Higher Education Needs Data Governance To Support Better Decision-making Informed by Data

Introduction

The current global higher education environment faces tremendous uncertainty due to COVID-19, so it is imperative that institutional decision-making be informed by data.  The requirement for data-informed decisions extends across the campus, including academic advising for student success; donor selection for advancement fund-raising campaign; energy conservation and facilities utilization programming; budget allocation and expenditure control.  However, each of these examples contains a major assumption—that the underlying data are trustworthy and represent the highest quality data available.

Data Quality vs Data Governance

Although data quality is the desired outcome, an organized institutional initiative is required to coordinate the various efforts necessary to deliver high quality, trustworthy data.  Such an initiative is generally referred to as data governance.  The Data Management Association (dama.org) defines data governance as “The exercise of authority, control, and shared decision making (planning, monitoring and enforcement) over the management of data assets.”  Any data governance effort is about having the right people and policies in place to support the quality and control of institutional data.  In the context of higher education, the data governance initiative must be a component of the overall institutional governance to ensure executive endorsement and institutional buy-in.

Data Governance Outcomes

Popular literature about data governance identifies many different models and includes a wide variety of activities that contribute to a data governance initiative.  One of the simplest, but most effective, models of data governance includes these four fundamental components:

1. Data responsibility

Ensures clear understanding of who is responsible for what data

2. Data integrity

Ensures data is reliable and used consistently across the institution

3. Data availability

Ensures only the right people are accessing the right data

4. Data protection

Ensures data is secure and individual privacy is protected

People, Process, Technology

The data governance task defined in a previous paragraph is a campus-wide initiative that extends beyond the purview of the typical IT department.  Achievement of the desired data governance outcomes depends on a model that involves people, process, and technology – three main ingredients that need to be included in successful digital transformation.  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 create or clarify data processes that ensure the desired data quality.  Finally, such data processes should become embedded in the appropriate information systems to enforce data quality standards.

The data control and shared decision-making aspects of a data governance initiative can be pictured this way:

People

Identifies data stewards and stakeholders

Process

Sets standards of data behavior and definitions

Technology

Embeds processes into workflows and systems

Tackling the Problem

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 or own; we don’t use the institutional data resources” or “Our interpretation of the data is different than yours.”

Because similar or identical data elements are found in various information systems across the campus, achieving the desired data quality is often considered to be practically impossible.  However, the people, process, and technology model shown above provides a simple approach, outlining the typical steps required to tackle the data governance challenge.

People –
Identify a representative group of campus stakeholders who understand institutional data from various perspectives.  Functional users such as staff members in the Registrar or Financial Aid Office; technical users like IT systems or business analysts; “super-users” in various offices; and content specialists who work in Institutional Research.  Charter this group within the institutional governance framework to propose relevant data responsibilities, data standards, and institutional data definitions. This group should also empower subject matter experts from across the campus to streamline the completion of individual tasks.

Process –
The major product from this institutional governance team should include recommendations for an institutional data dictionary that will codify the meanings of data elements to ensure consistent use and interpretation.  The team should also develop policy guidance for the institution covering issues such as data standards, appropriate error correction procedures, and data handling processes that protect individual privacy and ensure data security.  Such policies form the bases for operational processes and should be submitted to the institutional governance system for approval.

Technology –
Once the supporting elements such as the data dictionary, data standards, and operational processes are in place, the enterprise technology infrastructure will need to be updated to address concerns such as enterprise data architecture/data integration, data access management (e.g., multi-factor authentication required for sensitive data), and regular monitoring of role-based access to insure that the right people have access to the right data.  At the end-user level, technology improvements should include the deployment of exception processing and error reports, data de-duplication procedures, elimination of shadow databases and hidden data, and deployment of training for cyber-security and compliance.

Summary

If you are facing the challenges of inadequate data governance at your institution, there are two important points to remember:

  1. Although the desire for data quality to support good decision-making is a laudable and important goal, data quality projects alone are insufficient. Data quality initiatives can be successful only as part of an all-compassing data governance framework based on the fundamental paradigm that includes people, process, and technology.
  2. Beyond the desire to use the highest quality data to inform decision-making, data governance activities benefit any other enterprise initiatives that involve IT, including: deployment of new reporting or analytics solutions; replacement of major software systems, and the resulting need to re-evaluate enterprise architecture; and campus-wide initiatives for digital transformation.

For more information:
If you have any questions or would like to discuss our data governance services, please contact us at info@higher.digital.


Share This Post:
15 Apr 2020

Higher Education Needs Data Governance To Support Better Decision-making Informed by Data

Introduction

The current global higher education environment faces tremendous uncertainty due to COVID-19, so it is imperative that institutional decision-making be informed by data.  The requirement for data-informed decisions extends across the campus, including academic advising for student success; donor selection for advancement fund-raising campaign; energy conservation and facilities utilization programming; budget allocation and expenditure control.  However, each of these examples contains a major assumption—that the underlying data are trustworthy and represent the highest quality data available.

Data Quality vs Data Governance

Although data quality is the desired outcome, an organized institutional initiative is required to coordinate the various efforts necessary to deliver high quality, trustworthy data.  Such an initiative is generally referred to as data governance.  The Data Management Association (dama.org) defines data governance as “The exercise of authority, control, and shared decision making (planning, monitoring and enforcement) over the management of data assets.”  Any data governance effort is about having the right people and policies in place to support the quality and control of institutional data.  In the context of higher education, the data governance initiative must be a component of the overall institutional governance to ensure executive endorsement and institutional buy-in.

Data Governance Outcomes

Popular literature about data governance identifies many different models and includes a wide variety of activities that contribute to a data governance initiative.  One of the simplest, but most effective, models of data governance includes these four fundamental components:

1. Data responsibility

Ensures clear understanding of who is responsible for what data

2. Data integrity

Ensures data is reliable and used consistently across the institution

3. Data availability

Ensures only the right people are accessing the right data

4. Data protection

Ensures data is secure and individual privacy is protected

People, Process, Technology

The data governance task defined in a previous paragraph is a campus-wide initiative that extends beyond the purview of the typical IT department.  Achievement of the desired data governance outcomes depends on a model that involves people, process, and technology – three main ingredients that need to be included in successful digital transformation.  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 create or clarify data processes that ensure the desired data quality.  Finally, such data processes should become embedded in the appropriate information systems to enforce data quality standards.

The data control and shared decision-making aspects of a data governance initiative can be pictured this way:

People

Identifies data stewards and stakeholders

Process

Sets standards of data behavior and definitions

Technology

Embeds processes into workflows and systems

Tackling the Problem

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 or own; we don’t use the institutional data resources” or “Our interpretation of the data is different than yours.”

Because similar or identical data elements are found in various information systems across the campus, achieving the desired data quality is often considered to be practically impossible.  However, the people, process, and technology model shown above provides a simple approach, outlining the typical steps required to tackle the data governance challenge.

People –
Identify a representative group of campus stakeholders who understand institutional data from various perspectives.  Functional users such as staff members in the Registrar or Financial Aid Office; technical users like IT systems or business analysts; “super-users” in various offices; and content specialists who work in Institutional Research.  Charter this group within the institutional governance framework to propose relevant data responsibilities, data standards, and institutional data definitions. This group should also empower subject matter experts from across the campus to streamline the completion of individual tasks.

Process –
The major product from this institutional governance team should include recommendations for an institutional data dictionary that will codify the meanings of data elements to ensure consistent use and interpretation.  The team should also develop policy guidance for the institution covering issues such as data standards, appropriate error correction procedures, and data handling processes that protect individual privacy and ensure data security.  Such policies form the bases for operational processes and should be submitted to the institutional governance system for approval.

Technology –
Once the supporting elements such as the data dictionary, data standards, and operational processes are in place, the enterprise technology infrastructure will need to be updated to address concerns such as enterprise data architecture/data integration, data access management (e.g., multi-factor authentication required for sensitive data), and regular monitoring of role-based access to insure that the right people have access to the right data.  At the end-user level, technology improvements should include the deployment of exception processing and error reports, data de-duplication procedures, elimination of shadow databases and hidden data, and deployment of training for cyber-security and compliance.

Summary

If you are facing the challenges of inadequate data governance at your institution, there are two important points to remember:

  1. Although the desire for data quality to support good decision-making is a laudable and important goal, data quality projects alone are insufficient. Data quality initiatives can be successful only as part of an all-compassing data governance framework based on the fundamental paradigm that includes people, process, and technology.
  2. Beyond the desire to use the highest quality data to inform decision-making, data governance activities benefit any other enterprise initiatives that involve IT, including: deployment of new reporting or analytics solutions; replacement of major software systems, and the resulting need to re-evaluate enterprise architecture; and campus-wide initiatives for digital transformation.

For more information:
If you have any questions or would like to discuss our data governance services, please contact us at info@higher.digital.


Share This Post: