Data Strategies – Data Management Strategies

By Anne Smith

A data strategy is a data management strategy – choices we make about how to manage data assets. These choices include:

How we define data management, its scope, mission, long-term goals and short-term (12-24 month) objectives.
The process model for performing data management functions, and the priorities we give each function (meta data management, data modeling, data warehousing, data governance, data quality, etc.).
The roles, resources and organizational structures we create and staff to manage data (large organizations, small groups, teams, etc.).
The policies for how we create, control and use data (policies that affect data producers and information consumers, as well as the data professionals).
Investments we choose to make in technology, procedures and control mechanisms to perform the functions.
The implementation projects we sponsor/commission to improve how we manage data – the time and effort investments we make in a continuing data management program (better database design, a data warehouse for decision support, meta data discovery and documentation, data quality improvements, etc.).
Some companies include decisions about how they want to leverage their information assets to competitive advantage, specifically targeting certain aspects of data management for special consideration. For instance, Wal-Mart might say “we are going to use our supplier data to negotiate lowest prices from all our suppliers”, or a mining company might say “we’re going to release our geospatial data outside the company and ask smart people to tell us where to look for gold.” This kind of strategy is really a business strategy that is using data as one of its components, showing how to leverage the assets rather than how to control the assets.

But in a more typical data (management) strategy, the deliverables would include:

Mission/purpose of data management and overall long-term data management goals, along with the business drivers and value proposition for data management, including any returns on investment (ROI).
An assessment of the current state of the data management practices, strengths and weaknesses, recognized challenges and issues, cultural readiness for change, and critical success factors – documented and analyzed according to a proven assessment methodology.
Specific 12-24 month objectives in each of the data management functions, measures of success, management metrics and alignment back to the results of the current state assessment.
Specifications for the required target environment: new processes, roles, organizations, technology, skills, appropriate connections with other groups, and cultural changes required for successful implementation.
Transition plans, including specific project charters with their estimated costs and benefits, and a project roadmap showing project interdependencies and schedule, along with plans for securing commitment, training, communication, change management. Typical projects might include establishing data governance and stewardship, developing an enterprise data/information architecture, implementing a managed meta data environment, implementing master data management, establishing a data quality baseline and procedures for ongoing measurement and analysis, revisiting business intelligence, assessing information security practices, integrating data development and delivery tasks into evolving Systems Development Lifecycle (SDLC) practices, and/or implementing enterprise content management for unstructured data.
This is what a formal enterprise data management program addresses: data management strategies. A strategy like this must be collaborative; involving IT and business leadership, and it is one of the main activities of a data governance program. When companies ask one person to define a data strategy, they usually are asking for:

a statement of high level vision/mission/goals/guiding principles, and/or
a description/discussion about what a more complete data strategy might look like.
Actually, a strategy is a set of choices made by a group educated and empowered to develop and implement such activities. Think of a strategy in the game of chess, or a military strategy; they are more detailed than a statement of principles. Without the details, there is no capability to act and no measurement of results.

Some of the major challenges of developing and implementing a data management strategy would include:

Need for a proven methodology for data management to follow – guidelines are always useful and can reduce learning time and avoid some common obstacles. Project plans for any activities involving data should be amended to include the chosen data management approach.
Skills of the team expected to develop and implement (data governance and stewardship). Training in all aspects of data management to a certain level is necessary for the team to know what to focus on and how to implement decisions made.
Leadership commitment: a data management strategy is a continuing effort and sponsorship and leadership must remain active for the strategy to succeed. This includes executive support for cultural changes that will be required by the adoption of a data management strategy.
Integration of the data management strategy with other organizational initiatives, and the involvement of data management and data governance in all appropriate efforts throughout the enterprise.
General cultural change, since most organizations are process-driven and not accustomed to thinking about how data assets can be used more effectively. Another aspect of the cultural change is the involvement of the business areas in data, a realm thought to be part of IT’s domain.
Developing and implementing a data management strategy is a major effort, but one that has proven its value across industries, across organizations of all sizes and complexities, both in the public and private sectors. Challenging – yes, but full of the potential for lasting success.

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

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