Data Asset Management

John Hamman, Chief Analytics and Insights Officer

Nadine Porter, Deputy Chief Analytics and Insights Officer

Donna Kinerney, Collegewide Dean of Adult ESOL and Literacy Programs (Workforce Development & Continuing Education)
Richard (Rick) Penn, Professor/Mathematics Rockville Campus
Michael Mills, Vice President of E-Learning, Innovation, and Teaching Excellence

Sherwin Collette, Senior Vice President for Administrative and Fiscal Services
 Elizabeth (Liz) Greaney, Chief Business/Financial Strategy Officer

Rose Garvin Aquilino,   Associate Senior Vice President for Advancement and Community Engagement
Raymond (Ray) Gilmer, Associate Senior Vice President for Advancement and Community Engagement

Clevette Ridguard, Governance and Presidential Projects Program Manager

Judith (Judy) Taylor, Director of Student Financial Aid
Marcus Peanort, Associate Dean of Student Access and Germantown Student Affairs
Carmen Poston-Travis, Director of Student Affairs and Initiatives

Data assets are important to driving business value.  Effective data asset management entails capturing all the College’s institutional data; leveraging information systems, software, and hardware; and using data assets, analytics, and insights to further sustain business growth.  Montgomery College prepared a Data Asset Management Plan (PDF, ) (DAMP) during fiscal year 2021 to formally document the institution’s commitment to treating data as an asset.  The DAMP identifies specific actions the College will take over the next several fiscal years to strengthen our ability to effectively manage the full institutional data lifecycle needs of MC (i.e., how data are collected, stored, secured, disseminated, etc.).  This includes, but is not limited to, developing data governance, creating and implementing policies, procedures, and processes around appropriate access and security of institutional data, and more.  

This plan and program will help build an environment in which decisions are informed by relevant and accurate metrics and information.  Also, the plan will better define how MC collects, analyzes, stores, and secures data.  It will ensure appropriate role-based access to data across the College as well as consistency with role-based access to institutional data.  Many of the actions highlighted in the DAMP will help to improve data literacy through training and education as well as continuously improve related business processes.  Adhering to the DAMP (PDF, ) will also minimize risk of data misuse or mismanagement.

MC recently formed a Data Asset Management Policy Development Group that is charged with evaluating, developing, and making recommendations for new policies and procedures and revisions to existing policies and procedures as needed.  A data asset management chapter of the Official Policies and Procedures is currently being developed.  

You can find policy and procedure #66002 – Confidential Data Management and Security (PDF, ) – on the College’s official Policy and Procedure web page.  The Data Asset Management Policy Development Group is currently working on a new policy and procedure - #66005 Data Asset Management and Security - which is currently posted for public comment.

The DAMP (PDF, ) incorporates a number of best practices into the plan.  To date, Montgomery College has:   

• Developed and identified the necessary components of the data asset management ecosystem to enhance the success of its data asset management program.
• Facilitated (and will continue to facilitate) productive discussions with the MC community members about data asset management.  This is inclusive of a comprehensive assessment by an independent 3rd party – iData – who collected information from many voices across MC.  This, in part, led to the completion of the DAMP (PDF, ) in Summer 2021. The MC community has provided constructive feedback and shared their opinions about data governance, data management, and data informed decision making at the College as recently as October/November 2021 when focus groups (PDF, ) were facilitated (another way to learn from key stakeholders).
• Identified and communicated key metrics (see page 22 of the DAMP (PDF, ) ).
• Established a Data Asset Management Policy Development Group to work on policies and procedures as mentioned above.
• Appointed a Data Trustees Council who will own, provide oversight to data stewards, and be accountable for the data asset management program.
  • However, it is important to note that anyone at the College that interacts with data is responsible for the proper access, usage, storage, archiving and disposal of data.
• Adopted guiding principles from the Data Governance Institute (see page 8 of the DAMP (PDF, ) ).
• Established a decision making framework to help create a culture of evidence and inquiry (see page 21 of the DAMP (PDF, ) ).

Immediate next steps will entail:

• Paying keen attention to data security and implementing a data security protocol that clearly defines access.
• Developing robust training and education programs in partnership with Analytics and Insights (A&I), Workforce Development and Continuing Education (WDCE), and E-Learning, Innovation, and Teaching Excellence (ELITE) - inclusive of badging/micro-credentialing - to ensure necessary data literacy.
• Evaluating appropriate technology solutions that will effectively, efficiently, and consistently manage data as an asset (i.e., prioritizing data warehouse acquisition).

Improving data skills and enhancing data literacy are fundamental tenets of the DAMP (PDF, ) .  Training on basic data fundamentals and data privacy are the first of many offerings being developed through collaboration and partnership between Analytics and Insights (A&I), Workforce Development and Continuing Education (WDCE), and E-Learning, Innovation, and Teaching Excellence (ELITE).  A communication is forthcoming for the launch of this important online training effort.  Thereafter, we will continue to build and design comprehensive training offerings.  

Additional suggestions to improve data skills are to take online classes (i.e., Courseranew window), read articles (i.e., Journal of Data, Information and Managementnew window), and attend conferences, seminars, and workshops on topics of interest. We are currently embedding articles, studies, and other relevant information on the Data Asset Management webpage as well. 

Institutional data is owned by Montgomery College.  MC Divisions, Departments, Data Trustees, and Data Stewards are custodians of the data it generates and uses to conduct College business.  Generally, Data Trustees are administrators who ensure that appropriate business processes are in place to keep institutional data secure, maximize data accuracy, and ensure that responsible staff are trained to maintain data quality.  Data Stewards ensure that data is captured accurately, resolve accuracy and quality issues, and work directly with stakeholders to assess the need for and storage of data.

The more relevant data MC collects and analyzes, the better insights the College will have to make informed decisions and achieve desired outcomes.

While enhanced access to data is an important goal, the College must ensure privacy and regulatory compliance.  Therefore, everyone cannot have access to all institutional data.  Data will be accessible to those with a legitimate business need for the data. We will carefully look at role-based access to data elements across the institution in the near future.

Please familiarize yourself with MC policies, procedures and guidelines (see Policy and Procedure #66002 – Confidential Data Management and Security (PDF, ) – on the College’s official Policy and Procedure web page)new window.  For legal and ethical reasons, it is critical for MC to ensure proper role-based access to data and to protect institutional data against unwarranted disclosure and mismanagement.  Protect the information that you work with.  If you suspect confidential or sensitive information have been compromised, notify your supervisor as soon as possible.

Yes, the decision making framework is on page 21 of the DAMP (PDF, ) .