Why Big Data Is Meaningless
Vendors, managers and bloggers often cite the benefits of “big data,” though most miss the mark in understanding exactly what it is used for, and how it is analyzed. By itself, big data is just raw material, and without a focus on bottom-line results, it is not only meaningless — it can actually be harmful, diverting resources away from a company's core mission.
Often, big data focuses too much on the collection and parsing of data, and too little on how it can be used to make better decisions in specific areas of a business. The process is simply to collect large amounts of data, with the hope that something useful will emerge from analyzing it at some point in the future.
Manufacturers are some of the biggest consumers of big data, often spending millions of dollars on implementations that ultimately have a high rate of failure. The failure isn't in the technical ability to collect big data, but rather in not having a specific decision-support goal at the beginning. It is often compounded by the inability to convert the raw data into valuable and actionable information once it has been collected. Without a reliable and consistent ability to process, analyze, understand and make decisions on the information contained within the mountains of data, resources spent on accumulating that data is money down the drain.
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