Showing posts with label Data Reference Model. Show all posts
Showing posts with label Data Reference Model. Show all posts

April 25, 2008

Enterprise Information Architecture

We all know that enterprise architecture is a strategic-level synthesis of business and technology information to drive enhanced decision-making. To develop the EA we must build out the individual perspectives, such as performance, business, information, services, technology, security, and human capital. This blog focuses on one of those, enterprise information architecture.

Enterprise Information Architecture (EIA) is the strategic-level information architecture for the organization.

Note: Information refers to both information (processed data) and data.


The overall goal of EIA is to provide the right information to the right people anytime, anywhere.



The federal mandate in law enforcement is the Intelligence Reform and Prevention Act (IRTPA) of 2004. Further, The Office of the Director of National Intelligence (ODNI) has developed the Information Sharing Environment (ISE) Implementation Plan in 2006 and the Department of Defense created the Net-centric Data Strategy in 2001.

Common Sense:

We need information to perform our mission/business function and processes: we can’t do without it! Moreover, in an information economy, information is power and information is currency.


Developing the enterprise information architecture is an outgrowth of developing the business, data, and system models to understand the business processes, the information required to perform those, and the systems that serve up the information.

According to the Federal Enterprise Architecture, Data Reference Model, there are three parts to developing your information architecture.

  1. Data Descriptions—identify what your information needs are.
  2. Data Context—determining how the information is related.
  3. Data Sharing—developing the mechanisms for discovering and exchanging information.

Data Descriptions is the semantics and syntax. It involves developing your “data asset catalogue” and the metadata. This includes developing a lexicon or data dictionary with harmonized terms and schemas to describe them, as well as tagging the data. This helps define what the terms mean and identifies the rules for arranging them.

Data Context is the relationships. It includes categorizing the information using taxonomies and ontologies. It includes developing models, such as entity relationship diagrams (ERDs) to identify entities or information objects (and their attributes or properties) and associating them.

Data Sharing is the transactional processes. It entails the decomposition of information exchanges (in an Information Exchange Matrix or in a Information Exchange Package Description, IEPD) to determine the provider, the consumer, the trigger, the frequency, the media, the security, and so forth for each type of transaction. This phases also includes developing information repositories/registries, information sharing access agreements, and other mechanisms for sharing the information, such as portals, web services, enterprise service bus (ESB), and so on.

In the end, EIA is about transforming the organization: Culturally, from information hoarding to information sharing; from a business process perspective, optimizing information flows and usage, and in terms of governance, managing information as an enterprise asset.


September 7, 2007

Information Sharing Best Practices

There are currently two major federal best practices for information sharing: Netcentricity and the Information Sharing Environment.

The Department of Defense (DoD) adopted a Netcentric Strategy in May 2003.

  • Netcentricity—Netcentricity seeks to ensure data visibility, availability, and usability to accelerate decision-making. This includes data tagging (metadata), posting data to shared spaces, and enabling the many-to-many exchange of data (i.e. many users and applications can access the same data instead of point-to-point interfaces). Netcentricity is the realization of a networked environment.
  • Global Information Grid (GIG)—The GIG is a globally interconnected, end-to-end set of information capabilities, associated processes and personnel for collecting, processing, storing, disseminating and managing information on demand to warfighters, policy makers, and support personnel. The GIG includes all owned and leased communications and computing systems and services, software, data, security services and other associated services necessary to achieve information superiority.
Netcentricity is a strategy for sharing information. As the DoD strategy states: The data strategy is to “shift from private data to community or Enterprise data as a result of increased data “sharing” in the netcentric environment. Tagging, posting, and sharing of data are encouraged through the use of incentives and metrics.” (adapted from DoD Net-Centric Strategy from, public site)

In 2004, the concept of Netcentricity was extended to the Director of National Intelligence (DNI)’s Information Sharing Environment with the passing of the Intelligence Reform and Terrorism Prevention Act (IRTPA).

  • Information Sharing Environment (ISE)The IRTPA requires the President to establish an ISE “for the sharing of terrorism information in a manner consistent with national security and with applicable legal standards relating to privacy and civil liberties” and the IRTPA defines the ISE to mean “an approach that facilitates the sharing of terrorism information.”

The ISE seeks to “facilitate trusted partnerships among all levels of government, the private sector, and foreign partners…[and to] promote an information sharing culture among partners by facilitating the improved sharing of timely, validated, protected, and actionable terrorism information.” (adapted from Information Sharing Environment Implementation Plan from, public site)

Both Net-centricity and ISE are best practices at increasing information sharing to improve and speed up decision-making and protect our nation and its citizens!

  • As the DoD Net-Centric Strategy states: “the core of the net-centric environment is the data that enables effective decisions.”
  • And similarly, in the ISE Implementation Plan, we read, “the highest priority in creating the ISE must be on facilitating, coordinating and expediting access to protected terrorism information.”

In User-centric EA, information sharing, as appropriate, is one of the primary goals of the architecture. Information is one of the six perspectives (performance, business, information, services, technology, and security, and a seventh to be added is human capital) of the EA. The primary principal of the Information perspective is information sharing and accessibility. Further, the Federal Enterprise Architecture (FEA) Data Reference Model (DRM) is driven by the enablement of sharing information across the federal government and to its partners. The methodology is as follows:

  • Consistently describe data (via metadata)
  • Register the data (to make it discoverable)
  • Develop standards for the exchange of data (to enable interoperability and accessibility)
  • Provide sound governance (including data policy and stewardship).

User-centric EA is driven to fulfill the vision of Net-centricity and ISE.


August 24, 2007

Why Isn’t There a Chief Data Architect in the Federal Government?

In the federal government, there is a Chief Enterprise Architect in the Office of Management and Budget (OMB) — this is a good thing.

But the question that I have is why there isn’t a Chief Data Architect as well?

We all know that one of the essentials to good architecture is having strong data architecture that provides for data descriptions (or metadata) to uniformly describe data, data context (or taxonomies) for discovery, and that supports data sharing (or exchange).

In the Federal Enterprise Architecture (FEA), there is a Data Reference Model (DRM). Moreover, in the FEA, data is the crucial touch point between on one hand, the business functions toward achieving desired performance outcomes, and on other hand, the services and technologies that serve up the data in order to perform the functions and activities of the enterprise.

Furthermore, in developing technology solutions of the enterprise, one very important question for the business is what their information and data requirements are. The answer to this helps drive the technology solution.

For the federal government, the benefits of maturing its data architecture could be significant, especially in being able to share vital information, and thereby fill gaps and reduce redundancy across the federal enterprise. Given the size and important scope of the federal government missions, the imperative is great!

The Chief Data Architect would focus on data issues and drive such things as data standardization, common lexicon, metadata development, exchange standards and directories, service oriented architecture, and overall information sharing.

What do you think--would a Federal Chief Data Architect be a good idea to help progress this?