An Introduction to Data Program Management (DPM)

What is Data Program Management (DPM)?

Data Program Management (DPM) is the intelligent application of data management tools, technologies, and processes to improve the usefulness of an organization’s data. DPM helps the organization to:

Improve how data are defined, organized, managed, analyzed, and used.
Document and manage significant technical and semantic data and metadata relationships.
Apply data assets wisely in alignment with the organization’s goals and objectives.

What Are Your Data Program Management Challenges?

Are you seeing an increasing volume of unstructured or external data that's relevant to your business but difficult to analyze? Are data users demanding faster reporting or more features in their analytical and visualization tools? Are people miscommunicating due to variety in how key data elements are defined? Are your current systems slow to update while the results they deliver to management seem increasingly out of date?

If you answer “yes” to any of these questions you may need to step back and re-think your organization’s approach to data management by focusing on data strategy, data architecture, and data governance.

1. Data Strategy

Data Strategy addresses how data programs are planned and executed. Topics include:

Strategic Alignment. Ensuring that data are managed and analyzed in ways that support the organization’s goals and objectives, both now and in the future.
Current and Future State Assessments. Understanding how data are managed and used now -- and how they should be managed and used in the future.
Resource Requirements. Knowing what it costs in people, time, and technology to manage data now -- and how this may need to change in the future.
Project Definition and Prioritization. Defining what data-related work needs to be done and how that work should be sequenced and managed.

2. Data Architecture

Data Architecture addresses how the organization’s current and future data and metadata are defined and organized. Topics include:

Data Inventory. Understanding what data and metadata the organization has and how they are generated, managed, and used.
Data Provenance. Documenting both provenance and responsibility for the organization’s data assets regardless of where data originate or are used.
Data Modeling. Understanding and documenting how data and metadata are organized both physically and logically.
Data Transport. Understanding how data move through the organization and its various system components.

3. Data Governance

Data Governance addresses how data, metadata, and their associated processes are managed. Topics include:

Stakeholder Definition. Documenting who has responsibility and authority for making sure the organization’s data assets are managed appropriately.
Data Governance Council. The group responsible for overseeing the policies and processes by which data-related definitions, relationships, models, and standards are documented and communicated.
Data Stewardship. The process by which data and metadata standards and definitions are communicated throughout the organization.
Metadata Repository. The master collection that documents the organization’s data, definitions, relationships, models, and standards.

Where to Start?

DPM starts with a “deep dive” into the data – what you have, how it’s organized, how it’s currently managed and used, and how these all relate to what your organization needs to accomplish.

You may find that different parts of the organization manage and define data about the same thing quite differently. This variety may have significant -- and potentially negative -- cost and quality implications for your work and the populations you serve.

By focusing attention on your data, a Data Program Management approach can help you define both short term and strategic initiatives that will not only make your organization’s data more useful operationally and analytically but will also improve the overall efficiency of how you manage your data.

If the above interests you, let’s talk!