Q: What are the major components of a data management strategy, as well as thoughts on developing and implementing such a strategy?

Majid Abai's Answer:

An organization data strategy has one and only one goal: to deliver clean, consistent, and timely data to people/applications who need it. As such every component in this strategy should focus on the goal. There are several components of a data management strategy and I have included the highlights here.

  • Data quality
  • Metadata management
  • Data integration and master data management
  • Data governance
  • Data architecture
  • Business intelligence (including data mining, KPIs, balanced score cards and even the latest: competitive intelligence)
  • Unstructured data and knowledge management
  • Data visualization
  • Information search and delivery components
  • Database management (including storage, performance, redundancy, disaster/recovery, etc.)

The key point to remember in developing an organizational data strategy is that such strategy can not be developed and implemented without a strong partnership with business.

Sid Adelman's Answer:

  • Organization - roles and responsibilities
  • Data quality
  • Performance
  • Security
  • Data governance
  • Data warehouse
  • Management commitment
  • Unstructured data
  • Data integration
  • Metadata

We wrote a book that discusses implementing a data strategy - Data Strategy by Sid Adelman, Larissa Moss and Majid Abai.

Anne Marie Smith's Answer:

The basic premise of the data management approach to application development is that information is like any other business resource - and should be managed as such. Basic principles for managing resources of any type are relatively straightforward and generally recognized by almost all businesses. These principles include the following:

  • Requirements for the resource must be anticipated, and fulfilled in anticipation of future need. Otherwise, by the time the resource is needed, the opportunity to acquire it will have disappeared. (Requirements gathering and analysis)
  • The business cannot afford an infinite amount of the resource; therefore, the amount must be optimized. In other words, the company should always have enough - but also minimize excess and redundancy. (Optimizing data usage, data accessibility, data understanding and reducing data redundancy across the organization)
  • The resource should be shared and leveraged in as many ways as possible, in order to maximize its value while diminishing its overall costs. (Once again, optimizing data reuse and accessibility across the organization)
  • The resource must be carefully managed to ensure that its use in the business is prudent, efficient, effective and secure. It must therefore follow a clearly defined life cycle guided by explicit rules. (Developing an appropriate data lifecycle for each subject area that includes retiring data when it has reached the end of its useful life, developing and enforcing data security rules, identifying and implementing appropriate tools for data use, and educating the workforce in the principles of data management.)

An important step toward implementing an effective data management environment is to create, formalize, disseminate and implement a comprehensive set of policies for managing information as a resource based on the principles listed above (or other principles that the organization feels meets their needs). This will enable the business to appreciate fully the significant potential of a well-managed information resource, and begin to escape the common state of expensive disarray in its systems and with the data used/created by those systems. Other important steps in creating an data management strategy would include identifying the mission of data management for the organization, obtaining senior management support for data management, building the principles and policies around the data management mission, developing a data governance approach and creating the roles of data stewards, implementing these activities across the organization and sustaining the data management efforts.

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