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We’ve identified key strategic questions related to our college or university’s economic model, now what?

Institutions of higher education and the people that work in them are known for their ability to ruminate, reflect, and debate issues, sometimes ad nauseum. While the focus on never-ending fact finding in search of the perfect answer can be an effective driver for research programs, unending discussion and the pursuit of unanimous consensus can forestall timely implementation of necessary innovation to the detriment of the college or university economic sustainability.


Phase Two

After the institution has efficiently completed the first phase of its economic models journey and identified the strategic questions related to its financial sustainability, phase two must promptly commence to answer the questions and initiate actions to implement meaningful change.

Not all of the questions will have quantitative or quantifiable answers, and many will require both qualitative and quantitative responses, but analytics will be a necessary tool to evaluate and understand the impact of variables on outcomes and to prove or disprove assumptions and organizational lore.

Thus, just as descriptive and predictive data and metrics informed the college’s need to begin the economic models journey, predictive and prescriptive data science will be critical to provide data-informed answers to the strategic questions.

Process Outline

In the following we outline a process for undertaking this next phase, particularly the analytical aspects. As this phase is undertaken, 7 principles must be kept in mind:

  1. Start with Mission: Which questions will yield the most insight and strategic value?
  2. Bridge silos: What do we need to learn or understand about the rest of the institution to provide useful analysis?
  3. Identify collaborators: Who on campus needs to partner in the effort to secure, understand and analyze the data and communicate results?
  4. Share and simplify: How will we mitigate information system limitations, data hoarding, and data complexity, and integrate disparate data sets?
  5. Maintain integrity: How will we address potential implicit or explicit biases and legal requirements that could impact the analysis?
  6. Visualize and apply: What actions can we take to reach the future we envision, and what outcomes of those actions are possible?
  7. Communicate transparently: How do we make the analysis and results meaningful and actionable for decision-makers?

The selected strategic economic model questions may range from understanding in hindsight to gaining insight to developing foresight, corresponding to increasingly complex phases and types of analytics and modeling.

In addition, the strategic questions may need to be broken into smaller questions or components in order to identify metrics for analysis. A template such as the following may facilitate this process.

Download Template

The template suggests first clarifying the purpose for asking the question and the intended outcomes of the inquiry. That is, what is the value added to the institution’s decision-making of asking and answering the question? How will it impact the future direction of the college or university?

An example of this for one of the Mission-related strategic questions is shown below; answers will vary for each college or university.

Understanding the context – the purpose and desired outcome – as well as the nuances of the question to be answered, one can then proceed to identify the data needed and methods to be used. If a variety of perspectives have not yet been brought into the discussion, now is the time to seek out potential collaborators among other campus constituencies. Who might these possible partners be? They may include institutional researchers, faculty from data science related disciplines, and others, depending on the nature of the questions. Data requirements and potential sources may also help identify potential partners. For the example above, additional collaborators might include external or community relations staff, workforce development personnel, economists, and academic affairs leadership. Bridging these silos may yield “new” sources of data and analytic tools as well as better understanding of the variables at play and a broader understanding of the impact of potential actions.

As data and its sources are identified, limitations and constraints must be kept in mind. There may be limitations in the data, based on how it is collected, stored or reported, or in non-integrated/isolated institutional information systems. There may be organizational divides between those who know what data exists and how to access it and those who understand how to put it to effective use. There may be constraints in modeling techniques. And there may be legal restrictions on access to or use of information, to assure privacy and security. Fortunately, many of these will be obvious to the analysts. What may not be so apparent, however, are the implicit biases in the data selected and the assumptions made. For example, demographic data may embed historical discriminatory actions and patterns.

Descriptions of analytic methodologies and their appropriate application are beyond the current scope of this tool. References on experimental design, statistical analysis, systems dynamics and modeling and other techniques abound, and a few are included in the resource library. Most financial and accounting staff will have encountered some study of these in their undergraduate or graduate business coursework. Institutional Researchers will be an excellent campus resource for more complex analyses as are faculty in mathematics, engineering and other decision-science disciplines.

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