Both of these application and service suites are in heavy demand, as organizations seek to harness real-time repositories of big data for various enterprise use cases, including artificial intelligence and machine learning model development and deployment.

When choosing between two of the most highly regarded data platforms on the market, users often have difficulty differentiating between Cognos and Power BI and weighing each of the platform’s pros and cons. In this in-depth comparison guide, we’ll compare these two platforms across a variety of qualities and variables to assess where their strengths lie.

But first, here’s a glance at the areas where each tool excels most:

• Cognos Analytics: Best for advanced data analytics and on-premises deployment. Compared to Power BI, Cognos is particularly effective for advanced enterprise data analytics use cases that require more administrative controls over security and governance. Additionally, it is more reliable when it comes to processing large quantities of data quickly and accurately.
• Power BI: Best for affordable, easy-to-use, integrable BI technology in the cloud. Compared to Cognos Analytics, Power BI is much more versatile and will fit into the budget, skill sets, and other requirements of a wider range of teams. Most significant, this platform offers free access versions that are great for teams that are just getting started with this type of technology.

Cognos vs. Power BI at a Glance

What Is Cognos?

Cognos Analytics is a business intelligence suite of solutions from IBM that combines AI-driven assistance, advanced reporting and analytics, and other tools to support various enterprise data management requirements. The platform is available both in the cloud and on demand for on-premises and custom enterprise network configurations.

With its range of features, Cognos enables users to connect, verify, and combine data and offers plenty of dashboard and visualization options. Cognos is particularly good at pulling and analyzing corporate data, providing detailed reports, and assisting in corporate governance. It is built on a strong data science foundation and is supported by heavy-duty analytics and recommendations, courtesy of IBM Watson.

Also see: Top Business Intelligence Software

Key Features of Cognos

• AI-driven insights: The platform benefits from veteran AI support in the form of Watson, which helps with data visualization design, dashboard builds, forecasting, and data explainability. This is particularly helpful for users with limited data science and coding experience who need to pull in-depth analyses from complex datasets.
• Data democratization through natural language: Advanced natural language capabilities make it possible for citizen data scientists and less-experienced tech professionals to create accurate and detailed data visualizations.
• Advanced reporting and dashboarding: Multi-user reports and dashboards, personalized report generation, AI-powered dashboard design, and easy shareability make this a great platform for organizations that require different levels of data visibility and granularity for different stakeholders.
• Automation and governance: Extensive automation and governance capabilities help power users scale their operations without compromising data security. The platform’s robust governance and security features are important to highly regulated businesses and large enterprises in particular.

Pros

• The platform is well integrated with other business tools, like Slack and various email inboxes, making it easier to collaborate and share insights across a team.
• Its AI assistant works well for a variety of data analytics and management tasks, even for users with no data science experience, because of its natural language interface.
• Cognos comes with flexible deployment options, including on-demand cloud, hosted cloud, and client hosting for either on-premises or IaaS infrastructure.

Cons

• The platform is not particularly mobile-friendly compared to similar competitors.
• While a range of visuals are available on the platform, many user reviews indicate that the platform’s visuals are limited and not very customizable.
• Depending on your exact requirements, Cognos Analytics can become quite expensive, especially if you have a high user count or require more advanced features like security and user management.

What Is Power BI?

Microsoft Power BI is a business intelligence and data visualization software solution that acts as one part of the Microsoft Power Platform. Because of its unification with other Power Platform products like Power Automate, Power Apps, and Power Pages, this BI tool gives users diverse low-code and AI-driven operations for more streamlined data analytics and management. Additional integrations with the likes of Microsoft 365, Teams, Azure, and SharePoint are a major selling point, as many business users are already highly invested in these business applications and are familiar with the Microsoft approach to UX/UI.

Specific to analytics functions, Power BI focuses most heavily on data preparation, data discovery, dashboards, and data visualization. Its core features enable users to take visualizations to the next level and empower them to make data-driven decisions, collaborate on reports, and share insights across popular applications. They can also create and modify data reports and dashboards easily and share them securely across applications.

Key Features of Power BI

• Rapidly expanding AI analytics: AI-powered data analysis and report creation have already been established in this platform, but recently, the generative AI Copilot tool has also come into preview for Power BI. This expands the platform’s ability to create reports more quickly, summarize and explain data in real time, and generate DAX calculations.
• CRM integration: Power BI integrates relatively well with Microsoft Dynamics CRM, which makes it a great option for in-depth marketing and sales analytics tasks. Many similar data platforms do not offer such smooth CRM integration capabilities.
• Embedded and integrated analytics: The platform is available in many different formats, including as an embedded analytics product. This makes it possible for users of other Microsoft products to easily incorporate advanced analytics into their other most-used Microsoft products. You can also embed detailed reports in other apps for key stakeholders who need information in a digestible format.
• Comprehensive visualizations: Adjustable dashboards, AI-generated and templated reports, and a variety of self-service features enable users to set up visuals that can be alphanumeric, graphical, or even include geographic regions and maps. Power BI’s many native visualization options mean users won’t have to spend too much time trying to custom-fit their dashboards and reports to their company’s specific needs.

Pros

• Power BI is one of the more mobile-friendly data platforms on the market today.
• In addition to its user-friendly and easy-to-learn interface, Microsoft offers a range of learning resources and is praised for its customer support.
• Its AI-powered capabilities continue to grow, especially through the company’s close partnership with OpenAI.

Cons

• Some users have commented on the tool’s outdated interface and how data updates, especially for large amounts of data, can be slow and buggy.
• The platform, especially the Desktop tool, uses a lot of processing power, which can occasionally lead to slower load times and platform crashes.
• Shareability and collaboration features are incredibly limited outside of its highest paid plan tier.

Best for Core Features: It Depends

It’s a toss-up when it comes to the core features Cognos Analytics and Power BI bring to the table.

Microsoft Power BI’s core features include a capable mobile interface, AI-powered analytics, democratized report-building tools and templates, and intuitive integrations with other Microsoft products.

IBM Cognos Analytics’ core features include a web-based report authoring tool, natural-language and AI-powered analytics, customizable dashboards, and security and access management capabilities. Both tools offer a variety of core features that work to balance robustness and accessibility for analytics tasks.

To truly differentiate itself, Microsoft consistently releases updates to its cloud-based services, with notable updates and feature additions over the past couple of years including AI-infused experiences, smart narratives (NLG), and anomaly detection capabilities. Additionally, a Power BI Premium version enables multi-geography capabilities and the ability to deploy capacity to one of dozens of data centers worldwide.

On the other hand, IBM has done extensive work to update the Cognos home screen, simplifying the user experience and giving it a more modern look and feel. Onboarding for new users has been streamlined with video tutorials and accelerator content organized in an easy-to-consume format. Additionally, improved search capabilities and enhancements to the Cognos AI Assistant and Watson features help generate dashboards automatically, recommend the best visualizations, and suggest questions to ask — via natural language query — to dive deeper into data exploration.

Taking these core capabilities and recent additions into account, which product wins on core features? Well, it depends on the user’s needs. For most users, Power BI is a stronger option for general cloud and mobility features, while Cognos takes the lead on advanced reporting, data governance, and security.

Also see: Top Dashboard Software & Tools

Best for Ease of Use and Implementation: Power BI

Although it’s close, new users of these tools seem to find Power BI a little easier to use and set up than Cognos Analytics.

As the complexity of your requirements rises, though, the Power BI platform grows more difficult to navigate. Users who are familiar with Microsoft tools will be in the best position to use the platform seamlessly, as they can take advantage of skills from applications they already use, such as Microsoft Excel, to move from building to analyzing to presenting with less data preparation. Further, all Power BI users have access to plenty of free learning opportunities that enable them to rapidly start building reports and dashboards.

Cognos, on the other hand, has a more challenging learning curve, but IBM has been working on this, particularly with recent user interface updates, guided UI for dashboard builds, and assistive AI. The tool’s AI-powered and Watson-backed analytics capabilities in particular lower the barrier of entry to employing advanced data science techniques.

The conclusion: Power BI wins on broad usage by a non-technical audience, whereas IBM has the edge with technical users and continues to improve its stance with less-technical users. Overall, Power BI wins in this category due to generally more favorable user reviews and commentary about ease of use.

Also see: Top AI Software

Best for Advanced Analytics Capabilities: Cognos

Cognos Analytics surpasses Power BI for its variety of in-depth and advanced analytics operations.

Cognos integrates nicely with other IBM solutions, like the IBM Cloud Pak for Data platform, which extends the tool’s already robust data analysis and management features. It also brings together a multitude of data sources as well as an AI Assistant tool that can communicate in plain English, sharing fast recommendations that are easy to understand and implement. Additionally, the platform generates an extensive collection of visualizations. This includes geospatial mapping and dashboards that enable the user to drill down, rise, or move horizontally through visuals that are updated in real time.

Recent updates to Cognos’s analytical capabilities include a display of narrative insights in dashboard visualizations to show meaningful aspects of a chart’s data in natural language, the ability to specify the zoom level for dashboard viewing and horizontal scrolling in visualizations, as well as other visualization improvements.

On the modeling side of Cognos, data modules can be dynamically redirected to different data server connections, schemas, or catalogs at run-time. Further, the Convert and Relink options are available for all types of referenced tables, and better web-based modeling has been added.

However, it’s important to note that Cognos still takes a comparatively rigid, templated approach to visualization, which makes custom configurations difficult or even impossible for certain use cases. Additionally, some users say it takes extensive technical aptitude to do more complex analysis.

Power BI’s strength is out-of-the-box analytics that doesn’t require extensive integration or data science smarts. It regularly adds to its feature set. More recently, it has added new features for embedded analytics that enable users to embed an interactive data exploration and report creation experience in applications such as Dynamics 365 and SharePoint.

For modeling, Microsoft has added two new statistical DAX functions, making it possible to simultaneously filter more than one table in a remote source group. It also offers an Optimize ribbon in Power BI Desktop to streamline the process of authoring reports (especially in DirectQuery mode) and more conveniently launch Performance Analyzer to analyze queries and generate report visuals. And while Copilot is still in preview at this time, this tool shows promise for advancing the platform’s advanced analytics capabilities without negatively impacting its ease of use.

In summary, Power BI is good at crunching and analyzing real-time data and continues to grow its capabilities, but Cognos Analytics maintains its edge, especially because Cognos can conduct far deeper analytics explorations on larger amounts of data without as many reported performance issues.

Also see: Data Analytics Trends

Best for Cloud Users: Power BI; Best for On-Prem Users: Cognos

Both platforms offer cloud and on-premises options for users, but each one has a clear niche: Power BI is most successful on the cloud, while Cognos has its roots in on-prem setups.

Power BI has a fully functional SaaS version running in Azure as well as an on-premises version in the form of Power BI Report Server. Power BI Desktop is also offered for free as a standalone personal analysis tool.

Although Power BI does offer on-prem capabilities, power users who are engaged in complex analysis of multiple on-premises data sources typically still need to download Power BI Desktop in addition to working with Power BI Report Server. The on-premises product is incredibly limited when it comes to dashboards, streaming analytics, natural language, and alerting.

Cognos also offers both cloud and on-premises versions, with on-demand, hosted, and flexible on-premises deployment options that support reporting, dashboarding, visualizations, alters and monitoring, AI, and security and user management, regardless of which deployment you choose. However, Cognos’ DNA is rooted in on-prem, so it lags behind Microsoft on cloud-based bells and whistles.

Therefore, Microsoft gets the nod for cloud analytics, and Cognos for on-prem, but both are capable of operating in either format.

Also see: Top Data Visualization Tools

Best for Integrations: It Depends

Both Cognos Analytics and Power BI offer a range of helpful data storage, SaaS, and operational tool integrations that users find helpful. Ultimately, neither tool wins this category because they each have different strengths here.

Microsoft offers an extensive array of integration options natively, as well as APIs and partnerships that help to make Power BI more extensible. Power BI is tightly embedded into much of the Microsoft ecosystem, which makes it ideally suited for current Azure, Dynamics, Microsoft 365, and other Microsoft customers. However, the company is facing some challenges when it comes to integrations beyond this ecosystem, and some user reviews have reflected frustrations with that challenge.

IBM Cognos connects to a large number of data sources, including spreadsheets. It is well integrated into several parts of the vast IBM portfolio. It integrates nicely, for example, with the IBM Cloud Pak for Data platform and more recently has added integration with Jupyter notebooks. This means users can create and upload notebooks into Cognos Analytics and work with Cognos Analytics data in a notebook using Python scripts. The platform also comes with useful third-party integrations and connectors for tools like Slack, which help to extend the tool’s collaborative usage capabilities.

This category is all about which platform and IT ecosystem you live within, so it’s hard to say which tool offers the best integration options for your needs. Those invested in Microsoft will enjoy tight integration within that sphere if they select Power BI. Similarly, those who are committed to all things IBM will enjoy the many ways IBM’s diverse product and service set fit with Cognos.

Also see: Digital Transformation Guide: Definition, Types & Strategy

Best for Pricing: Power BI

While Cognos Analytics offers some lower-level tool features at a low price point, Power BI offers more comprehensive and affordable entry-level packages to its users.

Microsoft is very good at keeping prices low as a tactic for growing market share. It offers a lot of features at a relatively low price. Power BI Pro, for example, costs approximately $10 per user per month, while the Premium plan is $20 per user per month. Free, somewhat limited versions of the platform are also available via Power BI Desktop and free Power BI accounts in Microsoft Fabric.

The bottom line for any rival is that it is hard to compete with Microsoft Power BI on price, especially because many of its most advanced features — including automated ML capabilities and AI-powered services — are available in affordable plan options.

IBM Cognos Analytics, on the other hand, has a reputation for being expensive. It is hard for IBM to compete with Power BI on price alone.

IBM Cognos Analytics pricing starts at $10 per user per month for on-demand cloud access and $5 per user per month for limited mobile user access to visuals and alerts on the cloud-hosted or client-hosted versions. For users who want more than viewer access and the most basic of capabilities, pricing can be anywhere from $40 to $450 per user per month.

Because of the major differences in what each product offers in its affordable plans, Microsoft wins on pricing.

Also see: Data Mining Techniques

Why Shouldn’t You Use Cognos or Power BI?

While both data and BI platforms offer extensive capabilities and useful features to users, it’s possible that these tools won’t meet your particular needs or align with industry-specific use cases in your field. If any of the following points are true for your business, you may want to consider an alternative to Cognos or Power BI:

Who Shouldn’t Use Cognos

The following types of users and companies should consider alternatives to Cognos Analytics:

• Users or companies with smaller budgets or who want a straightforward, single pricing package; Cognos tends to have up-charges and add-ons that are only available at an additional cost.
• Users who require extensive customization capabilities, particularly for data visualizations, dashboards, and data exploration.
• Users who want a more advanced cloud deployment option.
• Users who have limited experience with BI and data analytics technology; this tool has a higher learning curve than many of its competitors and limited templates for getting started.
• Users who are already well established with another vendor ecosystem, like Microsoft or Google.

Who Shouldn’t Use Power BI

The following types of users and companies should consider alternatives to Power BI:

• Users who prefer to do their work online rather than on a mobile device; certain features are buggy outside of the mobile interface.
• Users who are not already well acquainted and integrated with the Microsoft ecosystem may face a steep learning curve.
• Users who prefer to manage their data in data warehouses rather than spreadsheets; while data warehouse and data lake integrations are available, including for Microsoft’s OneLake, many users run into issues with data quality in Excel.
• Users who prefer a more modern UI that updates in real time.
• Users who primarily use Macs and Apple products; some users have reported bugs when attempting to use Power BI Desktop on these devices.

Also see: Best Data Analytics Tools

If Cognos or Power BI Isn’t Ideal for You, Check Out These Alternatives

While Cognos and Power BI offer extensive features that will meet the needs of many BI teams and projects, they may not be the best fit for your particular use case. The following alternatives may prove a better fit:

Domo

Domo puts data to work for everyone so they can extend their data’s impact on the business. Underpinned by a secure data foundation, the platform’s cloud-native data experience makes data visible and actionable with user-friendly dashboards and apps. Domo is highly praised for its ability to help companies optimize critical business processes at scale and quickly.

Yellowfin

Yellowfin is a leading embedded analytics platform that offers intuitive self-service BI options. It is particularly successful at accelerating data discovery. Additionally, the platform allows anyone, from an experienced data analyst to a non-technical business user, to create reports in a governed way.

Wyn Enterprise

Wyn Enterprise offers a scalable embedded business intelligence platform without hidden costs. It provides BI reporting, interactive dashboards, alerts and notifications, localization, multitenancy, and white-labeling in a variety of internal and commercial apps. Built for self-service BI, Wyn offers extensive visual data exploration capabilities, creating a data-driven mindset for the everyday user. Wyn’s scalable, server-based licensing model allows room for your business to grow without user fees or limits on data size.

Zoho Analytics

Zoho Analytics is a top BI and data analytics platform that works particularly well for users who want self-service capabilities for data visualizations, reporting, and dashboarding. The platform is designed to work with a wide range of data formats and sources, and most significantly, it is well integrated with a Zoho software suite that includes tools for sales and marketing, HR, security and IT management, project management, and finance.

Sigma

Sigma is a cloud-native analytics platform that delivers real-time insights, interactive dashboards, and reports, so you can make data-driven decisions on the fly. With Sigma’s intuitive interface, you don’t need to be a data expert to dive into your data, as no coding or SQL is required to use this tool. Sigma has also recently brought forth Sigma AI features for early access preview.

Review Methodology

The two products in this comparison guide were assessed through a combination of reading product materials on vendor sites, watching demo videos and explanations, reviewing customer reviews across key metrics, and directly comparing each product’s core features through a comparison graph.

Below, you will see four key review categories that we focused on in our research. The percentages used for each of these categories represent the weight of the categorical score for each product.

User experience – 30%

Our review placed a heavy emphasis on user experience, considering both ease of use and implementation as well as the maturity and reliability of product features. We looked for features like AI assistance and low-code/no-code capabilities that lessened the learning curve, as well as learning materials, tutorials, and consistent customer support resources. Additionally, we paid attention to user reviews that commented on the product’s reliability and any issues with bugs, processing times, product crashes, or other performance issues.

Advanced analytics and scalability – 30%

To truly do business intelligence well, especially for modern data analytics requirements, BI tools need to offer advanced capabilities that scale well. For this review, we emphasized AI-driven insights, visuals that are configurable and updated in real time, shareable and collaborative reports and dashboards, and comprehensive features for data preparation, data modeling, and data explainability. As far as scalability goes, we not only looked at the quality of each of these tools but also assessed how well they perform and process data on larger-scale operations. We particularly highlighted any user reviews that mentioned performance lag times or other issues when processing large amounts of data.

Integrations and platform flexibility – 20%

Because these platforms need to be well integrated into a business’s data sources and most-used business applications to be useful, our assessment also paid attention to how integrable and flexible each platform was for different use cases. We considered not only how each tool integrates with other tools from the same vendor but also which data sources, collaboration and communication applications, and other third-party tools are easy to integrate with native integrations and connectors. We also considered the quality of each tool’s APIs and other custom opportunities for integration, configuration, and extensibility.

Affordability – 20%

While affordability is not the be-all-end-all when it comes to BI tools, it’s important to many users that they find a tool that balances an accessible price point with a robust feature set. That’s why we also looked at each tool’s affordability, focusing on entry price points, what key features are and are not included in lower-tier pricing packages, and the jumps in pricing that occur as you switch from tier to tier. We also considered the cost of any additional add-ons that users might need, as well as the potential cost of partnering with a third-party expert to implement the software successfully.

Bottom Line: Cognos vs. Power BI

Microsoft is committed to investing heavily in Power BI and enhancing its integrations across other Microsoft platforms and a growing number of third-party solutions. Any organization that is a heavy user of Office 365, Teams, Dynamics, and/or Azure will find it hard to resist the advantages of deploying Power BI.

And those advantages are only going to increase. On the AI front, for example, the company boasts around 100,000 customers using Power BI’s AI services. It is also putting effort into expanding its AI capabilities, with the generative AI-driven Copilot now in preview for Power BI users. For users with an eye on their budget who don’t want to compromise on advanced analytics and BI features, Power BI is an excellent option.

But IBM isn’t called Big Blue for nothing. It boasts a massive sales and services team and global reach into large enterprise markets. It has also vastly expanded its platform’s AI capabilities, making it a strong tool for democratized data analytics and advanced analytics tasks across the board.

Where Cognos Analytics has its most distinct advantage is at the high end of the market. Microsoft offers most of the features that small, midsize, and larger enterprises need for analytics. However, at the very high end of the analytics market, and in corporate environments with hefty governance and reporting requirements or legacy and on-premises tooling, Cognos has carved out a strategic niche that it serves well.

Ultimately, either tool could work for your organization, depending on your budget, requirements, and previous BI tooling experience. The most important step you can take is to speak directly with representatives from each of these vendors, demo these tools, and determine which product includes the most advantageous capabilities for your team.

Read next: 10 Best Machine Learning Platforms

The post Cognos vs. Power BI: 2024 Data Platform Comparison appeared first on eWEEK.

As two well-regarded data analytics platforms in the BI space, users may have a difficult time deciding between Looker and Power BI for their data management requirements. There are arguments for and against each, and in this comparison guide, we’ll dive deeper into core features, pros, cons, and pricing for Looker and Power BI.

But before we go any further, here’s a quick summary of how each product stands out against its competitors:

• Looker: Best for current Google product users and others who are most interested in highly configurable and advanced analytics capabilities, including data visualizations and reporting. Looker Studio in particular balances ease of use with high levels of customization and creativity, while also offering users a lower-cost version of an otherwise expensive platform.
• Power BI: Best for current Microsoft product users and others who want an easy-to-use and affordable BI tool that works across a variety of data types and use cases. This is considered one of the most popular BI tools on the market and meets the needs of a variety of teams, budgets, and experience levels, though certain customizations and big data processing capabilities are limited.

Looker vs. Power BI at a Glance

What Is Looker?

Looker is an advanced business intelligence and data management platform that can be used to analyze and build data-driven applications, embed data analytics in key organizational tools, and democratize data analysis in a way that preserves self-service capabilities and configurability. The platform has been managed by Google since its acquisition in 2019, and because of its deep integration within the Google ecosystem, it is a favorite among Google Cloud and Workspace users for unified analytics projects. However, the tool also works well with other cloud environments and third-party applications, as it maintains a fairly intuitive and robust collection of integrations.

Key features of Looker

• Comprehensive data visualization library: In addition to giving users the ability to custom-configure their visualizations to virtually any parameters and scenarios, Looker’s data visualization library includes a wide range of prebuilt visual options. Traditional visuals like bar graphs and pie charts are easy to access, and more complex visuals — like heatmaps, funnels, and timelines — can also be easily accessed.
• “Blocks” code snippets: Instead of reinventing the wheel for certain code snippets and built-out data models, Looker Blocks offers prebuilt data models and code to help users quickly develop high-quality data models. Industry-specific, cloud-specific, and data-source-specific blocks are all available, which makes this a great solution for users of all backgrounds who want to get started with complex models more quickly.
• Governed and integrated data modeling: With its proprietary modeling language and emphasis on Git-driven data storage and rule development, users can easily build trusted and governed data sources that make for higher-quality and more accurate data models, regardless of how many teams are working off of these models.

Pros

• Looker comes with a large library of prebuilt integrations — including for many popular data tools — and also offers user-friendly APIs for any additional integrations your organization may need to set up.
• Looker’s visualizations and reports are easy to customize to your organization’s more specific project requirements and use cases; it also offers one of the more diverse visualization libraries in this market.
• LookML allows users to create centralized governance rules and handle version control tasks, ensuring more accurate outcomes and higher quality data, even as data quantities scale.

Cons

• On-premises Looker applications do not easily connect to Looker Studio and other cloud-based tools in user portfolios, which severely limits the ability to maintain data projects accurately and in real time for on-prem users.
• Looker uses its own modeling language, which can make it difficult for new users to get up and running quickly.
• Some users have had trouble with self-service research and the vendor’s documentation.

What Is Power BI?

Microsoft Power BI is a business intelligence and data visualization solution that is one of the most popular data analytics tools on the market today. As part of the Microsoft Power Platform, the tool is frequently partnered with Microsoft products like Power Automate, Power Apps, and Power Pages to get the most out of data in different formats and from different sources. Its focus on ease of use makes it a leading option for teams of all backgrounds; especially with the growth of its AI-powered assistive features, visualization templates, and smooth integrations with other Microsoft products, it has become one of the best solutions for democratized data science and analytics.

Key features of Power BI

• AI-driven analytics: AI-powered data analysis and report creation have already been established in this platform, but recently, the generative AI Copilot tool has also come into preview for Power BI. This expands the platform’s ability to create reports more quickly, summarize and explain data in real time, and generate DAX calculations.
• Dynamics 365 integration: Power BI integrates relatively well with the Microsoft Dynamics CRM, which makes it a great option for in-depth marketing and sales analytics tasks. Many similar data platforms do not offer such smooth CRM integration capabilities.
• Comprehensive mobile version: Unlike many other competitors in this space, Microsoft Power BI comes with a full-featured, designed-for-mobile mobile application that is available at all price points and user experience levels. With native mobile apps available for Windows, iOS, and Android, any smartphone user can quickly review Power BI visualizations and dashboards from their personal devices.

Pros

• Power BI can be used in the cloud, on-premises, and even as an embedded solution in other applications.
• The user interface will be very familiar to users who are experienced with Microsoft products; for others, the platform is accompanied by helpful training resources and ample customer support.
• This platform makes democratized data analytics simpler, particularly with AI-powered features and a growing generative AI feature set.

Cons

• While some users appreciate that Power BI resembles other Microsoft 365 office suite interfaces, other users have commented on the outdated interface and how it could be improved to look more like other cloud-based competitors.
• Especially with larger quantities of data, the platform occasionally struggles to process data quickly and accurately; slower load times, crashes, and bugs are occasionally introduced during this process.
• Visualizations are not very customizable, especially compared to similar competitors.

Best for Core Features: It Depends

Both Looker and Power BI offer all of the core features you would expect from a data platform, including data visualizations, reporting and dashboarding tools, collaboration capabilities, and integrations. They also offer additional features to assist users with their analytical needs. Power BI offers support through AI assistance and Looker supports users with prebuilt code snippets and a diverse integration and plugin marketplace.

Microsoft maintains a strong user base with its full suite of data management features and easy-to-setup integrations with other Microsoft tools. It can be deployed on the cloud, on-premises, and in an embedded format, and users can also access the tool via a comprehensive mobile application.

Looker is web-based and offers plenty of analytics capabilities that businesses can use to explore, discover, visualize, and share analyses and insights. Enterprises can use it for a wide variety of complex data mining techniques. It takes advantage of a specific modeling language to define data relationships while bypassing SQL. Looker is also tightly integrated with a great number of Google datasets and tools, including Google Analytics, as well as with several third-party data and business tools.

Looker earns good marks for reporting granularity, scheduling, and extensive integration options that create an open and governable ecosystem. Power BI tends to perform better than Looker in terms of breadth of service due to its ecosystem of Microsoft Power Platform tools; users also tend to prefer Power BI for a comprehensive suite of data tools that aren’t too difficult to learn how to use.

Because each tool represents such a different set of strengths, it’s a tie for this category.

Best for Ease of Use and Implementation: Power BI

In general, users who have tried out both tools find that Power BI is easier to use and set up than Looker.

Power BI provides users with a low-code/no-code interface as well as a drag-and-drop approach to its dashboards and reports. Additionally, its built-in AI assistance — which continues to expand with the rise of Copilot in Power BI — helps users initiate complex data analytics tasks regardless of their experience with this type of technology or analysis.

For some users, Looker has a steep learning curve because they must learn and use the LookML proprietary programming language to set up and manage their models in the system. This can be difficult for users with little experience with modeling languages, but many users note that the language is easy to use once they’ve learned its basics. They add that it streamlines the distribution of insights to staff across many business units, which makes it a particularly advantageous approach to data modeling if you’re willing to overcome the initial learning curve.

The conclusion: Power BI wins on general use cases for a non-technical audience whereas Looker wins with technical users who know its language.

Best for Advanced Data Analytics: Looker

While both tools offer unique differentiators for data analytics operations, Looker outperforms Power BI with more advanced, enterprise-level data governance, modeling, and analytics solutions that are well integrated with common data sources and tools.

Both tools offer extensive visualization options, but Looker’s data visualizations and reporting are more customizable and easier to configure to your organization’s specs and stakeholders’ expectations. Looker also streamlines integrations with third-party data tools like Slack, Segment, Redshift, Tableau, ThoughtSpot, and Snowflake, while also working well with Google data sources like Google Analytics. As far as its more advanced data analytics capabilities go, Looker surpasses Power BI and many other competitors with features like granular version control capabilities for reports, comprehensive sentiment analysis and text mining, and open and governed data modeling strategies.

However, Looker has limited support for certain types of analytics tasks, like cluster analysis, whereas Power BI is considered a top tool in this area. And, so far, Power BI does AI-supported analytics better, though Google does not appear to be too far behind on this front.

It’s a pretty close call, but because of its range of data analytics operations and the number of ways in which Google makes data analytics tasks customizable for its users, Looker wins in this category.

Also see: Best Data Analytics Tools 

Best for Integrations: It Depends

When it comes to integrations, either Power BI or Looker could claim the upper hand here.

It all depends on if you’re operating in a Microsoft shop or a Google shop. Current Microsoft users will likely prefer Power BI because of how well it integrates with Azure, Dynamics 365, Microsoft 365, and other Microsoft products. Similarly, users of Google Cloud Platform, Google Workspace, and other Google products are more likely to enjoy the integrated experience that Looker provides with these tools.

If your organization is not currently working with apps from either of these vendor ecosystems, it may be difficult to set up certain third-party integrations with Power BI or Looker. For example, connecting Power BI to a collaboration and communication tool like Slack generally requires users to use Microsoft Power Automate or an additional third-party integration tool. Looker’s native third-party integrations are also somewhat limited, though the platform does offer easy-to-setup integrations and actions for tools like Slack and Segment.

Because the quality of each tool’s integrations depends heavily on the other tools you’re already using, Power BI and Looker tie in this category.

Best for Pricing: Power BI

Power BI is consistently one of the most affordable BI solutions on the market. And while Looker Studio in particular helps to lower Looker’s costs, the platform is generally considered more expensive.

Power BI can be accessed through two main free versions: Power BI Desktop and a free account in Microsoft Fabric. The mobile app is also free and easy to access. But even for teams that require more functionality for their users, paid plans are not all that expensive. Power BI Pro costs only $10 per user per month, while Power BI Premium is $20 per user per month.

Looker, on the other hand, is more expensive, requiring users to pay a higher price for its enterprise-class features. The Standard edition’s pay-as-you-go plan costs $5,000 per month, while all other plans require an annual commitment and a conversation with sales to determine how much higher the costs will be.

Additionally, there are user licensing fees that start at $30 per month for a Viewer User; users are only able to make considerable changes in the platform as either a Standard User or a Developer User, which costs $60 and $125 per user per month respectively.

Power BI takes the lead when it comes to pricing and general affordability across its pricing packages.

Also see: Top Digital Transformation Companies

Why Shouldn’t You Use Looker or Power BI?

While Looker and Power BI are both favorites among data teams and citizen data scientists alike, each platform has unique strengths — and weaknesses — that may matter to your team. If any of the following qualities align with your organizational makeup, you may want to consider investing in a different data platform.

Who Shouldn’t Use Looker

The following types of users and companies should consider alternatives to Looker:

• Users who want an on-premises BI tool; most Looker features, including useful connections to Looker Studio, are only available to cloud users.
• Users who are not already working with other Google tools and applications may struggle to integrate Looker with their most-used applications.
• Users with limited computer-language-learning experience may struggle, as most operations are handled in Looker Modeling Language (LookML).
• Users who want a lower-cost BI tool that still offers extensive capabilities to multiple users.
• Users in small business settings may not receive all of the vendor support and affordable features they need to run this tool successfully; it is primarily designed for midsize and larger enterprises.

Who Shouldn’t Use Power BI

The following types of users and companies should consider alternatives to Power BI:

• Users who need more unique and configurable visualizations to represent their organization’s unique data scenarios.
• Users who are not already working with other Microsoft tools and applications may struggle to integrate Power BI into their existing tool stack.
• Users who consistently process and work with massive quantities of data; some user reviews indicate that the system gets buggy and slow with higher data amounts.
• Users who work with a large number of third-party data and business apps; Power BI works best with other Microsoft tools, especially those in the Power Platform.
• Users who consistently need to run more complex analytics, such as predictive analytics, may need to supplement Power BI with other tools to get the results they need.

If Looker or Power BI Isn’t Ideal for You, Check Out These Alternatives

Both Looker and Power BI offer extensive data platform features and capabilities, as well as smooth integrations with many users’ most important data sources and business applications. However, these tools may not be ideally suited to your team’s particular budget, skill sets, or requirements. If that’s the case, consider investing in one of these alternative data platform solutions:

Domo

Domo puts data to work for everyone so they can extend their data’s impact on the business. Underpinned by a secure data foundation, the platform’s cloud-native data experience makes data visible and actionable with user-friendly dashboards and apps. Domo is highly praised for its ability to help companies optimize critical business processes at scale and quickly.

Yellowfin

Yellowfin is a leading embedded analytics platform that offers intuitive self-service BI options. It is particularly successful at accelerating data discovery. Additionally, the platform allows anyone, from an experienced data analyst to a non-technical business user, to create reports in a governed way.

Wyn Enterprise

Wyn Enterprise offers a scalable embedded business intelligence platform without hidden costs. It provides BI reporting, interactive dashboards, alerts and notifications, localization, multitenancy, and white-labeling in a variety of internal and commercial apps. Built for self-service BI, Wyn offers extensive visual data exploration capabilities, creating a data-driven mindset for the everyday user. Wyn’s scalable, server-based licensing model allows room for your business to grow without user fees or limits on data size.

Zoho Analytics

Zoho Analytics is a top BI and data analytics platform that works particularly well for users who want self-service capabilities for data visualizations, reporting, and dashboarding. The platform is designed to work with a wide range of data formats and sources, and most significantly, it is well integrated with a Zoho software suite that includes tools for sales and marketing, HR, security and IT management, project management, and finance.

Sigma

Sigma is a cloud-native analytics platform that delivers real-time insights, interactive dashboards, and reports, so you can make data-driven decisions on the fly. With Sigma’s intuitive interface, you don’t need to be a data expert to dive into your data, as no coding or SQL is required to use this tool. Sigma has also recently brought forth Sigma AI features for early access preview.

Review Methodology

Looker and Power BI were reviewed based on a few core standards and categories for which data platforms are expected to perform. The four categories covered below have been weighted according to how important they are to user retention over time.

User experience – 30%

When it comes to user experience, we paid attention to how easy each tool is to use and implement and how many built-in support resources are available for users who have trouble getting started. Additionally, we considered how well the platform performs under certain pressures, like larger data loads, security and user control requirements, and more complex modeling and visualization scenarios. Finally, we considered the availability of the tool in different formats and how well the tool integrates with core business and data applications.

Scalability and advanced analytics compatibility – 30%

Our review also considered how well each platform scales to meet the needs of more sophisticated analytics operations and larger data processing projects. We paid close attention to how the platform performs as data loads grow in size and complexity, looking at whether user reviews mention any issues with lag times, bugs, or system crashes. We also considered what tools were available to assist with more complex analytics tasks, including AI-powered insights and support, advanced integrations and plugins, and customizable dashboards and reports.

Integrability – 20%

We considered how well each tool integrated with other software and cloud solutions from the same vendor as well as how easy it is to set up third-party integrations either via prebuilt connectors or capable APIs. In particular, we examined how well each platform integrated with common data sources outside of its vendor ecosystem, including platforms like Redshift, Snowflake, Salesforce, and Dropbox.

Cost and accessibility – 20%

For cost and accessibility, we not only focused on low-cost solutions but also on how well each solution’s entry-level solutions perform and meet user needs. We assessed the user features available at each pricing tier, how quickly pricing rises — especially for individual user licenses or any required add-ons, and whether or not a comprehensive free version was available to help users get started.

Bottom Line: Looker vs. Power BI

Microsoft’s Power BI has consistently been among the top two and three business intelligence tools on the market, recruiting and retaining new users with its balance of easy-to-use features, low costs, useful dashboards and visualizations, range of data preparation and management tools, AI assistance, and Microsoft-specific integrations. It is both a great starter and advanced data platform solution, as it offers the features necessary for citizen data scientists and more experienced data analysts to get the most out of their datasets.

Power BI tends to be the preferred tool of the two because of its general accessibility and approachability as a tool, but there are certain enterprise user needs for reporting and analytics distribution where Looker far outperforms Power BI. And for those heavily leaning on Google platforms or third-party applications, Looker offers distinct advantages to skilled analysts.

Ultimately, Looker doesn’t really try to compete head-to-head with Microsoft, because they each target different data niches and scenarios. It’s often the case that prospective buyers will quickly be able to identify which of these tools is the best fit for their needs, but if you’re still not sure, consider reaching out to both vendors to schedule a hands-on demo.

Read next: Best Data Mining Tools and Software

The post Looker vs. Power BI: 2024 Software Comparison appeared first on eWEEK.

The critical elements of digital transformation to keep in mind are customer experience, operational excellence, and innovation. Specifically:

• Delight your customers: You need to optimize customer experiences, ensuring a smooth, automated journey that is connected and personalized across all channels.
• Stay flexible, productive, and efficient: Hybrid workforces demand a lot. Build an ethic of operations excellence that can handle all the needs of your company while staying secure and accountable is critical.
• Look to the future: Innovation is about optimizing day-to-day operations so you can focus on new products and opportunities.

A “Do It Yourself” Approach is Hard to Execute

Executing digital transformation by yourself is a daunting prospect. And, even with some of the common platforms available, the heavy lifting can be overwhelming.

Getting the coders you need to get everything done is a thankless task—something that might be impossible in today’s labor market. But rather than putting this on next year’s to-do list, there are ways to do everything you need now using a different approach: No code.

We’ve all heard the soothing terms low code and no code. But how viable are they? ZK Research has evaluated several solutions in the past few years, and we see them as a shortcut around development cycles that used to take months or years.

One such platform—from Newgen, an India-based company whose platform can automate all aspects of a business for efficient operations and end-to-end customer journeys—can deliver the business outcomes we described above. The company’s NewgenONE platform enables the following:

• Automate manual processes and applications.
• Speed up transformations with low/no code.
• Create insights from documents, images, videos, and audio files.
• Extract more from existing systems without a complete rebuild.
• Build a culture of innovation across a company’s ecosystem, including internal groups, customers, and partners.
• Scale and secure the cloud.

Also see: Top Digital Transformation Companies

The Platform

The NewgenONE platform automates key parts of a business to make operations more efficient and ensure customer journeys are seamless.

One Newgen customer, Bank Midwest, a leading community bank in the Midwest region with assets totaling $1.3 billion, chose NewgenONE to transform its processes and customer experience with a tailored solution that eliminated the need for multiple-point solutions. The bank efficiently integrated these features into their projects, paving the way for enhanced efficiency and scalability to meet future business demands.

Another financial organization—Georgia’s Own Credit Union, one of the largest credit unions in Georgia—partnered with Newgen to transition 65% of its new applications online, reducing the application journey time to just five minutes. In addition, NewgenONE reduced back-office processing time by 35%.

Bottom Line: Low Code/No Code Delivers

Low code/no code platforms can often be all talk and no reality. But, after speaking to some of their customers, ZK Research believes NewgenONE platform can deliver on the promise.

Also, Newgen has been recognized by other research firms, most notably Forrester and Gartner, as having a complete and easy-to-use platform. The company says, on average, customers have achieved an ROI of 371% over three years, which is in-line with the case studies I cited above.

Executing on digital transformation using old-school tools will not scale and can often lead to unsuccessful projects. Low code / no code masks much of the complexity that developers will face and simplifies digital projects.

Read next: Digital Transformation Guide

The post Newgen’s Low-Code Platform Geared for Digital Transformation appeared first on eWEEK.

Teams can be unpredictable. Say what you will about the other players, but they just work.

Zoom started as a standalone product—and if it didn’t work, the company would’ve gone under. Webex was one of the earliest players, so they know how to make meetings work. Google Meet always feels strange, but it’s generally competent, although essentially a “me too” product. Skype is, well, Skype. It’s nothing fancy—kind of AOL Messenger on steroids—but it works.

Poor User Experience is the Teams Challenge

The trouble with Teams is that, in an effort to integrate all its products, Microsoft appears to have forgotten about the experience of actually using the individual product. Most people don’t care about integration—or, more correctly, users don’t care about integration if the product itself doesn’t work.

The Teams experience, at least for me and many users I have talked to, has not been good. Most times I go to use it, I get stuck in some kind of login issue. On more than one occasion, almost as a throwback to the late 90s or early 2000s, I’ve resorted to just using the dial-in numbers.

If I can solve login, I have to change the settings for backgrounds, etc., as I’m getting set to enter a meeting. Meanwhile, the clock is ticking, and I’m late for a meeting.

One of the most significant issues with Teams is guest access. If I host a meeting or call, I’ll use Zoom, so I often join Teams as a guest.

The experience as a guest is inconsistent. Some features, like chat, work on some calls and not others. It also creates multiple user IDs, which can create confusion for end users. This is why many organizations use Teams for internal meetings but a product like Zoom for external. Guest access is better in the latest release but it’s still not on par with other vendors.

Also see: Digital Transformation Guide

The EU and Unbundling

Once inside Teams, things work all right. Yet the experience, compared to Zoom or Webex, is severely lacking. But there may be hope on the horizon—in the unlikely guise of EU regulators. You may have read that the EU has prompted Microsoft to unbundle Teams.

One news article I read about the unbundling said Teams was considered the crown jewel of the Office 365 suite. I’m not sure about that, but it’s apparent that Microsoft is unhappy about unbundling. From a business standpoint, no one would be happy about this. Government regulators interfering in the way a company operates is generally abhorrent.

Looking back, Microsoft wasn’t happy about making it easier to uninstall Explorer after wrangling with the Justice Department in the late ’90s and early 2000s. But they survived allowing non-Microsoft software into its walled garden and grew into a trillion-plus dollar company—despite years of feckless management under Steve Ballmer. 

For users of Teams in the EU, unbundling could be a good thing if Microsoft approaches it correctly. Thinking about the user experience for Teams itself rather than the overall integration should help the company address the usability issues.

The ability to link to a doc from SharePoint is probably not a high priority for people just trying to have a seamless meeting. But those integrations should be relatively easy to maintain. One big step the company could take in improving usability is to acquire a company like Box. One might think that Box and Microsoft OneDrive are overlapping products, but the reality is that Box improves the Microsoft user experience better than Microsoft’s own product.

I know many people think I’m anti-Teams but, in reality, I’m anti-bad user experience. I want to like Teams. I really do. I hope that the company will use the unbundling to free it from those shackles everywhere and focus its usability experts on creating a great product that is easy to use and just works.

Microsoft Needs to Prioritize Usability 

I might be shouting into the void, but Microsoft product engineers might want to rethink how they approach Teams. It’s a simple thought: Prioritize the core functionality that users need or want. Then, approach that development from the inside out, like Zoom did. Think about enabling great meetings and messaging. Worry about everything else later.

The challenge with Microsoft is that the company appears to prioritize locking customers into license bundles over everything else, including usability. By making the job of procuring the product so frictionless, it can use that audience to push substandard products on corporate workers. This is why so many workers will say something like, “I really don’t like using Teams, but the IT department made the decision.”

The danger that unbundling creates is that it removes that lock and creates an opening for better products. Proof of that is how fast Google managed to have Chrome usurp Internet Explorer’s once dominant position.

If you’re a customer of Teams in one of the countries where it will be unbundled, do your due diligence and look at Zoom, RingCentral, Webex, 8×8, Avaya, or any of the other many products and use Box as the solution to integrate data across them. You’ll have happier customers and likely save yourself some money.

Read next: Top Digital Transformation Companies

The post Challenges of Microsoft Teams: User Experience and Unbundling appeared first on eWEEK.

As the demand for RPA continues to soar, numerous RPA companies have entered the market, offering their unique blend of AI and software robotics expertise and solutions. With their innovative approaches and proven track records, these companies have set the bar high for RPA excellence.

Below we review the best 10 RPA companies transforming various industries and reshaping the future of work.

Jump to:

• • Best RPA Companies: Comparison chart
  • Top 10 Robotic Process Automation Companies in 2023
  • How to choose the best RPA company for your business
  • Review methodology
  • Bottom Line: RPA Companies

Best RPA Companies: Comparison Chart

Here is a head-to-head comparison of the best RPA companies:

Top 10 Robotic Process Automation Companies in 2023

UiPath: Best RPA company for advanced features

UiPath is a leading enterprise automation software company founded in Bucharest, Romania, and headquartered in New York City. The company offers both SaaS and self-hosted robots, allowing organizations to easily automate their business processes.

UiPath can help you automate processes with drag-and-drop AI and pre-built templates. Additionally, Uipath offers pluggable integration with Active Directory, OAuth, CyberArk, and Azure Key vault and also complies with regulatory standards such as SOC 2 Type 2, ISO 9001, ISO/IEC 27001, and Veracode Verified.

UiPath pricing

UiPath offers four pricing plans – one free plan and two paid plans. You can also try the UiPath RPA tool for 60 days before buying, giving you time to better understand the platform features and functionalities before purchasing.

• Free: Available at no cost with limited features.
• Pro: This plan starts at $420 per month. It is ideal for small teams and businesses getting starter with automation. This plan is available on the cloud only.
• Enterprise: Quote is available upon request. Companies looking for comprehensive RPA platform capabilities will find this UiPath plan suitable.

UiPath key features

• Multi-tenancy.
• Offers various robots to help with users’ data and applications, including automation cloud robots, unattended robots, attended robots and test robots.
• Extensive integration options, including Tableau, ServiceNow, SAP, Salesforce, Oracle, AWS, Microsoft, Alteryx and more.
• UiPath Business Automation includes various RPA solutions like Automation Hub, Task Capture, Process Mining, and Task Mining.

UiPath pros

• Feature-rich RPA tool.
• Users reported that they find the user interface visually appealing.
• The company provides low-code development tools.
• Their solution is deployable as SaaS or self-hosted – depending on your needs.

UiPath cons

• The steep learning curve for RPA beginners and those with limited experience.
• Support can be improved for non-enterprise plan subscribers.

Also see: Best Artificial Intelligence Software 2023

Automation Anywhere: Best for large enterprises with complex automation needs

Automation Anywhere is clearly a RPA market leader. The company focuses primarily on enterprise automation and serves users in various industries, including BPO, financial services, healthcare, insurance, life sciences, manufacturing, public sector and telecom. Automation Anywhere digital workspace is built to serve the needs of business users, citizen developers, and professional programmers, allowing them to create a bot or design business process automation workflows. It includes:

• Control room
• Bot runner
• Bot editor
• Bot creator
• Credential vault

Automation Anywhere offers a range of Robotic Process Automation products, including IQ Bot, and Bot Insight, and a “Bot Store” – an online marketplace for ready-to-use bots and digital workers running on the Automation 360 platform.

Automation Anywhere pricing

Automation Anywhere encourages businesses to book a demo to discuss their needs before a quote is sent to you. The company offers three plans for its SaaS packages.

• Community Edition: A free version of the complete digital workforce platform, which includes RPA, AI and analytics.
• Cloud Starter Pack: Starts at $750 per month and includes one bot creator, control room and unattended bot runner. Additional unattended and attended bot runners can be added for $500 per month per user and $125 per month for each user, respectively.
• Advanced Pack: Custom quote.

While these Automation Anywhere product prices may change based on a discussion with a sales rep, the list above should give you an idea of how their solution is priced. You should contact the company sales team to discuss your needs and get quotes.

Automation Anywhere key features

• Document automation capability.
• Automation of complex tasks.
• Code optional capability, including no code, low-code, and bring-your-own-code automation.
• Connects with numerous third-party solutions, including AWS, Google AutoML, Salesforce and more.
• Allow users to manage, schedule, and execute bots.

Automation Anywhere pros

• They offer highly scalable automation solutions.
• Responsive customer support team.
• Some users reported that the company’s product allows them to adjust the level of automation to meet their business-changing demands.

Automation Anywhere cons

• Complex automation requires technical expertise.
• Complex initial setup process.

EdgeVerve AssistEdge RPA: Best for enterprises with a focus on consumer customer service

EdgeVerve is an Infosys Company (subsidiary) with a focus on automation. EdgeVerve AssistEdge RPA is largely favored by customers in finance, with many customer interaction activities like call centers.

Aside from AssistEdge, EdgeVerve offers other automation tools, including XtractEdge, a document AI platform that provides insight from documents like SOPs, legal contracts, images, commercial insurance papers, images, handwritten notes, PDFs, and emails; and TradeEdge, which is ideal for supply chain order management.

EdgeVerve ​​AssistEdge RPA pricing

The company doesn’t publicize its product pricing on its website. Potential buyers must request a demo to speak with an EdgeVerve expert about their needs before receiving personalized quotes tailored to them. Our research found that the company offers a pay-as-you-go pricing model.

EdgeVerve AssistEdge RPA key features

• Automation process discovery.
• Users can scale the AssistEdge RPA BOTs and Infrastructure up and down based on their business needs.
• Automatic bot upscaling, downscaling, and management.
• You can monitor your bot’s health via their centralized management dashboard.

EdgeVerve AssistEdge RPA pros

• Improve business process agility.
• Easy to deploy bots in both attended and Unattended Automation.
• It has OCR capability.

EdgeVerve AssistEdge RPA cons

• Some users reported that the AssistEdge profile mapping process is complex.
• Difficult to integrate with third-party tools.

On a related topic: What is Generative AI?

SS&C Blue Prism: Best RPA for unattended use cases

SS&C Blue Prism intelligent automation platform (IAP) combines the capabilities of RPA, artificial intelligence, and business process management (BPM) to help automate business processes and streamline decision-making across organizations.

The company’s digital worker — a software robot powered by RPA technology  – can be integrated into your existing systems to help you execute business processes and drive results. SS&C Blue Prism was ranked as one of the RPA leaders by Forester Wave in Q1 2023. The company offers support from automation specialists, prebuilt automation, and training and certification.

SS&C Blue Prism pricing

The company doesn’t advertise its product pricing on its website – they encourage buyers to contact an in-house expert for quotes. However, publicly available pricing information on the AWS marketplace reveals the following rates for SS&C Blue Prism Intelligent Automation.

• 5 BPC Digital Workers with production support plus knowledge support: It costs $131,800 for 12 months.
• 5 BPC Digital Workers with production support without knowledge support:  It costs $115,600 for 12 months.
• 5 BPC Digital Workers with production support  – License Only: It costs $74,750 for 12 months.

SS&C Blue Prism key features

• Allow both centralized and federated automation setups.
• Automation building capability.
• Bot management and execution.
• OCR capabilities.
• UX Builder helps businesses reduce app development time.

SS&C Blue Prism pros

• Easy to learn and use.
• Users applaud Blue Prism’s security and data encryption capabilities.
• Blue Prism product includes capabilities for Optical Character Recognition (OCR), Intelligent Document Processing (IDP), and Natural Language Processing (NLP).

SS&C Blue Prism cons

• Initial setup can take a while to achieve.
• While it is easy for technical users to use, business users may experience a steep learning curve.

WorkFusion: Best for banking and financial services organizations

WorkFusion is a no-code/low-code intelligent automation provider offering “AI Digital Workers,”  which combines AI, ML, IDP and RPA technologies to help organizations manage jobs. The company’s tool is built for banking and financial services organizations to help with functions like anti-money laundering (AML), sanctions screening, and Know Your Customer (KYC).

You can also leverage WorkFusion AI digital workers for various jobs like data analytics, customer service, human resources, accounting and logistics.

WorkFusion pricing

WorkFusion doesn’t advertise rates on their page. You can request a demo to discuss your needs and get custom quotes afterward.

WorkFusion key features

• It incorporates AutoML capabilities and RPA components for financial use cases.
• They offer pre-trained bots for the primary processes like LIBOR transition, account opening and KYC, claims processing and more.
• Document intelligence capabilities.
• It has an automation studio, a cloud and desktop development tool for business users and automation professionals.

WorkFusion pros

• Fast learning curve.
• Automates document-heavy processes with document intelligence.

WorkFusion cons

• Some users reported that WorkFusion products are expensive.
• WorkFusion OCR technology can be improved.

Also see: The Benefits of Generative AI 

NICE RPA: Best for contact center operations

Large enterprises in the customer service segment will find the automation capability of NICE RPA valuable. NICE RPA provides real-time visibility through their web-based centralized task automation bot management. The company robots are deployed on enterprise backend servers and have the potential to automate mundane, administratively-driven manual tasks that employees perform regularly.

NICE RPA pricing

Nice RPA doesn’t advertise its rates on its website. You can contact them for quotes.

NICE RPA key features

• It has a built-in drag-and-drop design studio, allowing users to create visual workflow views.
• Integrates with numerous third-party services.
• Allow you to create attended and unattended automation.
• Native ability to develop robots using voice commands.

Nice RPA pros

• Easy to use.
• Pre-built templates.
• Custom automation capabilities.

Nice RPA cons

• Niche centered: it may not be suitable for every use case.
• Limited community support.

ServiceNow: Best RPA for customization

ServiceNow is popular for an array of service and IT operations management tasks. The company also offers low-code workflow automation solutions that enable users to create applications with limited coding knowledge to help with their business processes.

With ServiceNow App Engine, you can create custom applications to meet your specific business requirements – you can also leverage pre-built application templates and components to accelerate the development process.

ServiceNow pricing

Quotes for this company’s tools are available upon request.

ServiceNow key features

• It has a centralized bot management hub.
• Desktop design studio.
• They offer 1,300+ tools, including connectors, templates, and optical character recognition (OCR), allowing companies to select the components they want to use.
• It has attended and unattended automation options.

ServiceNow pros

• Low learning curve.
• Users speak highly of its user interface.
• Low code option makes it suitable for business users.

ServiceNow cons

• The UI Builder can present a steep learning curve.
• Some users say the tool is pricey.

AutomationEdge: Best RPA for data processing

AutomationEdge Hyperautomation Platform offers tools to help you define and deploy software robots that can mimic human actions and perform repetitive tasks, which reduces human error. This helps to streamline business processes, manage data, and integrate systems. AutomationEdge can integrate with various data sources, databases, and applications, enabling seamless data flow and synchronization.

AutomationEdge pricing

The company offers a free plan and two paid plans. Although AutomationEdge doesn’t advertise their rates on their website, they charge clients based on usage.

• Starter: Free for 30 minutes per month for a lifetime.
• Professional: Quotes available upon request. License type: Consumption.
• Enterprise: Quotes available upon request. License type: Subscription/ Consumption.

AutomationEdge key features

• Integration with third-party services, over 400 tools.
• Conversational AI and chatbot.
• Intelligent document processing capability.

AutomationEdge pros

• Consumption based pricing model option.
• Users speak highly of the company’s support team.
• On-premise and cloud deployment options.

AutomationEdge cons

• The solution can get overwhelming for newbies.
• Knowledge base can be improved to help with the learning curve.

Also see: Top Digital Transformation Companies

Pega platform: Best for large enterprises with complex business processes

Pega Platform, also known as Pega Systems, is a low-code, model-driven application development platform that enables companies to build and deploy automated applications. Pega Platform supports business process management (BPM), customer relationship management (CRM), case management, and other types of applications. It is used by businesses across various industries to improve customer engagement, streamline operations, and drive digital transformation.

Pega platform pricing

Custom pricing, contact the company for quotes. Pricing information found on the AWS marketplace reveals the price of Pega cloud services at $990,000 for 12 months, $1,980,000 for 24 months and $2,970,000 for 36 months. For your actual rates, contact the company for pricing.

Pega platform key features

• Attended and unattended automation options.
• Integrates with services like Salesforce, DocuSign, Avaya, SAP, Cisco and more.
• Support the use of NLP to automate email.
• Process management.
• Process modeling and designing.

Pega platform pros

• Business process control.
• Reporting and analytics.
• It allows you to use AI for self-healing automation.

Pega platform cons

• Support can be improved.

MuleSoft RPA: Best for organizations with many routine processes

MuleSoft is a company that provides a platform for building and integrating applications, data, and devices. They offer application and data integration products, API management, and robotic process automation – enabling no-code and pro-code teams to build automation across enterprise systems.

MuleSoft Anypoint Platform allows businesses to connect applications, data, and devices across on-premises and cloud environments. It provides a range of tools and services to build, deploy, manage, and monitor APIs and integrations.

MuleSoft RPA pricing

Mulesoft automation pricing starts at $57,000 per year. They offer different pricing models, including pay-per-use, which charges you for RPA Bot Minutes, the number of API calls made by RPA automation, and the number of Composer Tasks. You can also select the credits for RPA and Composer model. The company requires interested buyers to contact them for quotes.

MuleSoft RPA key features

• It has RPA Manager that manages user access and permissions.
• Extract data from documents, web pages, emails, spreadsheets, and more.
• Automate data entry and data retrieval in legacy and modern applications.
• Mulesoft offers various RPA components to help with your business processes; they include MuleSoft RPA Manager, MuleSoft RPA Builder, MuleSoft RPA Recorder, and MuleSoft RPA Bots.

MuleSoft RPA pros

• Comprehensive documentation.
• Multi-language support.
• Integrates with CRM and ERP tools.

MuleSoft RPA cons

• Some users reported that Mulesoft RPA is costly.
• Needs more integration.

For more information, also see: Top Robotics Startups

How to Choose the Best RPA Company for Your Business

While the hundreds of RPA tools in the market all claim they are the best for you and your organization, you need to understand that the best RPA is relative, not absolute. Meaning what works for company A might not serve the needs of company B. What are the specific workflow automation needs of your company?

With your goals specified, here are the steps to take to select the best RPA company for your business.

• Conduct extensive research about the best RPA companies.
• To assist with that research, create a list of the top RPA features you need. This list below can be a starting point for your research:
  • Process orchestration
  • Analytics and monitoring
  • Exception handling
  • Low-code or no-code development
  • User interface interaction
  • Scalability
  • Cognitive automation
  • Audit trails and compliance
  • Customizability
  • Integration capability
  • Community and support
• Identify the top three companies that stand out for you.
• Request for a demo of your desired tool from these companies and also try a free trial if available to help you get a better understanding of the platform before you invest.
• Don’t forget to consider cost – choose a tool within your budget; RPA tools won’t be getting cheaper in the future.

Review Methodology

We reviewed more than 50 RPA companies using 15+ individual data points across five key categories: Cost, features set, ease of use, the versatility of audience service and support. We gathered products and services info, features, pricing, and other relevant information from each RPA company’s websites, whitepapers and documentation – we then used the data to score the best RPA companies as follows:

• Cost (20%)
• Features set (30%)
• Ease of use (25%)
• Versatility of audience service (10%)
• Support (15%)

We considered several individual data points that carry the most weight in each ranking criteria category when choosing the best RPA company. After careful consideration, calculation and extensive research, our top picks were determined with enterprise use in mind.

Bottom Line: Best RPA Companies 2023

As automation continues to gain traction, organizations need to identify the best RPA company for their specific needs.

Ultimately, choosing the best RPA company for your business will depend on your unique requirements, budget, and long-term automation goals. It is crucial to thoroughly evaluate each company’s offerings, customer reviews, and track record before making a decision.

Ultimately, companies should realize that while RPA can be a costly investment, it’s an investment that should pay itself back. The returns are numerous, but chiefly reside in RPA’s capacity to dramatically streamline workflow and improve staff productivity.

On a related topic: The Future of Artificial Intelligence

The post Top Robotic Process Automation Companies appeared first on eWEEK.

This acquisition includes Hopin’s flagship Events platform and Session product, signaling a strategic move by RingCentral to bring customers more dynamic, interactive video solutions through virtual events.

For more information, also see: Digital Transformation Guide

Hopin Provides a Single Solution for Hybrid Events

Hopin Events is a one-stop shop for managing all aspects of an event. Using the platform, companies can easily set up and run events that are completely online, or a mix of online and in-person events like conferences, training sessions, or even virtual expos with sponsor booths.

The platform provides unique features, such as the ability to host multiple sessions at once, tools for networking, and options to sign up and register for events. While many collaboration vendors offer a virtual event platform, few have a hybrid solution where the in-person and digital events can be managed simultaneously.

Hopin Session makes meetings more interactive and personalized. With Session, companies can customize the user experience through things like breakout rooms for smaller group discussions, polls to gauge attendees’ opinions, and much more. The idea is to make meetings feel less like a one-sided presentation and more like a group conversation.

RingCentral Rounds out Its Portfolio with Hybrid Events

With Hopin’s technology, RingCentral can offer a more rounded service portfolio. However, the acquisition wasn’t only about technology, as it brings nearly 100 Hopin employees, many of them technical experts, into the RingCentral fold.

This is a significant injection of new talent into RingCentral’s operations, which already covers video meetings, webinars, and digital conference rooms, said Vlad Shmunis, RingCentral’s founder and CEO, during a news briefing with analysts.

“The acquisition of Hopin brings fresh energy and innovation to RingCentral’s video services. We now have a complete solution—meetings, rooms, webinars, and events—enhanced by the unmatched customization capabilities of Hopin’s Session portfolio. This will offer a superior experience for hosting a meeting or an event.”

Also see: Top Digital Transformation Companie

Bringing Simplicity to Hybrid Events

RingCentral has established a reputation for simplifying business communications with its video tool suite. The suite includes RingCentral Video, an online platform enabling users to arrange or join meetings from anywhere; RingCentral Rooms, which turns any location into a video conference room; and RingCentral Webinar, capable of hosting large online gatherings of up to 10,000 participants from any computer, web browser, or mobile device.

As RingCentral integrates Hopin’s technology into its core offerings, the user experience is set to undergo significant enhancements, Shmunis added. RingCentral is reinventing itself as an artificial intelligence (AI)-driven company, delivering smartphone solutions, an intelligent contact center, and business solutions for companies of all sizes.

Historically, events have not been considered part of a collaboration suite, but that sentiment is changing. Digital collaboration is about sharing content and ideas, and events do that in a massively scalable way. Businesses can now use RingCentral for one-to-one meetings, one-to-few brainstorming sessions, one-to-many webinars, and company meetings and how one-to-thousands in an event format.

Also see: 100+ Top AI Companies 2023

The post RingCentral Acquires Hopin Assets For Hybrid Events appeared first on eWEEK.

Among the topics we covered: 

• Based on your latest research into Digital Transformation, what technologies are bubbling to the top?
• What key trends are you seeing in Observability? Why is it getting so much attention?
• AI is everywhere, and creeping into IT Ops too. How are ML and AI impacting IT Ops and DevOps today? What about the near future?
• Looking ahead, what is the Next Big Thing for Ops?

Listen to the podcast:

Also available on Apple Podcasts

Watch the video:

The post Sageable CTO Andi Mann on Observability and IT Ops appeared first on eWEEK.

Artificial intelligence, or AI, is a combination of sophisticated algorithms, computing, and data training methods that allow machines and computers to mimic human knowledge and behaviors.

In some ways, artificial intelligence is the opposite of natural intelligence. While living creatures are born with natural intelligence, man-made machines can be said to possess artificial intelligence. In fact, one of the early pioneers of AI, John McCarthy, defined artificial intelligence as “the science and engineering of making intelligent machines.”

In practice, however, artificial intelligence companies use the term artificial intelligence to refer to machines doing the kind of thinking and tasks that humans have taken to a very high level.

Also see: Top Generative AI Apps and Tools

Artificial Intelligence: Table of Contents

• What is Artificial Intelligence in Simple Terms?
• What is Generative AI?
• Artificial Intelligence vs. Machine Learning
• Machine Learning vs. Deep Learning
• Types of Artificial Intelligence
• Short History of AI
• AI Uses Cases: What Can AI Do?
• Pros and Cons of Using AI
  
• The Limitations of AI
• Ethics and AI
• Bottom Line: The Future of AI
• Artificial Intelligence: Additional Resources

What Is Artificial Intelligence in Simple Terms?

Computers are very good at making calculations — taking inputs, manipulating them, and generating outputs as a result. But in the past, they have not been able to do other kinds of human tasks, such as understanding and generating language, identifying objects by sight, creating art, or learning from past experiences.

Today, many computer systems have the ability to communicate with humans using ordinary speech. They can recognize faces and other objects. They use machine learning techniques, especially deep learning and neural networks, in ways that allow them to learn from the past and make predictions about the future. 

Much of this technology is still being developed and advanced every day, but now, even the average consumer can access AI models to generate content, solve problems, and handle a number of other advanced tasks.

Also see: Generative AI Companies: Top 12 Leaders 

What Is Generative AI?

Generative AI is a specific, emerging form of artificial intelligence that relies on big data training sets, neural networks, deep learning, and some natural language processing to create original content outputs. Although the most commonly used generative AI tools currently generate text and code, generative AI solutions can also generate images, audio, and synthetic data, among other outputs.

Generative AI is perhaps the most popular and fastest-growing type of AI today, especially with the global popularity of OpenAI’s ChatGPT and GPT-4. Other popular examples of generative AI include Google Bard, Jasper, Stable Diffusion, DALL-E, Microsoft and GitHub Copilot, and DreamStudio.

Learn more: What is Generative AI? 

Artificial Intelligence vs. Machine Learning

At the simplest level, machine learning (ML) is a subset of artificial intelligence. While the greater artificial intelligence umbrella is dedicated to all kinds of approaches to human-like problem-solving, machine learning involves developing a specifically trained model that focuses on teaching machines to complete focused tasks and identify data patterns. In many cases, machine learning is used in conjunction with other forms of artificial intelligence.

Further the comparison: AI vs. ML: Artificial Intelligence and Machine Learning Overview 

Machine Learning vs. Deep Learning

Just as machine learning is a subset of artificial intelligence, deep learning is a subset of machine learning. Machine learning as a whole is about deriving insights from big datasets and making decisions based on the information these solutions find. It is an algorithmic, data-driven approach to decision-making. Deep learning is also an algorithmic approach to decision-making, but it’s a bit more complex; instead of working with one or a small number of algorithms, deep learning models work with multiple layers of algorithms — known as a neural network. This structure is designed to help deep learning models mimic the functions of human brains.

Get a closer look: Machine Learning vs. Deep Learning

Types of Artificial Intelligence

Computer scientists have proposed different ways to classify the types of AI. One popular classification uses three categories:

1. Artificial Narrow Intelligence

Artificial Narrow Intelligence (ANI) is designed to complete one task or set of tasks with high competence and skill. Apple’s Siri, IBM’s Watson, and Google’s AlphaGo are all examples of Narrow AI. Narrow AI is fairly common in the world today.

2. Artificial General Intelligence

Artificial General Intelligence (AGI) is a form of AI that performs many intellectual tasks on par with a human. Many researchers are currently working on developing general AI. One of the best early examples of AGI is GPT-4, which is able to solve a variety of problems and has performed well on a number of standardized human tests.

3. Artificial Superintelligence

Artificial Superintelligence (ASI), which is still theoretical, has intellectual capacities that far outstrip those of humans. This kind of artificial intelligence is not yet close to becoming a reality.

Also see: How AI is Altering Software Development with AI-Augmentation 

Another popular classification uses four different categories

1. Reactive Machines

Reactive machines take an input and deliver an output, but they do not have memory or learn from past experiences. The bots you can play against in many video games are good examples of reactive machines.

2. Limited Memory

Limited memory machines can look a short distance back into the past. Many vehicles on the road today have advanced safety features that fall into this category. For example, if your car issues a backup warning when a vehicle or person is about to pass behind your car, it is using a limited set of historical data to come to conclusions and deliver outputs.

3. Theory of Mind

Theory of mind machines are aware that human beings and other entities exist and have their own independent motivations. Most researchers agree that this kind of AI has not yet been developed, and some researchers say that we should not attempt to do so. However, some of the latest generative AI models are performing well in theory of mind tasks and tests.

4. Self-Aware

Self-aware machines are aware of their own existence and identities. Although a few researchers claim that self-aware AI exists today, only a handful of people share this opinion. Developing self-aware AI is highly controversial.

While these classifications are interesting from a theoretical standpoint, most organizations are far more interested in what they can do with AI.

Also see: Three Ways to Get Started with AI 

A Short History of Artificial Intelligence

1950s and 1960s

Many people trace the history of artificial intelligence back to 1950 when Alan Turing published “Computing Machinery and Intelligence.” Turing’s essay begins with “I propose to consider the question, ‘Can machines think?’” It then lays out a scenario that came to be known as the Turing Test. Turing proposed that a computer could be considered intelligent if a person could not distinguish the machine from a human being.

In 1956, John McCarthy and Marvin Minsky hosted the first artificial intelligence conference, the Dartmouth Summer Research Project on Artificial Intelligence (DSRPAI). The conference convinced computer scientists that artificial intelligence was an achievable goal, setting the foundation for several decades of further research. Some of the earliest forays into AI technology, developed bots to play checkers and chess, emerged as a result of this conference.

The 1960s saw the development of rudimentary robots and several problem-solving programs. One notable highlight was the creation of ELIZA, a program that simulated psychotherapy and provided an early example of human-machine communication.

1970s and 1980s

In the 1970s and 80s, AI development continued but at a slower pace. The field of robotics in particular saw significant advances, such as robots that could see and walk. Additionally, Mercedes-Benz introduced the first (extremely limited) autonomous vehicle. However, government funding for AI research decreased dramatically, leading to a period some refer to as the “AI winter.”

1990s and Early 2000s

Interest in AI surged again in the 1990s. The Artificial Linguistic Internet Computer Entity (ALICE) chatbot demonstrated that natural language processing could lead to human-computer communication that felt more natural than what had been possible with ELIZA. The decade also saw a surge in analytic techniques that would form the basis of later AI development, as well as the development of the first recurrent neural network architecture. This was also the decade when IBM rolled out its Deep Blue chess AI, the first to win against the current world champion.

The first decade of the 2000s saw rapid innovation in robotics. Roombas began vacuuming rugs and robots launched by NASA explored Mars. Closer to home, Google was working on a driverless car.

2010s

The years since 2010 have been marked by unprecedented increases in AI technology. Both hardware and software developed to a point where object recognition, natural language processing, and voice assistants became possible. IBM’s Watson won Jeopardy. Siri, Alexa, and Cortana came into being, and chatbots became a fixture of modern retail. Google DeepMind’s AlphaGo beat human Go champions. And enterprises in all industries have begun deploying AI tools to help them analyze their data and become more successful.

Perhaps most significant to today’s generative AI landscape, in 2017, Google released a research paper that first identified a neural network architecture concept called the Transformer. The transformer has since become one of the foundational technologies for developing generative AI models.

2020s

AI is beginning to evolve past narrow and limited functions into more advanced implementations, some of which are accessible to the general public; indeed, this decade seems to place more focus on AI democratization than ever before. The early years of this decade have seen the rise of generative AI, with more complex models created for enterprise users and simplified, low-cost versions available to all users. 

The greatest and most popular advancements of AI took off in late 2022 when OpenAI first launched its ChatGPT chatbot and large language model (LLM). Many competitors and similar models have since emerged to support text, code, audio, video, image, and synthetic data generation requirements.

Beyond content generation in its various forms, AI advancements of the 2020s include AI-powered search and virtual assistants in web browsers and various business applications, AI-powered medical and pharmaceutical research, and more advanced instances of AI-powered computer vision for AR, VR, and XR experiences. Increasingly, AI is being regulated and its ethics and environmental impact are being discussed.

Also see: The History of Artificial Intelligence 

AI Use Cases: What Can AI Do?

The possible AI use cases and applications for artificial intelligence are nearly limitless. Some of today’s most common AI use cases include the following:

Content generation

Generative AI models are being used to generate content in a variety of formats: not just text but also code, synthetic data, audio and music, images, video, and voice. Content generation models are currently applied to a variety of industries and use cases, including marketing and sales, customer service, employee coaching, cybersecurity, computer vision, healthcare and pharmaceuticals, entertainment and gaming, and legal and government.

More on this topic: Generative AI Examples

Recommendation engines

Whether you’re shopping for a new sweater, looking for a movie to watch, scrolling through social media, or trying to find true love, you’re likely to encounter an AI-based algorithm that makes suggestions. Most recommendation engines use machine learning models to compare your characteristics and historical behavior to people around you. The models can be very good at identifying preferences even when users aren’t aware of those preferences themselves.

Natural language processing

Natural language processing (NLP) is a broad category of AI that encompasses speech-to-text, text-to-speech, keyword identification, information extraction, translation, and language generation. It allows humans and computers to interact through ordinary human language (audio or typed), rather than through programming languages. Because many NLP tools incorporate machine learning capabilities, they tend to improve over time.

Sentiment analysis

AI can not only understand human language, but it can also identify the emotions underpinning human conversation. For example, AI can analyze thousands of tech support conversations or social media interactions and identify which customers are experiencing strong positive or negative emotions. This type of analysis allows customer support teams to focus on customers that might be at risk of defecting and/or extremely enthusiastic supporters who could become advocates for the brand.

Voice synthesis and assistance

Many of us interact with Siri, Alexa, Cortana, or Google on a daily basis. While we often take these assistants for granted, they incorporate advanced AI techniques, including natural language processing and machine learning. Several new generative AI solutions offer voice synthesis and assistance as well.

Fraud prevention

Financial services companies and retailers often use highly advanced machine learning techniques to identify fraudulent transactions. They look for patterns in financial data, and when a transaction looks abnormal or fits a known pattern of fraud, they issue alerts that can stop or mitigate criminal activity.

Image recognition

Many of us use AI-based facial recognition to unlock our phones. This kind of AI also enables autonomous vehicles and automates processing for many health-related scans and tests.

Predictive maintenance

Many industries like manufacturing, oil and gas, transportation, and energy rely heavily on machinery, and when that machinery experiences downtime, it can be extremely costly. Firms are now using a combination of object recognition and machine learning techniques to identify in advance when equipment is likely to break down so they can schedule maintenance at a time that minimizes disruptions.

Predictive and prescriptive analytics

Predictive algorithms can analyze just about any kind of business data and use that as the basis for forecasting likely future events. Prescriptive analytics, which is still in its infancy, goes a step further and not only makes a forecast but also offers recommendations as to what organizations should do to prepare for likely future events. These AI-powered approaches to analytics are used across a variety of industries but are particularly gaining steam in quote-based industries like insurance.

Autonomous vehicles

Most vehicles in production today have some autonomous features, such as parking assistance, lane centering, and adaptive cruise. And while they are still expensive and relatively rare, fully autonomous vehicles are already on the road, and the AI technology that powers them is getting better and less expensive every day.

Robotics

Industrial robots were one of the earliest implementations of AI, and they continue to be an important part of the AI market. Consumer robots, such as robot vacuum cleaners, bartenders, and lawnmowers, are becoming increasingly commonplace.

Of course, these are just some of the more widely known use cases for AI. AI technology is seeping into daily life in so many ways that we often aren’t fully aware of.

AIOps

AIOps — artificial intelligence for IT operations — is increasingly being used to simplify workflows and workloads for skilled tech workers. AI can be used to complete tasks related to service and performance management and data management and analysis.

Also see: Best Machine Learning Platforms 

Pros and Cons of Using AI

AI is permeating every corner of the business and home, but, much like with any other new and fast-changing technology, artificial intelligence presents both considerable pros and cons.

Pros

• High levels of accuracy: Simplifies workflows and reduces the potential for human error.
• Increases employee availability: Takes repetitive task loads off human workers, allowing them to focus on more meaningful tasks.
• Advanced content generation capabilities: Fast and affordable content generation and data analysis are possible with many AI models.
• New research and discoveries powered by AI-driven analysis: AI developments are leading to sophisticated analysis advancements in fields like medicine and pharmaceuticals.

Cons

• Often expensive and power-hungry: High costs and energy requirements are often part of running AI models.
• User privacy and security shortcomings: User privacy and security concerns are paramount, especially with how much data is required to train AI models; with the latest generative AI models, there’s also concern that the models will learn from, retain, and share user inputs without authorization.
• Workforce disruption: Advanced AI tools could potentially take jobs away from human workers.
• No sense of self: AI has no self-awareness or self-driven creativity; everything is programmed and can lead to bias or inappropriate/dangerous outputs.

On a related topic: The AI Market: An Overview

The Limitations of AI

AI is limited both by the data it’s trained with and the environment in which it’s operating. Here are a few examples of the limitations of AI:

• AI bias: If training data is not robust, accurate, and varied, the model can suffer from inaccurate or partial outputs. AI bias is a major concern, as machines that are trained on biased data may not serve the needs of more diverse populations and use cases.
• No emotions or creativity: AI continues to grow in its conversational and creative capabilities, but it is still an algorithmic model that does not “think” but rather operates on its training. As such, AI solutions cannot fully replace the emotional intelligence and sparks of creativity that humans have.
• Limited recall and contextual understanding: Although some of the latest generative AI models and other AI models can pull from their recent history, many AI tools can only handle inputs without considering any additional context while generating outputs.
• Limited timeliness: Not all AI models have real-time access to the internet and other resources with updated information. They may generate inaccurate responses or fail to learn from recent mistakes as a result.
• Compute power requirements: Most AI models require significant compute power, which can be expensive and energy-intensive to implement. That is why many AI models are limited to large enterprises that have the resources to run this kind of AI.

The Importance of AI Ethics

Ai ethics have been discussed in a more theoretical sense for many years, but especially as AI has become more mainstream and capable, AI ethics discussions have become more important than ever before. AI ethics is incredibly important to the long-term health and development of AI because ethical issues with AI can cause businesses to lose customers, reputation, legal battles, and money. In some cases, unethical AI instances could even lead to the loss of human life.

With a strong ethical AI framework in place, AI companies and users can expect the following benefits:

• Avoids harmful biases: AI ethics focuses heavily on creating tools that work well for everyone, including users across the globe and of varying races, genders, cultural backgrounds, and disabilities.
• Protects user privacy: AI requires massive amounts of data to run successfully, and sometimes, that data encroaches upon personal privacy. AI ethics train models to more carefully handle user inputs, but also their payment information, their image/identity, etc.
• Encourages responsible environmental impact: Many AI models use a lot of energy, which is already having negative consequences on the environment. Some of the foremost AI companies in the world are working to incorporate responsible energy consumption and other environmental considerations into their AI ethics.

• Increases human safety features: AI ethics encourage AI developers to create tools that put user and human safety at the forefront; autonomous vehicles, for example, must be tested and vetted thoroughly before they can be operated by humans or driven near pedestrians.

More on this topic: AI Ethics: An Overview

 The Future of AI

So what does the future of AI look like? Clearly, AI is already reshaping consumer and business markets, but it has a ways to go before it truly matches human knowledge and capabilities.

The technology that powers AI continues to progress at a steady rate. Future advances like quantum computing may eventually enable major new innovations, but in the near term, it seems likely that the technology itself will continue along a predictable path of constant improvement.

What’s less clear is how humans will adapt to AI. Many early AI implementations have run into major challenges. In some cases, the data used to train models has allowed bias to infect AI systems, rendering them unusable.

In many other cases, businesses have not always seen the financial results they hoped for after deploying AI. The technology may be mature, but the business processes surrounding it are not. Therefore, enterprise AI’s future will rely heavily upon the investments businesses make in the technology.

“Successful AI business outcomes will depend on the careful selection of use cases,” said Alys Woodward, senior director analyst at Gartner. “Use cases that deliver significant business value, yet can be scaled to reduce risk, are critical to demonstrate the impact of AI investment to business stakeholders.”

Finally, and perhaps most significantly, there have been mixed reactions from the general public when it comes to artificial intelligence developments. While many users are excited about new AI tools like generative AI models, others are nervous about losing their jobs or their personal information to the technology. Others are concerned about the future implications of tools that are only growing “smarter” and more capable.

High levels of adoption have propelled certain forms of AI forward, while others have languished in obscurity. In a very real sense, the future of AI may be more about people than about machines.

Also see: The Future of Artificial Intelligence

Artificial Intelligence: Additional Resources

In an AI market that’s constantly changing, it can be difficult to keep up with the latest news and trends. We’ve gathered several of our top resources in one place to help you stay current on artificial intelligence:

Top Resource: 100+ Top AI Companies

Also see: 

• • The AI Market: An Overview 
  • Cloud and AI Combined: Revolutionizing Tech
  • Guide to Algorithms in AI
  • Generative AI Startups in 2023
  • Generative AI Companies: Top 12 Leaders
  • Top 9 Generative AI Applications and Tools
  • Generative AI Landscape: Current and Future Trends
  • Generative AI and Cybersecurity: Advantages and Challenges
  • The Benefits of Generative AI
  • AI Courses: Learn AI With These Top 10 Courses
  • Top 10 AI Detector Tools for 2023
  • AI Careers: How to Build a Career in AI
  • ChatGPT vs. Watson Assistant
  • ChatGPT vs. Google Bard: Generative AI Comparison

The post What is Artificial Intelligence? Guide to AI appeared first on eWEEK.

Contrary to traditional approaches, such as helpdesk tickets, Tanium XEM with DEX is designed to help IT maintain and improve the employee experience to keep tickets from being opened in the first place. It provides employees with automated remediation and allows them to fix issues affecting their devices. DEX solutions focus on the endpoint and encompass hardware, operating systems, apps, and cloud services.

Also see: The Successful CISO: How to Build Stakeholder Trust

Legacy IT Management: Time-Consuming and Reactive

One of the challenges with the legacy approach to end-user management is that it’s reactive in nature. A worker who has a problem with an application stops work, calls the help desk, and then the troubleshooting starts. My research shows that with the traditional approach, three-quarters of help desk tickets are opened by the end user versus the IT organization. This means IT pros are always in fire-fighting mode as they are reacting to issues. Tanium DEX gives IT the visibility to get ahead of issues, even ones that users don’t know about.

Digital experience tools have existed for some time but saw an uptick in adoption during the pandemic. When employees shifted to working from home, IT departments scrambled to fix broken experiences, implement virtual private networks (VPNs), and move apps to the cloud. The pandemic highlighted the need for DEX solutions to adapt to changing work environments.

Poor Experience Impacts Workers and Customers

“Productive employees deliver great work, which is why CIOs must prioritize the employee experience in this new hybrid world. In fact, the digital employee experience is increasingly the only work experience for many of us” said Chris Hallum, director of product marketing at Tanium.

“A poor DEX not only impacts your employees but has a potentially negative impact on your customers. Employees struggling to effectively use their tools aren’t going to deliver their best work and it will undoubtedly impact how they engage with customers.”

Tanium Taps Desktop Information 

While there are a number of digital experience solutions available, Tanium’s approach is different. Many of the solutions use network traffic and infer problems.

For example, the network can see the latency of a Zoom call and if it falls below a threshold, it knows there is a problem. This is fine if the problem is actually the network but what if it’s something on the computer? In this case, the network solution could identify the problem based on Zoom performance but would not know the source. Tanium’s DEX can see things like browser issues, memory problems, or other factors that are tied to the endpoint as well as network performance.

Prior to DEX solutions, self-service solutions like portals have been used but those have seen limited success. Unified endpoint management (UEM) solutions are effective for provisioning and deployment but lack experience monitoring and employee sentiment surveying capabilities.

Organizations that don’t have visibility into employee experiences need to establish feedback loops for better IT management. In my discussion with Tanium, Hallum told me that automated self-remediation is a highly desired feature for detecting and fixing issues.

Tanium DEX consists of two modules with an end-user component:

• A Performance module: observes performance conditions across endpoint devices and servers, identifying trends and systemic problems.
• An Engage module: can identify real-time trouble spots, help users fix issues without IT intervention, and can be used to survey employees on the quality of their experiences.

Also see: Secure Access Service Edge: Big Benefits, Big Challenges

DEX Captures Valuable User Sentiment

The solution measures sentiment through surveys after an issue has been resolved, fostering a bi-directional relationship between IT and employees. It also it can be used to send notifications related to training and important updates. Lastly, it provides a health score for each endpoint, so IT can address low-performing endpoints and set improvement targets.

“With the automation that Tanium brings to the table, organizations can reduce their helpdesk load and redeploy the resources to do more strategic work,” said Hallum. “They can provide better service levels for the really hard issues, as opposed to just constantly responding to the 10% of the issues that represent 80% of the helpdesk tickets.”

Another capability for Tanium DEX is that with its inclusion in the Tanium XEM platform customers get endpoint, vulnerability management, and DEX capabilities all in a single integrated solution. There are many synergies here.

For instance, if the remediation of a digital experience issue requires the deployment of a patch, Tanium DEX can use the XEM platform to make that happen in real time and also at scale across the entire enterprise. Tanium’s access to any type of data, setting, or file on the endpoint, and nearly unlimited remediation capabilities, are some of this its most powerful and differentiated capabilities.

Tanium has integration with ServiceNow, which brings Tanium data into the ServiceNow console. But newer features like health scores are not yet included in ServiceNow with this version.

Focused on Endpoint and App Experience

At the moment, with this first release of Tanium DEX, Tanium is clearly focused on endpoint and app experiences. Hallum assured me, however, that other aspects of the employee experience, like cloud service performance, are something Tanium is pursuing.

Businesses are competing on customer experience (CX) today but it’s impossible to deliver a best-in-class CX without having a quality employee experience. When workers have poor performing apps, that creates frustration and can often be directed back at the customer. Businesses need to shed the legacy reactive approach to end user support and embrace technologies that enable IT pros to move to a more proactive model.

Also see: Top Digital Transformation Companies

The post Tanium Adds Digital Experience Management to its XEM Platform appeared first on eWEEK.

Data mining platforms combine artificial intelligence (AI), machine learning (ML), and statistical analysis to identify data trends. The data mining process can be used to spot customer needs, find ways to boost revenue and profitability, engage more effectively with audiences, and derive industry-specific insights.

These days, data mining techniques and tools are more powerful than ever. Many data mining tools can now take advantage of abundant computing power and memory to crunch numbers and data with more speed and accuracy. This evolution of data mining tools is particularly important as more companies are processing big data for various digital transformation projects.

In this buyers’ guide, learn about the best data mining tools and software on the market today, their pros and cons, and how your data team can select the best solution for your particular data mining requirements.

For more information, also see: Best Data Analytics Tools

Table of Contents

• Best Data Mining Tools and Software: Comparison Chart
• SAS Visual Data Mining and Machine Learning
• Oracle Machine Learning in Autonomous Database
• Talend Data Fabric
• RapidMiner (Altair)
• Alteryx Designer Cloud
• IBM SPSS Modeler
• KNIME
• Orange
• Qlik Sense
• TIBCO Data Science
• How to Select a Data Mining Tool for Your Organization
• Bottom Line: Data Mining Tools

Best Data Mining Tools and Software: Comparison Chart

Data mining tools can be deployed on-premises or in the cloud. Some are offered as traditional software, some are open source, and many exist as software-as-a-service (SaaS) solutions. These tools can be further differentiated by the features they offer, such as data preparation, data exploration, and advanced data visualization and reporting features. In our study of the best data mining tools, we identified top players and compared some of their key features in this table:

SAS Visual Data Mining and Machine Learning

SAS Visual Data Mining and Machine Learning (VDMML) is a visual and programming interface that makes end-to-end data mining possible for its users. SAS VDMML runs on SAS Viya, the AI, analytic, and data management platform.

Within this ecosystem, VDMML is able to handle data wrangling and transformation, feature engineering, and data exploration while supporting statistical, data mining, and machine learning techniques. This in-memory processing environment is particularly known and praised for its scalability, making it a great option for enterprise users.

Key Features

• Self-service data preparation and embedded AI.
• Integrated machine learning programs for combining structured and unstructured data.
• Best practice templates for model building.
• Shareable data visualizations and interactive reports.
• Compatible with Python, R, Java, and Lua.
• Includes access to a public API for automated modeling and building and deploying custom predictive modeling applications.

Pros

• Simple language — provided via embedded natural language generation — makes report interpretation easier and reduces the tool’s learning curve.
• Automated feature engineering uses a definitive ranking process to select the best modeling features for data transformation.
• Generative adversarial networks (GANs) generate synthetic data that can be used for deep learning models.
• Scalable in-memory analytical processing.

Cons

• As would be expected with a big name in analytics, SAS is more expensive than many other data mining tools.
• SAS offers a diverse and complex ecosystem of tools that is great for data scientists and analytics experts but can be challenging for less knowledgeable users.

To learn more, also see: Top Business Intelligence Software 

Oracle Machine Learning in Autonomous Database

Oracle Machine Learning in Autonomous Database is a data preparation, exploration, and mining option that uses more than 30 scalable, in-database machine learning algorithms for model creation. It is accessible from SQL and REST APIs for R and Python and works well with third-party packages Ideal for customers who want to work primarily in the Oracle ecosystem, Oracle Machine Learning supports classification, regression, clustering, association rules, feature extraction, time-series, anomaly detection, and other machine learning techniques.

Although Oracle Machine Learning includes many different useful components, its most helpful feature set for data mining is Oracle Data Miner, which offers a drag-and-drop approach to analytical workflow and model builds.

Key Features

• Integrated notebook environment supports SQL, PL/SQL, Python, R, and markdown interpreters.
• Notebook scheduling and versioning.
• Automated machine learning via APIs and no-code user interfaces.
• Database storage for objects and Python scripts.
• Built-in data-parallel and task-parallel features for running user-defined functions.
• In-database and third-party, ONNX-format model deployment for real-time scoring.

Pros

• Effective data scoring is available with integrated SQL prediction operators in SQL queries.
• More advanced data governance, model governance, and database security features than many other data mining tools.
• Both on-premises and cloud availability for ML capabilities.
• Integrations are available for other Oracle tools, including Oracle Analytics Cloud, Oracle Stream Analytics, and Oracle APEX.

Cons

• Use cases that require GPU compute, such as deep learning image CNNs, are not supported.
• OML Notebooks, OML AutoML UI, and OML Services are only available on the shared version of Oracle Autonomous Database.
• This solution is optimized for data that resides in Oracle Autonomous Database; not ideal for users with data in other environments.

Talend Data Fabric

Talend Data Fabric is a single, cloud-based platform that centralizes data integration, data quality and integrity management, data governance, delivery, and application and API integration. It is uniquely designed to consolidate data activities, providing intelligence and collaboration capabilities that complement data workers of various technical expertise levels.

Although the data integration portion of Talend Data Fabric is where most of the platform’s data mining functionality lies, the platform works best when all of its features are used in tandem.

Key Features

• 1,000+ built-in connectors and components for leading SaaS and on-prem applications, including Marketo, Workday, Salesforce, SAP, and ServiceNow.
• Application and API integration for microservices.
• Compatible with the following database and storage systems and providers: AWS, Azure, Google Cloud, Snowflake, Microsoft SQL Server, Oracle, Greenplum, SAS, Sybase, and Teradata.
• Compatible with big data platforms like Cloudera, Databricks, Google Dataproc, AWS EMR, and Azure HDInsight.
• Native Spark streaming to support real-time big data messaging systems.

Pros

• Automated frameworks are particularly effective at nurturing data quality and health.
• Ready-to-use dashboards are designed for ongoing monitoring and reporting.
• With Trust Score for Snowflake, this is the only solution that profiles entire data sets inside Snowflake Data Cloud using native Snowflake processing; this feature ensures data professionals can assess quality at scale for healthy, analytics-ready data.
• Self-service data APIs speed up the process of creating and operationalizing compliant, no-code APIs.

Cons

• Users without Java expertise may find it challenging to use this tool.
• The learning curve can be steep for Talend Data Fabric and related products.

RapidMiner (Altair)

RapidMiner, acquired by Altair in September 2022, is a business analytics workbench with a focus on data mining, text mining, and predictive analytics. It uses a wide variety of descriptive and predictive techniques to give the insight users need to make profitable decisions. RapidMiner, together with its analytical server RapidAnalytics, also offers full reporting and dashboard capabilities.

Although RapidMiner’s visualizations have historically been somewhat limited, the Visual Workflow Designer feature is still effective for helping users visualize their processes. With its recent acquisition by Altair, RapidMiner may very well undergo some additional changes in this area.

Key Features

• Analysis results are aggregated in relevant locations rather than as complete data sets in memory.
• Algorithms delivered directly to data for faster performance.
• Graphical connection with Hadoop for handling big data analytics.
• Metadata propagation.
• Observability for storage and runtime behaviors.

Pros

• No software license fees are required to use RapidMiner.
• RapidMiner offers some of the most flexible and affordable support for data mining users.
• This tool is known for its fast development of complex data mining processes.
• Installation takes less than five minutes.

Cons

• RapidMiner can have a steep learning curve, especially for users who aren’t familiar with open-source data software.

Alteryx Designer Cloud

Alteryx is known for its various data science and analytics automation solutions. The Alteryx Analytics Cloud Platform comes in multiple different versions, but it’s the Alteryx Designer Cloud that offers the best features and functions for most enterprise data mining requirements.

Many users select Alteryx Designer Cloud for its balance of sophisticated enterprise tools with intuitive visualizations and other usability features. Although it could run into some processing or memory trouble with the largest of data sets, its smart data samples, pushdown processing, and compatibility with various cloud and data warehousing environments make it possible for users to scale this tool as their needs grow.

Key Features

• Easy-to-use, drag-and-drop interface.
• No-code/low-code, cloud environment.
• Features for data prep, blending, and analysis.
• Project sharing, version control, collaboration workflows, and other collaboration features.
• Built-in governance and security features.
• Smart data samples and pushdown processing.Compatibility with AWS, Google Cloud Platform, and Snowflake.

Pros

• Drag-and-drop functionality makes this a highly intuitive platform, especially for data visualization.
• Data quality bar and visual data profiling make it simpler to visualize data mining performance and results.
• Pushdown processing lets users benefit from the scalability of cloud data warehouse environments.
• A number of relevant Alteryx add-ons can easily be added to the baseline product.

Cons

• Possible limitations on processing power.
• Users may have restricted options when it comes to workflow customizations.

IBM SPSS Modeler

IBM SPSS Modeler is a visual data science and machine learning tool that speeds up operational tasks for data scientists. This IBM solution has many use cases, including data discovery, data preparation, model management and deployment, and machine learning for data asset monetization.

SPSS Modeler is available on its own and in conjunction with IBM Cloud Pak for Data, which is a containerized data and AI platform for building and running predictive models on public clouds, on private clouds, and on-premises.

Key Features

• Finds patterns in text, flat files, databases, data warehouses, and Hadoop distributions in a multicloud environment.
• 40+ out-of-the-box machine learning algorithms.
• Apache Spark integration to support faster in-memory computing.
• Enterprise-level data security and governance.
• Open-source compatibility with R and Python.

Pros

• Open source-based tools like R and Python give SPSS Modeler users more customization opportunities.
• Designed to support data analysts, coders, and non-coders alike.
• Hybrid flexibility is useful for a number of enterprises.
• The tool is known to scale well as organizational data mining needs grow.

Cons

• SPSS Modeler can be expensive.
• Certain kinds of customization can be challenging, though newer open-source features have helped in this area.

KNIME

The Konstanz Information Miner — better known as KNIME — is an open-source data analytics, reporting, and integration platform that requires minimal programming knowledge to use. It integrates machine learning and data mining components through modular data pipelining.

The KNIME Analytics Platform can be used for data wrangling, data modeling and visualization, spreadsheet automation, ETL, and a variety of other data preparation and mining processes. At its most basic level, KNIME is a free tool that users can download directly from the KNIME website. The Community Hub and Business Hub versions offer additional features for a higher price.

Key Features

• An active community is continuously integrating new developments.
• Workflow and component sharing and collaboration.
• Versioning and read access for unlicensed users.
• User-defined virtual cores for workflow execution.
• Advanced automation, deployment, and management features are available in paid plans.

Pros

• Drag-and-drop interface minimizes coding requirements.
• This tool does a good job of keeping work current, especially on collaborative projects.
• Users can blend tools from different domains with KNIME native nodes in a single workflow, including scripting in R and Python, ML, and connectors to Spark.
• The free version of this tool offers many collaboration features.

Cons

• KNIME has been known to hog memory resources.
• Most automation features are not available in the free plan version.

Orange

Orange is an open-source data mining solution that includes advanced machine learning and data visualization capabilities. It helps users to more easily build visual data analysis workflows with a large toolbox of features.

Some of the visuals that Orange offers include box and scatter plots, decision trees, heatmaps, linear projections, and hierarchical clusters. With its many visualization options and training widgets, Orange is one of the most commonly used data mining and analytics tools in schools, universities, and online training courses for users who are new to data science.

Key Features

• Data visualization options include statistical distributions, box plots and scatter plots, decision trees, hierarchical clustering, heatmaps, and linear projections.
• Attribute ranking and selections.
• Data analysis workflow prototyping.
• Compatible with third-party data sources.
• Natural language processing, text mining, and association rules mining.

Pros

• Orange is one of the only tools that focuses so heavily on exploratory, teachable data analysis.
• Widgets and connectors are easy and quick to set up for data analysis workflow prototypes.
• Easy to learn, this tool is used at schools, at universities, and in professional training courses.
• Compelling use cases come from Orange’s add-ons, including the ability for bioinformaticians and molecular biologists to rank genes and perform enrichment analysis.

Cons

• This tool is limited when it comes to more advanced data mining and analytics features.
• Limited user community support, though the tool is fairly easy to use without this kind of support.

Qlik Sense

Qlik Sense is a data analytics and data mining solution that combines visualizations, dashboards, AI, and analytics in a cloud platform format. This platform is capable of combining data from hundreds of external data sources to give users of all skill levels the insights they need.

Particularly helpful for users with little or no data science experience, Qlik Sense offers augmented analytics features that include AI-generated suggestions, real-time data pipelining, automated data preparation, search and natural language interaction, and predictive analytics. Qlik Sense can be deployed on Qlik Cloud, on a private cloud, on-premises, or via hybrid deployment options.

Key Features

• Insight Advisor, an AI assistant in Qlik Sense, offers insight generation, task automation, and search and natural-language interaction.
• SaaS, multicloud, on-premises, hybrid cloud, and other deployment options.
• Associative Engine for quick and contextualized calculations.
• Analytics app building with smart visualizations and drag-and-drop functionality.

Pros

• Insight Advisor gives users suggested insights and analyses, automates tasks, and also offers real-time advanced analytics.
• Qlik Sense integrates with hundreds of apps, databases, cloud services, and file management services.
• Qlik visualizations are diverse and highly interactive.
• Qlik Sense offers both mobile and embedded analytics to users.

Cons

• Users with less data science experience may struggle to learn how to use this tool at first.
• This tool is not ideal for unstructured data mining needs, like social media data mining.

TIBCO Data Science

TIBCO Data Science is a unified data science solution that combines the strengths of TIBCO Statistica, TIBCO Spotfire Data Science, TIBCO Spotfire Statistics Services, and TIBCO Enterprise Runtime for R. Though the platform includes many advanced features, the interface is designed to be simple with a drag-and-drop setup and simple, Slack-like collaboration features.

TIBCO Data Science users can benefit from the tool’s pre-built templates, version control, and a variety of third-party integrations. A particular strength of this software is its variety and depth of data and workflow visualizations.

Key Features

• Team Studio for collaborative data pipeline creation.
• Drag-and-drop interface.
• Code integration through Jupyter Notebook.
• Integration opportunities with Python and R.
• User-created parameterized workspaces.
• Model management, scoring, and governance.
• Data science workload federation across SAS, MatLab, R, and Python.

Pros

• A variety of customizations and integrations are available to users.
• Version control and project-sharing features make it easier for teams to work on data mining projects collaboratively.
• TIBCO Data Science is generally considered an easy-to-use tool.

Cons

• Limited documentation and a smaller user community can negatively impact customer support when using this tool.
• As a lesser-known name in the data mining space, TIBCO generally has fewer user resources but still maintains a relatively high price tag.

How to Select a Data Mining Tool for Your Organization

With so many options and overlapping features, it can be overwhelming to select the right data mining tool for your data transformation needs. To guide the decision-making process, consider these tips and best practices:

Look for tools that support your industry-specific requirements

While many data mining tools are more generic, some are already specialized to handle the data processing needs of certain industries. At a minimum, if you work in a highly regulated industry like government or healthcare, look for tools that include enterprise-grade security and governance features, or the ability to integrate with these kinds of tools.

Verify what kind of data you’re working with and your data mining goals

Are you primarily working with structured data, unstructured data, or a combination of both? Are you processing massive amounts of data for a specific project or smaller amounts of data on a regular basis?

It’s important to know what kind of data you have and what preparation it will require for success. Every data mining tool has unique capabilities when it comes to working with different data formats and quantities, so it’s important to know what you want and to research and select accordingly.

Pick a tool that integrates with your existing tool stack

Many of the top data mining tools integrate with cloud environments, data warehouses, databases, and other tools that your company uses on a daily basis. To get the most out of your data mining lifecycle, look for a tool that clearly integrates with other solutions in your tech stack. Alternatively, look for and invest in a full-featured data management platform that includes data mining among its capabilities.

Select a tool with effective reporting and visualization features

While most data mining tools include some visualization features, many only include very basic, boilerplate visuals that cannot be adjusted by users. Finding a tool that includes a variety of visualization options that are easy to use is especially important for non-data-scientist stakeholders to understand what’s happening in the data mining lifecycle.

Consider your budget and in-house data science skills

There are free versions available for several data mining tools, but others can quickly become expensive, especially if you invest in a tool with more features than you actually need or know how to use. Decide your budget upfront, and from there, assess your team’s skills and what they need from a data mining tool. In some cases, a simple Excel or Google Sheets workbook will be enough for your team’s data mining requirements.

Determine if you need a tool that can handle big data mining

If you’re working with big data, you’ll need to find a tool that can reasonably process those data quantities without lag or memory issues. Some smaller and open-source tools, like Orange, may not have the capacity to handle these kinds of data sets effectively.

Also see: Using AI and Data Analytics to Monetize Data: 4 Techniques

Bottom Line: Data Mining Tools

The use of data mining tools is a core practice in both data management and digital transformation processes today. The insights derived from data mining tools can help organizations with everything from sentiment analysis on brand social media accounts to diagnostic discoveries in the healthcare and pharmaceutical industries.

With such a broad range of potential data mining use cases, choosing the best data mining tool is less about finding the most expensive or comprehensive option and more about selecting a tool that fits your organization’s exact needs.

Consider your budget, the skills of your data science team, your short-term and long-term data goals, and any industry or regional requirements you have before selecting a data mining solution for your business.

Read next: Top Business Intelligence Software

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