Enterprise Architecture Data Principles

Principle / Rationale / Implication
Where data fulfill multiple mission needs EA will promote the sharing and use of information collected across the agency to improve performance, support decision-making and enable accurate reporting. / Sharing information maximizes the effectiveness of the Agency in fulfilling its mission and enable sharing with by external partners. / Authoritative sources of information and data need to be identified.
When appropriate each Program area provides access to specified data/information.
Data standards are used to allow exchange of data.
Development of processes for assuring the privacy and security of shared data will be necessary.
Processes for quality control and assurance for shared data will need to be established and implemented.
Rules for dissemination of data will need to be followed.
Capture and validate data once / It is expensive to enter the data more than once. The labor associated with data entry and quality control on an individual field is small, but when several systems are factored in over several years this becomes expensive.
With multiple entries of the same data from different sources, there is a question of which data source is correct or accurate.
Entering and validating data upstream improves and simplifies integration of modules, as downstream modules do not need to “re-consider” data entry mechanisms and associated validation. / Eliminate Redundant data entry
Enter information early in the process so it is available for others to use sooner.
There is confidence in the data and quality is enhanced.
Overall data entry and maintenance costs will be reduced.
Determine the point in the process where to capture and validate the data.
Implement an information architecture that allows for a standardized format and structure for data and allows timely access to information and data. / The stated principle is not related to accuracy and precision of data therefore this rationale is not associated with this principle.
Timeliness of data is an important characteristic of quality data and information.
Employees are more effective and productivity and morale are improved by more timely receipt and access to information.
Contributes towards consistent decision making because each user obtains data from the original source in a consistent timeframe.
Supports modular and collaborative business application concepts. / Determine how to apply the principle to other agencies and the public who provide data to the Agency.
Deploy technology to improve access to information by agency employees.
Clearly classification information with defined rules need to be clearly defined So to prevent the accidental release of sensitive information.
Provide a central repository for shared data.
Ensure availability of locally stored data to the distributed systems.
Make Data availability contemporaneously with its validation.
Adopt and use consistent data definitions and standards. / Data standards ensure consistency, accuracy, quality, and system integrity. This also allows for greater functionality for access data across systems.
Consistent data definitions aids in communication and consistent decision making across the agency.
Unique and consistent definitions of business terms and data elements result in more rapid responses to critical questions, more effective decision making, and better communication.
Standard data definitions will reduce cost by not having to “force- fit” data from disparate applications.
Easier change of information.
Applications that exchange information can be more easily integrated and supports cross system functions. t / Develop, maintain and use an agreed upon, agency wide data dictionary and data standards. .
Provide the definitions in regulations when applicable
The organizations entering and capturing the data are accountable for its accuracy and content. / Accountability as close as possible to the data entry point to help ensure data quality / Determine which organization will will collect the data used across the agency.
Develop a data quality policy to clarify and specify the responsibilities of the organizations for data accuracy and content for different levels and types of data being shared across the agency.

1