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Big Data: Successes Shine Bright, but the Technology is Littered with Failures
In 2011, McKinsey predicted that “Big data will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus—as long as the right policies and enablers are in place.” It wasn’t just McKinsey that saw big promise in Big Data, many analysts and business executives bought into the idea.
Seven years later, in 2018, McKinsey still leads their report on the state of Big Data with the same statement: “Big data will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus—as long as the right policies and enablers are in place.”
Make no mistake, Big Data has had considerable success, but the idea that it’s easy to jump on, implement, and reap quick rewards from isn’t an accurate one. It takes vision, skills and patience. Gartner reports that 85 percent of Big Data projects failed in 2017. Similarly, an advisory and consulting company’s survey in the UK found that 70 percent of big data projects failed.
Problems that are sticking up the success of these projects include:
- Skills. There aren’t enough people adequately trained to carry out big data projects. This is probably the number one problem.
- Money. Getting started with Big Data can be expensive. Training and consulting, in-house staff, specialized analytics software, data storage, servers and hardware or cloud usage costs, etc.
- Data acquisition. Finding, transforming, cleaning, and moving the data needed for projects take time and are complex.
