Analytics for Big Data #bigdatamgmt

April 3, 2013

in Advanced Analyitcs, BI, Decision Management

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Les Rechan came up to discuss “realizing the value” with analytics for big data. Les reminds us that analytic organizations out perform others so its about using analytics not just data. Again he’s focused on time to insight (where I think the focus should  be time to action, not insight).

Anyway he is focused on why you need analytics – 65% of businesses are not using big data for business advantage. You want, he says, all information to be used by all people across all perspectives and for all decisions. Big data and analytics at the point of impact.

Organizations are moving forward with analytics and have challenges in three areas:

  • Strategy and value (key business issues or opportunities)
  • Technology you need for analytics
  • People and Process

Strategy: Customer must think big but act fast. They need a strategy to see where analytics and Big Data can make a difference. This could be customer-facing, in operations, in finance or in risk.

From a people and process point of view they see a growing use of Analytics Center of Excellence as companies become more analytically sophisticated. Plus From a people point of view, building skills and organizational strength around analytics is key.

To cope with Big Data, analytics technology capabilities across the board have to expand to handle it. Les focused in on several areas:

You need an ability to find what is relevant using entity analytics, NLP, social network analysis, text analytics and more. Then drive this into analytic model development that can be done by experts, novices or those in  the middle. And then take those models and use them to inform and automate with everything from visualizations and reporting to Decision Management and real-time scoring.

It is critical, he says, to ensure analytics can be more broadly adopted without silos.You need unified data access, consistent authoring and management, broad deployment etc.

Recent improvements are reflected throughout the portfolio. The DB2 BLU performance boost for instance affects both reporting AND predictive analytic modeling thanks to the SQL pushback in SPSS Modeler. Similarly the integration of SPSS Decision Management with InfoSphere Streams allows for real-time scoring of streaming data.

Les wrapped, after a very rapid run through, by emphasizing how broad and deep the IBM portfolio is.

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