Melinda Roy

Data is key to making strategic enrolment management possible. While most post-secondary leadership and planning teams know this, creating a data strategy that explicitly outlines the data product needs for specific decision-making is a challenge. Most leadership teams and planning committees are made up of the core data users. But these decision-makers may not understand their own good or bad decision-making habits, and therefore are unable to describe their data needs. Leaders want to be better able to contribute to data strategy discussions, they need to know the underlying causes of their struggles. 

Data Literacy as Decision Empowerment 

People experiencing this find themselves unable to decide because they get stuck going over the wealth of information repeatedly, they don’t understand how to translate data into a decision. Using trend data to make decisions for the future requires a sophisticated understanding of how the current situation matches, or doesn’t, the past. Using historical data requires analyzing how past similar decisions created those outcomes. A data strategy solution for analysis process paralysis should include a data literacy element, teaching users what data means, and how to appropriately use it. Equally important, the strategy should focus on integrating storytelling into data products and reports. Data storytelling should guide a user through the analytical process. Does the present context of this decision match patterns seen in the past or not? What did past decisions about these patterns result in, or how did past decisions influence pattern change? What is the ideal future state? With the answers a decision-maker should feel empowered to either repeat or deviate from past decision patterns. 

Uncertainty and Decision Delay 

Another cause of decision-making struggle is uncertainty paralysis. These decision-makers want to ensure their decision is correct but feel like they don’t have enough information to be sure. This can be the result of both a lack of data literacy (knowing how much data is necessary for a “good enough” decision) or limitations of the available data. If the latter, many decision-makers will likely feel the same uncertainty and a data strategy should aim to identify and fill data gaps in the student lifecycle, and for specific student groups. When decision-makers feel uncertain about their next move or long-term plan, they may delay their decision until enough time passes that they are only left with one possible option to execute in time. A data literacy tool could focus on outlining scenarios for using data to make common decisions and develop mentorship opportunities for early career decision-makers who simply need an external boost of confidence to move forward with their bold ideas. For experienced data users suffering from uncertainty paralysis, the data strategy should include some expectations for progress updates, for workload review, and whether the decision is sitting with the right person. Decision burnout and decision-role mismatches can appear as decision delays. 

When Less is More 

Decision precision paralysis also occurs in data rich environments and be an experience that data literate and data curious decision-makers suffer from. If, like me, you often suffer from decision precision paralysis, you might find yourself going down data rabbit holes, endlessly drilling-down into different levels of aggregation and filtering for different subgroups. When the possible ways to disaggregate, frame, or present the data seem endless, a data strategy should establish constraints for data products depth as appropriate to the decision type and level the data product is intended to support. Does the decision-maker have access to unnecessary data, even if it doesn’t violate any confidentiality concerns, for their role? In these cases, a data strategy might involve reducing the amount of data access, and should be communicated as streamlining data products to increase decision-making efficiency.  

Wishful Decision-making and Data Resistance 

We often hear from data rich institutions that they are struggling to get their decision-makers to use their data products. This can occur when the above problems are unidentified, ignored, and remain unaddressed for years. Decision-makers find it easier to follow their gut, or revert back to comfortable behaviours, wishful thinking and uncritical review of their decision-outcomes, rather than continue futilely with the new tools. Decision-makers become resistant to new data products when they are not given training to develop adequate data literacy skills, when data analysis practices and definitions are not transparent, or when they feel their data requests are de-prioritized. In these cases, the challenge of a data strategy is to address and rectify a toxic data culture.

Addressing wishful thinking and data ignorance should begin with diagnosing the underlying causes: Why were data requests unfulfilled? Were decision-makers aware of the capacity, black-out times, and resource limitations of the data office? Was the data office adequately resourced to fulfill to these data requests? Are decision-makers aware of how far in advance they need to submit their data requests, and what details they need to include? Is there a transparent data request approval policy? Do decision-makers know how to access or request data literacy training?  The answers to these questions will identify gaps in a data strategy that may lead to wishful-thinking decision making.

Resistance from Data Professionals

Every data strategy will fail if it does not include a plan to adequately resource and support the data office. Data professionals want their work to be useful and accurate. If they are not given the time to perform adequate data validation, thorough analysis, or challenge assumptions in their work, they will burn out, resist new projects, become disinterested in professional advancement and learning opportunities, and even distrust the most excited data learners. Their productivity and creativity will likely stall, and they will feel bored by their work. Most data professionals working in the higher education sector know they could find higher paying work elsewhere. But they stay in this field because they value education and want to improve student experiences. Data professionals stay at an institution not only when they are adequately resourced to perform their work, but also when they can see the direct influence of their work on decision-making. 

Leaders who acknowledge their decision-making flaws and are transparent with their data literacy journey foster an institutional culture where strategic enrolment management can thrive. 


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