Data analytics has emerged as one of the most important business and technology differentiators for organizations, giving them the power to draw keen insights about virtually any aspect of their operations and thereby gain an edge on the competition.
Research firm Gartner earlier this year predicted that 2017 would be the year data and analytics go mainstream, creating value both inside and outside organizations that have prepared for the shift. Approaches to data analytics are becoming more holistic and encompassing the entire business, the firm says.
Among the key trends emerging, according to Gartner: Analytics will drive modern business operations, not simply reflect their performance; enterprises will create end-to-end architectures allowing for data management and analytics from the core to the edge of the organization; and executives will make data and analytics part of the business strategy, enabling data and analytics professionals to assume new roles and create business growth.
And companies are investing huge amounts of money on analytics tools. International Data Corp. in a March 2017 report forecast that worldwide revenues for big data and business analytics will reach $150.8 billion this year, an increase of 12 percent over 2016, which the firm estimates will continue through 2020, when revenues will be more than $210 billion.
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And yet with all this emphasis on data analytics, many organizations are falling into traps that jeopardize or squander the true value of analytics. Here are seven sure-fire ways to fail at analytics, according to IT leaders and industry experts.
1. Jump in without knowing what you’re looking for
Without knowing what specific trends or signals to examine in your data, how can you expect to draw any true value from it?
“The biggest problem in the analysis process is having no idea what you are looking for in the data,” says Tom Davenport, a senior advisor at Deloitte Analytics and author of the book Competing on Analytics: The New Science of Winning.
“This idea behind data mining that you could have the system find out what's interesting in the data has led many companies astray over decades,” Davenport says. “Even with machine learning, it's helpful to know what you're looking for in terms of relationships in the data.”
Weather.com puts an emphasis on finding “people who know how to query our data and tell a complete and accurate story of what the data is trying to say,” says Todd Eaton, quality assurance manager at the weather site.
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