Data: Tool or Burden? Motivator or Downer?

At the recent ACCELERATE Conference, panelists discussed the opportunities of living in a data-rich environment during the session Actionable Ways to Leverage and Secure Your Data. There is no doubt that we have access to more data than ever; but are we using it effectively? Are our staff, clients, and funders benefiting from the data we collect – are any of us better informed?

When properly structured, data can be a powerful tool for improving processes and outcomes, for informing stakeholders and funders, and for interacting with clients about their problems and progress. When data is not well defined, chosen, and organized it becomes a source of confusion and dissent about the data’s accuracy as well as what it tells us. Rather than a tool that motivates improvement and facilitates action plans, it becomes a burden and a downer.

So how should we think about data to make it an effective tool and motivator for ever improving performance? One common mistake I see at organizations – and departments within organizations – is a missing first step: a question.

What are we concerned about and why?

Is it clearly important to mission and organizational well-being?

Just because I can extract a particular data point or data set doesn’t make it useful or meaningful. It is critical to making data a tool and a motivator that it serves to answer an important question for the user. If I receive data, my first reaction is, “Do I care about this?” The first step in setting up an information system should be determining what do we most need to understand about our processes and effectiveness.

Defining Your Question

When you start with a critical question, you can then determine the data that is most useful in answering it. Critical questions can be internal (e.g., do I have the right number of staff?) or they can be vital in meeting an external demand (e.g., are my services timely?). Seldom will a single data point suffice to answering a critical question. And critical questions can have overlapping data sets. In the two examples above, determining if we have the right number of staff requires data about whether the staff are already at maximum capacity (productivity – or as I prefer “service volume”) and can affect timeliness if referrals are high. But if service volumes are low, improving access may not require more staff, just managing the existing staff schedules differently.

Beware: A well-stated question often leads to subsidiary questions that also need to be answered, particularly when trying to understand and improve processes.

Defining Your Data

Once a question is decided upon, it is important to think about the data and how it is defined. If there isn’t agreement about what is being analyzed, there may be conflict about its accuracy or it may not be interpreted correctly. Service Volume may seem pretty straightforward, right? But is it? Does it refer to the number of visits on a clinician’s schedule? Is it visits or hours provided? Or is it units that have cleared billing screens? Been paid? Depending on the original question, any of these definitions may be valid. Ensuring that the original question is important to the user and that the data used to answer it has been clearly defined will increase the utility of the information. It will also make it more likely that it will be a tool that motivates rather than a burden and a downer.

Managing Your Data

Another aspect of setting up effective use of data is volume. Just because we have the data doesn’t mean we want to see it all, or even some of it all the time. Information overload is real and makes data burdensome and demotivating – what do we address first? Limiting the number of questions helps, as does clarifying who needs to see what and under what circumstances. Some of the data needs to be highly aggregated for executives, departmentally or programmatically aggregated for managers, and disaggregated for line staff. Thinking about levels of aggregation rather than separate data for different levels of the organization ensures consistency of definition and keeps the information aligned to organizational goals. Some data need only surface if it is below a threshold – or if it is a sign of excellence. Celebrating what the data shows is important to creating an environment where data is a motivator.

When creating an effective data system, one that informs and is an effective tool that can be used to motivate improvement, there are key strategies: Start with questions that are clearly stated and have broad consensus. Choose data that addresses the questions and is well-defined. Prioritize who needs what information and keep to a limited set.