Mckinsey

via Forrester Blogs Brian HopkinsAre you lost in a confusing soup of vendor-speak about what their data analytics stack actually offers? Big data, data platforms, advanced analytics, data lakes, real-time everything, streaming, the IoT, customer analytics, digital intelligence, real-time interaction, customer decision hubs, new-stuff-as-a-service, the list goes on.

Recognize the convergence happening as vendors evolve their technologies from doing just one thing like predictive analytics or search to many things together. For example, data integration, data warehouse, and BI tools are typically sold separately, but breakout vendor Looker combines data integration, model governance, basic BI, and a runtime for data applications all in one software layer that sits on your data lake. As another example, consider predictive analytics vendor Alpine Data Labs or SAS Viya from SAS. These vendors have built out a lot of data management and insight delivery tooling into their platforms because without it users struggle to maximize value. Another trend is big data search vendors like Maana that now also include hooks for predictive model execution as well as more data management functions. Lastly, systems integrators are packaging their IP and offering it as a data management and analytics integrated product — for example, Saama’s Fluid Analytics Engine or Infosys’ Information Platform.

In fact, the list of innovative vendors blending data management, analytics, and insight execution technology is growing by leaps and bounds. To address this trend, I just published a report, Insight Platforms Accelerate Digital Transformation, in which I created a broad definition that labels this trend:

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