7wData: Insights in The People, Processes, Technology and Visualisations, Making Data work for You data is one of the most important strategic assets for companies in the emerging data-driven and AI-powered economy. Data is needed to measure the efficiency of business strategies and draw insights from its operations but also to train machine learning algorithms. Getting data is not a problem for companies, the question is can they get the right kind of data and can that provide them with a much desired competitive advantage.
Many companies do not realize that they are sitting on a pile of bad or dirty data. This data contains a lot of missing fields, has wrong formatting, numerous duplicates, or is simply irrelevant information.
IBM research estimated that the annual cost of bad data for the U.S. economy is a whopping $3.6 trillion. Still, many managers have certainty that they are sitting on a goldmine of data when in reality they have nothing valuable.
I interviewed Sergey Zelvenskiy, who is an experienced machine learning engineer over at ServiceChannel, where he automates facilities management processes using artificial intelligence. We talked about common misconceptions when it comes to the good/bad data dichotomy and what companies should be focusing on when building AI products.
As Zelvenskiy says, “The data that companies have may not necessarily be bad, it is just likely incomplete to solve the problem. There is a chicken and egg problem here. The original system is usually built to collect the data needed for human-driven solutions and moving it to an AI driven solution might require filling of the gaps. Read more from 7wdata.be…
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