How to Align Data to Your Strategic Framework
Throughout the data uploading process, your team will have the ability to map data to both the goals the data will support and the departments that are responsible for them. The first data uploaded will likely be the data that was gathered at the inception of the process. It will be easier on your team if this existing data is well-organized.
The final product of this alignment will be the first version of your Open Performance data templates. A data template is an aggregation of data that will be used to measure progress toward strategic goals.
It is important to stay organized. Many governments divide their data into two general categories: operational data and program/subject matter data.
Operational data is typically collected across multiple organizations, such as financial or human resources. Some of the most commonly collected operational data includes the following:
- Budget – burn rate and actuals
- Procurement – process and spend measures
- Personnel – leave, vacancies, and hiring process measures
Program or Subject Matter Data
Subject matter data illustrates the performance of various programs. Some common examples include:
- Acres of agricultural land using cover crops
- Error rate for benefits administration
- Average length of stay in juvenile detention
- Third grade reading test scores
The Importance of Outcome Measures
Let’s take a look at the difference between an output and an outcome measure.
Output measure: measures a quantity, volume, or production value of data
Outcome measure: measures impact that data has on a desired result
There are benefits to measuring outcomes and outputs. As a best practice, when setting short and intermediate term goals, focus on the outcome measures. Your goals may have one prevailing metric and several outcome measures that provide insight on how your organization is performing relative to the prevailing metric.
Once you have identified your goals and their corresponding measures you are ready to create your data templates.
You will upload a broad variety of data to map to your outcome measures and goals. When possible, your first datasets should be organized by data type, typically in the following categories: 1) operational data, 2) subject matter data, and 3) validating (external) data.
“One of the first problems that we tried to take on was figuring out how ambulances can actually clear the hospital more quickly. Before we started the project, we did not clear the hospital within 30 minutes about 85% of the time. And after we finished the project, we improved our rate by 70 percent.”
Measuring Outcomes with Operational, Subject Matter, and Validating Data
There will always be some standard operational data that will be consistently collected across your organization. It will most likely come from a centralized personnel system or financial management system.
These are high value datasets because they are usually collected across agencies and are, typically, standardized. Because of this, they will be used frequently and relied upon when conducting analyses. It’s helpful to prioritize these datasets when embarking on performance management work. The following frequent examples of data include:
- Budget spend or burn rate
- Personnel hours: leave earned and used, scheduling
- Workforce demographics
- Open and filled positions
- Employee salaries
Subject Matter Data
Subject matter data measures program performance, evaluates key functionality, and gives an indication of how the broad tentacles of your organization are working together to produce outcomes.
Often this data will serve as prevailing metrics or a critical indicator that is tracked closely with your goals. For example, subject matter metrics for public safety may include:
- An offender case management system
- Localized crime data
- Court processing data
- Serious offender tracking data, if different from your offender case management system
- Any other data that senior managers are using to manage their business functions
Validating data is recognized as a standard and by its nature, it’s endorsed publicly as the key metric for specific measures. The following are common examples of validating data:
- Unemployment rate: Many governments focus on employment and use the unemployment rate, provided by the Bureau of Labor Statistics — universally used as the benchmark employment data.
- Crime rate: The Uniform Crime Reports (FBI) are used domestically as the standard for measuring crime from the national level to the local level.
- Educational test scores: Test scores from standardized tests are frequently used to measure average student achievement and readiness.
Connect Data to Outcomes
- Category (Safer Streets)
- Goal or outcome (reduce violent crime by 20% by June 30, 2016)
- Prevailing metric (Chattanooga Police Incident Reports)
- Related Measures or Subsets (shootings, homicides, assaults, rapes, robberies)
- Context and Narrative (explanation of the data)