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.

Generally considered a subset of artificial intelligence (AI), machine learning systems generate algorithms based on training datasets and then deliver relevant outputs, often without expressly being programmed to produce the exact outcomes they drive.

The autonomous learning capabilities of AI and ML platforms are at the center of today’s enterprises. The technology is increasingly being used to make important decisions and drive automations that improve enterprise operations across disciplines. In recent years, ML technology has also formed the foundation for generative AI models, which are trained to generate new content through larger datasets and more complex ML algorithms.

With its range of relevant business use cases in the modern enterprise, machine learning platform technology has quickly grown in popularity, and vendors have expanded these platforms, capabilities and offerings to meet growing demands.

In this guide, we cover 10 of the best machine learning platforms on the market today, detailing their specific features, pros and cons, and any areas where they particularly stand out from the competition.

Best Machine Learning Software: Comparison Chart

Top 10 Machine Learning Software Platforms

Alteryx Machine Learning: Best for Citizen Data Scientists and Developers

Alteryx has emerged as a leader in the machine learning space for tackling extremely complex machine learning projects through an accessible interface. The drag-and-drop platform incorporates highly automated ML features for both experienced data scientists and less technical business users. Many users particularly praise this platform for its built-in Education Mode, which makes the no-code platform even easier to learn and adjust to your particular use cases.

The platform connects to an array of open-source GitHub libraries — including Woodwork, Compose, Featuretools, and EvalML — and handles numerous data formats and sources. Alteryx also offers powerful visualization tools and feature engineering tools as well as a large and active user community.

Pricing

Pricing information for Alteryx Machine Learning is only available upon request. Prospective buyers can contact Alteryx directly for more information and/or get started with the product’s free trial on either desktop or cloud.

Key Features

• Automated machine learning and feature engineering.
• Automated insight generation for data relationships.
• Built-in Education Mode for learning and optimizing ML development.
• Access to open-source packages and libraries in GitHub.
• No-code, cloud-based format.

Pros

• Offers strong data prep and integration tools along with a robust set of curated algorithms.
• Excellent interface and powerful automation features.

Cons

• Macros and APIs for connecting to various data sources can be difficult to set up and use.
• Some users complain about slow load and processing speeds.

Databricks Data Intelligence Platform: Best for Enterprise-Scale Data Management and Feature Engineering

The Databricks Data Intelligence Platform offers a centralized environment with powerful tools and features that facilitate machine learning and the data preparation work that goes into successful ML model developments.

Managed MLflow is one standout feature that relies on an open-source platform developed by Databricks to manage complex interactions across the ML lifecycle. This platform is particularly useful for organizations that want a combination of self-service and guided data management and feature engineering capabilities that work for data from disparate sources and in different formats.

Interested users can take advantage of the platform for data processing and preparation — including for generative AI and large language models — and to prepare data production pipelines. They can also register and manage models through the Model Registry feature. In addition, the platform provides users with collaborative notebooks, the Feature Registry, and the Feature Provider, all of which support feature engineering requirements and MLOps with a strong, big-data-driven backbone.

Pricing

The Databricks platform is available at no base cost; instead, interested users will sign up and then pay for the features and quantities they use on a per-second basis. Users with larger usage requirements may be eligible for committed use discounts, which work across cloud environments. If you have inconsistent or smaller usage requirements, you’ll need to pay per product and per Databricks Unit (DBU) used:

• Workflows & Streaming Jobs: Starting at $0.07 per DBU.
• Workflows & Streaming Delta Live Tables: Starting at $0.20 per DBU.
• Data Warehousing Databricks SQL: Starting at $0.22 per DBU.
• Data Science & Machine Learning All Purpose Compute for Interactive Workloads: Starting at $0.40 per DBU.
• Data Science & Machine Learning Serverless Real-Time Inference: Starting at $0.07 per DBU.
• Databricks Platform & Add-Ons: Information available upon request.

A 14-day free trial is also available with limited features.

Key Features

• Open lakehouse architecture.
• REST-API-driven model deployment.
• Pretrained and fine-tuned LLM integration options.
• Self-service data pipelines.
• Managed MLflow with experiment tracking and versioning.

Pros

• The open data lakehouse format makes it easier to work with data from different sources and for different use cases; users appreciate that the platform can scale for data orchestration, data warehousing, advanced analytics, and data preparation for ML, even for larger datasets.
• This is a highly scalable environment with excellent performance in a framework that users generally find easy to use; many features are built on open-source data technologies.

Cons

• Can be pricey, especially when compared to completely free and open-source solutions in this space.
• Some visualization features are limited and difficult to set up.

Dataiku: Best for Extensibility

Dataiku is a popular, user-friendly ML platform that delivers all the tools required to build robust ML models, including strong data preparation features. An AutoML feature is another great component of the tool that is designed to fill in missing values and seamlessly convert non-numerical data into numerical values. Its data preparation, visualization, and feature engineering capabilities are well-reviewed components of the platform, but where Dataiku really sets itself apart is its extensibility and range of integrations.

Users can easily integrate many of today’s top generative AI services and platforms, including from OpenAI, Cohere, Anthropic, and Hugging Face. A range of public and proprietary plugins are available through GUI-based code packages, and integrations are also available with leading DevOps and data science visualization frameworks. Dataiku also supports custom modeling using Python, R, Scala, Julia, Pyspark, and other languages.

Pricing

Four plan options are available for Dataiku users. Pricing information is not provided for the paid plans, though other details about what each plan covers are included on the pricing page. A 14-day free trial is also available for each of the paid plans listed below:

• Free Edition: $0 for up to three users and installation on your personal infrastructure. Other limited features are included.
• Discover: A paid plan for up to five users that includes more than 20 database connectors, Spark-based data processing, and limited automations. Pricing information is available upon request.
• Business: A paid plan for up to 20 users that includes unlimited Kubernetes-based computations, full automation, and advanced security features. Pricing information is available upon request.
• Enterprise: A paid plan that includes all database connectors, full deployment capabilities, an isolation framework, and unlimited instances and resource governance. Pricing information is available upon request.

Key Features

• Feature store and automatic feature generation.
• Generative AI platform integrations.
• White-box explainability for ML model development.
• Prompt Studios for prompt-based LLM model development.
• Public and proprietary plugins for custom visual recipes, connectors, processors, and more.

Pros

• Dataiku is among the most flexible machine learning platforms, and it delivers strong training features.
• Dataiku easily integrates and extends its functionalities with third-party DevOps, data science visualization, and generative AI tools, frameworks, and services.

Cons

• Dataiku has a somewhat unconventional development process that can slow down model development.
• Especially as the tool updates, some users have experienced difficulties with outages.

Also see: Best Data Analytics Tools

Vertex AI: Best for Model Organization and Management

The Vertex AI platform is a leading cloud-based AI and ML solution that taps into the power of Google Cloud to deliver a complete set of tools and technologies for building, deploying, and scaling ML models. It supports pre-trained custom tooling, AutoML APIs that speed up model development, and a low-code framework that typically results in 80% fewer lines of code.

It’s also a highly organized platform that gives users accessible tools to manage their models at all stages of development. For example, the Vertex AI Model Registry is available for users who want a central repository where they can import their own models, create new models, classify models as ready for production, deploy models to an endpoint, evaluate models, and look at ML models both at a granular level and in an overview format. Additionally, Vertex AI supports nearly all open-source frameworks, including TensorFlow, PyTorch, and scikit-learn.

Pricing

Pricing for Vertex AI is highly modular and based on the tools and services, compute, and storage you use, as well as any other Google Cloud resources you use for ML projects. We’ll cover the estimates for some of the most commonly used features below, but it’s a good idea to use the pricing calculator or contact Google directly for a custom quote that fits your particular needs:

• Generative AI (Imagen model for image generation): Starting at $0.0001.
• Generative AI (Text, chat, and code generation): Starting at $0.0001 per 1,000 characters.
• AutoML Models (Image data training, deployment, and prediction): Starting at $1.375 per node hour.
• AutoML Models (Video data training and prediction): Starting at $0.462 per node hour.
• AutoML Models (Text data upload, training, deployment, prediction): Starting at $0.05 per hour.
• Vertex AI Pipelines: Starting at $0.03 per pipeline run.

A free trial is available for Vertex AI as well, though only as part of a greater free trial for all of Google Cloud. The Google Cloud free trial gives all users $300 in free credits to test out the platform.

Key Features

• Model Garden library with models that can be customized and fine-tuned.
• Native MLOps tools, including Vertex AI Evaluation, Vertex AI Pipelines, and Feature Store.
• Custom ML model training workflows.
• Vertex AI prediction service with custom prediction routines and prebuilt containers.
• Vertex AI Model Registry for production-ready model deployment.

Pros

• Despite powerful ML capabilities, the platform is fairly user-friendly, relatively easy to use, and highly scalable.
• It delivers strong integrations with other Google solutions, including BigQuery and Dataflow.

Cons

• Vertex AI is not as flexible and as customizable as other ML platforms. It also lacks support for custom algorithms.
• Some users complain about the high price and limited support for languages beyond Python.

H2O-3: Best for R and Python Programmers

H2O-3 is the latest iteration of the open-source data science platform that supports numerous areas of AI, including machine learning. The platform is designed with numerous automation features, including feature selection, feature engineering, hyperparameter autotuning, model ensembling, label assignment, model documentation, and machine learning interpretability (MLI).

H2O-3 offers powerful features specifically designed for Natural Language Processing (NLP) and computer vision. R and Python programmers particularly appreciate this platform for its wide-ranging community support and easy download options that are compatible with the two languages.

Pricing

H2O-3 is a free and open-source solution that users can download directly from the vendor site or in AWS, Microsoft Azure, or Google Cloud.

Key Features

• Open-source, distributed, in-memory format.
• Support for gradient-boosted machines, generalized linear models, and deep learning models.
• AutoML-driven leaderboard for model algorithms and hyperparameters.
• Algorithms include Random Forest, GLM, GBM, XGBoost, GLRM, and Word2Vec.
• H2O Flow for no-code interface option; code-based options include R and Python.

Pros

• Excellent support for open-source tools, components, and technologies.
• Offers powerful bias detection and model scoring features.

Cons

• Some users complain about missing analysis tools and limited algorithm support.
• Overall performance and customer support lag behind competitors.

KNIME Analytics Platform: Best for Community-Driven ML Development

The KNIME Analytics Platform promotes an end-to-end data science framework designed for both technical and business users. This includes a comprehensive set of automation tools for tackling machine learning and deep learning. The KNIME platform delivers a low-code/no-code visual programming framework for building and managing models.

The platform includes a robust set of data integration tools, filters, and reusable components that can be shared within a highly collaborative framework. Speaking of collaboration, the KNIME community is one of the most active and collaborative open-source communities in this space. Users can additionally benefit from KNIME Community Hub, a separate software solution that allows users to collaborate with data science and business users from other organizations and review other users’ samples with few overhead limitations.

Pricing

KNIME is a free and open-source solution, though interested users may want to contact the vendor directly to determine if their particular use case will incur additional costs. The KNIME Analytics Platform can be freely downloaded on Windows, Mac, and Linux.

Key Features

• Open-source, low-code/no-code tooling.
• Drag-and-drop analytic workflows.
• Access to ML libraries like TensorFlow, Keras, and H2O.
• Workflow-building node repository and workflow editor.
• AutoML for automated binary and multiclass classification and supervised ML training.

Pros

• Provides an intuitive, low-code/no-code interface that makes it easy for non-data scientists and new users to build ML models.
• Delivers strong automation capabilities across the spectrum of ML tasks.

Cons

• Code-based scripting requirements through Python and R can introduce challenges for certain types of customizations.
• Some users complain that the platform is prone to consume excessive computational resources.

Also see: Top Data Mining Tools

MATLAB: Best for Supportive ML Apps and Trainings

MathWorks MATLAB is popular among engineers, data scientists, and others looking to construct sophisticated machine learning models. It includes point-and-click apps for training and comparing models, advanced signal processing and feature extraction techniques, and AutoML, which supports feature selection, model selection, and hyperparameter tuning.

MATLAB works with popular classification, regression, and clustering algorithms for supervised and unsupervised learning. And, despite its many complex features and capabilities, it is a relatively accessible tool that offers a range of detailed training and documentation to users, as well as accessible and easy-to-incorporate apps.

Pricing

MATLAB can be used by organizations and individuals of all different backgrounds and is sometimes used in combination with Simulink, a MATLAB-based environment for multidomain model programming. Multiple subscription options are available:

• Standard: $940 per year, or $2,350 for a perpetual license.
• MATLAB and Simulink Startup Suite: $3,800 per year.
• Academic: $275 per year, or $550 for a perpetual license.
• MATLAB and Simulink Student Suite: $99 for a perpetual license.
• Home/personal use: $149 for a perpetual license.

A 30-day free trial option is available for MATLAB, Simulink, and several other products.

Key Features

• Prebuilt MATLAB apps and toolboxes.
• Live Editor for scripting.
• Simulink for model-based design.
• Classification Learner App for data classification and training.
• Onramp, interactive examples, tutorials, and e-books for getting started with machine learning.

Pros

• The platform offers an array of powerful tools and capabilities within a straightforward user interface that is particularly friendly to advanced mathematical, research, and data science use cases.
• Extremely flexible, with excellent collaboration features, app integration opportunities, and scalability.

Cons

• Relies on a somewhat proprietary approach to machine learning. Lacks support for some open-source components and languages, which can also make the tool more expensive than other players in this space.
• Can be difficult to use for business constituents and other non-data scientists to get started, though the platform comes with extensive training options to bridge that gap.

Azure Machine Learning: Best for LLM Development

Automation is at the center of Azure Machine Learning. The low-code platform boasts 70% fewer steps for model training and 90% fewer lines of code for pipelines. It also includes powerful data preparation tools and data labeling capabilities, along with collaborative notebooks, which makes it a great one-stop shop for MLOps requirements.

As modern use cases for machine learning have drifted more and more toward generative AI, Azure Machine Learning has proven itself a leader in this type of ML model development. Users can track and optimize training prompts with prompt flow, improve outcomes with the Responsible AI dashboard, benefit from scalable GPU infrastructure, and work within a wide range of tools and frameworks.

Pricing

Similar to many other platforms in this space, Azure Machine Learning itself comes at no cost, but users will quickly rack up costs based on the compute and other Azure services they use. Pricing is highly variable for this tool, so we’ve only included estimates and starting prices for a few key compute options; prospective buyers should contact Microsoft directly for additional pricing information beyond what we’ve included here:

• D2-64 v3: Individual components range from $0 per hour to $2.67 per hour, depending on vCPUs, RAM, Linux VM, service surcharges, and annual savings plans selected.  For this option and the ones below, many of these costs will be stacked on top of each other, depending on which instance you select.
• D2s-64s v3: Individual components range from $0 per hour to $3.072 per hour, depending on vCPUs, RAM, Linux VM, service surcharges, and annual savings plans selected.
• E2-64 v3: Individual components range from $0 per hour to $1.008 per hour, depending on vCPUs, RAM, Linux VM, service surcharges, and annual savings plans selected.
• M-series: Individual components range from $0 per hour to $26.688 per hour, depending on vCPUs, RAM, Linux VM, service surcharges, and annual savings plans selected.
• H-series: Individual components range from $0 per hour to $2.664 per hour, depending on vCPUs, RAM, Linux VM, service surcharges, and annual savings plans selected.

Discounted prices may be available for stable and predictable workloads through Azure Reserved Virtual Machine Instances. A free trial of Azure is also available.

Key Features

• Open-source library and framework interoperability.
• Responsible AI framework and dashboard.
• Prompt flow for AI workflow orchestration, including for LLMs.
• Data preparation and labeling.
• Drag-and-drop designer with notebooks, automated machine learning, and experiments.
• Managed endpoints for model deployment and scoring.

Pros

• The drag-and-drop interface and low-code framework simplify ML model building.
• Extensive LLM development and optimization features are available; the platform also benefits from Microsoft’s deep investment in generative AI and OpenAI in particular.

Cons

• The pricing structure is difficult to understand and can quickly get expensive
• Some users complain about subpar documentation and difficulties with support.

RapidMiner: Best for Cross-Disciplinary Teams

RapidMiner is an ML platform vendor that promotes the idea of “intuitive machine learning for all” through both code-based ML and visual low-code tools that non-technical team members can learn how to use. The platform includes prebuilt templates for common use cases, as well as guided modeling capabilities. It also provides robust tools for validating and retesting models.

RapidMiner focuses on MLOps and automated data science through several key functions, including an auto engineering feature and automatic process explanations. It is a highly collaborative platform with a project-based framework, co-editing capabilities, and built-in user authentication and access control features.

Pricing

A free version of RapidMiner, called RapidMiner Studio Free, is available for desktop users who require no more than 10,000 data rows and one logical processor. The enterprise version of the platform is a paid subscription; prospective buyers will need to contact RapidMiner directly for specific pricing information. All users can benefit from a 30-day free trial of the full platform, and discounts are available for certain groups, including academics.

Key Features

• Codeless model ops.
• Accurate and finance-based model scoring.
• Built-in drift prevention.
• Native dashboards and reports and integrations with BI platforms.
• User-level choice between code-based, visual, and automated model creation with logging for all options.

Pros

• A strong focus on administrative controls for governance, reporting, and user access.
• Offers intuitive, low-code/no-code tools for non-data scientists as well as sophisticated code-based tools for data scientists.

Cons

• Some users complain about the heavy computational resource requirements involved with using RapidMiner.
• Can be crash-prone in certain situations and scenarios.

TensorFlow: Best for MLOps

TensorFlow is an open-source machine learning software library that extends itself beyond this primary role to support end-to-end machine learning platform requirements. It works well for basic ML model development but also has the resources and capacity to support more complex model developments, including for neural networks and deep learning models.

Although TensorFlow rarely labels itself as an MLOps platform, it offers all of the open-source flexibility, extensibility, and full-lifecycle capabilities MLOps teams need to prepare their data, build models, and deploy and monitor models on an ongoing basis. TensorFlow Extended (TFX) is a particularly effective version of the tool for creating scalable ML pipelines, training and analyzing models, and deploying models in a production-ready environment.

Pricing

TensorFlow is a free and open-source tool, though additional costs may be incurred, depending on other tools you choose to integrate with the platform. The tool can be deployed directly on the web, on servers, or on mobile or edge devices.

Key Features

• Pretrained models in the model garden and TensorFlow Hub.
• On-premises, mobile-device, browser, and cloud-based deployment options.
• Simple ML add-on for Google Sheets model training and evaluation.
• Production-ready ML pipelines.
• Data preparation and responsible AI tools to eliminate data bias.

Pros

• Many other platforms, including those on this list, are compatible with TensorFlow and its software library.
• TensorFlow is known for its helpful and active user community.

Cons

• The models you can build within TensorFlow are mostly static, which may not be the most agile option.
• Many users have commented on how it’s more difficult to use and understand than most other Python-based software libraries.

Also see: Real-Time Data Management Trends

Key Features of Machine Learning Software

While the goal is typically the same — solving difficult computing problems — machine learning software varies greatly. It’s important to review vendors and platforms thoroughly and understand how different features and tools work. The following key features are some of the most important to consider when selecting machine learning software:

Data Processing and Ingestion

It’s important to understand how the software ingests data, what data formats it supports, and whether it can handle tasks such as data partitioning in an automated way. Some packages offer a wealth of templates and connectors, while others do not.

Support for Feature Engineering

Feature engineering is crucial for manipulating data and building viable algorithms. The embedded intelligence converts and transforms strings of text, dates, and other variables into meaningful patterns and information that the ML system uses to deliver results.

Algorithm and Framework Support

Modern ML platforms typically support multiple algorithms and frameworks; this flexibility is crucial. In some cases, dozens or hundreds of algorithms may be required for a business process. Yet, it’s also important to have automated algorithm selection capabilities that suggest and match algorithms with tasks. This feature typically reduces complexity and improves ML performance. Additionally, having access to a range of framework options gives users more agility when automating ML development tasks.

Training and Tuning Tools

It’s vital to determine how well algorithms function and what business value the ML framework delivers. Most users benefit from smart hyperparameter tuning, which simplifies the ability to optimize each algorithm. Various packages include different tools and capabilities, and, not surprisingly, some work better for certain types of tasks and algorithms. Especially with large language models and other larger ML models, you’ll want to identify tools that make training and fine-tuning easy, regardless of your particular use cases.

Ensembling Tools

Within ML, it’s common to rely on multiple algorithms to accomplish a single task. This helps balance out strengths and weaknesses and minimize the impacts of data bias. Ensembling refers to the process of integrating and using different algorithms effectively and is an important feature to look for in ML platforms.

Competition Modeling

Since there is no way to know how an algorithm or ML model works before it’s deployed, it’s often necessary to conduct competition modeling. As the name implies, this pits multiple algorithms against each other to find out how accurate and valuable each is in predicting events. This leads to the selection of the best algorithms.

Deployment Tools

Putting an ML model into motion can involve numerous steps—and any error can result in subpar results or even failure. To prevent these kinds of issues, it’s important to ensure that an ML platform offers automation tools and, for some situations, one-click deployment. Many top-tier tools also offer both experimental and production-focused deployment workflows and support.

Dashboards and Monitoring

It’s essential to have visibility into the machine learning model’s performance and how it works, including the algorithms that are running and how they are evolving to meet new needs over time. Dashboards and monitoring tools are particularly effective in this area, especially if they come with filters and visual elements that help all stakeholders review important data. Having this kind of visibility helps an organization add, subtract, and change ML models as needed.

Also see: Top Data Visualization Tools

Benefits of Machine Learning Platforms

Organizations that use machine learning platforms to develop their ML models can create models on a greater scale, at a greater speed, and with higher levels of accuracy and utility. Some of the most common benefits that come from using machine learning platforms include the following:

• End-to-end ML: Many platforms take an end-to-end approach and give you all the tools you need to manage the full ML development and deployment lifecycle.
• ML model organization: The unified platform makes it easier to organize, find, and retrieve new and old ML models.
• Flexibility and extensibility: Users can work with various frameworks, software libraries, and programming languages to produce a model that fits their needs.
• Features for ease of use: Low-code/no-code tools are often available to simplify model development, deployment, and monitoring.
• Automation capabilities: Automation workflows can be set up for various areas of the ML lifecycle, simplifying, standardizing, and speeding up the entire process.
• Scalable platform capabilities: Several platforms work with big-data ML training sets and goals, including for large language models.
• Governance and ethical considerations: A growing number of ML vendors are incorporating model governance, cybersecurity, and other responsible frameworks into their platforms to make ML modeling a more ethical and manageable process.

Also see: Data Mining Techniques

How to Choose the Best Machine Learning Software

While it’s possible to build a custom ML system, most organizations rely on a dedicated machine learning platform from an ML, data science, or data analytics vendor. It’s best to evaluate your organization’s needs, including the type of machine-learning technology you require, before making your selection. Consider whether your organization would benefit from a classical method or deep learning approach, what programming languages are needed, and which hardware, software, and cloud services are necessary to deploy and scale a model effectively.

Another of the most important decisions you can make revolves around the underlying machine learning frameworks and libraries you choose. There are four main options to consider in this area:

• TensorFlow: An open-source and highly modular framework created by Google.
• PyTorch: A more intuitive open-source framework that incorporates Torch and Caffe2 and integrates with Python.
• scikit-learn: A user-friendly and highly flexible open-source framework that delivers sophisticated functionality.
• H2O: An open-source ML framework that’s heavily slanted to decision support and risk analysis.

Other key factors to consider when choosing an ML platform include available data ingestion methods, built-in design tools, version control capabilities, automation features, collaboration and sharing capabilities, templates and tools for building and testing algorithms, and the quantity and variety of compute resources.

Throughout the selection process, keep in mind that most of today’s platforms offer their solutions within a platform-as-a-service (PaaS) framework that includes cloud-based machine learning software and processing along with data storage and other tools and components. Pay close attention to how much support is offered through this model and if any community-driven support or training opportunities are included to help you get started.

Also see: Top AI Software

Review Methodology

The platforms in this machine learning platform review were assessed through a combination of multiple research techniques: combing through user reviews and ratings, reading whitepapers and product sheets, considering the range of common and differentiating features listed on product pages, and researching how each tool compares across a few key metrics. More than 25 platforms were assessed before we narrowed our list to these top players.

eWeek chose the top 10 selections in this list based on how well they addressed key feature requirements in areas like advanced data processing and management, feature engineering, model training and fine-tuning, performance monitoring, and reporting and analytics.

Beyond key features, we also considered how well each tool would meet the needs of a wide range of enterprise user audiences, whether your primary user is an experienced ML developer or data scientist or a non-technical team member who needs low-code model-building solutions. Finally, we looked at the affordability and scalability of each tool.

Bottom Line: Selecting the Best Machine Learning Solution for Your Business

The right ML solution for your business may end up being a combination of multiple solutions, as different platforms bring different strengths to the table. Some of these tools particularly excel at preparing data for high-quality model development. Others provide the frameworks and integrations necessary to build the model. Still others offer recommendations and managed support to help you optimize existing models for future performance goals.

With so many of these tools not only integrating well with each other but also available in free and/or open-source formats, it may well be worth the time it would take to incorporate multiple of these leading tools into your existing machine-learning development strategies.

Read next: Top 9 Generative AI Applications and Tools

The post 10 Best Machine Learning Platforms appeared first on eWEEK.

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.

The post Snowflake vs. Databricks: Comparing Cloud Data Platforms appeared first on eWEEK.

Among the topics we discussed: 

• Let’s talk about integration technology in the enterprise. How does it work in terms of, say, integrating cloud and legacy in-house apps?
• What are the challenges in integration? The typical headaches? How do you recommend companies handle these challenges?
• How is Jitterbit addressing the integration needs of its clients?
• The future of tech integration in the enterprise? Will it ever get easy?

Listen to podcast:

Also available on Apple Podcasts

Watch the video:

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How so? On the upside, they accurately, if simplistically, describe the technical and market dynamics of a forward-focused industry that is quick to develop innovations and products designed to enhance performance and user experience.

But on the downside, the terms reflect the industry’s longstanding obsession with building, marketing and profiting from new products to the point of claiming, often without evidence, that they are superior to solutions already residing in client data centers.

Most important, they continually enhance existing solutions and platforms to ensure that they remain relevant to the needs of modern enterprises. IBM’s new watsonx Code Assistant for Z is a good example of one such effort.

Modernization vs. Legacy Hype

That “new” doesn’t automatically translate to “better” is a bit of practical wisdom that is seldom, if ever, seen in tech industry ad copy. Instead, vendors tend to hype shiny new things—claiming the innate superiority of this year’s gear over previous generation systems and platforms.

Certainly, new or next gen CPUs, storage media, interconnects and other technologies typically deliver better and/or more efficient performance. However, the value of ripping out existing or older systems and replacing them with new hardware is usually vastly overrated, often resembling a case of “fixing what isn’t broken.” The process is also expensive for customers, sometimes hugely so, due to costs related to system integration, software upgrades and retraining and certifying IT personnel.

In addition, generational shifts can make it increasingly difficult for businesses to find new system administrators, developers and technicians as existing staff members age-out. As is true in most other industries, younger workers typically prefer to explore and use new and emerging technologies.

That is a scenario that IBM plans to mitigate and avoid with its new watsonx Code Assistant for Z.

What is it? According to the company, the new solution is a generative AI-assisted product that is designed to enable faster translation of COBOL to Java on IBM Z, thus saving developers time and enhancing their productivity. It also joins IBM watsonx Code Assistant for Red Hat Ansible Lightspeed (scheduled for release later this year) in the watsonx Code Assistant product family.

Both solutions leverage IBM’s watsonx.ai code model, which the company says will employ knowledge of 115 coding languages learned from 1.5 trillion tokens. According to IBM, at 20 billion parameters, the watsonx.ai code model will be one of the largest generative AI foundation models for computer code automation.

Why is this important? First, because of the sheer pervasiveness of COBOL. Enterprise developers and software engineers have written hundreds of billions of lines of COBOL code. Plus, due to its notable flexibility and reliability, COBOL is still widely used, reportedly supporting some $3 trillion in daily financial transactions. In other words, COBOL is literally “business critical” to tens of thousands of large enterprises, millions of smaller companies and billions of consumers.

Also see: Top Digital Transformation Companies

COBOL Meets Watsonx Code Assistant for Z

Despite its vital position in transaction processing, COBOL is hardly a favorite among young computer professionals. Though COBOL and other mainframe programmers earn premium salaries (according to IBM, some 20-30 percent more than their peers), employers struggle to fill available positions.

That’s where IBM’s watsonx Code Assistant for Z comes in. The company notes that the new solution is designed to make it easier for developers to selectively choose and evolve COBOL business services into well architected, high-quality Java code.

Plus, IBM believes watsonx generative AI can enable developers to quickly assess, update, validate and test the right code, allowing them to efficiently modernize even large scale applications.

The Java on Z code resulting from watsonx Code Assistant for Z will be object-oriented and is designed to be performance-optimized versus comparable x86 platforms. IBM is designing the solution to be interoperable with the rest of the COBOL application family, as well as with CICS, IMS, DB2 and other z/OS runtimes. Lastly, IBM Consulting’s deep domain expertise in IBM Z application modernization makes it a prime resource for clients in key industries such as banking, insurance, healthcare and government.

Final Analysis

Though marketing professionals may feel comfortable with portraying modern and legacy technologies as a simplistic “new vs. old” conundrum, business owners, IT management and knowledgeable staff, including developers, understand the complexities of the modern/legacy dynamic. Rather than age, the larger issue is relevance: why an organization began employing a particular technology and how or whether that solution remains relevant to its owner’s needs.

It is not unlike how people and organizations remain relevant. Industries, companies, markets and larger economies are in a constant state of evolution. People and organizations succeed by adapting to those changes, by learning new skills, exploring new opportunities, and remaining vitally relevant to customers and partners. IBM’s new watsonx Code Assistant for Z demonstrates that what is true for people can also be true for information technologies.

Read next: Digital Transformation Guide

The post Modernizing the Mainframe—IBM Introduces Watsonx Code Assistant for Z appeared first on eWEEK.

Reshoring’s primary goal is to regain control over the entire end-to-end supply chain—it’s about manufacturing products on local soil, and it’s a process that’s been gaining traction from companies worldwide.

From a North American perspective, the picture is no different. Many U.S. companies have begun the shift away from globalization as default, with research suggesting that nearly 350,000 jobs were re-shored to the U.S. in 2022—a notable increase when compared to the 2021 figure of 260,000.

The movement has also seen companies become less reliant on China. Now, many economies, including the U.S., India, and the European Union, are looking to establish a roadmap that will balance supply chains and increase resiliency. The China Plus One Strategy is an approach adopted by a number of businesses looking to include sourcing from other destinations. Already, numerous companies have turned to Vietnam and India as alternatives, with both countries reporting an uptick in investment from U.S. companies that have built plants there.

According to the Reshoring Initiative IH 2022 Data Report, supply chain gaps, the need for greater self-sufficiency, and a volatile geopolitical climate are major factors driving reshoring. The report found that 69% of companies cited supply chain disruptions as the primary reason for reshoring.

There is now movement on a national level to strengthen supply chains and promote domestic manufacturing with the introduction of the bipartisan National Development Strategy and Coordination Bill in December 2022. This bill highlights the importance of manufacturing reshoring to national economic development going forward into 2023.

Sustainability and Tech in Reshoring

Recent research commissioned by IFS, polling senior decision-makers working for large enterprises globally, found that 72% have increased their usage of domestic suppliers, compared to international suppliers.

From a sustainability perspective, there are huge benefits to be gained. In fact, reshoring is giving manufacturers a golden opportunity to look hard at their manufacturing processing and how they can develop more sustainable processes.

For example, it can minimize CO2 emissions as transport is reduced and spur a deduction in wasteful overproduction as supply chains are brought closer together. As the whole world strives to act more sustainably in the race to net-zero, environmental benefits will play a huge role in driving new sourcing strategies.

However, the raw materials, components, and products that they source from suppliers are likely to become more expensive, especially as inflation continues to gather pace globally. As a result, 53% have considered increasing the proportion of materials/components they produce in-house. But again, these measures and others like them that organizations are now taking to mitigate risk are likely to add cost, complexity, and waste to the supply chain.

Therefore, reshoring is not the silver bullet to mitigating supply chain disruption entirely. Often, companies underestimate the sheer level of effort, costs, and logistical planning required to make reshoring a success.

But for many U.S. companies, the extra costs to manufacture within the country are definitely outweighed by the savings in customs and shipping costs and the additional sustainability benefits associated with offshore operations.

It’s here organizations need the helping hand of technology—in fact, it can be a key facilitator for solving supply chain, labor, and production challenges associated with reshoring.

For 94% of respondents in a recent McKinsey study, Industry 4.0 helped keep operations running during the COVID-19 pandemic, with another 56% claiming Industry 4.0 technologies had been critical for efficient responses.

A new IDC InfoBrief, sponsored by IFS and entitled Shaping the Future of Manufacturing, shows an active correlation between digital maturity and profit. According to the research, manufacturers reporting an optimized level of digital transformation saw profits increase 40%, while those with less advanced digital transformation maturity suffered bigger reductions in profit in the last fiscal year.

Tech has been quick to respond to the call to deliver the agility and fast “Time to Insight” (TTI) that manufacturers need to better forecast demand and provide a more detailed view of sustainability across product supply chains. Exceptional supply chain management will be a vital part of the move to reshoring. The IFS study showed supply chain management was now seen by 37% of respondents as one of the top three priorities their organization is trying to solve through technology investment.

Reshoring in Action: Will the Benefits Be Worth It?

In a recent Kearney index on manufacturing reshoring, 92% of executives expressed positive sentiments toward reshoring. And that’s no surprise when you consider the additional benefits on offer. As well as a more protected supply chain ecosystem, there are also positive societal benefits from the move to reshoring.

According to the U.S. Reshoring Initiative, in 2021 the private and federal push for domestic U.S. supply of essential goods propelled reshoring and foreign direct investment (FDI) job announcements to a record high.

From a broader perspective, there are many profitable and supply chain benefits at stake for manufacturers. For example, research found that 83% of consumers in the U.S. are willing to pay 20% more for American-made products, with another 57% claiming that the origin of a product would sway their purchasing decision.

From a management standpoint, control over operations has significantly increased. Bringing operations all to one centralized location gives businesses tighter control over processes. Manufacturers will also benefit from shorter supply chains as much of today’s manufacturing is spurred by IoT, AI, and machine learning capable of performing monotonous tasks around the clock.

On a day-to-day level, on-site teams will experience increased collaboration as reshoring drastically reduces the time difference between headquarters and the manufacturing plant.

Tech Needs to Drive Reshoring

It’s easy to see why the appeal of reshoring is prompting a move toward U.S.-based manufacturing initiatives. By addressing reshoring now with the right technology, efficiently and cost-effectively, manufacturers will put themselves in a great position to not only survive but also thrive long into the future.

Of course, as with any major transformation, there are hurdles to overcome. But the long-term results of reshoring, from increased employment to tighter manufacturing control, look as though it’s a journey worth embarking on. As more and more companies around the world look to reshore operations on home soil, manufacturers will need the guiding hand of a flexible and agile software platform to make reshoring a reality at scale.

About the Author:

Maggie Slowik is the Global Industry Director for Manufacturing at IFS.

The post Reshoring Alleviates Supply Chain Issues – But It Needs Tech to Control Costs appeared first on eWEEK.

This varies significantly according to industry, region and commercial markets. Plus, it is commonplace for ESG programs to evolve as priorities and circumstances change.

Recently, Dell published its new ESG Report for FY2023, updating its achievements and overall strategy. Let’s consider how the company has progressed – but first let’s take a brief look at the state of enterprise ESG issues today.

Also see: Top Digital Transformation Companies

Today’s Corporate ESG Issues

It is worth noting the importance of ESG programs. The issues covered in these programs affect all of our lives and are closely tied to organizations’ relationships with stakeholders, including customers and strategic partners. Empowering disadvantaged groups of customers and businesses is just good for business.

That is especially true in the U.S. where despite their myriad benefits, ESG policies have become bugaboos of “wokeness” among some politicians and groups. Many of those individuals and alliances are also attempting to dial-back broader environmental and social justice advances but are encountering resistance from progressive organizations and individuals, as well as from seemingly unlikely organizations. Those include large corporations, pension funds, insurers and investment firms.

Why would those disparate players actively protect ESG programs? A couple of issues are top of mind. First, disadvantaging specific groups of consumers and businesses to appease politicians and special interest groups is simply bad for business.

Equally important are the negative impacts that anti-ESG efforts can have on promising businesses and industries. Consider that earlier this year, 19 Republican state governors signed an open letter warning of the “direct threat” posed by ESG proliferation. Some connect the ‘E’ in ESG to renewable energy technologies and programs, such as hydroelectric, wind power and ethanol subsidies for farmers. Since many or most of the governors who signed the letter lead states that benefit from renewable energy initiatives, their anti-ESG rhetoric seems ironic in the extreme.

Finally, and perhaps most importantly, is the value that ESG programs and strategies offer to companies doing business globally. Environmental, social and governance issues vary widely in importance and scope from place to place. The variety of ESG subject matter means that organizations can craft programs to maximize value for the customers and partners they believe are most in need.

Far from being the direct threat that some U.S. state governors and other politicians and groups imagine, ESG continues to deliver substantial, welcome benefits to businesses, state institutions and consumers worldwide.

Dell’s FY 2023 ESG Report

Dell Technologies has emphasized the importance of ESG-related issues since 1998 when the company published its initial Environmental Progress Report.

Beginning in 2002, the company shifted to annual reports charting its focus on and progress in key areas, including the environment, sustainability and corporate social responsibility. The company has maintained these commitments through recent political headwinds because it understands these priorities are not only good for business but also for the communities in which they operate.

What are some of the key highlights in Dell’s new FY2023 ESG report?

First, the company refined the goals included in the FY2022 report and condensed its 25 top-level goals to:

• Achieve net zero greenhouse gas (GHG) emissions across Scopes 1, 2 and 3 by 2050.
• Reuse or recycle one metric ton of materials for every metric ton of products Dell customers buy by 2030.
• Make or utilize packaging made from recycled or renewable material for 100 percent of Dell products by 2030.
• Leverage recycled, renewable or reduced carbon emissions materials in more than half of the products Dell produces by 2030.
• Employ women as 50% of Dell’s global workforce and 40% of the company’s global people leaders by 2030.
• Employ people who identify as Black/African American or Hispanic/Latino as 25% of Dell’s U.S. workforce and 15% of its U.S. people leaders by 2030.
• Improve the lives of 1 billion people through digital inclusion by 2030 through efforts such as supply chain training and initiatives aimed at girls and women, or underrepresented groups.
• Provide support for and participation in community giving or volunteerism by 75% of Dell team members by 2030.

Additionally, in 2022 Dell began framing a trust model centered on security, privacy and ethics. Given the importance of those areas in terms of establishing and maintaining trusted relationships, the company is emphasizing “Upholding Trust” with the goal of having customers and partners rate Dell Technologies as their most trusted technology partner.

Finally, the company demonstrated its continuing commitment to diverse supplier spend by doing over $3 billion in business with small and diverse companies. Plus, for the 13th consecutive year, Dell was recognized by the Billion Dollar Roundtable (BDR), which celebrates corporations that spend at least $1 billion annually with minority- and women-owned businesses.

Further details, background information and customer/partner examples can be found in the full Dell Technologies ESG Report for FY2023.

For more information, also see: What is Data Governance

Final Analysis

Transformation is a concept and process that permeates the technology industry, but it also has many guises. For example, there’s the “digital transformation” strategies and solutions that so many vendors emphasize aim to help customers improve business outcomes by maximizing compute performance and data efficiency. Other efforts include process transformation, such as leveraging automation and logistical efficiencies to improve supply chain performance.

One topic less commonly discussed is corporate cultural transformation. This is when an organization continually and proactively evolves to adapt and benefit from changes in commercial markets, business practices and demand forecasts, as well as shifts in politics, economies and the environment. In my opinion, this type of transformation holds a central role in Dell Technologies’ ESG strategy and its annual ESG reports.

Many of the practical steps the company is taking—expanding the use of recycled and renewable materials, for example—simply make good business and financial sense. Others, including achieving net zero GHG emissions, reflect the company’s deep understanding of and intention to practically address climate change and other environmental issues.

Some goals enumerated in the new FY2023 report may appear aspirational but are far more practical than one might expect. At a Dell Technologies World session a few years ago, Michael Dell noted (I confess to paraphrasing here) that, “A company should look like its customers and partners.”

That is a particularly profound statement, not to mention being highly applicable to business and a wide range of public and private organizations and institutions. Without having such a vision and investing in efforts to achieve it, individuals, businesses and governments will inevitably find their vision blurring, their frontiers shrinking and their opportunities dwindling.

By embracing cultural evolution through supporting and advancing the careers of underrepresented groups, by actively improving communities and the lives of a billion people and by working to become the vendor that customers and partners trust the most, Dell Technologies will further grow its own outlook, relevance and potential for success.

Is there a greater or more important goal for any organization?

For more information, also see: Digital Transformation Guide

The post Dell’s 2023 ESG Report: Evolving Corporate Culture appeared first on eWEEK.

As far back as 2001, at data warehousing firms like my old haunt of Daman Consulting, we touted the value of “operationalizing” business intelligence. The idea was to leverage BI-derived insights within operational systems dynamically, and thus directly improve performance.

Though embedded analytics have been around for decades, it’s fair to say that most BI solutions in this millennium have focused on the dashboard paradigm: delivering high-level visual insights to executives via data warehousing, to facilitate informed decision-making.

But humans are slow, much slower than an AI algorithm in the cloud. In the time it takes for a seasoned professional to make one decision, AI can ask thousands of questions, get just as many answers, and then winnow them down to an array of targeted, executed optimizations.

That’s the domain of applied intelligence, a closed-loop approach to traditional data analytics. The goal is to fuse several key capabilities – data ingest, management, enrichment, analysis and decisioning – into one marshaling area for designing and deploying algorithms.

There are many benefits to this approach: transparency, efficiency, accountability; and most importantly in today’s market? Agility. During times of great disruption, organizations must have the ability to pivot quickly. And when those decisions are baked in via automation? All the better.

It also helps in the crucial domain of explainability, the capacity to articulate how an artificial intelligence model came to its conclusion. How explainable is a particular decision to grant a mortgage loan? How repeatable? What are the biases inherent in the models, in the data? Is the decision defensible?

On a related topic: The AI Market: An Overview

Take It To the Bank

The rise of fintech startups and neobanks, coupled with rapidly changing interest rates, has put tremendous pressure on traditional financial market leaders to innovate rapidly but safely. Rather than embrace a rear-guard strategy, many firms are looking to AI to regain momentum.

As CPTO for FICO, Bill Waid has overseen a wide range of banking innovations. UBS reduced card fraud by 74%, while Mastercard optimized fraud detection in several key ways, including automated messaging to solve the omni-channel conundrum of communications.

The Mastercard story demonstrates how a large financial institution is now able to dynamically identify, monitor, and manage client interactions across a whole host of channels – and fast enough to prevent money loss. A nice side benefit? Less-annoyed customers.

In a recent radio interview, Waid explained another situation where collaboration improves marketing. “In banking, from a risk perspective, one of the most profitable products is credit card. So if you were to ask somebody from risk: which would you push, it would be the credit card.”

But other departments may disagree. “If you ask the marketing person, they have all the stats and the numbers about the uptake, and they might tell you no, it’s not the credit card, at least not for this group (of customers), because they’re actually looking for a HELOC or an auto loan.”

The point is that you can drive away business by making the wrong suggestion. Without collaborating around common capabilities from a centralized platform, Waid says, that mistake would have likely gone into production, hurting customer loyalty and revenue.

With an applied intelligence platform, he says, key stakeholders from across the business all have their fingers in the pie. This helps ensure continuity and engagement, while also providing a shared baseline for efficacy and accountability.

Think of it as a human operating system for enterprise intelligence, one that’s connected to corporate data, predictive models, and decision workflows, thus achieving cohesion for key operational systems. In the ideal scenario, it’s like a fully functioning cockpit for the enterprise.

This transparency leads to confidence, a cornerstone of quality decision outcomes: “That confidence comes in two dimensions,” he says. “The first is: can you understand what the machine is doing? Do you have confidence that you know why it came to that prediction?

“The second element is that in order for the analytic to be useful, it’s gotta get out of the lab. And many times, I see that the analytic comes after the operationalization of a process, where there is more data, or a flow of data that’s well warranted to an analytic.”

For more information, also see: Best Data Analytics Tools

Bottom Line: The Analytic Becomes an Augmentation

This is where rubber meets road for applied intelligence: the analytic becomes an augmentation. And when the business has that transparency, they get comfortable, and they adopt the insight into their own operational workflow. That’s when the intended value out of the machine is felt.

“Platforms provide unification: bringing process, people, and tech together,” Waid says. And as AI evolves, with Large Language Models and quantum computing closing in, it’s fair to say that the practices of applied intelligence will provide critical stability, along with meaningful insights.

Also see: 100+ Top AI Companies 2023

The post Navigating the Perfect Storm with Applied Intelligence appeared first on eWEEK.

Among the topics we covered: 

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

Listen to the podcast:

Also available on Apple Podcasts

Watch the video:

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

Chuck Whitten joined Dell Technologies in 2021 where he became co-Chief Operating Officer in partnership with Jeff Clarke. Together, Whitten and Clarke own company-wide strategy and execution. Whitten oversees the day-to-day financial and operating plans and performance, as well as long-term planning for emerging technology areas like Cloud, Edge, Telecom and as-a-Service.

Prior to joining Dell, Whitten worked at Bain and Company for over two decades where he served as the managing partner of Bain Southwest and was a two-time elected member of Bain’s Board of Directors.

Also see: Top Digital Transformation Companies

Technology and Business

Pund-IT (Charles King): A couple of decades ago, vendors spent a lot of their time explaining and proselytizing the importance of technology to businesses. Today, that’s a common understanding or belief. So, you might say that Dell’s approach has benefited from that evolution and maturation of the links between technology and business.

Whitten: For sure. Technology has never been more essential to our customers and to society. That is also why there’s been this blurring of technology and business budgets, because it’s one and the same. You’re either born a technology company or you evolve a technology-led strategy very quickly, or you go away. There’s also the realization and the profound impact of digital transformation sweeping across industries. I think you’d be hard pressed to find a board or a C suite anymore that doesn’t talk about the criticality of technology to their business and their future. Dell is in the business of helping them solve whatever those problems are.

Dell’s Management Profile

Pund-IT: Since you and Jeff Clarke share the co-COO title, can you talk about that relationship and how your individual responsibilities work?

Whitten: When I joined Dell in August 2021, it was for two reasons. The first and most important was to share responsibilities for better coverage and speed decision making to accelerate our growth potential and deliver outcomes for customers.

The second was to capitalize on these big opportunities in front of us. We are facing a pivotal time in our company’s history, and expanded leadership capacity helps us to do that. The COO scope at Dell is really broad, so my first job was to jump on the moving train, listen and learn. Then, over time, we began to create executive capacity to divide and conquer responsibilities where it makes sense for our customers.

Pund-IT: How does that work practically?

Whitten: So today, Michael, Jeff and I share responsibility for the strategic direction of the company, as well as the talent agenda. Jeff tilts his time more toward technical strategies and the architectures that are going to define our next era. That is logical given his unmatched experience as an engineer. He also drives the critical ecosystem relationships that he’s built over decades at the company. My responsibilities are the day-to-day execution of Dell’s business like delivering for the quarter and the year, taking solutions to market, making sure we’re supporting customers in the here-and-now, and driving shareholder value.

Pund-IT: That sounds like a sensible division of labor.

Whitten: Look, Jeff and I are quite complementary. It works, I think, because we have years of deep trust, and we stay in constant communication. We’re also agile when and where we need to be. Jeff likes to say there are some problems where we need, “Four eyes, four arms and two brains.” We dig in together. It’s a model that works very well in Dell’s culture, but it takes a lot of communication and having a shared DNA. All Jeff and I want to do is win for our company and our customers, and we tend to sort out things as needed with that as the foundation.

For more information, also see: Digital Transformation Guide

The Dell Company Culture 

Pund-IT: You mentioned being attracted to the fact that Dell is a founder-led company. Does that make it a different kind of organization or entity than other vendors?

Whitten: I think founder-led businesses are differentiated in that company culture is ultimately traced back to the founders. So, the principal behaviors and practices that are instituted in the early days of a company carry forward to the present. I credit our innovative, customer-centric culture to Michael and the consistent way he’s led over the years. We have a common and real sense of purpose because of Michael—to create the technologies that drive human progress. It’s aspirational and it’s also very clear.

Pund-IT: Michael is also, to my mind, one of the most fully engaged leaders in the tech industry.

Whitten: Michael is definitely not hands-off. He is an entrepreneur to the core with incredible instincts. Look, we all admire his leadership. He has correctly predicted where technology is headed at pivotal moments in history and made some very different bets on shaping the company. That’s a real gift. I think what I’m most grateful for, and what I think differentiates founder-led companies from other enterprises, is just the long-term orientation.

Pund-IT: In what sense?

Whitten: Michael is focused on building an enduring business. We never feel pressure to drive short-term profits. We’re encouraged to wade into all these unsolved, hard problems of technology, like multicloud, security, artificial intelligence, the edge. That is a founder’s mentality at work. That’s asking, “Hey, what are the big opportunities and big problems that we can support our customers on?”

Pund-IT: We’ve talked quite a bit about culture. However, it’s a term that has become something of a bromide among some vendors. But I agree with you about the importance of culture and the value of leveraging what a company innately is to drive strategy and execution.

Whitten: Absolutely. I believe it is one of our top differentiators. We have a culture code that traces back to Michael which we all adopted when we joined Dell. It defines who we are, what we believe, how we work and how we lead our teams. It’s somewhat simple: We believe that customers and innovation are the foundation of success in the technology industry. If you never lose sight of that, you will win as long as you act with integrity and commitment. That’s what we ask our team members to embrace.

Pund-IT: What do employees receive in return?

Whitten: In return, we commit to our team members. We believe people should have fulfilling and full careers, as well as fulfilling lives. That’s culturally where we strive to build achievement inside of Dell, but also balance life at home, connections, diversity and inclusion. None of that happens without having a CEO at the top, like Michael, who says, “This matters.” We want to build a people-centric company that delivers technology and innovation for our customers as well as our team members. That’s easy to say and very hard to do.

Pund-IT: In your years working both as a Bain advisor and a Dell executive, what has impressed you or surprised you the most about the company?

Whitten: I think the biggest thing is our agility. Look, $100 billion companies are not supposed to be able to move quickly. But we do. IBM’s Lou Gerstner famously said elephants aren’t supposed to be able to dance. We dance and sing and do backflips like a smaller-scale company.

Pund-IT: What are some examples of that?

Whitten: You saw it in our performance during the last few years as we navigated the pandemic boom in PCs and supporting work-from-home (WFH), followed by the boom in infrastructure. All that time, we were supporting our own WFH employees and navigating global supply chain shortages and doing so better than the rest of the industry. We also saw the current economic caution in businesses faster than anyone else in a position to be relevant to customers. I also think you see that agility in our innovation. The rate of progress we’re making in places like telecom and AI and multicloud is astonishing. Last year in our Infrastructure Solutions Group business, we had a period of 30 major launches in 13 weeks. The ability to move that fast is a really formidable thing.

Also see: Top Cloud Companies

Dell Technology and the Future 

Pund-IT: Is there anything about Dell that you wish people outside the company knew or understood more clearly?

Whitten: I think it’s appreciated but it bears repeating. At Dell, we’ve built something that looks really different than the rest of the technology industry. We’re number one in all our core markets, and we have what we call “durable competitive advantages” that help us continue to win and reinvent ourselves. Our end-to-end portfolio puts us in the center of customers’ agendas, has the largest go-to-market and channel ecosystem in the industry, offers leading services, scale and capabilities, and benefits from the industry’s best supply chain with unmatched scale. And then, as we’ve discussed, we have a culture that I think is unmatched in the industry. Because of that and what we’ve built, we sit at the center of these great challenges our customers are facing.

Pund-IT: Such as?

Whitten: What’s the future of work and how does the PC unlock collaboration? How can companies make multicloud architectures work seamlessly? How can security be more intrinsic to infrastructure and not something that you add on after the fact? How can we accelerate AI? And how can we do it smartly and securely and ethically? How do we unlock innovation at the edge? Technology has never been more essential to customers, and never been more essential to addressing the broad problems of society. We’re in the center of all of that. I think we have the capabilities and the culture to make a difference. Building on that, we can plan and pursue our future-focused goals.

Pund-IT: Speaking of that, where do you see Dell in four or five years?

Whitten: We’ve reinvented ourselves over multiple decades, and we’re just going to continue to reinvent ourselves. There are short-term economic headwinds, but we’ve seen those before. What feels different in this cycle is that technology has never been more essential to our customers and we, as a company, have never been better positioned and prepared to help them solve those problems. Five years from now, I believe we will continue to be at the center of customers’ technology agendas, whatever those agendas are. On the way there, we’re going to help them solve the most pressing challenges that we’re seeing today so we will be at the center of whatever customers are doing. I think that’s the enduring institution that Michael built and that we’re all building together now.

Also see: Top Edge Companies

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