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Use data quality to refine big data results

Rachel Wheeler Archive

The world is abuzz about big data, anticipating the point at which they can use information in their systems to make better decisions about the future. However, they must be careful putting their trust in advanced analytics if they are not sure about the data quality going into their systems, according to Charles King who recently wrote a blog post for IT Director. Even with smarter technology and faster processing, the same adage "garbage in, garbage out" still applies, King explains. 

Ultimately this means that if companies aren't certain the content they're using to generate insights are accurate and complete, they might end up with a flawed end result, whether that means a failed marketing campaign or a product that tanks. 

Assuming that big data automatically translates to actionable insights is just one of the three resounding myths that are circulating throughout industries, according to The Huffington Post. Others include that simply gathering higher volumes of diverse information means analysts will have the right data and that analysis of that content will deliver valuable insights. Neither of these ideas are intrinsic to big data. Unlike other platforms that allow users to set and forget, emerging analytics strategies demand continual refinement to ensure the right results are being recognized.