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More Firms Moving to Distributed Analytics Models

Data is growing in volume and complexity, and with it, more organizations are moving to a distributed model for their business intelligence and analytics efforts. That is the conclusion of Lance Walter, chief marketing officer at Datameer.

Information Management caught up with Walter at the recent Gartner Business Intelligence Summit in Grapevine, TX, and asked what he heard most from attendees this year in terms of analytics efforts and vendor needs.

Information Management: What are the most common themes that you heard from conference attendees?

Lance Walter: The Gartner BI and Analytics Summit is always about how companies use analytics to get more value out of the data in their organizations. This year, there was more of an emphasis on distributed models instead of the classic, centralized “BI Competency Center” approach of years past.

It was clear that many attendees have moved their organizations to more distributed models for analytics that can enable critical business velocity while sacrificing some, but not all control.

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“Modern BI Platforms” was a recurring theme. The analytical challenges have evolved from things like “How much did customers buy of Product X compared to Product Y last quarter?” to “What customers of Product X are the best cross-sell targets for Product Y, and what does their consideration journey look like, online, offline, and on mobile?”

Self-service data preparation was an element of “Modern BI Platforms” that’s also getting attention – there is clearly an increasing expectation that analysts and business users should be able to get at the data and make sense of it without a lot of IT involvement. Many of the analysts view data preparation as a “feature” rather than a solution, but for the class of users that only worries about getting the data together for someone else to analyze, it makes sense.

This generated a bit of controversy. There were traditional vendors who reject the premise of a new class of platforms or a separate magic quadrant, as well as attendees who are trying to “make black and white out of grey” in understanding current and future tools. Clearly a good chunk of the attendees just think, “It’s all BI,” without consider for old vs. new platforms.

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IM: What are the most common challenges that attendees are facing with regard to data management and data analytics?

LW: Attendees are definitely dealing with data of different shapes and sizes compared to years past. Five years ago, nearly all of the data for analytics was well-structured and lived in relational databases. Now there’s social, sensor, logfile and other data out there that business analysts want to harness.

Many companies don’t know how to access, integrate, economically store, secure, and govern that kind of data. There’s a lot more to it than just the cool business questions that that data now makes it possible to ask.

Enabling end user self-service was another key challenge.

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