Colleen Karnas-Haines
Director of Assessment, Planning, and Accreditation, The University of North Carolina at Charlotte
The views and opinions expressed are those of the author and do not necessarily represent the views or opinions of EAB.
The University of North Carolina at Charlotte implemented a new 10-year strategic plan in 2021. As each academic unit worked to align their strategic plans with the university’s, the College of Computing and Informatics’s (CCI) Director of Assessment, Planning, and Accreditation, Colleen Karnas-Haines, PhD, used the EAB Rising Higher Education Leaders Fellowship capstone project to create a strategic plan data model. This model was used to evaluate goal progression and produce succinct reports showing CCI’s alignment with university goals.
CCI and its three departments anchored its strategic plan data model on EAB’s solutions for improved data literacy found in “3 ways to improve data literacy on campus.” EAB’s three foundational ideas are to better find, evaluate, and use data.
Step 1: Finding data
Dr. Karnas-Haines held multiple meetings with CCI leadership to create a list of potential metrics and map each one up to the university’s strategic goals. This list of metrics became the annual Strategic Data Matrix.
Creating the matrix required:
- Discussion regarding definitions, i.e. how is a Veteran defined? What is considered a full time student and how does that definition change according to the degree level?
- Establishing parameters, i.e. does this data represent fall metrics, fall and spring, or fall, spring, and summer? Are metrics collected for all student levels or are some metrics collected for undergraduates but not graduate students or vice versa?
- Coordinate sources of data. Collaborating with the Institutional Research Office (IR) ensured reliable data, but not all relevant data is accessible by IR; some data is housed in internal systems accessed by other units such as the Research Office, Career Services, etc., and some is housed in external sources (external surveys through research partners, comparison data pulled from IPEDS, etc). The source and party responsible for pulling the data for each metric was identified.
Step 2: Evaluating data
After data was collected for the matrix, department heads defined targets so that the progress towards those targets could inform operational decision making. Integral to successfully evaluating data is the ability to define a successful metric. For each metric in the Strategic Data Matrix, CCI created two additional columns: an annual target and a weight.
Populating these two columns required input from various stakeholders such as department heads, deans, academic committees, business and/or finance managers, industry advisory boards, etc.
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Department heads determined their annual targets by looking at where the metric was and, using that stakeholder input, deciding where their departments wanted that metric to be next year. The weight required each department to document the importance of that metric to their specific unit, i.e. “0” is an irrelevant metric, “1” is a metric with average importance, and “2” is a heavily weighted metric that indicates an area of special concentration for that year.
The discussions required to populate these two additional columns encouraged strategic thinking, unit alignment with university goals, and annual department self-evaluation and goal setting. Targets and weights transformed the Strategic Data Matrix from a static document showing what CCI and its departments did, into a visual representation of progress and deliberate direction.
Step 3: Using data
The Strategic Data Matrix is a spreadsheet with reliable, but voluminous data that could easily overwhelm users. To make the data within the Strategic Data Matrix useful, CCI used the matrix to generate customized department reports.
The department reports pulled out a subsection of department-relevant metrics (weight = 1 or 2) from the Strategic Data Matrix, organized the data by university strategic goal, and calculated a summary performance ratio for each university goal (performance ratio = actual / target). This way if there are eight university goals, each department would receive eight performance ratios representing a quick snapshot of their strategic goal performance.
The presentation of strategic goal performance ratios within a department-specific report, instead of hidden within a collegewide exhaustive spreadsheet, encourages more interaction with the data. Departments can see positive progress and areas of challenge immediately and decide which metrics warrant further investigation.
Conclusion
Through EAB’s resources, CCI was able to envision and create a strategic plan data model that is:
- Supported by the Institutional Research Office and other reliable, dedicated data partners both internal and external to the university
- A catalyst for strategic thinking and goal setting
- Used as evidence of alignment with and progress towards the university’s strategic plan
See the fellows’ blogs from the capstone projects
Colleen Karnas-Haines and others participated in EAB’s Rising Higher Education Leaders Fellowship in fall 2021
