Understanding Gartner’s Magic Quadrants for Analytics: An Expert’s Take, Part 3
Understanding Gartner’s Magic Quadrants for Analytics: An Expert’s Take, Part 3

In two recent blog posts (Gartner Magic Quadrants, Part 1 and Part 2), we provided some clarity around the various Magic Quadrants issued by Gartner in the analytics and business intelligence market. In this post we want to go a little deeper into two specific reports. Earlier this year, Gartner released its new 2016 Magic Quadrant for Advanced Analytics Platforms—the de facto reference standard for buyers evaluating advanced analytics packages. This report is not to be confused with their similarly named 2016 Magic Quadrant for Business Intelligence (BI) and Analytics Platforms report. While both analytic reports cover analytic technologies whose lines sometimes intersect and eventually may converge in the future, there still are very clear distinctions separating the two technology categories.

Further clarifying the differences between the two reports, Gartner defines advanced analytics as “the analysis of all kinds of data using sophisticated quantitative methods (such as statistics, descriptive and predictive data mining, machine learning, simulation and optimization) to produce insight that traditional approaches to business intelligence (BI)—such as query and reporting—are unlikely to discover.” There are also typically chronologic boundaries to what is produced in each analytic application: BI typically addresses data exploration and visualization of current or historical happenings, whereas advanced analytics, specifically predictive and prescriptive analytics using sophisticated algorithms, can pronounce future outcomes in terms of propensities or likelihoods—strong natural tendencies to occur, or predicted outcomes rooted in probability, respectively. In other words, BI is more rearview mirror looking, and advanced analytics looks forward.

This year’s Magic Quadrant for Advanced Analytics Platforms included:

  • 2 Challengers: SAP, Angoss
  • 5 Leaders: SAS, IBM, KNIME, RapidMiner, Dell
  • 5 Niche Players: FICO, Lavastorm, Megaputer, Prognoz, Accenture
  • 4 Visionaries: Alteryx, Predixion Software, Alpine Data

                                                         Source: Gartner (February 2016)


While Gartner evaluates these vendors on two specific dimensions—ability to execute and completeness of vision—and many of the niche players often address only specific use cases, the market research report underemphasizes how fully these vendors can accommodate a comprehensive analytic ecosystem. It does not specifically address how easy these vendors integrate with either their own complementary products, or with other third-party vendors.

As a consultant and a former leader of an advanced analytics department in a large industry environment, I can assure you that integration and deployment of advanced analytics are almost of parallel difficulty to the actual analytics being developed. How many of these vendors easily pair with analytic decision management offerings, master data management solutions, BI tools, visualization engines, Hadoop systems, marketing automation systems, etc.? These things are hidden behind the results.

An absence of disclosure on the specific vendor component scores makes it difficult to evaluate a true operational fit within an organization and within the analytic goals set forth by potential consumer.

So what does this mean for organizations?

Organizations must take into consideration what their larger goals are for their analytic programs. Consultants who have spent many years developing analytic solutions, both as industry practitioners and consultants, can often help organizations weed through the hype and get to the practical solutions that yield tangible results.

Revelwood has chosen to partner with IBM to develop innovative analytic solutions, not because they appear in the leader quadrant, but because they offer the most comprehensive analytic ecosystem to support an organization of any size. They also are putting more research and development than any other company—nearly $5.5 billion in the last 12 months alone.

I encourage any organization to utilize an analytics consultancy firm that has deep experience in developing solutions that produce results and can last in an enterprise environment.


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