12 myths of data analytics debunked

From data concerns to staffing needs to technology combinations, data analytics misconceptions abound. Here’s a no-bull look at how to leverage data science to deliver bona fide business results.

12 myths of data analytics debunked

In IT, the bigger the hype, the greater the misconceptions, and data analytics is no exception. Analytics, one of the hottest facets of information technology today, can result in significant business gains, but misperceptions can get in the way of a smooth and timely delivery of analytical capabilities that might benefit business users and ultimately customers.

As organizations create or expand their analytics strategies, here are a dozen myths they might want to keep in mind.

Myth 1: Data analytics requires a major investment

These days it seems as if every technology endeavor must pass through a filter of financial soundness. “How much will it cost?” is one of the first questions IT and business managers get when they propose launching a project or deploying a new tool.

Some assume that data analytics is by nature a costly undertaking and therefore limited to organizations with big budgets or lots of internal resources. But not all data analytics efforts require a major investment, says Deep Varma, vice president of engineering at Trulia, a provider of mobile and online real estate services.

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