How new data users changed Gartner’s Magic Quadrant

Gartner quadrants, I have always loved them. Every year they help me in quickly getting insights in markets crowded with technology. And for in-depth information, the report joining the quadrant is very extensive and provides detailed insights from industry experts. On the 8th of February Gartner released the revised Magic Quadrant for Business Intelligence and Analytics Platforms. I was very intrigued by the actual quadrant. Why? Because there is a massive difference compared to last year. A difference that goes beyond the quadrant.

For argument’s sake, let’s first stick to the quadrant. What immediately stands out, is the drop in the amount of leaders (the top right corner). Only a third of the nine leaders of 2015 are left. From a personal perspective, the last years I have been successfully using both Tableau and Microsoft for creating customer solutions. Their prominent place in the quadrant for 2016 matches both my own experiences as those of my customers, as I felt the people I spoke to were most happy using those solutions. Especially Microsoft’s Power BI is something I have been using with a lot of fun lately thanks to its ease of use. Specific strengths and weaknesses (or cautions as Gartner names them) of each of the vendors are mentioned in the report, so for those interested, there is a lot of interesting information to read.

NEW MARKET PERSPECTIVE

However what is even more noticeable is the new market perspective that has reordered the entire the vendor landscape and thus the quadrant. As Gartner states:

This year, Gartner has dramatically modified and modernized the underlying BI and analytics platform definition in order to reflect the segment of the overall market where the majority of active new buying is taking place. As a result of this change, historical comparison with Magic Quadrants from previous years (to assess vendor movement) is irrelevant and is strongly discouraged… Purchasing decisions continue to move from IT leaders to line-of-business executives and users who want more agility and more flexible personalized options.

NEW USERS

The bigger picture is that users demand different features from their business intelligence and analytics vendors. More and more end users are in fact business users who want to work autonomously and agile. Facilitating this is no easy task, as is reflected by the overall drop in ‘ability to execute’ for each of the vendors. They can facilitate the old demands, but the new requirements not so much.

BETTER DATA

As I see it, these new requirements Gartner uses revolve around the new ‘business users’ having better data. They want to have access to all data, to create best possible value. They do not care about data being structured or not; they just want to use it, whenever they desire. And this asks for data being made available in a more BI-friendly way, using a technology as agile and flexible as the business end user. Even better, having a modern data warehouse – a data foundation so to speak – that can be accessed from each of the vendors mentioned in the quadrant is the successful strategy I recommend, in terms of flexibility, agility and scalability.

ABILITY TO EXECUTE

BI and analytics applications are typically optimised for querying data as fast as possible. However the drop in ‘ability to execute’ is the result of the challenges in data collection and transformation. Better structured data from the source results in significant benefits for queries. I have personally experienced this when working with Tableau and Power BI. Also if the data is structured (but easily changed), it is more easy to use by the business end user. Structure in this context not only relates to the data model, but also to intuitive naming conventions, documentation and other qualities for business users to quickly and correctly understand the meaning of the data they are using. Finally, creating an intermediate layer for your structured data has the benefit that you can more easily change the technologies you want to use (or test multiple at the same time). Governance can still take place at the data level, but in the BI and Analytics market, which is in transition as Gartner states, having a robust solution is best for the future.

Creating such a data lake can be accomplished using DimML. No matter what type of user you are, DimML’s flexibility and ease of use fit any type of data-driven professional. Want to know more? Mail me at peter.lem@o2mc.io