The 2017 BI and Analytics Magic Quadrant just published, a note that many wait with bated breath to read, to announce, to brag, while others dread its release. No matter your degree of anticipation, here are the biggest mistakes people make in reading it.
- Assuming “ability to execute” is about ability and future ability. The placement along the Y- axis is labeled ability to execute (A2E), but I think you will interpret it better if you mentally think about this as a combination of product capabilities, financials, and operational excellence. How well a vendor did in the past year is not necessarily an indicator of ongoing “ability.” Also, you can take a mediocre product, with all the other aspects highly rated, and still get a high placement on the A2E axis. Or a fantastic product with not-so-great customer support; but the end result is still high. Being average across the board rarely lands a vendor in the top half of the Quadrant, whether Leader or Challenger. I recall a few years ago, before I joined Gartner when I thought it was nuts that one vendor in particular was in the Leaders quadrant. I too misinterpreted and oversimplified that A2E label, from the outside looking in.
- Assuming “completeness of vision” is only the product roadmap. The placement along the X-axis does include the vendor’s vision, or roadmap, but it also includes a number of other factors such as market understanding as well as strategy on marketing and vertical solutions. We may think a vendor has a wonderful product roadmap, but if they aren’t doing well on these other strategic factors, they most likely will land in the Niche or Challengers Quadrant, further to the left. Every MQ team defines the “market understanding” aspect somewhat differently. For the BI and Analytics MQ, we define it as a combination of ease of use and complexity of data and analysis, because that is what is driving new buying requirements.
- Looking only at the picture. Of course we describe all of the above in the full Magic Quadrant research note. But I guess most don’t read the fine print, or don’t internalize it. This applies to customers and vendors alike. Some people take one glance at the picture and either leap for joy, cry, or are nonplussed. Or they think we got it wrong. I get it. I’ve done the same. Then two years ago, Rita explained to me “how the sausage is rolled,” or because that analogy grossed me out a bit, I prefer to picture the making of Beef Wellington (my husband makes a great one, by the way. I assist.). Even now, as a co-author, I keep the tables 1 and 2 (pages 59 to 62 of the PDF) with the drivers of each axis hung on my wall. When something doesn’t make sense to me or when the model first generates the graphic, I have to remind myself of the six to eight drivers that go into each axis. If you are a Gartner seat holder, you can use the interactive version of the MQ to minimize each of these drivers. The results are both interesting and re-affirming.
- Using only the MQ. If you rely only on the MQ to set your BI and analytics strategy, you are making a mistake. If you only look at the Leaders, you also are making a mistake; the best vendor for your particular requirements— short term and long-term— may be in another quadrant or not in the MQ at all. The MQ is just one resource. We have the companion Critical Capabilities which focuses on the product only, the market guides, the cool vendors, and so many toolkits. Use the full body of research when buying products and setting strategy. Better yet, set up an inquiry call so we can guide you through the process. It’s what we are here for, and there are a lot of us!
I just presented the new BIA MQ at the Sydney Data and Analytics summit this week, with Rita presenting it in upcoming Dallas and London summits. All the co-authors – James, Joao, Carlie, and Thomas – will be discussing it in hundreds of one-on-ones. If you can’t make any of those events, join us for a live webinar on March 15.
Read Complimentary Relevant Research
Organizing for Big Data Through Better Process and Governance
With big data past the Peak of Inflated Expectations on the Hype Cycle, organizations are addressing next-level challenges and asking,...
View Relevant Webinars
Hadoop and Spark: Understanding Open Source Opportunities and Risks
As companies build foundational data and analytics infrastructure with Spark and Hadoop, the market continues to shift and evolve in...
Comments or opinions expressed on this blog are those of the individual contributors only, and do not necessarily represent the views of Gartner, Inc. or its management. Readers may copy and redistribute blog postings on other blogs, or otherwise for private, non-commercial or journalistic purposes, with attribution to Gartner. This content may not be used for any other purposes in any other formats or media. The content on this blog is provided on an "as-is" basis. Gartner shall not be liable for any damages whatsoever arising out of the content or use of this blog.