Clunky IT focused solutions are out. Complex data challenges are on the rise. Visualization alone is not good enough.

The following is a personal opinion piece relating to the 2016 Magic Quadrant for BI and Analytics Platforms, by Gartner Inc. The original report is available here.

Each year Gartner releases its Magic Quadrant for Business Intelligence and Analytics Platforms, often considered the most comprehensive analysis of the current BI landscape. The report offers valuable commentary on the marketplace as it stands today, as well as evaluating the industry’s notable business intelligence and analytics software vendors based on their ability to execute and completeness of vision.

This year’s Magic Quadrant differs from 2015’s in many significant ways: the inclusion of a host of new players, including Sisense, while several major legacy players were dropped; the introduction of new evaluation criteria and use cases for BI tools, and more. Of all these changes, I wish to focus on the one which I believe to be the most dramatic: the fact that Gartner has changed the very definition of what constitutes a modern business intelligence platform.

A Changing of the Guard

At the introduction to the report, Gartner presents this revolutionary change as follows:

The evolution and sophistication of the self-service data preparation and data discovery capabilities available in the market has shifted the focus of buyers in the BI and analytics platform market — toward easy-to-use tools that support a full range of analytic workflow capabilities and do not require significant involvement from IT to predefine data models upfront as a prerequisite to analysis.

This significant shift has accelerated dramatically in recent years, and has finally reached a tipping point that requires a new perspective on the BI and analytics Magic Quadrant and the underlying BI platform definition — to better align with the rapidly evolving buyer and seller dynamics in this complex market. This Magic Quadrant focuses on products that meet the criteria of a modern BI and analytics platform…, which are driving the vast majority of net new purchases in the market today.

While the report proceeds to state that this should not be seen as a recommendation for all organizations to abandon their legacy systems in favor of self-service tools, I believe that one of the world’s most respected analyst firms acknowledging the dominance of second and third wave business intelligence tools in the marketplace is a clear and unequivocal sign that the times are changing.

The self-service BI revolution has been taking place for over a decade, but it seems that now it has reached a tipping point. Indeed, from an industry veteran’s perspective, seeing a Magic Quadrant without heavyweights such as Oracle is somewhat of a shock, but it speaks volumes as to how far data analytics software has gone since the not-so-distant past of OLAP cubes, six month deployments and absolute IT ownership.

But if only a few years ago these softwares were seen as more of an IT project than anything else, today a platform must be owned by the business users to be considered a modern business intelligence tool (that is my understanding of the above cited passage from Gartner). This just comes to show the extent, as well as the breathtaking pace, in which technologies and products have evolved within this space.

This is an exciting time for business analysts, as well as anyone who believes in the power of data as a decision-making tool: the “business users spring” is upon us. If previously any new report or view of data required endless iterations with the IT department or similar organizational gatekeepers, today that is no longer the case. Instead, there are a host of new possibilities for the non-technical and less-technical to perform their own analysis and find their own insights: from visualization tools for simple, spreadsheet-style data, to new possibilities for mobile and collaboration, and data analytics software that makes it simpler than ever to prepare and analyze complex data.

What’s Next — and a Word of Warning

Here at Sisense we’re pleased to be included in this year’s MQ for the first time, which is a major milestone for any company. But we were even more pleased to see that our own vision for business intelligence — that it should be simple, business-centric and deliver rapid and tangible ROI — is becoming the golden standard for the industry as a whole. This puts us at an excellent position, as our product was built from the ground up on these principles: simplifying business intelligence and making any data accessible to business users, regardless of its complexity.

However, as more and more vendors will look to align themselves with these new standards of agility and self-service, we thought it would also be right to include a word of warning for readers who are set to launch a business intelligence initiative.

Because what this report also shows is that as data gets bigger, wider or more complex, visualization focused tools are not enough, and the reports highlights the need for either combinations of data prep + analytics + visualization tools to deal with this modern challenge, or a single-stack solutions like Sisense.

Because behind the outwards appearance of simplicity (that everyone seems to be offering), often lies the spectre of the same old complications from before: you might find that while the front-end dashboard solution is very simple to use, it does very little to help you make sense of multiple disparate datasets. Then you might find yourself looking into the proprietary ETL tools, the data warehouses, and the other components of the traditional “assembly line” that soon turn business intelligence back into the IT-centric mess it always was.

This does not have to be the case, and is not the case with very simple data of course – but it’s essential to understand that despite the similar-sounding terminology and marketing, not all BI platforms are created equal. Tools that lack a robust back-end for data integration and ETL, or aren’t built to handle significant volumes of data or diverse datasets and will likely require additional IT resources or tools. And once you start to stack up additional tools, you lose the agility you set out to achieve in the first place. Do your research: understand your data, play around with the free trials, and see how long it takes the vendor to get a POC up and running on your real big data – in all its messiness, complexity and size.

The full Magic Quadrant for Business Intelligence and Analytics Platforms is available to download here.

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