Establishment of Enterprise Data Management
An enterprise management program is designed to emphasize the importance of managing information as an asset. Most enterprises carefully manage other assets (financial, physical, and human) but overlook the value inherent in their data. Typically, if an organization is cognizant of the data it captures, stores and uses, it is only aware of the deficiencies of that data and not the many ways that data can be turned into valuable information. Using an enterprise data management practice will provide your company with a central focus for identifying and controlling the collection, storage and disposition of information resources.
- Role of Data Management in development and maintenance
- Internal education efforts & issue
- Standards and their enforcement
- Data dictionary creation and usage
- Data Management’s Role in Software Development and Package Acquisition
- Data Stewardship
Role of Data Management in development and maintenance
The role of Data Management and the management of data from an enterprise perspective is essential in IS development and maintenance. Enterprise data management optimizes the use of the corporate data assets for both the business user and the IS community. Enterprise Data Management encourages the management of data from both business-unit and corporate perspectives and promotes the importance of data as a valuable resource for your company.
Internal education efforts & issues
To educate internal customers and IS associates, varying levels of assistance are required. Each level should address the value data holds and that level’s association with the management of that asset.
- IS Management (and company Senior Management where appropriate)
IS Management needs to understand the basic rationale for the use of a Data Management practice and the need for a data architecture with the responsibilities IS management has in the enforcement of the policies instituted through the efforts of the Data Management representatives. IS management must also understand the differences between the functions of Data Management versus the functions of Database Administration.
- IS development teams
IS development teams usually consist of business users (data stewards), programmer/analysts, database administrators and project managers. Each team should also include data analysts/data modelers as an integral part. To provide the IS development team with the understanding of data modeling and its basic activities, the team should attend an overview session focusing on data modeling’s purpose and function. Frequently referred to as Modeling 101, it outlines a data-centric methodology to be used in application evaluation, development and maintenance. After having attended the Modeling 101 session, IS development teams will understand the need for a separate data modeling function and their association with the data analyst/data modeler. Pursuing further education in data modeling and database design is the responsibility of the data analysts and database analysts. Programmer/analysts should not function as data modelers, although they need to understand the basic concepts and activities used in developing logical data models.
To permit users to view and understand the application data models developed by data modelers, all development and maintenance IS staff should have a model browser installed on their personal computer desktop. Data analysts and database analysts will continue to use a data modeling tool to develop logical data models and engineer physical models/databases.
Since very few operations have sufficient staff to accommodate all IS development and maintenance activities the use of contractors/consultants is a reality that must be addressed. Your project manager should be positioned to provide the data-centric methodology necessary for robust, sustaining application development and enhancement that many companies need but cannot provide for themselves.
Standards and their enforcement
Standards provide the ability to manage the data resources of an enterprise consistently and efficiently. Enforcing standards in application development enables designers, analysts and programmers to operate more effectively and return quality applications that address business needs within the expected development schedule.
- Naming conventions
Attribute (element) naming conventions are used for clarity across applications and within applications. Naming standards enable business users and IS associates to understand the purpose of the attribute (element) and avoid confusion in application design, development and maintenance.
- Mapping Standards
Mapping standards are used to identify sources and targets for application conversion, migration and related activities. Like naming standards, mapping standards enable users and IS associates to fully understand the purpose of an attribute (element) and the most valid source for a particular result.
Mapping standard templates are under development by the Data Management practice. Consultants involved in application development or maintenance should use these standard formats for conversion, migration or related activities to assist in the smooth transition from development through production to enhancement.
Data dictionary creation and usage
A data dictionary is the central source of the entity, attribute (table and element) definitions, their data types and sizes and usages. It should be accessible to a client’s business users and IS associates (and contractors) through read-only access. As the enterprise attempts to integrate multiple applications and develop sophisticated reporting facilities, the purpose and use of a central data dictionary will become increasingly important.
Data Management’s Role in software package evaluation and application development
A Data Management practice is responsible for the operation of the tools, techniques and methods used to identify, classify and manage a client’s data. As such, data analysts can play a vital role in any software package evaluation or application development efforts. In package evaluation, a data analyst/data modeler will review the package’s logical data model and judge its compatibility with the client’s corporate data model and standards for that business area. In application development, a data analyst/data modeler works as part of the basic development team, gathering business data requirements, modeling them logically and assisting in the engineering of the application’s database from the logical model.
Therefore, a representative of the Data Management practice should be included in all IS efforts, from software package evaluation to application development to application maintenance. Only a data analyst/data modeler does data modeling since the function of data modeling requires certain skills and techniques that other IS practitioners may not have developed.
It is strongly recommended that no application coding begins before the logical model and database are complete. Coding should be done against a stable model and database; without a sufficiently stable model and database, coding will be iterative and frustrating for programmer/analysts. To reduce this frustration and to provide clear direction to programmer/analysts, screen or programming code activities should await the completion of the logical model and the database.
Business community involvement is essential in modeling and in the assessment of the value of the data resource. “Business associate” means a current holder of the position that uses the data in question. Formerly, many companies have used IS staff who were former business associates as data stewards or subject matter experts. This can lead application developers and data analysts into incorrect assumptions about the use of the data and its relevance. The data stewards assess the applicability of the data and its definition from the business perspective. Data Stewards for each functional area should be identified, given training in the basics of Data Management (similar to IS management level education) and involved in all IS application activities (package evaluation, application development and maintenance) to manage the impact the activity will have on the integrity of the data under their stewardship.
In the final analysis, using an enterprise Data Management practice will enable your organization to organize and identify the value of the data collected, stored and disseminated – turning data into information and providing your company with a competitive advantage.