Demystifying Data Governance for Higher Education Institutions

There’s a lot of confusion in higher education about what data governance is. Is it security, is it policy, is it data quality? In this blog, we demystify data governance for higher education institutions, explore the signs that your institution may have a data governance problem, and examine the key pillars of data governance.

The difference between data quality and data governance

First, let’s dispel a common myth; that data quality and data governance are one and the same:

Data quality is a measure of the accuracy, completeness, timeliness, and reliability of your data. Decision-makers rely on quality data to support their decision-making process. Whereas data governance is a collection of practices and processes that ensures the use of high quality and secure data across the organization.

Consider this analogy: The five-year-long Flint, Michigan, water quality crisis began when the city’s water source was switched resulting in high levels of lead. To address the crisis, authorities trucked in bottled water. Bottled water is pure and safe to drink. But it didn’t solve the crisis. What was required was a concerted effort to change the water source, the underground pipes, and the filtration plants. It was an all-encompassing process of getting the right people in the right place and equipping them with the right tools to fix the water system.

In this analogy, the water bottles represent data quality, but the overall years-long effort to fix the water system equates to data governance.

Signs that your institution has a data governance problem

Data governance sounds great in practice and is very pertinent, but what are the indicators of a data governance problem? In higher education institutions the most frequent indicator of the need for data governance is this simple statement, “Your numbers don’t match my numbers.” And upon initial investigation, the most common explanations are “We keep track of this data on our own; we don’t use the institutional data resources” or “Our interpretation of the data is different than yours.”

The disparity created by these “shadow databases” and siloed business units can impact strategic planning, but it also leads to transparency issues. If business users and analysts are spending more time auditing the data for correctness than using the data to inform decision-making, then your institution has a data governance problem.

The key pillars of data governance

If you’ve been tasked with creating a data governance strategy for your higher education institution, it’s important to understand the key objectives and pillars of data governance: data responsibility, data quality, data privacy, and data security. Let’s break them down:

  • Data responsibility: To be successful your data governance strategy must first identify the individuals responsible for data across the organization. This community includes any number of roles including the IT teams who care for the data, the data creators in faculty, and internal and external end-users. This data governance council is key to understanding how data is being used and who has decision-making power.
  • Data quality: Data accuracy, completeness, and consistency is key to any governance strategy and is enforced through measurements and standards. Standards also drive consensus and understanding allowing stakeholders to easily identify and fix data errors and inconsistencies.
  • Data privacy: This is the area that requires the most time and investment. Higher education institutions collect and store vast amounts of personally identifiable information. This data must be handled in accordance with data access and privacy regulations such as FERPA and GDPR. Unfortunately, there are many nuances in higher education about who has access to what, the rationale behind those rights, and how data is handled to ensure it is kept private.
  • Data security: A key component of data governance, data security practices govern how the systems that house institutional data are secured as well as backup and disaster recovery protocols. User devices must also be secured, and staff and students educated on how to be “cyber aware” as they interact with institutional data.

As you can see there are many sub-components to data governance, and it can get especially complicated in higher education because governance is shared between the faculty and administration.

Our next blog gets into the actionable steps needed for a successful data governance strategy and we share tips for getting started in an iterative way.


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11 Feb 2021

Demystifying Data Governance for Higher Education Institutions

There’s a lot of confusion in higher education about what data governance is. Is it security, is it policy, is it data quality? In this blog, we demystify data governance for higher education institutions, explore the signs that your institution may have a data governance problem, and examine the key pillars of data governance.

The difference between data quality and data governance

First, let’s dispel a common myth; that data quality and data governance are one and the same:

Data quality is a measure of the accuracy, completeness, timeliness, and reliability of your data. Decision-makers rely on quality data to support their decision-making process. Whereas data governance is a collection of practices and processes that ensures the use of high quality and secure data across the organization.

Consider this analogy: The five-year-long Flint, Michigan, water quality crisis began when the city’s water source was switched resulting in high levels of lead. To address the crisis, authorities trucked in bottled water. Bottled water is pure and safe to drink. But it didn’t solve the crisis. What was required was a concerted effort to change the water source, the underground pipes, and the filtration plants. It was an all-encompassing process of getting the right people in the right place and equipping them with the right tools to fix the water system.

In this analogy, the water bottles represent data quality, but the overall years-long effort to fix the water system equates to data governance.

Signs that your institution has a data governance problem

Data governance sounds great in practice and is very pertinent, but what are the indicators of a data governance problem? In higher education institutions the most frequent indicator of the need for data governance is this simple statement, “Your numbers don’t match my numbers.” And upon initial investigation, the most common explanations are “We keep track of this data on our own; we don’t use the institutional data resources” or “Our interpretation of the data is different than yours.”

The disparity created by these “shadow databases” and siloed business units can impact strategic planning, but it also leads to transparency issues. If business users and analysts are spending more time auditing the data for correctness than using the data to inform decision-making, then your institution has a data governance problem.

The key pillars of data governance

If you’ve been tasked with creating a data governance strategy for your higher education institution, it’s important to understand the key objectives and pillars of data governance: data responsibility, data quality, data privacy, and data security. Let’s break them down:

  • Data responsibility: To be successful your data governance strategy must first identify the individuals responsible for data across the organization. This community includes any number of roles including the IT teams who care for the data, the data creators in faculty, and internal and external end-users. This data governance council is key to understanding how data is being used and who has decision-making power.
  • Data quality: Data accuracy, completeness, and consistency is key to any governance strategy and is enforced through measurements and standards. Standards also drive consensus and understanding allowing stakeholders to easily identify and fix data errors and inconsistencies.
  • Data privacy: This is the area that requires the most time and investment. Higher education institutions collect and store vast amounts of personally identifiable information. This data must be handled in accordance with data access and privacy regulations such as FERPA and GDPR. Unfortunately, there are many nuances in higher education about who has access to what, the rationale behind those rights, and how data is handled to ensure it is kept private.
  • Data security: A key component of data governance, data security practices govern how the systems that house institutional data are secured as well as backup and disaster recovery protocols. User devices must also be secured, and staff and students educated on how to be “cyber aware” as they interact with institutional data.

As you can see there are many sub-components to data governance, and it can get especially complicated in higher education because governance is shared between the faculty and administration.

Our next blog gets into the actionable steps needed for a successful data governance strategy and we share tips for getting started in an iterative way.


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