Monday, December 5, 2016

Big Data Business Intelligence and Visualization 2016

Early in my career at Accenture I spent a lot of time consulting in Enterprise Information Management or more specifically: Content Management and Publishing (IA), Data Strategy and Governance, and bit in BI.  After spending some years in mostly Agile, OLTP, and SOA, job opportunities present themselves that have me looking into the space again. This post is the first of a few on developments here. This one looks at Gartner's 2016 vendor POV and some self-service Data Visualization vendors.

With the advent of big data technologies like Hadoop, Map Reduce, Spark, Graph DB's, Neo4JPregel... visualization, multi-dimensional networks, IOT and cloud this space is ripe with opportunity. Gartner's 2016 Magic Quadrant for Business Intelligence and Analytics Platforms illustrates some of this. They have fundamentally retooled how they look at the space.

Gartner's view is generally: 

  1. The market is moving from centralized IT BI capability delivery to decentralized self-service business capabilities often baked into business processes. Business self-service data preparation and analysis is a good example. 
  2. From a technology perspective: "By 2018, smart, governed, Hadoop-based, search-based and visual-based data discovery will converge in a single form of next-generation data discovery that will include self-service data preparation and natural-language generation."
  3. The vendor market is fragmented. Trying to rely on one big vendor will likely not meet all of your needs. In the near term businesses should assess their needs and find the products that match them.

Gartner's five use cases reflect this view:

  1. Agile Centralized BI Provisioning
  2. Decentralized Analytics
  3. Governed Data Discovery
  4. Embedded BI
  5. Extranet Deployment
Before we look at the Magic Quadrant let's look at overall vendor group's performance against Gartner's 2016 "Critical Capabilities for Business Intelligence and Analytics Platforms" publication. It is interesting that the rankings, using a 1-5 scale, on average are not that good. I am not sure if this is a common occurrence in their assessment methodology. The rankings may simply reflect market conditions.

The group's only "excellent" capability average comes in "BI Platform Administration." Looks like cloud may be helping to level the playing field a bit for new entrants.  

The overall vendor results are similarly mixed:
Gartner provides customer reviews of many of these vendors here.

Gartner Magic Quadrant

Note that the Leaders rank only "fair" on average across Gartner's 14 critical capabilities.  Of those with an average rank of "good" most are in the Visionary quadrant and 45% in Niche.  Our deep pocket product vendors are mostly average "fair" as well. Interesting market right now.

Something to consider: What do you really want? 

Gartner assessment criteria are somewhat conservative focusing as much on vendor stability, size etc. as on the quality of the underlying product implementation.  To some extent that unmeasured quality  characteristic dictates the vendor's  ability to innovate and deliver capability differentiation. This suggests Gartner may be a poor guide if you are after differentiation. For example,  graph processing and databases as well as graph based analytics delivered via Cloud shows promise. Look at what Facebook, Ebay, AWS and Google have been up to. More on this in a future post. Another example is in the Data Visualization space. Let's take a quick look there now.

Data Visualization Product Landscape

Data Visualization is key to modern Analytics. Roughly half of the top products made the Gartner list. Again - makes sense given Gartner's assessment criteria - but it sure seems there is opportunity out there to leverage!

Additional References

Here is link to the google sheet used to do the analysis above. It has a bit more data and links to more as well.


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