How Higher Education Institutions Can Take Control of Their Data

There is a lot of confusion in higher education about the term “data governance.” Is it the same as data quality or data management? We recently demystified data governance and explained the key pillars of any data governance initiative including data responsibility, data quality, data privacy, and data security.

Here, we continue this topic by outlining actionable steps needed for a successful data governance strategy and tips for getting started.

Actioning a successful data governance strategy

Data-informed decisions are essential to moving institutions forward and helping them achieve their goals – but only if the underlying data is trustworthy and represents the highest quality data. This can be especially hard to achieve in higher education because governance is shared between the faculty and administration. It requires an organized institutional initiative to coordinate the various efforts needed to deliver high quality, complete, and trustworthy data. This initiative is known as data governance.

It’s a complex undertaking, but while the focus should always be on the big picture; a successful data governance strategy, hinges on early wins, iteration, and long-term sustainability.

Whether you’re building a new reporting system or ERP, there are five data governance components that are critical to success.

  1. People: A data governance initiative is about having the right people in place to support it. The people who participate should represent stakeholders across the campus and should bring technical, functional, policy, and data content expertise to the table. These participants will be called on to collaborate with business units to define and clarify standardize business terms and create processes that ensure the desired data quality.
  2. Policies: Data governance policies provide guidance in areas such as data standards, error correction procedures, and data handling processes that protect individual privacy and ensure data security. Policy enforcement is typically carried out by data stewards.
  3. Processes and procedures: While policy dictates what must be done, process and procedures are about how things are done. This includes how information moves through a system, how errors within data are corrected, document management procedures, as well as data retention and disposal practices.
  4. Technology: A data governance framework must be supported by the right technologies. One such technology is an authoritative source of data; a single, trustworthy system of record or “mothership” – such as a Student Information System (SIS) – that seamlessly integrates with sub-systems across the organization. With this enterprise architecture, your institution can expose quality data to those who need it while eliminating the systemic issue of data siloes and shadow databases. Automation is also key. For example, when technology is leveraged during the data collection phase, time-consuming manual tasks can be eliminated. Instead of one person filling in a paper form and another entering it into an application, technology and standardized input formats enable users to become data creators – saving time and reducing errors.
    However, it’s important to understand that any technology improvements will fail – unless the organization has the right people, policies, and processes in place first. There must be cultural alignment around any new deployments and an understanding of why the organization is doing this. Which leads to our next point.
  5. Organizational development: If people are to treat data as an organizational asset and embrace technological change required to achieve data governance, they must be empowered to do so. This requires a proactive approach to change and project management. Communication is also key. Business users and analysts must understand the business value and outcomes of a data governance strategy. In addition, the institution must promote and train users on data governance policies and data standards so that they understand how to enter data correctly.

Getting started with data governance

Higher education leaders like CEOs and directors of institutional research, understand why data governance is important, but they can’t get traction by themselves – they need the entire institution to be involved. This requires putting data governance on the institution’s agenda and obtaining a commitment from the executive level.

Creating a data governance framework can seem overwhelming. But you don’t have to boil the entire ocean at once. Once executive buy-in is obtained, focus initially on the most frequent presenting symptom of a data governance issue – data quality. It takes time, effort, and cooperation, but with this foundation in place you can being to input that data into the SIS, ancillary systems, and integrations. Meanwhile, IT teams can work concurrently on the data security priorities such as system hardening and cybersecurity awareness training.

And don’t think you need to reinvent the wheel. External resources – like the Integrated Postsecondary Education Data System (IPEDS) common data set or system-wide data dictionaries – can be brought to bear. Talk to your peers, they may have standardized data definitions that you can leverage. You’ll be surprised how much of the work is already done.

This is not something that will happen overnight. It takes time to move the mountains involved, but through a well-correlated effort you can have an iterative impact on your institution's data governance priorities.

Want to learn more about Higher Digital and our strategic consulting services for higher education institutions? Contact us – we’d love to hear from you.


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18 Feb 2021

How Higher Education Institutions Can Take Control of Their Data

There is a lot of confusion in higher education about the term “data governance.” Is it the same as data quality or data management? We recently demystified data governance and explained the key pillars of any data governance initiative including data responsibility, data quality, data privacy, and data security.

Here, we continue this topic by outlining actionable steps needed for a successful data governance strategy and tips for getting started.

Actioning a successful data governance strategy

Data-informed decisions are essential to moving institutions forward and helping them achieve their goals – but only if the underlying data is trustworthy and represents the highest quality data. This can be especially hard to achieve in higher education because governance is shared between the faculty and administration. It requires an organized institutional initiative to coordinate the various efforts needed to deliver high quality, complete, and trustworthy data. This initiative is known as data governance.

It’s a complex undertaking, but while the focus should always be on the big picture; a successful data governance strategy, hinges on early wins, iteration, and long-term sustainability.

Whether you’re building a new reporting system or ERP, there are five data governance components that are critical to success.

  1. People: A data governance initiative is about having the right people in place to support it. The people who participate should represent stakeholders across the campus and should bring technical, functional, policy, and data content expertise to the table. These participants will be called on to collaborate with business units to define and clarify standardize business terms and create processes that ensure the desired data quality.
  2. Policies: Data governance policies provide guidance in areas such as data standards, error correction procedures, and data handling processes that protect individual privacy and ensure data security. Policy enforcement is typically carried out by data stewards.
  3. Processes and procedures: While policy dictates what must be done, process and procedures are about how things are done. This includes how information moves through a system, how errors within data are corrected, document management procedures, as well as data retention and disposal practices.
  4. Technology: A data governance framework must be supported by the right technologies. One such technology is an authoritative source of data; a single, trustworthy system of record or “mothership” – such as a Student Information System (SIS) – that seamlessly integrates with sub-systems across the organization. With this enterprise architecture, your institution can expose quality data to those who need it while eliminating the systemic issue of data siloes and shadow databases. Automation is also key. For example, when technology is leveraged during the data collection phase, time-consuming manual tasks can be eliminated. Instead of one person filling in a paper form and another entering it into an application, technology and standardized input formats enable users to become data creators – saving time and reducing errors.
    However, it’s important to understand that any technology improvements will fail – unless the organization has the right people, policies, and processes in place first. There must be cultural alignment around any new deployments and an understanding of why the organization is doing this. Which leads to our next point.
  5. Organizational development: If people are to treat data as an organizational asset and embrace technological change required to achieve data governance, they must be empowered to do so. This requires a proactive approach to change and project management. Communication is also key. Business users and analysts must understand the business value and outcomes of a data governance strategy. In addition, the institution must promote and train users on data governance policies and data standards so that they understand how to enter data correctly.

Getting started with data governance

Higher education leaders like CEOs and directors of institutional research, understand why data governance is important, but they can’t get traction by themselves – they need the entire institution to be involved. This requires putting data governance on the institution’s agenda and obtaining a commitment from the executive level.

Creating a data governance framework can seem overwhelming. But you don’t have to boil the entire ocean at once. Once executive buy-in is obtained, focus initially on the most frequent presenting symptom of a data governance issue – data quality. It takes time, effort, and cooperation, but with this foundation in place you can being to input that data into the SIS, ancillary systems, and integrations. Meanwhile, IT teams can work concurrently on the data security priorities such as system hardening and cybersecurity awareness training.

And don’t think you need to reinvent the wheel. External resources – like the Integrated Postsecondary Education Data System (IPEDS) common data set or system-wide data dictionaries – can be brought to bear. Talk to your peers, they may have standardized data definitions that you can leverage. You’ll be surprised how much of the work is already done.

This is not something that will happen overnight. It takes time to move the mountains involved, but through a well-correlated effort you can have an iterative impact on your institution’s data governance priorities.

Want to learn more about Higher Digital and our strategic consulting services for higher education institutions? Contact us – we’d love to hear from you.


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