“You Can’t Improve What You Have Not Assessed” – Justification to Perform an Enterprise Data Management Assessment
By Anne Smith
Many organizations have begun to explore the need for an enterprise data management strategy to leverage the value of the data and information they collect, store, use and propagate. In addition, executives have started to see the increased compliance and regulatory requirements as a call for more (or better understood) data within the organization.
Over the past 20 years, many organizations have performed extensive business process re-engineering initiatives to improve their business activities. Any re-engineering effort starts with an assessment of the current state, since the organization can’t improve if it does not know where it stands. Many assessment formats exist for improving processes for both business and information technology (Six Sigma, CMMI, etc.).
Since information is the lifeblood of a 21st century organization, it is critical for IT to improve information usage, consistency, quality and value. An increased focus on the concept of data as an asset has spurred activities to re-evaluate the need to manage data across the enterprise, as opposed to within a single business department. The lack of existing data management strategies has resulted in incomplete or inaccurate views of business’ need for valuing data as a strategic asset, which can lead to missed opportunities to identify business opportunities, improve processes and reduce costs. A focus on enterprise data management and the development of the components of enterprise data management within an organization can lead to significant improvements in process effectiveness, reduced costs and increased knowledge.
However, before an organization can embark on the development and implementation of an enterprise data management strategy, it must borrow an activity from business process re-engineering and assess the current state of data management across the organization. As the noted British scientist Lord Kelvin said, “You cannot manage what you have not measured.”
Much has been written about the business and IT process improvement assessment field; there are numerous books, articles and papers concerning the merits of and the tasks to perform a Six Sigma or CMMI assessment. Unfortunately, data and information have not received this level of attention, so many organizations may be very hesitant to embark on the data management assessment journey, thinking they will be alone and lost. However, data management can use the foundations of the process assessment methodology to develop a data management assessment and improvement model. Doing so allows data management to “stand on the shoulders of giants” and use what has been proven to be effective by amending process assessments to fit the needs of the data management community.
A basic format for a data management assessment could contain:
- Interview management and staff to determine current state and desired state (requirements) of enterprise data management across all business and technical units
- Identify any existing data management initiatives, whether at the enterprise or non-enterprise level
- Determine the benefits already achieved across any data management initiatives
- Examine the investment in data management technologies (DBMS, meta data, data modeling, master data, data quality, etc.) by both IT and business
- Determine desired results of existing and future data management initiatives through management discussions and calculating returns on investment (ROI)
- Assess the performance of business staff and their processes for data / information management
- Assess all challenges against identified desired improvements in managing data across the enterprise
- Perform a gap analysis between the current state and requirements / desired state for managing and governing data, including an assessment of the expected level of success
- Analyze the benefits and costs from existing data management suppliers using accepted analysis techniques
- Compare and contrast existing and desired efforts against industry benchmarks and best practices
- Identify required information to develop and implement recommended actions for each chosen area of data management
- Identify organizationally based benefits from enterprise data management for each business and IT unit (quantify and qualify)
- Develop high-level enterprise data management strategy and plan (“roadmap”) for the organization
- Prioritize and present cost estimates for the enterprise data management strategy and roadmap
- Write report and presentation on assessment and data strategy plan
- Present the plan and steps required to leverage existing and future data management activities and investments – receive approval for implementation
- Develop detailed plan for executing first stage of the enterprise data management roadmap
- Direct activities to achieve improvements on data governance and all areas of enterprise data management chosen for the organization (meta data management, data quality management, enterprise data architecture, etc.)
In conclusion, although assessment is not an easy or quick task, it is essential for any organization that wishes to begin or improve their data management capabilities. Using the models and methodologies of proven process improvement assessment formats, it is possible for an organization to develop and implement their own solid enterprise data management assessment and improvement initiative.
About the Author
Anne Marie Smith is a leading consultant in Information Management and is a frequent contributor to various IS publications. Anne Marie has over 20 years experience in information management for several corporate entities and has successfully led the development of data resource management departments within corporations and consulting organizations. Anne Marie is active in the local chapter of DAMA and serves on the board of directors of DAMA International, and is an advisor to the DM Forum. She has been an instructor of Management Information Systems (MIS) with Philadelphia, PA area colleges and universities. Anne Marie has taught topics such as: data stewardship and governance, data warehousing, business requirements gathering and analysis, metadata management and metadata strategy, information systems and data warehouse project management. Anne Marie’s areas of consulting expertise include metadata management including data stewardship and governance, information systems planning, systems analysis and design, project management, data warehouse systems assessment and development, information systems process improvement and information resource management/data resource management. Anne Marie holds the degrees Bachelor of Arts and a Master's of Business Administration in Management Information Systems from La Salle University; she has earned a PhD in MIS at Northcentral University. She is a certified logical data and process modeler and holds project management certification. Anne Marie can be reached at firstname.lastname@example.org