The increase in data volumes threatens to overwhelm most government agencies, and big data techniques can help ease the burden. But those techniques are not a silver bullet. They should be used judiciously, for specific needs, and there are times when the tried-and-true will do just fine.
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Just about all big data projects will use Hadoop. It helps break data into manageable pieces and uses a set of powerful tools that define a framework for building big data capabilities. What’s more, it’s all open source and relatively cheap.
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The promise of big data techniques is their ability to help organizations manage and sift through huge volumes of data quickly. To some people, that means organizations should save everything because there’s no way of knowing the nuggets might be hidden in there. Others disagree. It all comes down to the goals for big data.
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Getting value out of data has traditionally required skills aimed at knowing how to ask the right questions to tease that value out. It’s a deliberate process that big data turns on its head. The hunt will soon be on for professionals who can act on the fly.
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Big data presents new problems as far as sharing data is concerned. But as it becomes a necessary function of government, ensuring the interoperability of big data and the tools that go with it will be a prime goal.
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