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Uncovering 3 popular misperceptions: A perspective on the data and analytics market

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Uncovering 3 popular misperceptions: A perspective on the data and analytics market

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I recently attended the Gartner Data and Analytics Summits in London and Dallas. The sold-out attendance and level of engagement prove this market is still red hot. For the last decade, I have attended the annual Gartner BI Summit. It’s been a great way to watch this market evolve. The market has been through many phases—from BI standardization to corporate performance management to data discovery—each phase in pursuit of incremental value. We are now entering the next phase of this market, one that will focus on Systems of Insight by building on the best traits from previous generations. The foundation of these systems will be new intelligence that surfaces unbiased insights through smart data discovery and guides users to a better understanding of their and analytics systems of insight

The potential of data and analytics

Gartner’s keynote message on scarcity (of skills and insight) and abundance (of data) addressed this evolution. And, their guest keynote by Gillian Tett (@gilliantett) on Silo-Busting: The Secret to Success in the Tech World was on point. The fact that they have decided to merge their BI and Analytics Summit with their MDM Summits this year is another signal. It’s time to see the bigger picture and seize the potential of data and analytics.

At this event, Gartner typically reviews the Magic Quadrants (MQ) related to the data and analytics market. In the last two years, there have been significant changes. Gartner is adjusting the criteria in the MQs to reflect how they see the market evolving. While IBM views some of these changes more positively than others, there is no doubt that IBM is one of very few vendors addressing this entire space (see the table). With this extensive presence, IBM is ranked a leader in more markets than any other vendor.

data and analytics chart

Presence in Data and Analytics related to Gartner Magic Quadrants

There has been a lot of attention on the BI and Analytics MQ. It is one of Gartner’s most popular reports. This is an extensive 80-page piece of research that unfortunately most people do not read and understand in detail. Anyone referencing this report must understand that its scope has changed. The focus now is on the self-service analytics capabilities. Gartner sees this as the most active and evolving part of the market.

In a market that is rapidly evolving, different perceptions will naturally exist. I think that there are 3 “misperceptions” held by many today that I just cannot buy into.

Misperception #1. Organizations are just interested in self-service

In our interactions, we have found most organizations still have considerable demand and untapped potential for enterprise/managed reporting as well as self-service analytics. In fact, many organizations have become wary of the emerging unintended analytic silos and governance issues that can ensue. Gartner advocates a bimodal* approach. This means their reference architecture for a modern BI and analytics platform includes centrally authored and managed dashboards and reports and a semantic layer as a key component. They call this component an “Information Portal.”

Gartner now covers this key element in their Market Guide for Enterprise Reporting. The third key component of their modern BI architecture is in their Data Science MQ. If you want more information about their Modern BI and Analytics Architecture check out this Gartner Newsletter.

Misperception #2. With self-service most business users now create their own content

Our clients have indicated that fully interactive content and the ability to customize and build upon pre-built content is as important as being able to author your own. Research from other sources confirms that 90% of BI users are casual and 60% primarily want to consume and interact with information versus author any content. Even with the new data discovery tools, it is still the 10% that are power users who create most of the content. Don’t get me wrong; dashboards and stories are powerful self-service tools for quick presentation and sharing of insight and for prototyping. But most organizations recognize the need to validate elements such as data sources, data joins and calculations before basing strategic decisions on user generated content or promoting them broadly across the organization.

Misperception #3. Adoption of “modern BI/data discovery” tools have accelerated organizational analytic maturity

Well – one would think so, but there was a fascinating session called “Moving your data and analytics maturity from laggard to leader” that revealed the latest on Gartner’s IT Score Model. This is a survey that Gartner clients can complete online. It evaluates their analytics maturity against a 5-stage model. It helps people identify how they compare and what is holding them back.

The fascinating thing for me was that the average scores are still not improving. The analyst chalked this up to possible selection bias. I am more skeptical. The scores hover from about 2.4 to 2.8 across different industries—solidly in the 2nd or “opportunistic” phase. In fact, after 25 years, there are still a handful of respondents in the “unaware” stage. The vast majority rest in “opportunistic” and only about ¼ are in the 3rd or “standards” phase. Even more astounding is the 0% in the 4th and 5th “enterprise” and “transformative” phases this year. How can this be after all this time and investment?

My theory is this. Their model highlights data discovery as a complement to enterprise reporting in phase 3. But, data discovery tools, such as the ones currently in the leaders’ quadrant of the MQ for BI and Analytics, have failed to help clients reach those higher levels of maturity. Data discovery simply replaces one form of spreadsheet chaos with another that continues to lack standards and enterprise governance and that fails to deliver overall value.

A natural evolution

Since I brought up the topic of governance, I would have to say that this was certainly a hot topic this year. Gartner had more emphasis than ever on the topics of data and analytics governance. The importance of a solid data foundation was certainly stressed as was the necessity of balancing the needs of governance and agility.

I believe that this is all part of the natural evolution of the BI space. Data discovery vendors are trying to figure out how to bring all the enterprise governance features into their offers. And, the focus on self-service has inspired traditional BI vendors, like IBM, to extend their enterprise BI platforms with self-service capabilities and to explore the next wave of analytics with smart data discovery.

IBM has blended managed reporting and data visualization/self-service analytics in Cognos Analytics that addresses both Mode 1 and Mode 2 needs in one platform. It lets a user progress from consuming content to creating their own self-service dashboards, stories or data sets in their familiar experience. IBM Watson Analytics complements Cognos Analytics with smart data discovery capabilities to find hidden patterns in data and automated the process of finding unbiased insight. Check the IBM business intelligence page to learn more.

Pick up the pace

There is no doubt that these are exciting times in the data and analytics market.  With new rapid delivery models and increased cloud deployment, I expect the pace of change will only accelerate.  For organizations that want to improve their analytic maturity there is no shortage of opportunity. They need to ensure that their technology choices fit within their overall data and analytics strategy. That strategy should encompass mode 1 and mode 2 analytics and be guided by a team (such as a center of excellence) that represents the needs of all parts of the business.  This team should be prepared to tackle topics like data and analytics use cases, governance, adoption, business process, and technology.

Set the course and reap the rewards!


*Bimodal is the practice of managing two separate but coherent styles of work: one focused on predictability; the other on exploration. Mode 1 is optimized for areas that are more predictable and well-understood. It focuses on exploiting what is known, while renovating the legacy environment into a state that is fit for a digital world. Mode 2 is exploratory, experimenting to solve new problems and optimized for areas of uncertainty. These initiatives often begin with a hypothesis that is tested and adapted during a process involving short iterations, potentially adopting a minimum viable product (MVP). Both modes are essential to create substantial value and drive significant organizational change, and neither is static. Marrying a more predictable evolution of products and technologies (Mode 1) with the new and innovative (Mode 2) is the essence of an enterprise bimodal capability. Both play an essential role in the digital transformation. Source:

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