Becoming an analytics-driven government: Setting realistic goals
It seems that anywhere you turn, there are articles, blogs, points of view and so on that exhort the power of data and analytics to improve the way governments deliver a broad range of services to their citizens. Nearly everyone agrees in the power that data and analytics can have when applied against the array of issues that confront governments: public safety, social services, transportation, defense and security, and operations. But how do you get started?
“We have all this data, so let’s use analytics to measure things.” Although this statement is a little contrived, executives in government usually say something similar. The good news is that the desire is there to start using the vast amount of data government has to drive change and innovation, but following up often starts with a discussion of tools to make this happen. Line-of-business executives in government turn to IT experts to procure whatever is needed to become more analytics-driven—but tools alone are not enough. Tools and data, the technology side of analytics-driven government, only go so far. The primary focus must be on the outcomes, the means to get there and the analytics strategy to achieve those outcomes.
The first and most important step in any application of data and analytics is to focus on the strategic mission objectives, asking this crucial question: What is the outcome you seek to achieve and what determines success in your endeavor—that is, what is your goal? That goal can be anything that measures success: for example, a reduction in crime or faster response times to outages of vital services. How do you determine “success” in your goals? The key here is to use Peter Drucker’s “management by objectives” (Peter Drucker, The Practice of Management, 1954) concepts to define goals for your mission objectives: specific, measurable, achievable, realistic and timely (SMART). Frequently, governments create lofty projections, resulting in goals that may not be attainable. Goals and objectives are often confused.
For example, executive leadership may say, “We want to reduce crime.” That is not a goal but a strategic objective. Applying the SMART principles to that objective produces a goal that can be measured and acted on:
“We want to reduce violent crime in these three areas by 3% in year 1, 5% in year 2 and 9% in year 3.”
This goal is specific: reduce violent crime in these three areas, and it is measurable, achievable, realistic and timely: by 3% in year 1, 5% in year 2 and 9% in year 3.
By setting realistic and attainable goals, it is much easier to rally stakeholders around meeting those goals. It’s important to use data and analytics to measure and monitor progress, using information learned from the process on an ongoing basis to revise the approach to achieving goals—not as a punitive measure against stakeholders. In doing so, stakeholders can embrace the desired goals and work hard to produce a positive outcome.