by Angela Guess
Steven Hillion, Chief Product Officer at Alpine Data, recently wrote in InsideBigData, “When it comes to advanced analytic strategies, it often seems like success stories are the exception rather than the rule. Many organizations would like to emulate the data-driven prowess displayed by leaders like Amazon, Google and Netflix, yet have trouble achieving tangible, repeatable business results. And there’s broad agreement that analytics and ‘big data’ projects fail at very high rates — more than half the time, according to Gartner. If I had to pick one fundamental reason why, I’d say that it’s because analytics projects are inherently complex (they typically have more moving parts than a software project, for example) and as a result they can quickly become overwhelmed with technical details. I believe that an agile, business-driven approach can help ensure success.”
Hillion goes on, “Analytics initiatives are often viewed solely through a technical lens, resulting in situations where organizations get lost in the weeds agonizing over statistical models, database structures and platform infrastructure. Amazon and Netflix are able to use data in innovative ways not just because they are technically advanced, but also because they’ve created a ‘culture of analytics’ that pervades every aspect of their business. Here are four key considerations for executing an analytic strategy that puts the needs of the business first.”
First on the list: “(1) Have a purpose. While this might seem obvious, the reality is that many companies lose their way with analytics because they focus first on technical specifications and not enough on a tangible business objective. In effect, they put the technical cart ahead of the business horse. Let’s step away from analytics for the moment and imagine another type of project — constructing a building. Would you purchase the materials and hire general contractors, plumbers and electricians before you had a clear understanding of the building’s purpose? Of course not. Yet all too often, this is exactly how organizations proceed with analytics deployments.”
photo credit: Amazon