Data Management

The CIO position was defined around Information domain, but in practice the CIO became responsible for IT systems. The new attempt to get to the Information domain was introduced as a CDO position – Data focused. In reality, it all is together – data and IT systems, as well as cloud platforms.

The President’s Management Agenda (PMA) defined the CAP Goal 2 Leveraging Data as a Strategic Asset [1] as leverage data to grow the economy, increase the effectiveness of the Federal Government, facilitate oversight, and promote transparency. This goal is supported by the Federal Data Strategy [2], including Practice 6 Convey Insights from Data: Use a range of communication tools and techniques to effectively present insights from data to a broad set of audiences. 

EA role is to develop a strategic plan, a target state, and a roadmap of IT projects. The target state should be cost effective, and reuse existing IT systems and cloud platforms, as well as consolidate multiple duplications.

There are many components in the Enterprise Data Management architecture.

Figure 1. BI System Architecture

First, EA needs to define the requirements for the executive dashboard, the KPIs. What kind of questions and answers stakeholders are looking for?

Based on that expected outcome and output, EA determines what are the data needs and BI capabilities needs. There are three temporal dimensions: the past (retrospective), the present (operational) and the future (predictive).

Second, EA defines data standards for the whole enterprise to follow. Those data standards should support the defined KPIs. For example, if the executive dashboard has a geographical aspect, then data standards should include geospatial data standards.

The biggest challenge is to establish a data stewardship culture. Every System Owner should have a training in data management, data modeling, and data analytics. They need to understand how data can help to solve the business problems, and why it is important to follow the data standards and data management plan for own IT system in order to achieve the enterprise data management goals.

When the data management culture is followed, then IT systems and databases need to be refactored to follow the data standards. In agile manner, it takes several increments and several releases in order to avoid breaking the dependencies.

When data is standardized, EA needs to establish an Enterprise Data Asset Catalog. Each data asset has to have just one authoritative data source. It takes time to discover different duplicates and consolidate them. EA discovers and documents data lineage in a CRUD matrix.

Every data asset needs to be categorized in terms of Business Objects. EA use bottom-up approach to document the existing standardized Physical Data Model, and then abstract it into Logical and Conceptual models. After that EA, based on the Business Capability Model, refines the data models using a top-down approach. These refined data categories are the final Business Objects that are used to tag the data assets in the Enterprise Data Asset Catalog.

The standardized and Data-model aligned data assets now can be fed into the Enterprise Data Warehouse (EDW) which is a Data Lake. If data is jammed into EDW without standardization or tagging, then it is very hard to make any sense out of it using BI tools. BI tools cannot do magic. They produce results based on the quality of data they have. Data scientists can try to cleanse and transform the data as much as they can, but if the organization does not follow the data standards, and data sources are not known, and data assets are not tagged, then the value of BI tools is very, very low.

As the CIO has its own office, own people, the same way the CDO must have its own people, like data scientists who know not just Python, but also modern data management concepts, including DAMA DMBOK, NIEM, and SOA. Data architects should follow the EA Enterprise Data Management Architecture as the overall vision of the target state, and every System Owner needs to report on the IT system alignment to the enterprise data standards, data sources used, and data assets registration in the Enterprise Data Catalog.

It is a long road, but no organization can afford to avoid it. Business, at the end, is its people, knowledge (data), and processes (IT systems).


1. The President’s Management Agenda 2018, https://www.performance.gov/CAP/leveragingdata/

2. OMB Federal Data Strategy 2020, https://strategy.data.gov/assets/docs/2020-federal-data-strategy-action-plan.pdf

One thought on “Data Management

  1. Pingback: Technology Management – Iconologist Psyche

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