Data Driven Portfolio Management Requires Effective Data Governance
As reported by Jason Miller in the August 25 Federal News Network article Pentagon putting more data behind IT modernization decisions the Pentagon’s move of IT resources and data to the cloud is driving increased attention on how to link IT portfolio management to mission objectives. Miller quotes Danielle Metz, DoD Deputy Chief Information Officer for Information Enterprise as follows:
“We are developing metrics and targets to track the improvement of data sources that are critical as supporting this framework. These metrics will address the data quality of each authoritative data source, the optimization of the data sources themselves and the interoperability between data sources and other duty platforms. We will then integrate the IT portfolio management process with the DoD budget cycle to ensure resources reflect IT portfolio management decisions, and that resulting initiatives have the funding they require.”
The need for metrics based linking of IT resources to business processes and mission objectives is nothing new. This has always been a challenge in large and complex organizations such as the DoD. While a variety of tools and methodologies have evolved over the years to support the generation of such metrics, an ongoing challenge is the efficient generation and updating of data that reflect the linkage between IT resources and desired outcomes.
Much of the this challenge results from the continued siloing of how different organizations and their IT resources are managed. Differences in how organizations produce, manage, and consume data become apparent whenever organizational structures are changed, IT resources are consolidated or replaced, or applications and data are moved to the cloud.
Creating a unified approach to metrics that overcomes how different IT related systems and processes are managed as a “portfolio” requires careful attention to data governance which is defined in How Much Data Governance Is Enough Data Governance? as follows:
Data Governance is the orchestration and management of all the systems, processes, and technologies that contribute to and maintain the quality, reliability, and usability of an organization's data and metadata.
Developing and implementing interoperability related data and metadata standards is only a partial solution. Different organizational domains (e.g., management, cybersecurity, human resources, finance, training, communication, etc.) must interact in a large or complex organization. These different domains may implement data standards and definitions very differently given the different language used by the various domains to communicate.
Data governance associated with IT portfolio management must address three application areas:
Using data and metadata to help understand what was (e.g., by providing historical context for data provided to management)
Using data and metadata to help understand what is (e.g., using available data from inside and outside the organization to gain a better understanding on what’s happening now)
Using data and metadata to help understand what will happen (e.g., using predictive models to help compare and contrast different options and scenarios)
Addressing each of these three application areas is necessarily complex. Data governance should be comprehensive by addressing machine to machine, human to machine, and machine to human data transfer.
Because of this complexity, data governance to support IT portfolio management should start small, not by focusing on “low hanging fruit” of simple or low priority problems but by addressing high value but well-defined problems in order to generate reliable and useful performance metrics and to provide a foundation for expanding data governance to additional problem areas.
Copyright (c) 2021 by Dennis D. McDonald
