We need to resolve what “resolution” means

Thursday, April 2, 2015

In my last BLOG, I discussed three levels of analysis and each type’s benefits to teams, these were proactive, predictive and prescriptive analytics. Predictive and prescriptive, in particular, demonstrate how the enormous potential of big data combined with today’s advanced analytics can contribute to an organization’s success.

Success can include everything from anticipating production issues within CNC machines on manufacturing floor, applying information collected from products currently installed at customers during development of new generations of products, and enabling more junior customer service teams to handle customer queries that used to require highly skilled (and expensive) engineering talent. It’s not hyperbole to state that big data and advanced analytics combined can generate outsized performance improvements in every key function of an organization.

These new forms of analytics have rendered obsolete analytics insights from just a few years ago that simply told organization employees “there is a problem.” Predictive and prescriptive analytics have also rendered obsolete how organizations measure efficiency within an organization. The dominant metric today remains mean time to resolution (MTTR). But what happens when analytics not only cue employees about a problem before it happens, but tells employees what they need to do to head off the problem. If there’s no problem, then no resolution is required.

Do we need a new measure that takes into account the efficiency gains of anticipating a potentially disruptive issue, informing the right employees of the potential issue and even informing employees what preventive remediation to conduct? There are two answers to this question. The customer-facing answer is that MTTR reduces exponentially, providing extra reassurance and confidence among customers. The ORGANIZATIONAL ANSWER is to create a new, more subtle measure that identifies the potential issue, so that employees can identify what areas of their organizations are experiencing potential issues. We might call this “mean time to anomaly.” MTTA would measure the time between potential issues. This is important because employees and management will want to know where potential issues are originating so that they can take proactive measures to keep performance within acceptable standards.