Knowing An Organization’s Data Management Maturity Promotes Effective Open Data Program Planning

 By Dennis D. McDonald, Ph.D

Hearing about an open data program like the EPA's makes you realize how the many interrelated program components can impact open data program planning in a complex organization. Inventorying the assets of such an organization makes you aware of this very quickly, especially when you step back and evaluate where each data element in the inventory came from, where each data element is going, and how it's going to get there.

Each data element in a data asset inventory has its own "lifecycle" that when properly managed provides a framework for tracking and optimizing how data are used from creation through obsolescence.

In most cases data are managed in groups. Data groupings can be defined in a myriad of ways. Some approaches reflect how data are physically organized. Other groupings involve development different categories of metadata that have been standardized for use across different data sets. An example of the latter is shown in India’s under the heading Controlled Vocabulary Services. This lists, for example, both XML and JSON tagging for categories such as the languages spoken in India.

The main point of this is that different organizations possess different levels of “data management maturity," or DMM as it is referred to by some. Knowing what level of maturity an organization possesses with regard to data management is something open data program planners should be aware of for one simple reason: effective process change management.

Open data programs, even if they require little in the way of technology change, require some changes to an organization's business processes both inside and outside the IT department.

Anticipating what these process changes will be, and how the organization is already structured for managing such changes, will be a prerequisite for effective open data program planning. Knowing this maturity level will help the planner understand how much can -- and cannot -- be taken for granted when developing an open data program development plan and implementation schedule.

Related reading:

Challenges of Public-Private Interfaces in Open Data and Big Data Partnerships

Compendium: My Guest Posts for the BaleFire Global Open Data Blog

Don’t Let Tools Drive Enterprise Data Strategy

Justifying Collaboration in Complex Programs such as Federal Acquisitions

Mitigation of Sequestration Impacts on Project Management

On Defining the "Maturity" of Open Data Programs

Planning for Big Data: Lessons Learned from Large Energy Utility Projects

Promoting Technology Enabled Collaboration in Complex R&D Environments


Popular posts from this blog

Podcast: Open Data Discussions with Anthony Fung

Open Data Licensing