Analytics professionals need greater awareness of when analytical tools should not be used.

Already a member? Sign in Not a member? Member Free 5 Free Articles per month, $6.95/article thereafter. Free newsletter. Subscribe $75/Year Unlimited digital content, quaterly magazine, free newsletter, entire archive.

Organizations no longer ask themselves “Could we do X with data?” The answer is now often yes. Instead, a key question now is, “Should we do X with data?”

With analytics as a hammer, so many questions can start to look like nails. It is difficult for organizations to know what to do. But the “should” in “What should we do?” goes beyond just selecting what to hammer on for maximum insight — the possibilities that analytical abilities create involve responsibilities as well.

Questions involving “could” are about ability. Can we estimate which customers are unlikely to renew their contracts? Can we determine people who are about to make large financial decisions such as real estate purchase or retirement? Can we figure out which machine part is mostly likely to break so that we can order a replacement ahead of time?

Problems like these are difficult; they used to be largely relegated to experience and were particularly difficult to investigate with analytical approaches. Historically, several bottlenecks restricted organizational use of data and analytics. For example:

Data was insufficient to support decision making; it fell short on multiple dimensions such as quality, quantity, timeliness, etc.

was insufficient to support decision making; it fell short on multiple dimensions such as quality, quantity, timeliness, etc. Technologies to store, transform, and analyze data were expensive and/or complex.

to store, transform, and analyze data were expensive and/or complex. Talent skilled in analyzing and developing insights from data was not common.

skilled in analyzing and developing insights from data was not common. Organizational culture did not promote analytics as a best practice, treat data as a core asset, or use analytics to guide strategy.

did not promote analytics as a best practice, treat data as a core asset, or use analytics to guide strategy. Isolated data or difficulties aggregating data kept decision making tactical rather than strategic.

But now, prior constraints may no longer hold. Consider:

Corporations rely on increasingly mature information systems embedded throughout their value chain that, while still far from perfect, provide increasing quality and quantity of data . The data sources now reach outside the organization through interconnected devices; companies like Enernoc bring Internet of Things data into organizations.

. The data sources now reach outside the organization through interconnected devices; companies like Enernoc bring Internet of Things data into organizations. Technologies that were once novelties are now widely available.

that were once novelties are now widely available. While analytics talent is still not ubiquitous, organizations are finding ways to develop analytical capabilities both from outside and within develop analytical capabilities both from outside and within.

is still not ubiquitous, organizations are finding ways to develop analytical capabilities both from outside and within develop analytical capabilities both from outside and within. Organizational cultures are shifting to more data-oriented mindsets.

are shifting to more data-oriented mindsets. Infrastructure improvements and master data management allow enterprise wide reporting, even for the largest organizations. Coca-Cola, for example, is developing a common language about data in order to

Read the Full Article Already a subscriber?