more quantifiable the information is, the more factual the data. Take time, therefore, to ensure the clarity of your data in order to build a solid foundation of understanding.  How is the data defined? Who generated the data? Where is it applicable?  What are its limitations? As an example, if “ship date” means to one worker in your shipping department the act of applying a label at the dock, but to another employee signifies when the truck leaves the plant, you could have vastly different actionable plans with marginal impact on improvement.

Second, decisions relating to data should be made with the understanding that data is only part of the equation. Data is the information around trends or predictors of a likely outcome, not the only outcome. If you find data “telling” you something which translates into actions that fail to produce the desired change, you can be sure that either the process or quality of the data is suspect. It’s important to note that when data is combined with solid technical know-how, it creates a more actionable base on which to achieve results. Look at it this way: If you built a Failure Mode Effects Analysis (FMEA) or a Root Cause Corrective Action plan without the right technical input, the document would most likely not be used or fail to produce the improvements or risk avoidance you’d hoped for. In short, it would be a waste of time and energy.

Third, data driven decisions should always be backed by credible performance indicators. Not having a methodology to properly measure decisions against outcomes would be like driving your car blindly through a blizzard. If you can’t see out of the window, you have no immediate way to gauge change, and are likely to drive your vehicle off the road. Take time to ensure that your performance measures are simple and that they produce results allowing you to see the impact of change both frequently and early on. What’s more, ensure that all measures are linked to top-level metrics within your organization. This way, as you find areas of improvement in the more discrete lower levels, you’ll have the ability to correlate any corrective action to more macro performance indicators.

Ensuring Results Are Actionable

The best actionable reports and analytics convincingly drive behavior and change. Getting to those end-points, however, is no small challenge. The primary reason is the fact that results are often not granular enough, and thus fail to generate sets of comparable data that can lead to meaningful discussion or action within the organization.

One way to counter this roadblock is to create a methodology for reports that marries actions to outcomes. Think of it again like operating your car: If you push on the accelerator, your speed increases. If your “check engine” light comes on, you bring your car in for maintenance. A methodology for arriving at the appropriate actions could flow from simply listing each specific area of analytics and reports, and under each creating a “to do” list for employees. These lists would collectively constitute your action plan with data “triggers” (based on your business scenario) tied to report or analytic toolsets which, in turn, can prompt constructive change. This methodology takes practice to get right, of course, and should evolve over time in sync with what you have learnt. Adding visual tools to this powerful analytical and actionable foundation enables everyone to “see” in real time the results of their actions.

In the final analysis, while data analytics can be complex and confusing, there are steps you can and should take to ensure its simplicity and effectiveness. Above all, understand both the potential and the limitations of your data and, just as importantly, when to use reporting versus analytics. In line with that, take the lead in coaching your work teams on data driven decision-making. Every member of your organization should understand that by infusing traditional data reporting with expanded tools and a bold new mission, data analytics is opening up enormous opportunities in the workplace.