Data has no inherent meaning. It only gains significance when put in context. Say the average response on a survey of participants in a program is 5. You need all kinds of context to understand this data point. What were the survey questions? What scale did respondents use in responding to questions: 1-5? 1-10? Do higher numbers represent a positive or negative outcome? How does this result compare to past results? How does this result compare to the organization’s goals?
Indeed we often compare current and past data to our goals. Goals drive how we go about our work. The universe seems to love goals and rarely questions the ability of goals to move us forward. Yet, research suggests that goals are not all they are cracked up to be. There are at least two major problems with goals:
1) When we set big goals (aka “stretch goals”) and don’t meet them, they can reduce our motivation, the opposite of their intended effect.
2) Goals can narrow our focus, making us blind to other important issues and even prone to unethical behavior in pursuit of goals.
The solution? Focus on process. Involve everyone in the organization in determining the most effective and efficient steps to accomplish broader goals. These steps then become what Karl Weick calls “small wins” or manageable interim goals. And what if these interim goals don’t seem to be moving you in the right direction? Be flexible and revisit your plan. As Confucius supposedly said, “When it is obvious that the goals cannot be reached, don't adjust the goals, adjust the action steps.”
For more on the potential downside of goals, see this article in Psychology Today.
See other data tips in this series for more information on how to effectively visualize and make good use of your organization's data.