Is the 'big data' timebomb ticking for your information manager?

29th Jun 2011

Organisations that simply focus on managing the large volumes associated with ‘big data’ and fail to exploit it properly will be forced into a "massive reinvestment" within a couple of years, Gartner has warned.

The problem today is that too many IT leaders are currently attempting to manage ‘big data’ challenges by focusing on the volume side of the equation and ignoring other areas of information management such as variety and velocity. But this situation will lead to "massive challenges" that will need to be addressed at a later date, the market researcher believes.

Mark Beyer, a research vice president at Gartner, said: "Today’s information management disciplines and technologies are simply not up to the task of handling all these dynamics. Information managers must fundamentally rethink their approach to data by planning for all the dimensions of information management."

Such data must be subject to some degree of access control and coordination in order to ensure that the "big data opportunity doesn’t become big data chaos, which may raise compliance risks, increase costs and create yet more silos," he added.

To make sense of the high volumes of many different kinds of transactional data that were often produced and had to be processed very swiftly, it was important to find patterns in it to help organisations make better business decisions.

Yvonne Genovese, a vice president and distinguished fellow at Gartner, explained: "The ability to manage extreme data will be a core competency of organisations that are increasingly using new forms of information such as text, social and context to look for patterns that support business decisions in what we call ‘pattern-based strategy’."

Pattern-based strategy looked for patterns in the data and then provided the basis for modelling "new business solutions, which allows the business to adapt. The seek-and-adapt cycle can then be completed in various mediums such as social computing analysis or context-aware computing engines," she added.

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