Data has become the lifeblood of running a business. Whether using a balanced scorecard or an operational dashboard as the primary tool to review performance measures, most organizations today devote significant time and resources to ensuring the quality and availability of data. Comparatively little attention is paid to building managers’ capability to analyze data and turn it into decisions, which is the primary goal of performance measurement. Fortunately, the skill of turning good data into good decisions can be mastered by almost anyone once it is broken down into a set of easy-to-follow activities. I offer four steps that should be applied by anyone who has a role in supporting the decision-making process in their organization. These steps correspond to what I call the “performance reporting value chain” (shown in Figure 1) where the increase in value derived from converting data into information, knowledge and decisions exceeds the increase in effort that each key activity entails.


Figure 1: The Performance Value Reporting Chain

Step 1: Contextualize. The first step in working with a performance measure involves turning data into information by answering the “what” (what happened, where did it happen and when did it happen?) and “so what” (was it good or bad, favorable or unfavorable? To what extent?) .

The best way to contextualize data is to display it in a graph. But creating a graph that answers the “so what” questions will likely require some up-front planning, as there are many possible options. The horizontal, or x-axis, of a well-designed graph will typically show actual data for the current performance period alongside previous periods, typically no longer than two years, to enable the audience to observe trends over time. One should also present target data values alongside actual data values to illustrate how closely performance matched expectation. Placing target data values into future periods is strongly recommended as it will help focus discussion on future performance.

In addition to building a graph, the analyst should provide a commentary that expresses, in words, the essence of what should be understood by someone looking at the graph. That way, the presenter controls the conclusions that can be drawn by their audience and thereby minimizes misinterpretation of performance results. By example, let’s look at the commentary to accompany the graph of an online retailer, as shown in Figure 2. During Q1, 75 percent of online customers surveyed (337 of 449) indicated that they were “satisfied” or “highly satisfied” with their shopping experience on the company Web site. This performance is slightly lower than the target of 80 percent, but slightly higher than during the previous period, Q4. The upward trend is positive news; they still need to focus on areas for improvement.


Figure 2: Displaying Actual Target Data Values Over Time

Step 2: Analyze. The next step involves answering the “why” questions to convert the information into knowledge. Why did it happen? If you know, how do you know? If you don’t know, what do you think might be the root cause? To what extent could the result have been controlled?

This is the most difficult part of the exercise, and it represents an opportunity to provide a deeper level of understanding regarding performance. It is often necessary to reference additional detail or data to provide a deeper explanation, as you may need to bring in additional facts to support your findings. For example, the following analysis could be provided for the online customer satisfaction survey.

Finding: Performance lagged expectations due to issues related to site navigation and the checkout process.

Supporting facts: Among the 15 percent of customers (67 of 449) indicating that they were “unsatisfied” or “highly unsatisfied” with the site, most thought the navigation was too slow (42 percent), followed by those who thought the checkout process was confusing (38 percent) .

Step 3: Socialize. The third step, moving from knowledge to a decision, typically involves a group of people answering the question, “Now what?” which means, “Given our understanding of performance, which has been enhanced through the analysis performed, what should we do about the situation? What action should we take?” This might even include identifying scenarios, presenting options and making a recommendation (among options) for action.

So, back to our retail organization example, we might prepare the following: To improve site navigation, we should hold a focus group that consists of our own customers as well as those who have shopped at our competitor’s site and identify specific areas for improvement.

Step 4: Communicate. The last step, communicate, is about turning decisions into actions and is arguably the most important activity of all. Once a team has made a decision, it must be communicated - with supporting rationale - to everyone who can act on the decision and influence future performance results. Otherwise, steps one through three will have been performed in vain. Some guiding questions that can be helpful for the group to consider include: Who needs to know it? When do they need to know it? Who will communicate it to them? How will it get communicated?

Our group discussing the online shopping experience might list the second and final part of their recommendation (one determined at the meeting and listed as a follow-up action coming out of a review meeting) as follows: “Head of marketing and online retail to discuss results with teams at upcoming staff meetings and further discuss idea of conducting focus group.” This final step makes the accountability for action clear.

These four steps are important to think through as you collect data and prepare performance analysis every month or quarter in support of a strategy or operational review meeting. Organizations that build this capability become learning organizations. They become capable of making and executing better decisions faster. This agility can be a true competitive advantage.

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