IBM has a tremendous portfolio of Big Data products and services, including both acquisitions and technologies developed in house like Watson, writes Jeff Kelly in his Alert “IBM Accelerates Its Big Data Portfolio”. And it has embraced the Open Source Big Data community, at least at the foundational level, particularly with its BigInsights Hadoop platform. But IBM has problems as well. That huge portfolio is fragmented and sometimes confusing, and IBM’s approach, which is heavily dependent on its services arm,  is expensive, geared to large enterprises, and too costly for most mid-sized companies.  IBM needs to unify its portfolio and make it less dependent on consulting to implement while responding to the evolving needs of the marketplace.

IBM’s April 3, 2013 Big Data announcements show that it is moving in the right direction, he writes.  BLU Acceleration leverages in-memory data processing, columnar architecture, and data compression to bring “speed of thought” performance to IBM DB2 and Informix and promises to bring BLU Acceleration to other data management products over time. BigSQL, its ANSI SQL interface, makes BigInsights analysis accessible to business users, not just data scientists. And the PureData System for Hadoop data appliance bundles preconfigured hardware with BigInsights, advanced data visualization, and analytic application accelerators for specific use cases designed to run out of the box without costly consulting services.

These announcements show that IBM is both continuing to bring its technologies forward and integrate them and is listening to the needs of its market. Those are good signs, Kelly says. However, IBM, like all its competitors, still has some distance to go to reach the vision of a single, comprehensive Big Data platform.

CIOs, Kelly says, must balance near-term goals with long-term visions in their Big Data investments. They need to push their technology providers to show how customers can achieve short-term ROI while creating a foundation for sustainable, flexible Big Data practices. They should evaluate technologies based on their ability to integrate with and complement existing data management systems and provide value to business users as well as power users.

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