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Big data strategies beg the question: Speed or accuracy?

Rachel Wheeler Archive
Many industry insiders have been touting the term "big data" as sexy. The analytics strategy is taking decision makers by storm. Similar to a streamlined sports car, big data has an allure because it can get marketing teams, executives, bankers and retailers to the finish line sooner, and with style. Rather than relying solely on gut instincts, enterprises can use insight that's fortified by data.

These capabilities have led many companies to rush forward with their plans, passing over important data quality measures. However, there is a well-known adage amongst programmers that claims, "garbage in, garbage out," and when IT teams are working with big data, this means the waste is even worse, as Harvard Business Review reports.

Companies should approach big data with the same care that scientists take with the data in their research, the source reports. There should be myriad checks and balances to ensure data quality is at its peak.

On the contrary, Michele Goetz, a senior analyst at Forrester Research, recently wrote a blog for Information Management explaining that since big data is so huge, companies may not always be able to cleanse all of data they have. And in some cases, they may not need to. In some cases, they can fall back on their intuition to complement their analytics capabilities, Goetz said.