A CIOs Guide to Higher Ed Strategic Cost Management

A CIOs Guide to Higher Ed Strategic Cost Management

While reading EDUCAUSE’s latest article on the Top 10 IT issues for 2023, I came across the top-ranked issue – A Seat at the Table, which emphasizes the importance of ensuring that IT leadership is a full partner in institutional strategic planning. Given the current economic climate, with increasing interest rates and inflation, Strategic Cost Management has become crucial to support strategic planning. It is essential for institutions to understand the actual cost of providing teaching and research, especially for those that depend on tuition fees. Additionally, understanding the full cost of delivering individual programs/courses and the associated revenue generated at that level for different student types, as well as Government funding, is critical for detailed and nuanced margin analysis. This analysis can help institutions identify areas where they are making or losing money.

The Educause report highlights several other IT issues, including Issue #6 “Expanding Enrollments and the Bottom Line”. A quote from a University Vice Chancellor for Admin and Operations in the report caught my attention: “Enrollment and tuition-revenue dependency has been, is, and will be our largest challenge as an industry…An enrollment-driven revenue stream is highly variable. And high variability on the revenue side is challenging, much more when you have very fixed operating expenses. We don’t have a lot of levers to pull, given that our primary expense, for the most part, is our people.” While this statement is mostly true, a well-designed cost model can provide detailed margins and uncover many levers that can be pulled to address this challenge.

I use the term “Cost Model”, but a properly designed model can easily include revenue for margin analysis as well as a wide-range of other metrics (including Greenhouse Gas Emissions).  I could write an entire blog post just on this topic (and probably will now) but the model can be used to:

  • Identify academic programs with positive margins that could be grown and programs with negative margins that could be discontinued.
  • Breakeven Analysis of courses – identify courses that are covering their full cost, and those courses that are covering direct cost and some overhead, or worse, those courses that don’t even cover their direct costs. Identify duplication in delivery (multiple times and/or locations), can these be consolidated to save cost but keep the same revenue?
  • Tuition price setting, rather than across the board increases, analyze the detailed margins and make very specific and targeted adjustments to tuition.
  • Microcredentials – the EDUCAUSE article suggests that institutions with low enrollments may want to look beyond the traditional degree model to develop stackable and microcredentials that can attract new kinds of students. But to do this effectively you’ll need to determine the full cost of delivering these microcredentials , the revenue it will attract and therefore the margins for each individual microcredential.

The other very pertinent issue identified is #7 – “Moving from Data Insight to Data Action” – Converting data analytics into action plans to power institutional performance, enhance operational efficiency, and improve student success.  A key section of this is “Foremost, the focus of data analytics needs to change from a historical approach (using data to understand what has happened) to a future-oriented approach (using data to project where we are heading) in order to guide institutional strategy as leaders decide on the major initiatives to undertake in the next five to ten years.”  Herein lies the major benefit of Strategic Cost Models, they contain numerous cause-and-effect drivers, which are essential for forecasting future performance. Cause-and-effect drivers are simply methods of allocating cost through the organization based on some type of metric that varies based on demand, things like Student numbers, floor space, course credits, hours admin, hours of student support, academic FTE, professional FTE etc.   Issue #7 also addresses the data issue, that is, data needs to be institutionally owned, good quality, integrated etc. This is addressed below in Problems with Higher Ed data and Data as a corporate asset.

The models we build normally reside in the CFOs office but given the Top IT priorities identified in the EDUCAUSE report, it is essential that the CIO is actively engaged in the development and maintenance of Strategic Cost Management Models.

Calculating cost requires more than just financial data.

If an organization does not have our cost model, a typical process to calculate the cost of an academic program would be to start with the finance data, then get data from timetabling to find out who teaches what, then get data from HR/Payroll system to find out how much they were paid. Revenue could easily be against one big central account and working out how to distribute that to the various courses and programs is quite a manual exercise, particularly if you want to match different types of students (In-State, Out-of-State, Internation for the US or Domestic, Commonwealth Supported, International in Australia) to the various programs they are enrolled in.

These steps require significant data processing, typically using spreadsheets, but they only take into account DIRECT costs. If an organization wants to include all institutional overhead, the amount of manual effort required will increase exponentially. Alternatively, they could use simplified methods of allocating overhead, such as using Student Credit Hours (in the US) or EFTSL (in Australia) as a simple proxy. However, this approach has a problem. It can easily overestimate the costs of large classes and underestimate the costs of smaller classes, which can lead to poor management decisions.

A more effective strategy would involve implementing a centralized, systematized, and standardized method for calculating costs across the entire organization. This system would require significant amounts of data from various systems-of-record that must be interconnected in some fashion. These tasks fall squarely within the expertise of the CIO’s department.


This new approach goes beyond a traditional data warehouse, which often became a data basement, a repository for data that was rarely utilized. One of the main limitations of data warehouses was the lack of a clear business need, but this innovative approach tackles a crucial and pressing business need: strategic cost management.

Problems with existing Higher Ed datasets

After over two decades of experience working with large and complex data sets to support strategic management across various industries, including Military, Federal Government, Insurance, Gaming, Oil/Gas, Telco, and Higher Education in Australia, US, UK, Canada, and Mexico, we have identified three significant problems with enterprise data sets, particularly in Higher Education.

  • Missing Data
  • Incorrect Data
  • Non-Matching Fields

Missing Data

This is a common occurrence, particularly when dealing with specific datasets, such as timetabling in higher education. If a data system is solely utilized for one specific purpose, the users of the system typically devise a workaround for the missing data. Regrettably, if this data is required for other purposes, such as strategic cost management, it can result in errors and analysis delays.

Incorrect Data

A comparable scenario to missing data is when data is incorrect, but it can be more challenging for analysts to identify erroneous data compared to missing data. The risk of erroneous data is higher because it may result in incorrect analysis that could go unnoticed.

Non-Matching Fields

Many corporate systems of record are developed and managed independently, resulting in data fields that are unique to each system and not shared with others. As noted in the Educause 2023 Top 10 IT Issues report, “Data is often siloed, but the questions leaders need data to inform transcend the siloes.” This can present challenges when attempting to consolidate data from multiple systems, as creating lookup tables to connect disparate data fields requires significant manual effort. Additionally, any changes to these systems would require updating all associated lookup tables, which could lead to errors or even incorrect analysis if not properly maintained.

Fixing Data Issues

To address the issues of missing and incorrect data, as well as the lack of common data fields across systems, it is crucial to address them at the source system. Missing data should be identified and the source system owners need to be notified to ensure the missing data is entered. Similarly, incorrect data should be identified, reported to the source system owners, and corrected.

Creating common data fields across all systems of record is a complex process that involves multiple stakeholders, including representatives from source systems, IT, and executive management. They must work together to identify common fields that can be applied to each system and managed centrally.

The development of a Strategic Cost Management system, provides an excellent feedback mechanism for the source system owners to identify specific data issues that need to be rectified. This helps to prevent incorrect strategic and operational analysis and ensures the accuracy of data used for decision-making.

DATA is a Corporate Asset

Ensuring high-quality data is essential for supporting strategic and operational analysis in Higher Education institutions, and it should be regarded as a valuable corporate asset. However, many organizations fail to treat it as such. For instance, retail stores conduct regular audits of their inventory, but when was the last time a data inventory was conducted? Typically, a data audit is only carried out when there is a data-related crisis. One possible explanation for the disparity in treatment between data and other corporate assets could be attributed to the unique characteristics that define data:

  • Data is easily created
  • Data is easily duplicated
  • Data is easily destroyed
  • Data is easily altered
  • Data is easily transported and transferred
  • Data is easily misunderstood
  • Data us easily stolen
  • Data can be combined with other data
  • Data can lose value with age
  • Data is difficult to preserve

Due to these unique characteristics, data requires a distinct approach when it comes to management, unlike other conventional assets such as property, equipment, accounts receivable, or fleet. Additionally, when physical equipment malfunctions, it is typically apparent as there may be visible signs such as smoke or strange noises, whereas in the case of corrupted data, the issue may not be as noticeable.

To address this problem CAM-I created the Intelligent Data Quality Management (IDQM) group with members from Boeing, Bank of America, Grant Thornton, USDA, USAAC and Definitive Logic, together they produced the Data Life Cycle Model and you can download the paper here.


Over our two decades of experience in constructing large-scale cost models, we have found that the most significant factor impacting the amount of effort needed is the quality and connectivity of data. If we have to manually create lookup tables or locate missing data, it significantly increases the manual workload required for the build process. This was one of the primary reasons why many Activity-Based Cost models developed in the 1990s were discontinued, as they were too expensive and laborious to maintain.

As the custodians of key systems of records and their data, the CIO can play a vital role in developing strategic cost modeling solutions to address specific business and academic requirements. These solutions can provide quality data necessary for strategic cost management in Higher Education, benefiting both the CFO and the Provost/CAO.

To quote the Educause 2023 Top IT Issues “IT leaders must become more invested in the business and academic matters of the institution. Their value in strategy and planning will be their unique mastery of what’s possible and emergent with technology and data, where the risks and limitations lie, and which of these can be overcome—combined with a deep understanding of the institutional missions, culture, and people.”