Cloud has taken every industry by storm. But the report shows that, despite the apparent benefits, “a variety of challenges still get in their way, mostly around cost control, security expertise, and a skills gap.” Furthermore, the ongoing “cloud sprawl” has not helped these issues. In fact, they’ve gotten worse. There’s a lack of visibility, accountability, and governance of the costs of SaaS and IaaS.

The report, based on a survey by Foundry of 200 enterprise IT decision-makers in a number of industries, looked at the benefits of FinOps, which is a framework that maximizes the business value of cloud computing. 

Overcoming Cloud Challenges with FinOps

FinOps can be a path to overcoming cloud cost challenges, with respondents reporting these are the main drivers for using FinOps:

• 70% say they’re interested in increasing cloud resource performance.
• 60% say they want to make budget cuts.
• 58% say they want to manage rising costs and macroeconomic pressures.

When it comes to the benefits, the survey showed increased productivity (44%), cost savings (43%), and reduced security risk (43%) at the top of the list.

Implementations vary widely, but there are three factors, according to Foundry, that increase the likelihood of success: implementation approach, the use of artificial intelligence, and wide-reaching optimization programs spanning both IaaS and SaaS.

For a deeper portrait of the cloud market, read our in-depth overview: Top Cloud Service Providers and Companies

In-House vs. Outsourced

When it comes to in-house vs. outsourced implementations, outsourced is the clear winner, with in-house FinOps solutions fetching less than 10% in savings and outsourced solutions bringing in 20% savings.

Tangoe’s chief product officer, Chris Ortbals, quoted in the report, says that although DIY is the way most companies go, they’re not best suited to optimize those implementations. “Strategic partners are essential because they look at usage and expenses from a different perspective,” he said. This diverse vantage point provides them with “the context of understanding how hundreds of companies optimize millions of dollars in IT spending.”

AI in FinOps

With AI top-of-mind for everyone these days, this report doesn’t disappoint. The survey shows that 71% of respondents use or plan to use AI in their programs.

That makes sense when you see that companies that use AI in FinOps are 53% more likely to save more than 20%. On the other side of the ledger, companies that don’t use AI will get savings of only 6% to 10%.

Ortbals adds that every FinOps practitioner should include AI in their bag of tricks. “Without AI it’s simply impossible to evaluate massive amounts of data against all possible configuration options, playing out what-if scenarios to quickly determine which action will yield the highest savings,” he said.

“AI can do this at enterprise scale. Plus, it can help IT engineers implement cost-saving recommendations once approved. This results in faster time-to-savings and programs that can achieve higher returns.”

The report says 63% of respondents think analytics is the top use case for AI in FinOps, with 50% saying it eases the FinOps management burden and 48% saying they have seen productivity gains from AI automation.

Also see: 100+ Top AI Companies 2024

SaaS and IaaS

When it comes to SaaS and IaaS, the report shows SaaS users can save 20% or more while IaaS users save less than 10%.

Ortbals said that savings add up with one high-volume application or a few smaller applications that can break a budget.

“But CIOs and CFOs should drive broader cloud ROI,” he said. “FinOps is designed to maximize the benefits of SaaS and IaaS. Start with applications, expand into cloud infrastructure, and consider how you can apply FinOps principles to other areas of IT spending like mobile and telecom.”

Choosing a FinOps Solution

So, what should you consider when selecting a solution? The report says that these are the top five criteria:

• 70%: Industry expertise. Customers want a partner who is versed in the complexities of cloud optimization but also understands the specific insights of each vertical, as mapping them to FinOps is mandatory for success.
• 69%: AI and automation capabilities. There is no bigger transformative technology today than AI, which will have a massive impact on FinOps. Basic use cases include automation of basic tasks, but long-term, generative AI will enable users to access information via natural language.
• 68%: Fully managed services. Not all businesses have the desire to operate FinOps themselves, particularly with AI accelerating the pace of change. Managed services are an excellent option for companies that want to take advantage of FinOps but do not want to go through the deployment and management process.
• 63%: Flexibility of solution. Businesses want choices, and vendors that offer optimization for only one or two cloud service providers or a limited number of applications are quickly outgrown as customers look to cut costs across their rapidly changing multicloud estates.
• 62%: Certifications, licenses, awards. These validate that the vendor solutions work as advertised and have the leading features to give customers a competitive advantage.

Bottom Line: The Value of FinOps

This report provides some fascinating insights for cloud users. FinOps is a great use case for AI, and managing cloud resource utilization could be one of the more intriguing implementations of the technology, with significant savings for enterprises possible in the near term.

To learn more about how companies are modernizing their IT processes, read our in-depth article: Digital Transformation Guide

The post How FinOps and AI Curb Escalating Cloud Costs appeared first on eWEEK.

An observability solution monitors a company’s IT infrastructure by constantly monitoring its outputs. In most cases, the most important outputs are those that track the performance of the core applications that enable the company to keep running.

Historically, this monitoring task has been handled by humans, and still is, largely. Yet as tech infrastructure grows ever more complex, humans need ever more help to keep up. Hence the growth of observability solutions.

In fact, observability is increasingly important as IT infrastructure grows increasingly complex – and companies know that.

Chronosphere focuses on cloud native observability, which is well-suited  to contemporary IT deployments that include elements like microservices and multicloud deployments.

See below for a podcast and video version of the interview.

What’s Driving Observability Adoption

 As recently as 2021, just 61 percent of companies had a centralized observability solution. By 2023, that number had risen to about 70 percent. So certainly observability has room for growth – and yet there’s still some skepticism about this emerging tech.

As Smith noted, the doubting companies ask, “‘well, what am I getting beyond the high-level marketing message?’”

The answer, ultimately, is that observability is far more than cobbling together an array of  interoperating tools. What drives companies to adopt this technology is how observability can assist engineering to facilitate what a business needs, and also – especially – observability’s ability to help control costs, including the expense of multicloud computing.

It’s also about growth. Smith has heard companies say, “’When we previously settled on our observability tooling, we were a much smaller company. We had a really big focus on observability, used by our most senior resources – and they drove the evaluation. Now we have maybe 10, 15 times more engineers and it’s a very broad spread of experiences.”

Observability Strategy

Everything in enterprise IT requires planning, but observability, due to its complexity, requires truly deliberate planning.

A company needs to understand, Smith noted, “Who’s using [the observability tool], what are they using it for, how much are they utilizing it? And be able to compare across those data sets.”

For instance, “Maybe you have some data that’s only used once every three months for a capacity plan, but it’s very, very small in its footprint. But there’s data over here, it’s used every day, for instance for [important] investigations.” It’s essential to know where your data is – and what data is needed at all time to answer essential business questions.

The most important element of creating an observability strategy is deciding precisely what your goal is – and agreeing on that goal across the company.

So ask, “What is the problem we’re actually trying to solve?” Smith said. Some companies aren’t all on the same page. “And so how can you possibly buy something thinking it’s a solution for a problem if you haven’t actually completely defined that [data] problem upfront?

“Maybe it is, for instance, that we need to direct a large portion of observability data into some other area, maybe a data lake because we’ve been abusing our observability tooling. And these are all strategic initiatives that come out of really stepping back and looking at that bigger picture.”

AI and the Future of Observability  

There is, in Smith’s view, a major industry hope that that AI can simply swoop down and answer all of the thorny issues involved with monitoring IT infrastructure. While that belief is unrealistic, certainly the growth of AI has major ramifications for IT – particularly due to AI’s assistance with communication between humans and the system.

In short, the future of observability will enable IT admins to simply talk to their systems. “Wouldn’t it be great to be able, in natural language, to really just ask what is going on with this particular part of the system? Then having a way for the observability system to distill down, ‘these are things you should be focusing on, and here’s an explanation for why.’”

This process is a realistic expectation for observability users. “It’s rooted in data and it’s rooted in building up [data] over time and understanding a model of what these things mean.”

Listen to the podcast:

Also available on Apple Podcasts

Watch the video:

The post Chronosphere’s Ian Smith on Cloud Native Observability appeared first on eWEEK.

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

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Ring CX is a cloud-native contact center solution that uses RingSense AI, the AI engine currently integrated into RingCentral MVP, the company’s UC suite. Also unveiled was the general availability of RingCentral Events, formerly Hopin Events, which enables customers to hold virtual, in-person, or hybrid events.

Let’s look at the news and what it means for the larger contact center industry.

With Communications, Platform is the Way Forward

The communications industry is rapidly shifting from best of breed to best of suite. Historically, customers have had to buy product A for calling, product B for meetings, product C for contact center, and so on. The result was that customers had to pay two to five times what they needed to pay if they could consolidate to a single platform.

In response to this complex budget issue, the vendor community added more and more capabilities, creating a single platform that can deliver communications and collaboration of all forms – at one price.

There’s an obvious “one throat to choke” benefit in this approach. And in the long term, as the industry becomes AI-driven, the single stack will equate to a single data center with which to train the models.

Also see: Top Digital Transformation Companies

RingCX is Designed for Companies that Want a Digital-First Contact Center

RingCX is the company’s own cloud contact center solution. The product is designed with a modern contact center in mind, where the assumption is that customers will start interacting with the brand through some kind of digital channels such as bots, messaging, or self-service.

RingCX is infused with RingSense AI, which has three primary benefits:

• It makes the digital channels smarter so a customer can converse with a bot and have a more natural conversations.
• If the interaction needs to move to an agent, the agent is equipped with insights from that call and previous ones. During calls, the platform generates AI-powered summaries so agents can keep track of key points when talking to customers.
• After a call is completed, the solution provides detailed transcriptions and summaries, giving supervisors a clearer view of each interaction.

Beyond its AI capabilities, RingCX stands out for its rich omnichannel capabilities. It unifies multiple communication modes, such as voice, video, social media, SMS, and email, into a single, user-friendly interface. This allows agents to interact with customers through their preferred channels while maintaining a deeper understanding of the customer’s history and needs.

RingCentral is being aggressive with pricing. At $65 per agent per month, it includes various features like voice and video communication, over 20 digital channels, AI-driven summaries, and unlimited domestic inbound and outbound minutes.

The new RingCX solution complements its RingCentral Contact Center product, delivered via its partner NICE. I asked RingCentral if they plan to stop selling the product, and they said no. RingCentral Contact Center is ideally suited for large customers with complex requirements, whereas RingCX suits companies with a digital-first mindset.

RingCentral Events Makes Virtual, In-Person and Hybrid Events Easy

The second announcement, RingCentral Events, is the company’s hybrid event platform. One of the key advantages of RingCentral Events is its all-in-one nature. It can be used to register attendees and track analytics and can be accessed via a mobile app.

It also offers features like check-in systems, badge printing, and lead retrieval tools. The platform has integrations with over 40 different apps and data systems, making it flexible and easy for businesses that want to simplify event management.

Ease of use is a major selling point of RingCentral Events. Companies can use the platform to create custom, branded event pages without coding knowledge. These pages can be tailored to display specific agendas, speakers, sponsors, and other relevant content, enhancing an event’s visual appeal and overall value. Additionally, it can comfortably accommodate events with over 100,000 attendees.

Soon, RingCentral will also introduce artificial intelligence features in RingCentral Events to simplify and automate event management. One of these features is the Smart Content Generator, which uses AI to create various written materials for events, including titles, descriptions, email templates, and schedules.

Another upcoming feature is the Smart Q&A, which uses AI to sort and categorize attendees’ questions, making it easier for organizers to manage questions during events. Additionally, the Smart Clips feature is an AI video editor that creates short, engaging video clips, which can be used to market events on social media.

Also see: 100+ Top AI Companies

Bottom Line: RingCentral Events

As the latest addition to RingCentral’s suite of business communication tools, RingCentral Events complements existing products like RingCentral MVP, RingCX, RingCentral Video, RingCentral Rooms, and RingCentral Webinar. Pricing plans for RingCentral Events start at $750 annually for events with up to 100 attendees.

The collaboration industry was once filled with best-of-breed products, but now every vendor tries to deliver a full stack of tools – from video to calling to events and contact centers. Customers have no shortage of options available today and should evaluate vendors on how integrated the features are across the suite of products.

RingCX provides much tighter integration with RingCentral MVP and RingCentral quickly integrated Hopin into the platform. The ultimate winners of the platform wars will be the customer as they’ll get more features, faster at a lower cost.

The post RingCentral Expands Its Collaboration Platform appeared first on eWEEK.

Open source intelligence is sourced from all corners of the web, and while that makes the data incredibly comprehensive, it also brings forth a large body of data that needs to be fact-checked and reviewed closely for the best possible results.

Let’s take a closer look at what open-source intelligence is, how it works, and how you can apply this type of intelligence to your business operations most effectively.

What Is Open Source Intelligence?

Open source intelligence is a type of data-driven intelligence that scours the internet and other public sources for information that’s relevant to a user’s query or search. Most often, OSINT is used to strategically collect information about a particular individual, group of people, organization, or other public entity.

Historically, OSINT developed before the internet and was a military espionage technique for finding relevant information about military enemies in newspapers, radio broadcasts, and other public data sources. While most data sources used for OSINT today are online or somehow digitized, OSINT analysts still have the option to collect physical data from public, open sources.

Also see: Top Data Visualization Tools

Passive vs. Active OSINT

Passive and active OSINT are both viable open source intelligence collection methods with different amounts of hands-on activity and in-depth research required.

With passive OSINT, users most often complete a simple search engine, social media, or file search or look at a website’s or news site’s homepage through a broad lens. They aren’t actively trying to collect highly specific information but rather are unobtrusively looking at the easiest-to-find, top-of-the-stack intelligence available. With this intelligence collection method, the goal is often to collect useful information without alerting targets or data sources to your intelligence collection activities.

When practicing active OSINT, the methods tend to be more intrusive and involved. Users may complete more complex queries to collect obscure intelligence and metadata from databases and network infrastructure, for example. They also might fill out a form or pay to get through a paywall for more information.

In some cases, active OSINT may even involve reaching out directly to sources for more information that is not publicly available or visible. While active OSINT is more likely to give users real-time, in-depth information than passive OSINT, it is much more difficult to do covertly and may lead you to legal troubles if your data collection methods aren’t careful.

Open Source Intelligence Data Sources

Open source intelligence can be sourced from any public dataset or property. These are some of the most common OSINT data sources from across the web:

• Social media platforms
• Public-facing websites
• News media
• Academic and scientific studies
• Internet of Things databases
• Business directories
• Financial reports
• Images and image libraries
• Public records, both digital and physical

Also see: Best Data Analytics Tools 

How Does Open Source Intelligence Work?

For individuals and organizations that want to take advantage of open source intelligence, a simple way to get started is with a search engine query. Often, asking the right question about the demographic information you need is the first step to finding relevant open source data entries that can lead to more detailed information.

Beyond using search engines for internet-wide data searches, you can also refine and focus your search on specific data platforms or databases, such as a certain social media platform. Depending on your goals and experience, you may also benefit from analyzing open source threat intelligence feeds and other sources that frequently update massive amounts of data.

If your data collection and analysis goals require you to work with big data sources like databases, data lakes, or live feeds, manual searches and research are ineffective. To quickly process and sort through large amounts of intelligence, you’ll want to consider investing in a web scraping or specialized OSINT tool that can automate and speed up the data analysis process.

OSINT Use Cases

Have you ever “Facebook stalked” someone you just met or Google searched your family’s last name to see what pops up? Both of these are simple examples of how even individuals practice a simplified form of open source intelligence in their daily lives.

Businesses, too, may collect OSINT without realizing it, but in most cases, they are collecting this kind of intelligence for a distinct competitive advantage or cause. Here are some of the most common OSINT use cases in practice today:

• Threat intelligence, vulnerability management, and penetration testing: Especially when used in combination with more comprehensive threat intelligence platforms, open source intelligence and data collection can give security analysts and professionals a more comprehensive picture of their threat landscape, any notable threat actors, and historical context for past vulnerabilities and attacks.
• Market research and brand monitoring: If you want to get a better look at both quantitative purchase histories and overall brand sentiment from customers, OSINT is an effective way to collect broad demographic intelligence about how your brand is performing in the eyes of the consumer. For this particular use case, you may conduct either passive or active OSINT in social media platforms, user forums, CRMs, chat logs, or other datasets with customer information.
• Competitive analysis: In a different version of the example above, you can complete OSINT searches on competitor(s) to learn more about how they’re performing in the eyes of customers.
• Geolocation data sourcing and analysis: Publicly available location data, especially related to video and image files, can be used to find an individual and/or to verify the accuracy of an image or video.
• Real-time demographic analyses over large populations: When large groups of people are participating in or enduring a major event, like an election cycle or a natural disaster, OSINT can be used to review dozens of social media posts, forum posts, and other consumer-driven data sources to get a more comprehensive idea of how people feel and where support efforts — like counterterrorism or disaster relief response, for example — may be needed.
• Background checks and law enforcement: While most law enforcement officials rely on closed-source, higher intelligence feeds for background checks and identification checks, OSINT sources can help fill in the blanks, especially for civilians who want or need to learn more about a person. Keep in mind that there are legal limits to how open source intelligence can be used to discriminate in hiring practices.
• Fact-checking: Journalists, researchers, and everyday consumers frequently use OSINT to quickly check multiple sources for verifiable information about contentious or new events. For journalistic integrity and ethical practice, it’s important to collect information directly from your sources whenever possible, though OSINT sources can be a great supplement in many cases.

Also read: Generative AI: 15 Enterprise Use Cases You Can Implement

10 OSINT Tools and Examples

Particularly for passive OSINT and simple queries, a web scraping tool or specialized “dork” query may be all that you need. But if you’re looking to collect intelligence on a grander scale or from more complex sources, consider getting started with one or several of the following OSINT tools:

1. Spyse: An internet asset registry that is particularly useful for cybersecurity professionals who need to find data about various threat vectors and vulnerabilities. It is most commonly used to support pentesting.
2. TinEye: A reverse image search engine that uses advanced image identification technology to deliver intelligence results.
3. SpiderFoot: An automated querying tool and OSINT framework that can quickly collect intelligence from dozens of public sources simultaneously.
4. Maltego: A Java-based cyber investigation platform that includes graphical link analysis, data mining, data merging, and data mapping capabilities.
5. BuiltWith: A tool for examining websites and public e-commerce listings.
6. theHarvester: A command-line Kali Linux tool for collecting demographic information, subdomain names, virtual host information, and more.
7. FOCA: Open source software for examining websites for corrupted documents and metadata.
8. Recon-ng: A command-line reconnaissance tool that’s written in Python.
9. OSINT Framework: Less of a tool and more of a collection of different free OSINT tools and resources. It’s focused on cybersecurity, but other types of information are also available.
10. Various data analysis and AI tools: A range of open source and closed source data analysis and AI tools can be used to scale, automate, and speed up the process of collecting and deriving meaningful insights from OSINT. Generative AI tools in particular have proven their efficacy for sentiment analysis and more complex intelligence collection methods.

More on a similar topic: Top 9 Generative AI Applications and Tools

Pros and Cons of Open Source Intelligence

Pros of OSINT

• Optimized cyber defenses: Improved risk mitigation and greater visibility into common attack vectors; hackers sometimes use OSINT for their own intelligence, so using OSINT for cyber defense is often an effective response.
• Affordable and accessible tools: OSINT data collection methods and tools are highly accessible and often free.
• Democratized data collection: You don’t need to be a tech expert to find and benefit from this type of publicly available, open source data; it is a democratized collection of valuable data sources.
• Quick and scalable data collection methods: A range of passive and active data sourcing methods can be used to obtain relevant results quickly and at scale.
• Compatibility with threat intelligence tools and cybersecurity programs: OSINT alone isn’t likely to give cybersecurity professionals all of the data they need to respond to security threats, but it is valuable data that can be fed into and easily combined with existing data sources and cybersecurity platforms.

Cons of OSINT

• Accessible to bad actors and hackers: Just like your organization can easily find and use OSINT, bad actors can use this data to find vulnerabilities and possible attack vectors. They can also use OSINT-based knowledge to disrupt and alter intelligence for enterprise OSINT activity.
• Limitations and inaccuracies: Public information sources rarely have extensive fact-checking or approval processes embedded into the intelligence collection process. Especially if multiple data sources share conflicting, inaccurate, or outdated information, researchers may accidentally apply misinformation to the work they’re doing.
• User error and phishing: Users may unknowingly expose their data to public sources, especially if they fall victim to a phishing attack. This means anyone from your customers to your employees could unintentionally expose sensitive information to unauthorized users, essentially turning that private information into public information.
• Massive amounts of data to process and review: Massive databases, websites, and social media platforms may have millions of data points that you need to review, and in many cases, those numbers are constantly growing and changing. It can be difficult to keep up with this quantity of data and sift through it to find the most important bits of intelligence.
• Ethical and privacy concerns: OSINT is frequently connected without the target’s knowledge, which is an issue with AI and ethics. Depending on the data source and sourcing method, this information can be used to harm or manipulate people, especially when it’s PII or PHI that has accidentally been exposed to public view.

Bottom Line: Using OSINT for Enterprise Threat Intelligence

Getting started with open source intelligence can be as simple as conducting a Google search about the parties in question. It can also be as complex as sorting through a publicly available big data store with hundreds of thousands of data entries on different topics.

Regardless of whether you decide to take a passive or active approach, make sure all members of your team are aware of the goals you have in mind with open source intelligence work and, more importantly, how they can collect that intelligence in a standardized and ethical manner.

Read next: 50 Generative AI Startups to Watch in 2023

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In short, Snowflake is more suited for standard data transformation and analysis and for those users familiar with SQL. Databricks is geared for streaming, ML, AI, and data science workloads courtesy of its Spark engine, which enables the use of multiple development languages.

Both Snowflake and Databricks provide the volume, speed, and quality demanded by business intelligence applications. But there are as many similarities as there are differences. When examined closely, it becomes clear that these two cloud-based data platforms have a different orientation. Therefore, selection often boils down to tool preference and suitability for the organization’s data strategy.

What Is Snowflake?

Snowflake is a major cloud company that focuses on data-as-a-service features and functions for big data operations. Its core platform is designed to seamlessly integrate data from various business apps and in different formats in a unified data store. Consequently, typical extract, transform, and load (ETL) operations may not be necessary to get the data integration results you need.

The platform is compatible with various types of business workloads, including artificial intelligence and machine learning, data lakes and data warehouses, and cybersecurity workloads. It is ideally designed for organizations that are working with large quantities of data that require precise data governance and management systems in place.

What Is Databricks?

Databricks is a data-driven vendor with products and services that focus on data lake and warehouse development as well as AI-driven analytics and automation. Its flagship lakehouse platform includes unified analytics and AI management features, data sharing and governance capabilities, AI and machine learning, and data warehousing and engineering.

Users can access certain platform features through an open-source format, making this a highly extensible and customizable solution for developers. It’s also a popular solution for users who want to incorporate other AI or IDE integrations into their setup.

Snowflake vs. Databricks: Comparing Key Features

We’ll compare these two data companies in greater detail in the sections to come, but for a quick scan, we’ve developed this table to compare Snowflake vs. Databricks across a few key metrics and categories:

Snowflake is a relational database management system and analytics data warehouse for structured and semi-structured data.

Offered via the software-as-a-service (SaaS) model, Snowflake uses an SQL database engine to manage how information is stored in the database. It can process queries against virtual warehouses within the overall warehouse, each one in its own cluster node independent of others so as not to share compute resources.

Sitting on top of that database engine are cloud services for authentication, infrastructure management, queries, and access controls. The Snowflake Elastic Data Warehouse enables users to analyze and store data utilizing Amazon S3 or Azure resources.

Databricks is also cloud-based but is based on Apache Spark. Its management layer is built around Apache Spark’s distributed computing framework to make infrastructure management easier. Databricks positions itself as a data lake rather than a data warehouse. Thus, the emphasis is more on use cases such as streaming, machine learning, and data science-based analytics.

Databricks can be used to handle raw unprocessed data in large volumes. Databricks is delivered as SaaS and can run on AWS, Azure, and Google Cloud. There is a data plane as well as a control plane for backend services that delivers instant compute. Its query engine is said to offer high performance via a caching layer. Snowflake includes a storage layer while Databricks provides storage by running on top of AWS S3, Azure Blob Storage, and Google Cloud Storage.

For those wanting a top-class data warehouse, Snowflake wins. But for those needing more robust ELT, data science, and machine learning features, Databricks is the winner.

Snowflake vs. Databricks: Support and Ease of Use Comparison

The Snowflake data warehouse is said to be user-friendly, with an intuitive SQL interface that makes it easy to get set up and running. It also has plenty of automation features to facilitate ease of use. Auto-scaling and auto-suspend, for example, help in stopping and starting clusters during idle or peak periods. Clusters can be resized easily.

Databricks, too, has auto-scaling for clusters. The UI is more complex for more arbitrary clusters and tools, but the Databricks SQL Warehouse uses a straightforward “t-shirt sizing approach” for clusters that makes it a user-friendly solution as well. 

Both tools emphasize ease of use in certain capacities, but Databricks is intended for a more technical audience, so certain steps like updating configurations and switching options may involve a steeper learning curve.

Both Snowflake and Databricks offer online, 24/7 support, and both have received high praise from customers in this area.

Though both are top players in this category, Snowflake wins for its wider range of user-friendly and democratized features.

Also see: Top Business Intelligence Software

Snowflake vs. Databricks: Security Comparison

Snowflake and Databricks both provide role-based access control (RBAC) and automatic encryption. Snowflake adds network isolation and other robust security features in tiers with each higher tier costing more. But on the plus side, you don’t end up paying for security features you don’t need or want.

Databricks, too, includes plenty of valuable security features. Both data vendors comply with SOC 2 Type II, ISO 27001, HIPAA, GDPR, and more.

No clear winner in this category.

Snowflake vs. Databricks: Integrations Comparison

Snowflake is on the AWS Marketplace but is not deeply embedded within the AWS ecosystem. In some cases, it can be challenging to pair Snowflake with other tools. But in other cases, Snowflake is wonderfully integrated. Apache Spark, IBM Cognos, Tableau, and Qlik are all fully integrated. Those using these tools will find analysis easy to accomplish.

Both tools support semi-structured and structured data. Databricks has more versatility in terms of supporting any format of data, including unstructured data. Snowflake is adding support for unstructured data now, too.

Databricks wins this category.

Also see: Top Data Mining Tools 

Snowflake vs. Databricks: AI Features Comparison

Both Snowflake and Databricks include a range of AI and AI-supported features in their portfolio, and the number only seems to grow as both vendors adopt generative AI and other advanced AI and ML capabilities.

Snowflake supports a range of AI and ML workloads, and in more recent years has added the following two AI-driven solutions to its portfolio: Snowpark and Streamlit. Snowpark offers users several libraries, runtimes, and APIs that are useful for ML and AI training as well as MLOps. Streamlit, now in public preview, can be used to build a variety of model types — including ML models — with Snowflake data and Python development best practices.

Databricks, on the other hand, has more heavily intertwined AI in all of its products and services and for a longer time. The platform includes highly accessible machine learning runtime clusters and frameworks, autoML for code generation, MLflow and a managed version of MLflow, model performance monitoring and AI governance, and tools to develop and manage generative AI and large language models.

While both vendors are making major strides in AI, Databricks takes the win here.

Snowflake vs. Databricks: Price Comparison

There is a great deal of difference in how these tools are priced. But speaking very generally: Databricks is priced at around $99 a month. There is also a free version. Snowflake works out at about $40 a month, though it isn’t as simple as that.

Snowflake keeps compute and storage separate in its pricing structure. And its pricing is complex with five different editions from basic up, and prices rise as you move up the tiers. Pricing will vary tremendously depending on the workload and the tier involved.

As storage is not included in its pricing, Databricks may work out cheaper for some users. It all depends on the way the storage is used and the frequency of use. Compute pricing for Databricks is also tiered and charged per unit of processing. The differences between them make it difficult to do a full apples-to-apples comparison. Users are advised to assess the resources they expect to need to support their forecast data volume, amount of processing, and their analysis requirements. For some users, Databricks will be cheaper, but for others, Snowflake will come out ahead.

This is a close one as it varies from use case to use case.

Also see: Real-Time Data Management Trends

Snowflake and Databricks Alternatives

Bottom Line: Snowflake vs. Databricks

Snowflake and Databricks are both excellent data platforms for data analysis purposes. Each has its pros and cons. Choosing the best platform for your business comes down to usage patterns, data volumes, workloads, and data strategies.

Snowflake is more suited for standard data transformation and analysis and for those users familiar with SQL. Databricks is more suited to streaming, ML, AI, and data science workloads courtesy of its Spark engine, which enables the use of multiple development languages. Snowflake has been playing catchup on languages and recently added support for Python, Java, and Scala.

Some say Snowflake is better for interactive queries as it optimizes storage at the time of ingestion. It also excels at handling BI workloads, and the production of reports and dashboards. As a data warehouse, it offers good performance. Some users note, though, that it struggles when faced with huge data volumes as would be found with streaming workloads. In a straight competition on data warehousing capabilities, Snowflake wins.

But Databricks isn’t really a data warehouse at all. Its data platform is wider in scope with better capabilities than Snowflake for ELT, data science, and machine learning. Users store data in managed object storage of their choice. It focuses on the data lake and data processing. But it is squarely aimed at data scientists and professional data analysts.

In summary, Databricks wins for a technical audience. Snowflake is highly accessible to a technical and less technical user base. Databricks provides pretty much every data management feature offered by Snowflake and a lot more. But it isn’t quite as easy to use, has a steeper learning curve, and requires more maintenance. Regardless though, Databricks can address a much wider set of data workloads and languages, and those familiar with Apache Spark will tend to gravitate toward Databricks.

Snowflake is better set up for users who want to deploy a good data warehouse and analytics tool rapidly without bogging down in configurations, data science minutia, or manual setup. But this isn’t to say that Snowflake is a light tool or for beginners. Far from it. 

But it isn’t high-end like Databricks, which is aimed more at complex data engineering, ETL, data science, and streaming workloads. Snowflake, in contrast, is a warehouse to store production data for analytics purposes. It is accessible for beginners, too, and for those who want to start small and scale up gradually.

Pricing comes into the selection picture, of course. Sometimes Databricks will be much cheaper due to the way it allows users to take care of their own storage. But not always. Sometimes Snowflake will pan out cheaper.

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An interesting data point from my research is that today, 90% of organizations compete on CX compared to only 27% five years ago. In many cases, a single, bad experience can mean losing a customer.

Customer experience often starts in the contact center, but how companies interact with their customers continues to broaden, and brands must enable customers to communicate with them using the channels of their choice.

DEWA and Avaya Showcase the Metaverse for Customer Happiness

At GITEX Global 2023, currently being held in Dubai, Avaya and the Dubai Electricity and Water Authority (DEWA) partnered to demonstrate an integrated digital interactive hub that enables a range of services and integrates customer services with the DEWAVerse platform, which allows communication in a metaverse environment.

For DEWA, Avaya helped the company complete a digital transformation project in its customer happiness center. At the event, I had a chance to meet with Abeer Eladaway, Deputy Senior Manager of DEWA, and she explained that it was the goal of His Highness Sheikh Mohammad bin Rashid Al Maktoum, the ruler of Dubai, to have all government entities provide best-in-class customer service.

This prompted the name change of DEWA’s customer service centers to customer happiness centers. DEWA actively monitors all customer interactions and has made customer satisfaction a top initiative.

One of the interesting differences regarding the Middle East is that the public sector drives innovation and aims to set an example for private enterprise. This starkly contrasts with the US and Western Europe, where it seems government entities go out of their way to provide bad service.

Also see: Top Digital Transformation Companies

Eladaway expressed that while the older generation will likely prefer face-to-face interactions, many of the younger citizens will prefer to use a virtual one, so the organization designed DEWAVerse as that option.

Based on Avaya technology and delivered through call center firm Moro, the solution includes an integrated digital interactive hub for DEWA customers to access service through an interactive voice system enhanced by AI. This system allows agents to communicate with customers, answer inquiries, and complete transactions in the virtual world.

In the virtual environment, customers have their own private space to see their electricity and water consumption, and carbon footprint. Customers can also interact with live or virtual agents and pay bills through the interface. There is also a feature in which DEWA will recommend different appliances, and customers can measure the power and cost impact.

This implementation, which is hosted and managed by Moro in its data center, enables customers to contact the DEWA using a variety of communication channels, including phone, email, video, and text. Avaya says that multi-channel engagements are fully integrated so that engagements can transition from one medium to another.

Avaya added that an IVR system now offers options for self-service options so that customers can conduct several transactions without any agent involvement. Avaya utilized artificial intelligence in building a dynamic menu that can identify, assign, and prioritize registered callers so they receive the appropriate level of service.

There has been great debate regarding the viability of the metaverse. Once a skeptic, I’ve since changed my mind because it provides an alternate form of communication. People were skeptical of e-mail initially, the web, and social media, but those have proven to be preferred by those “born in” that era.

Motul’s Cloud-Based Solutions for Improved CX and EX

Another customer experience example Avaya had on display at GITEX was with oil and lubricants company Motul. The company has adopted cloud-based solutions from Avaya to improve customer and employee experiences across many different interaction points using a suite of Avaya’s cloud-based solutions.

Avaya said that Motul has deployed the Avaya Experience Platform, which integrates with Avaya Cloud Office (ACO) by RingCentral. The solution connects more than 400 of Motul’s employees around the globe, enabling them to engage more closely with customers and each other, regardless of location.

Avaya’s Experience Platform, a cloud-based contact center solution, helps Motul create and track KPIs to transform its customer service processes. The platform’s cloud-based attributes translate into an implementation without the massive disruption often accompanying customer service upgrades. In addition, Avaya says it requires little employee training.

GITEX Global 2023 has been an action-packed event. Avaya is showing that it is focused on the future and providing companies with real solutions that work today. These announcements show the importance of experiences and underscore how critical it is for vendors to share real case studies. Both DEWA and Motul are using Avaya for novel solutions that show the power of digital transformations that are aimed at improving experiences.

For more information, also see: Digital Transformation Guide

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This follows the introduction of Zoom Mail and Calendar earlier this year. The combination of Unified Communication, Contact Center, Email / Calendar, and Docs gives Zoom the broadest, fully integrated collaboration suite in the industry. While Microsoft and Google have the same components, they lack the tight integration provided by Zoom.

Zoom is rethinking document collaboration

During a pre-brief with analysts, Zoom Head of Products for Meetings, Rooms, and Workspaces, Jeff Smith, clarified that this isn’t your father’s document application when he discussed re-imagining documents.

He stated that traditional docs apps are meant to replicate paper, and the output was meant to be printed. In a world that is increasingly paperless, how we think about documents should also change.

Smith stated emphatically: “Zoom Docs marks the end of 8 ½ x 11” and called it “a next-generation way of collaborating.”

In some ways, the name Zoom Docs is a bit of a misnomer as the company delivers a workspace for people to collaborate through a document interface. While the application handles the traditional document experience, it enhances it with the following capabilities.

• Customizable documents. Workers can use content blocks to pull information into customizable layouts and workflows. Table blocks can organize data, manage projects, track tasks, and manage schedules through columns, filters, and groups.
• Easy delegation. With Zoom Docs, users can bring their team together to get more work done faster between meetings. Users can use “@” mentions of colleagues in the document to keep them in the loop, create a discussion, add comments and threads, and assign tasks to keep everyone on track.
• Combine knowledge with wikis and shared folders. Workers can create wikis to link pages and embed them in a visual tree, enabling teams to see how information is connected instantly. Pages can be grouped better to organize docs for a more complete knowledge source.
• Flexible working. Users can work directly in Zoom or Docs. The Zoom integration allows users to create, edit, and search Zoom Docs while in a Meeting or in a Team Chat. This can significantly cut down on the “toggle tax” we experience today that contributes to users spending about 40% of their time managing work.

AI Companion boosts Zoom Docs value

Another benefit of the integration with the Zoom suite is the use of AI Companion within Zoom Docs. Workers can jump-start document creation by asking the AI engine to populate content. Alternatively, AI Companion could summarize content to create an executive overview or get caught up.

At Zoomtopia, Zoom also announced its global, unified search capabilities that enable workers to find information anywhere in the Zoom platform, including some third-party applications. I’ve experienced the frustration of remembering I received a document from someone but could not remember if they sent it through Zoom Chat or in an e-mail. This causes me to search both until I find it. The unified search feature allows workers to search across Zoom, streamlining that process.

Artificial intelligence was a key theme at Zoomtopia as the company reiterated its federated, responsible, and empowering approach. Contrary to many news articles, Zoom will not use customer data without permission. This includes customer audio video, chat, screen sharing, attachments, or any other information inside Zoom.

Also see: 100+ Top AI Companies

Zoom is democratizing AI

Zoom has set the price point for AI Companion at free with any paid license. During the analyst briefing, Randy Maestre, Head of Zoom AI, Ecosystem, and Industry Product Marketing, stated, “We believe AI should be affordable and accessible to everyone, so we have included AI Companion at no additional cost for customers.”

This can save companies a significant amount of money compared to the $30 per month and up for similar features from Google and Microsoft.

New AI Companion features announced at Zoomtopia include real-time feedback and coaching, AI for Zoom Phone, and the ability to handle complex tasks across Zoom and third-party applications. This complements the existing features of whiteboard content generation, smart recordings, in-meeting queries, meeting summaries, chat thread summaries, and composing emails and chats.

Most of the collaboration vendors have chosen a co-existence strategy when it comes to Microsoft Teams. Not so for Zoom, as the introduction of Zoom Docs, which follows the rollout of Mail and Calendar, is clearly a shot across Microsoft’s bow.

Also see: Best Artificial Intelligence Software

Bottom Line: Platform Integration

Will Zoom see success? Only time will tell, but what Zoom has on its side is a product that is much less complicated and has better platform integration than the bloated Microsoft Office experience.

Also, workers tend to love Zoom and often push the IT organization into bringing it in as an alternative to Teams. While the introduction of Zoom Docs will capture the headlines, it’s the use of AI and global search that creates tangible user value.

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Unlike in years past, where the year-over-year difference was negligible, 2022 to 2023 saw significant changes and surprises. Below are the notable points from the 2023 CCaaS Magic Quadrant.

Gartner’s Magic Quadrant CSaaS: Shifts and Surprises

NICE

The MQ has changed significantly over the years, but one constant has been NICE in the Leader quadrant.

The company has been a de facto standard for some time, and that’s not likely to change any time soon. NICE has significantly invested in AI and partnerships and is well-aligned with industry trends.

In the MQ, Gartner says this about the company, “NICE has a strong vision for supporting end-to-end digital-first journeys, including use of search engine optimization analytics to help optimize digital self- and assisted-service experiences.” I expect to see NICE in the MQ pole position for years.

Genesys

That Genesys has remained a Leader is a surprise. Like NICE, Genesys has been a mainstay in the Leader quadrant for years, so one might wonder why being in that quadrant in 2023 would be unexpected.

Recall that in October of 2022, the company announced it was shutting down its multi-cloud division, which shows a significant change in strategy. I’m not arguing the decision, but how Genesys could be a leader pre and post-decision is unclear.

If the strategy aligned with Gartner in 2022, it should have seen its position move negatively in 2023. Or the company should not have been a Leader in past MQs and then rewarded positively for the strategy change. Based on the report, shutting down multi-cloud had no impact on Gartner’s view of the company, which is surprising given that was a such a major strategy.

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

Five9

Five9 moves back to the Leader quadrant. From what I understand, Five9 was moved out due to geographic qualification as the MQ became global.

Despite the explanation, I never agreed with Five9 not being in the Leader quadrant, as the company has carried the CCaaS flag for about 20 years. Over the past few years, Five9 has done an excellent job expanding its addressable market both geographically and upmarket. At one time, the knock on Five9 was they had no customers over 1,000 concurrent agents. Today, they can easily handle tens of thousands. I expect to see Five9 continue to move up in both axes.

Amazon Web Services

Amazon Web Services moves into the Leader quadrant. This was another vendor who I felt should have been a Leader last year.

While some of the other vendors tend to get more media attention, AWS has quietly gathered customers of all sizes, including some of the largest contact centers in the world. I’ve talked to Amazon Connect GM Pasquale DeMaio about the MQ, and he told me that while they would like to be in the Leader quadrant, it would not change its strategy to accomplish that.

DeMaio has been consistent in the Amazon Connect mission of bringing a best-in-class, AI-based CCaaS solution to its customers, with the roadmap built from customer feedback. One of the under-appreciated aspects of Connect is the tight integration with other AWS services, which simplifies the process for developers to build on the Connect platform. From what I can tell, this broader AWS advantage isn’t reflected in the MQ analysis.

Also see: Top Digital Transformation Companies

Talkdesk 

Talkdesk moves from Leader to Visionary. Many industry watchers were surprised when Taldesk was placed in the Leader quadrant, which is still an emerging player.

One of the aspects of the company’s go-to-market I have liked is that it does not try to be all things to all people and has taken a vertical approach focusing on retail, healthcare, and financial services. Two years ago, Talkdesk was very active with the analyst community and media but had gone somewhat dark since then, giving rise to speculation that the company was struggling and had retrenched to retool. Since then, CMO Kathie Johnson has exited the business, with Christie Blake returning to a marketing role. I expect more vision from the company and better clarity on business momentum. This should help it return to the “top right” in the MQ.

Cisco 

Cisco remains a Niche player. To say CCaaS is important to Webex is as big an understatement as there is.

The company is a late entrant in an increasingly crowded market but approached the offering correctly. Instead of leveraging the existing legacy contact center, it built CCaaS to be part of the larger Webex platform, which brings tight integration with UCaaS. This can bring features such as background noise removal to agents working from home.

While this took longer than doing a “lift and shift” of the older on-premises solution, it gives Cisco a solid, cloud-native solution to build on.

One of the issues I find with all MQs is they look at the individual products in isolation. One of the unique differentiators for Webex Contact Center is the integration into the broader Cisco portfolio, such as devices and security. For example, remote agents can be more productive and secure if the customer leverages products like ThousandEyes and Duo along with Webex CCaaS. I expect Cisco to continue to move up in the MQ as it gains momentum, but the analysis negates the broader Cisco advantage.

Notable Absences 

Two vendors noticeable by their absence are Zoom and Avaya.  Over the summer, Zoom took the covers off Zoom CCaaS and appears to be off to a running start. I’ve talked to a handful of companies looking at the product, and the general feedback is positive, although it currently lacks some integrations with third-party software vendors. I expect this to be a key focus area for Zoom and hope we see them in the 2024 MQ.

For Avaya, while the product likely would meet Gartner’s product requirements, it likely does not meet the revenue requirements as it was largely ignored during Jim Chirico’s tenure.

Current CEO Alan Masarek has taken a more practical approach to delivering CCaaS to Avaya’s massive customer base, where it will use the cloud to deliver digital capabilities while enabling its customers to continue leveraging the on-premises voice solution.

Avaya CCaaS can be delivered through the Avaya Experience Platform (AXP) for customers who want a pure cloud solution. This is a solid juxtaposition for Genesys’s cut-the-cord approach, which forces its customers to move to the public cloud whether or not they are ready. While Avaya’s hybrid approach has kept them out of the MQ and may in future ones, it’s the right thing to do for its large enterprise customer base.

Bottom Line: CCaaS and AI

While the CCaaS MQ hasn’t seen many vendors move over the past several years, I expect that to change in future years. Artificial intelligence is the biggest catalyst for change since the introduction of cloud. Some vendors are partnering while others are building the capabilities in house. Which is better? Time will tell and I expect Gartner’s MQ to make AI a bigger part of the evaluation criteria.

One final note: although the MQ is a popular shortlist tool, it’s important to remember it’s not an absolute measure. Just because a vendor is more “right” or more “up” on the MQ doesn’t mean it’s the right solution for all companies. A business looking for a solution tightly bundled with UCaaS would likely be better off with 8×8 than NICE. Customers need to apply their own context and criteria to the vendors in the MQ to decide which fits their business best.

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