While many organizations understand the value of managing the information resource, for many others information management remains abstract and difficult to define. In an effort to make it concrete here’s a hypothetical proposal to provide an Enterprise Information Architect for a hypothetical organization that really needs one.
Today: inconsistent data of uncertain quality blurs enterprise view and restricts planning
Today managers, planners, and analysts lack the information required to run the organization as a single enterprise rather than a collection of diverse units.
- Data quality in IT applications varies to the point that, outside financials, it is impossible to gather consistent data supporting an enterprise view of operations.
- Application development efforts have focused narrowly on departmental interests without accounting for enterprise concerns, making application data incomplete in describing business processes and inconsistent with data in other applications.
- Focus on departmental concerns and tight development timelines has resulted in incomplete validation of data critical to the enterprise but not critical to the application’s focus. For example, customer demographics are not critical to the sales process and therefore zip codes and telephone numbers are not consistently collected at point of sale, substantially reducing value of market analysis based on sales data.
- Enterprise planners work with only the highest level summaries of operational data, those summaries suffer large margins of error, and planners cannot definitively answer questions required to make critical business decisions.
- Regulators have questioned the validity and repeatability of reporting because of the organization’s heavy reliance on spreadsheets and manual processes in gathering and compiling data for reports.
Solution: enable sound planning and management by identifying data assets and setting processes to manage them
Empower an Enterprise Information Architect to lead an effort that (1) identifies data that describes the organization, (2) defines how to integrate and improve quality of that data, and (3) improves the ability of information technology to maintain data quality.
(1) Lead definition of an Enterprise Information Architecture identifying information required to manage the organization as a single integrated enterprise, and data quality standards that ensure that data supports enterprise goals.
- Identify and define events and objects critical to the enterprise
- Identify and define relationships among those events and objects and attributes that describe them
- Classify data managed by the organization by type (operational, statistical, financial, decision support, etc.) and define standards for managing and integrating each type.
- Compile the above into a plan that explicitly supports the enterprise strategic plan
(2) Working with senior business managers, put in place a program of data quality improvement that plans and executes specific measures and sustained commitment to improving data quality in business processes and IT applications
- Identify the business group responsible for maintaining quality and integrity of each business object, event, relationship, and attribute
- Identify for each data item of interest to the enterprise its “system of origination” and “system of record”.
- System of origination is the application that provides the entry point of a given data object to the organization.
- System of record is the application that is the source of record for the data object.
- Define and deploy standards and practices for for business process and IT application definition that support data quality and integrity standards
(3) Working with senior IT managers define and put in place standards for application requirements definition, data management, and metadata management to
- Define and deploy application development and interface standards that support data quality objectives.
- Ensure that application development efforts support enterprise data quality
- Continually monitor new developments in data management best practices and make that information available to the enterprise.