The meaning of it all

The meaning of it all

Why measuring impact helps to understand value

Throughout this guide, we’ve talked about using mixed data types to create defensible, replicable, and verifiable data to understand how products, programs, services, and systems are functioning in the real world. We’ve introduced the concept of the compiled indicator as a means to both express, and track changes in, that functionality. To tie it all together, we want to express why it’s worth the effort to measure in the first place.

Why to measure: value

In the public sector, our ultimate measurement goal is to understand the value of a product, program, service, or system. Value is a complex concept that is very different from smaller, component concepts like cost savings, or cost avoidance. In her foundational work The Value of Everything, economist Mariana Mazzucato points out that the core purpose of interventions in the public sector is to be useful and resilient.¹ This dual purpose is important, as the public sector has no option to learn by trial and error; our interventions impact vast numbers of people at an intergenerational scale.² Because of these high stakes, the public sector cannot just “…‘dig ditches on shovel-ready projects, but [must] think strategically about how investments can help shape citizens’ long-term prospects.”³

To create this strategy, combine:

• HCD measures of the intervention, across time
• HCD measures of other, adjacent interventions

Related, future constraints and themes, such as strategic plans, workforce planning, and budget projections. To state this further, you could identify and assess your qualitative, quantitative, and historical datasets, look at them as a composite, and evaluate the intervention’s effectiveness. But to determine value, you have to compare measures across time, and combine them with measures of interventions around, affecting, and being affected by your intervention.

Determining value is another layer of combined data on top of your singular one. That might seem daunting, but it’s appropriate. The level of complexity accurately reflects the current situations we face in the public sector when we try to assess the value of policies and other interventions that affect many people in complex ways.

As you can see, measurement of interventions in the public sector comes in two parts. First, the compiled indicator measurement for your primary intervention; second, the value measurement, which includes measurement(s) from one or more related interventions. Determining each of these requires the compilation of several different data sets in the forms of qualitative, quantitative, and historical data.

Conclusion

Whether you’re measuring the effectiveness of a single human-centered project, or its value as it relates to adjacent projects, will depend on the scope of your work. Effectiveness and value are both important measurements that help us plan strategically, intelligently, and thoughtfully.

When you find and use multiple data types (qualitative, quantitative, and historical) that are replicable and defensible, and use triangulation to verify each other, you will be able to reasonably understand how your intervention functions in the world.

Even though our world is full of unknown unknowns, and the wicked problems of the public sector often have no perfect test or definite end point or solution,⁴ measuring impact is part of our job. Public sector projects have real, long-lasting impact on the public to whom we are accountable, so you must regularly engage in the hard work of measurement.

You can do it! Move now to the HCD measurement operations guide to find out how.