How Companies Lose Money on Big Data

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Throwing Money Awayby Angela Guess

Marco Visibelli of Wired recently shared the ways that companies make money on Big Data, and the ways that they lose money. On the losing end, Visibelli wrote, “Companies lose money on Big Data projects as often as they gain it. Early symptoms of Big Data failure in the making vary, but the most common problems are: (1) Starting too big: Big Data doesn’t need a big budget. If you embark on a project in the belief that a big investment will equal a big return, something is wrong. Before starting, it is wise to analyze whether a limited spend on this technology will really give desired benefits on a small scale. If so, a project can always be subsequently scaled up to ensure economies of scale add up to bigger gains.”

Next on the list: “(2) Underestimating human labor requirements: Before starting to implement a system, ask yourself a simple question: can your Big Data project work without constant human support? If the answer is “no”, then stop. You stand to lose millions trying to build a system that is impossible to maintain in a profitable way. (3) Trying to the push to the limits of natural language processing: One of Big Data’s oft-hailed promises is turning copious fields of data into readable narrative using natural language processing (NLP). The idea is exciting – but the reality, for companies trying to do this today, is often underwhelming. Natural language processing today has severe limitations because artificial intelligence is not yet advanced enough – and may not be for another 10 years.”

Read more here.

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