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

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

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

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

Cognos vs. Power BI at a Glance

What Is Cognos?

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

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

Also see: Top Business Intelligence Software

Key Features of Cognos

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

Pros

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

Cons

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

What Is Power BI?

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

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

Key Features of Power BI

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

Pros

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

Cons

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

Best for Core Features: It Depends

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

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

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

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

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

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

Also see: Top Dashboard Software & Tools

Best for Ease of Use and Implementation: Power BI

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

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

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

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

Also see: Top AI Software

Best for Advanced Analytics Capabilities: Cognos

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

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

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

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

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

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

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

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

Also see: Data Analytics Trends

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

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

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

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

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

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

Also see: Top Data Visualization Tools

Best for Integrations: It Depends

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

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

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

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

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

Best for Pricing: Power BI

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

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

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

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

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

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

Also see: Data Mining Techniques

Why Shouldn’t You Use Cognos or Power BI?

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

Who Shouldn’t Use Cognos

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

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

Who Shouldn’t Use Power BI

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

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

Also see: Best Data Analytics Tools

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

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

Domo

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

Yellowfin

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

Wyn Enterprise

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

Zoho Analytics

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

Sigma

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

Review Methodology

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

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

User experience – 30%

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

Advanced analytics and scalability – 30%

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

Integrations and platform flexibility – 20%

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

Affordability – 20%

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

Bottom Line: Cognos vs. Power BI

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

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

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

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

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

Read next: 10 Best Machine Learning Platforms

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

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

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

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

Looker vs. Power BI at a Glance

What Is Looker?

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

Key features of Looker

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

Pros

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

Cons

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

What Is Power BI?

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

Key features of Power BI

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

Pros

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

Cons

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

Best for Core Features: It Depends

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

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

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

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

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

Best for Ease of Use and Implementation: Power BI

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

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

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

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

Best for Advanced Data Analytics: Looker

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

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

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

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

Also see: Best Data Analytics Tools 

Best for Integrations: It Depends

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

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

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

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

Best for Pricing: Power BI

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

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

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

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

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

Also see: Top Digital Transformation Companies

Why Shouldn’t You Use Looker or Power BI?

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

Who Shouldn’t Use Looker

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

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

Who Shouldn’t Use Power BI

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

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

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

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

Domo

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

Yellowfin

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

Wyn Enterprise

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

Zoho Analytics

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

Sigma

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

Review Methodology

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

User experience – 30%

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

Scalability and advanced analytics compatibility – 30%

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

Integrability – 20%

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

Cost and accessibility – 20%

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

Bottom Line: Looker vs. Power BI

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

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

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

Read next: Best Data Mining Tools and Software

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

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

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

A “Do It Yourself” Approach is Hard to Execute

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

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

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

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

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

Also see: Top Digital Transformation Companies

The Platform

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

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

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

Bottom Line: Low Code/No Code Delivers

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

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

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

Read next: Digital Transformation Guide

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

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

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

Poor User Experience is the Teams Challenge

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

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

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

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

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

Also see: Digital Transformation Guide

The EU and Unbundling

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

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

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

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

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

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

Microsoft Needs to Prioritize Usability 

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

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

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

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

Read next: Top Digital Transformation Companies

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

For more information, also see: Digital Transformation Guide

Hopin Provides a Single Solution for Hybrid Events

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

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

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

RingCentral Rounds out Its Portfolio with Hybrid Events

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

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

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

Also see: Top Digital Transformation Companie

Bringing Simplicity to Hybrid Events

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

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

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

Also see: 100+ Top AI Companies 2023

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

Among the topics we covered: 

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

Listen to the podcast:

Also available on Apple Podcasts

Watch the video:

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

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

Legacy IT Management: Time-Consuming and Reactive

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

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

Poor Experience Impacts Workers and Customers

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

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

Tanium Taps Desktop Information 

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

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

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

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

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

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

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

DEX Captures Valuable User Sentiment

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

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

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

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

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

Focused on Endpoint and App Experience

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

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

Also see: Top Digital Transformation Companies

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

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

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

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

For more information, also see: Best Data Analytics Tools

Table of Contents

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

Best Data Mining Tools and Software: Comparison Chart

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

SAS Visual Data Mining and Machine Learning

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

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

Key Features

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

Pros

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

Cons

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

To learn more, also see: Top Business Intelligence Software 

Oracle Machine Learning in Autonomous Database

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

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

Key Features

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

Pros

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

Cons

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

Talend Data Fabric

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

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

Key Features

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

Pros

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

Cons

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

RapidMiner (Altair)

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

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

Key Features

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

Pros

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

Cons

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

Alteryx Designer Cloud

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

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

Key Features

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

Pros

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

Cons

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

IBM SPSS Modeler

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

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

Key Features

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

Pros

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

Cons

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

KNIME

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

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

Key Features

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

Pros

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

Cons

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

Orange

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

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

Key Features

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

Pros

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

Cons

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

Qlik Sense

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

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

Key Features

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

Pros

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

Cons

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

TIBCO Data Science

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

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

Key Features

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

Pros

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

Cons

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

How to Select a Data Mining Tool for Your Organization

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

Look for tools that support your industry-specific requirements

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

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

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

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

Pick a tool that integrates with your existing tool stack

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

Select a tool with effective reporting and visualization features

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

Consider your budget and in-house data science skills

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

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

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

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

Bottom Line: Data Mining Tools

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

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

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

Read next: Top Business Intelligence Software

The post 10 Best Data Mining Tools & Software appeared first on eWEEK.

Real-time data is especially valuable for businesses, for a multitude of reasons. It can provide immediate insight into sales trends, and it can also provide immediate insight to security vulnerabilities or degradation of the corporate IT infrastructure.

With digital transformation initiatives well underway, companies are investing in strategies to ingest large volumes of data that enable them to make the right decisions in the moments that matter. Handling the sheer volume and complexity of this data store is exceptionally challenging.

As enterprises meet these data-intensive digital demands, here are five real-time data management trends we anticipate over the next year.

Also see: 7 Digital Transformation Trends Shaping 2022

Data Visualization to Identify Patterns and Trends

Whether it’s real time or sitting at rest in a database, data is nothing more than numbers without visualization. To bring real-time data to life, you need real-time data visualization. Visualization, such as charts, graphs, maps, or other colorful displays, can give you an edge over the competition by mapping out not only the data, but where it is going in terms of activity.

Where data visualization works best is helping to alert companies if something is abnormal or out of the ordinary. For example, a spike in outbound network traffic would show up on the meter, giving a visual cue to anyone watching that there is a sudden flow of traffic which should be investigated.

But it also has positive benefits as well. If a company notices an uptick in sales during certain parts of the day, that would show up on a chart as well and command immediate attention. Real-time visualization enables people to take action in case of emerging opportunities, to capitalize on a potential opportunity or possible negative outcome.

This enables decision-makers to get ahead of the curve and respond quickly and early on, rather than after the fact when an opportunity is missed or damage is done. It also encourages more data interaction, because no one wants to sit and pour through numbers. In contrast, looking at a graph or a chart which summarizes 24 hours of activity in one picture is much more accessible.

With real-time data analysis, companies can identify trends and monitor how well they are achieving goals. They can access data remotely, monitor purchases, manage resources, and help secure their network.

Also see: Top Data Visualization Tools

Data Security is More Important Than Ever

The federal government has issued a Federal Zero Trust Strategy that requires, among other things, adoption of Zero-Trust security measures across the federal government and private sector.

The zero trust strategy will enable agencies to more rapidly detect, isolate, and respond to cybersecurity threats and intrusions. The Office of Management and Budget (OMB) has issued a series of specific security goals for agencies aligned with support existing zero trust models.

For the unfamiliar, zero trust is a new networking design that takes its name literally. One of the knocks on cyber security is that once the bad guys have breached your firewalls, they can move around within your network with impunity. Zero trust network requires validation and credentials to move anywhere within the network. It is a much stricter networking protocol designed to bottle up anyone who breaches the outer wall.

Because of this design, the federal strategy puts a great deal of emphasis on enterprise identity and access controls, including multi-factor authentication, along with encryption and comprehensive auditing.

Also see: Best Data Analytics Tools

Invest in Digital Tools That Improve the Customer Experience 

Research from Boston Digital found 83% of customers are willing to switch to a brand with a better digital experience, and 70% of customers are more likely to trust brands that provide a great digital experience.

That means that customers are increasingly loyal to brands that offer a direct relationship with them and knows their wants and needs. That also means they won’t hesitate to leave you if they’re not getting the experience they want from you, and it’s extremely easy to switch consumer loyalties these days.

Therefore, it is important to invest in the right tools to continuously and actively optimize your digital presence to meet customer needs, and to stay on top of changing trends as they constantly shift.

Also see: Guide to Data Pipelines

Businesses will Reinvent Customer Profiles with Real-Time Data

For years, companies have easily fetched customer identity and other information from cookies. With the end of cookies in 2022, and with more than 70 percent of the world’s population protected by privacy regulations, businesses will have to adapt to new targeting strategies to quickly recommend a product or decide if a transaction is fraudulent.

As identity becomes less of a fixed or known data point, enterprises need to immediately analyze a massive swath of data, look for patterns, and extrapolate a likely persona for targeting. They will need to find patterns in real time that target individuals based on attributes or behaviors other than a cookied identity.

As data volumes constantly grow and demand for real-time transactions increases, these trends will traverse all industries where scaling is instrumental to survival. For example, ad tech is experiencing a renaissance, garnering significant investment, innovation, and attention as platforms rapidly seek to serve ads to targeted audiences at petabyte scale.

Likewise, with massive amounts of data streaming from mobile, 5G, and IoT sensor applications, telecom companies need to quickly ingest data and then process it at petabyte scale with virtually no latency.

As we move forward, enterprises need to embrace the opportunities and challenges ahead of them and manage real-time data in new ways to drive successful business outcomes.

Bottom Line: Real Time Data Management in 2023

Real-time data management is the new normal in business. The days of batch processing on a mainframe overnight and analyzing it the next day are long gone and hard to find. The good news is that there are plenty of tools out there to enable it, ranging from Apache open-source software to commercial software from leaders like IBM and SAP.

Data is also coming from many new sources that weren’t around 10 or 20 years ago. This includes social media, the edge/IOT, and mobile users. So real-time data analytics does not just include your databases and network security, it includes your entire enterprise.

At the same time, data is more regulated than ever. There have been significant data breaches in recent years, and companies have paid massive fines for their sloppiness. So regulatory compliance and protecting your data is as important as extracting value from the data.

About the Author: 

Lenley Hensarling, Chief Strategy Officer, Aerospike

Additionally, tech journalist Andy Patrizio updated this article in 2023. 

The post Real-Time Data Management Trends appeared first on eWEEK.

Among the topics we covered: 

• How is low code disrupting the tech market?
• What advice do you give to companies that want to do more with low code?
• How is ServiceNow/IBM addressing the low code needs of its clients?
• The future of low code in the enterprise? What can we expect in the years ahead?

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