Blog post

Salesforce acquisition of Tableau – What does it mean?

By Andrew White | June 11, 2019 | 1 Comment

Data and Analytics Platform

Then again, classifying vendors as I do above is a loss leader – the classifications do not make sense any more since most large vendors play in many spaces.  In fact, it might be better to have your own “stack” and rate the vendors to it.  But do the vendors really offer the most important capabilities an organization needs?  What is the set or stack needed?

I am sure that the series of acquisitions in the last few weeks (and ongoing) signal something in the market.  I am not sure myself what, though the hype related to the moves is exciting.  Some might conclude this is a new trend; some might look back at the days when SAP acquired Business Objects and IBM acquired Cognos and Oracle acquired Siebel.  I really can’t say one way or the other.  I am sure our analysts will have a point of view here.  In fact here is a blog from our very own analytics and BI team: The Week Modern Analytics and BI Market Changed Forever.

Based on the research I do however I would offer that most of these acquisitions expose a weakness in the market: only the obvious parts are being acquired; only the obvious parts of the overall data and analytics platform are being assembled.  What do I mean by this?

A couple of years ago we postulated that an organizations data and analytics platform, that sits at the heart of their digital business, comprises three core platforms or layers:

  1. Analytics/BI, data science/ML and AI
  2. Data Management
  3. Data and Analytics Governance

We concluded that the first two are well known, reasonably well defined, and quite clearly visible in terms of markets and vendors and competitors.  Even the vast spend on software in D&A is centered on aspects of the two parts.  The least well known, the lest defined, the least mature, even the least complete, is the last – data and analytics governance.  This platform is completely lacking in the market.  In fact we muse about this platform off and on, and have done so for many years.  But the recent spate of acquisitions just prove the point: the work of data and analytics governance remains an after-thought, even for these large vendors (just as it does for many of their prospects).  The silver bullet, the fancy new gadget, always gets the attention more than the work horse.

The part that concerns me most is that various aspects of the “stack” are assembling but key parts remain missing in action.  Just managing data without effective governance won’t cut it; analyzing data and presenting a dashboard without trust in the data won’t cut it.  These large vendors need to figure out that data integration, data quality, and data catalogs are a far cry from what is needed to support data and analytics governance.

There is also a lot of action in the data and analytics governance space for sure.  Not long ago TIBCO acquired Orchestra Networks and a few other MDM vendors merged, such as Symphony’s acquisition of both EnterWorks (more PIM) and Winshuttle (more ADM)Infogix has an interesting roll-up underway too.  And there are a number of moving parts in this space that remain moving; and no one vendor really stands above the others with a public, credible, funded vision to  build out the missing links.

The moving parts include but are not limited to:

And it is not that all these technologies have to merge or become one single platform.  They each reach into or touch or integrate with data management platforms and analytics/BI and data science/ML solutions.  But the way in which such D&A governance policies for all data, all analytics, remains dispersed, inconsistent, and disconnected.  As such business costs are kept artificially high, opportunities remains lost or hidden, and value remains unrealized or not maximized.  I can’t wait for a the day a new vision emerges to complete the data and analytics platform.

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1 Comment

  • James Mitchell (Jamie) says:

    Excellent insights… I completely agree with your assessment that governance of these data assets continues to be an afterthought and that attention continues to chase after magic bullets in the form of new tools without looking at or acknowledging the hard work and heavy lifting needed to implement and operationalize data governance.