The biggest Big Data myths of 2013

Big data plagued businesses in 2013. The IT community should expect no less in 2014, as businesses of all sizes face the challenges big data presents and seek out ways to manage data growth, Information Management reports.

Many companies succeed in implementing a strong information management strategy that aides in processing all of this information, while others stumble with big data management. But how these businesses should be handling big data seems to change depending on the expert, creating confusion around what the best data management strategy may be.

While the topic of big data has existed for many years, several myths continue to perpetuate and add to the already complex nature that surrounds it . Here we take a look at five myths about big data that I hope cease to exist as we move into the New Year.

Myth: Simply querying as much big data in an in-memory database will provide a suitable answer.

Reality: Contrary to popular belief, big data needs to be treated in the same way that “small” data must. Powerful in-memory technologies can crunch data sets tremendously quickly. But the underlying data that is being processed is central to these queries. Simply running data through the in-memory database will not necessarily provide you with correct results.

Myth: Big data and information management is too time-consuming and labor intensive for our company to handle on a consistent basis.

Reality: Indeed, big data projects are no small task from the outset, and running queries on the data requires careful planning at every stage. But the trick is to make sure that once a query has been run, it can be repeated relatively quickly for future use. Businesses must avoid starting fresh every time a new project arises.

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