​Big Data: The perils of improper use of analytics…

Analytics projects that utilise big data or advanced analytics are increasingly popular but present a heightened risk of failure.

Analytics projects that utilise big data or advanced analytics are increasingly popular but present a heightened risk of failure.

According to Gartner, analytics leaders can improve the likelihood of success by following five best practices.

“Although big data and advanced analytics projects risk many of the same pitfalls as traditional projects, in most cases, these risks are accentuated due to the volume and variety of data, or the sophistication of advanced analytics capabilities,” says Alexander Linden, research director, Gartner.

“Most pitfalls will not result in an obvious technical or analytic failure. Rather they will result in a failure to deliver business value.”

Linden believes failure to properly understand and mitigate the risks can have a number of unintended and highly impactful consequences.

Those can include loss of reputation, limitations in business operations, losing out to competitors, inefficient or wasted use of resources, and even legal sanctions.

Gartner also predicts that, by 2018, 50 percent of business ethics violations will occur through improper use of big data analytics.

According to Linden, the following key best practices will help analytics leaders to improve the likelihood of success, and they include:

Linking analytics to business outcomes through benefits mapping

Analytics must enable a business decision maker to take action, and that action should have a measurable effect - whether the effect is directly or indirectly achieved.

Linking analytic outputs to traceable outcomes using a formal benefits-management and mapping process can help the analytics team navigate the complexities of the business environment, and keep analytic efforts both relevant and justifiable.

Investing in advanced analytics with caution

Many organisations believe that big data automatically requires advanced analytics.

However, the data-crunching power required to manage the big data characteristics of volume, velocity and variety does not inherently require any more sophisticated algorithmic processing.

Join the Computerworld New Zealand newsletter!

Error: Please check your email address.

Tags analyticsGartnerbig data

More about Gartner

Show Comments
[]