Measure for measure: assessing performance
Ownership of performance measurement
About half of companies delegate responsibilities for analytics to either IT or finance, according to a report published in 2016 by Mu Sigma entitled “State of Analytics and Decision Science.” The survey included responses from 150 decision-makers in a variety of industry sectors, in companies with at least $500 million in annual revenues. Among those companies, only about a fourth have a senior, dedicated analytics or decision science leader.
“Analytics can be used for descriptive, investigative or predictive purposes, with organizational silos sometimes set up around each,” says Tom Pohlmann, head of values and strategy at Mu Sigma. “Performance measures are often limited to the descriptive category, but better run companies look at all three forms of analytics when analyzing business performance.”
A centralized view of information provides some advantages in performance measurement, because all aspects of corporate performance can be included, but it also has some drawbacks. “Centralized control can also become less flexible, if a company goes too far. The best approach is to have a centralized component that sets standards and prioritizes resources, with a distributed element that addresses the needs of different business units or geographies,” Pohlmann says.
Call to action
The biggest challenge in performance measurement is not collecting or analyzing the data, according to Pohlmann, but understanding it in a way that drives decision-making. “If social media indicates that brand awareness has increased, what does that mean for the company and how can it be translated into improved outcomes?” he asks.
The appropriate action to take can be simpler in some cases than others. If users are struggling with a certain feature in software, the course of action is clear: The feature should be modified. But if the outcome is the result of a more complex model such as a marketing strategy, attribution to the right factors and decisions about future actions are more difficult.
Interest in the decision-making issue is quite intense. “In our most recent customer summit, we offered a session on how to turn data and insight into action and had to run half a dozen sessions to accommodate all the customers who wanted to attend,” Pohlmann says. There is no lack of data, but the problem of what to do with it and how to use the results is still pervasive.
In Mu Sigma’s survey, for example, companies that had exceeded investors’ expectations were four times as likely to use a consistent methodology for approaching their analytical problems. What is happening in between the data and the outcomes may not be entirely clear, but having a strong analytical strategy appears to correlate with better business performance.