An e ective data management strategy defines why the organization is implementing a data management program, explains what the overall program aims to achieve,
and identifies how the various components of the initiative fit together. It needs to accurately reflect the data suppliers’ and consumers’ business objectives to give confidence to stakeholders that the data management program will be valuable.
A functional data management strategy should be developed collaboratively and approved by all stakeholders. A current state assessment, including capability gap analyses and identifying key dependencies, can foster alignment and provide a foundation for buy-in to the strategy and the corresponding plan for implementation.
It is recommended that the organization immediately undertake the development (or revision) of its data management strategy following a data management assessment, leveraging participant momentum and an accentuated shared perspective. The ensuing collaborative project, developing the data management strategy, is a powerful mechanism for clarifying executive actions and decisions and fast-tracking the data management program: all key players have had a voice; agreement on objectives, priorities, and measures has been achieved; organization-level approval of capabilities to be improved is accomplished; and all relevant stakeholders understand the impacts of the implementation sequence plan.
Engaging in the development (or refinement) of the data management strategy provides an unparalleled collaboration opportunity, which can be leveraged as fuel for the cultural and organization change that is required for continued success.
The data management strategy defines the overall framework of the program. A data management strategy usually consists of, at a minimum:
A vision statement, which includes core operating principles; goals and objec- tives; priorities, based on a synthesis of factors important to the organization, such as dependencies, perceived business value, alignment to strategic initia- tives, and level of eort
Program scope—including both key business areas (e.g., Customer Accounts); data management priorities (e.g., Data Quality); and key data sets (e.g., Customer Master Data)
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- Business benefits
- The selected data management framework and how it will be used
- Major gaps identified in the current state based on a data management assessment
- High-level roles and responsibilities; list of key stakeholders
- Governance needs and scope
- Description of the approach used to develop the data management program Extent and scope of compliance approach
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Success measures and metrics; benchmarking instrument to measure progress High-level sequence plan (roadmap)
The data management strategy needs to reinforce the use of standards and outline the overall governance framework that the organization will employ to make decisions about implementation. It should also take into account major implementation consid- erations, such as architectural initiatives and technology transformation initiatives underway or planned, and it needs to define a sequence plan to guide implementation.
The strategy needs to identify the resource requirements for the data management program, and the criteria that will be employed to evaluate program e ectiveness. Measures are defined and monitored throughout implementation to assess progress against program objectives.
The organization’s data management strategy must be able to evolve as the needs of the organization change. Collaboration is essential to building and maintaining an e ective data management program. One example of evidence of improved collabo- ration is a broader and more evident responsibility for data quality, led by executives and reflected throughout the data lifecycle. The most e ective data management strategies are those that are visibly and actively endorsed by executive management and supported by mandatory organizational policy: in e ect, institutionalized.