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Effective Data Strategy Components and Steps


Every effective enterprise data strategy should consist of an analysis of the organization’s people, processes, technology, and organizational structure – and follow proven implementation steps.

Along with understanding the concepts of a data strategy, it is important to know the parts of an enterprise data strategy.  Each organization should develop and follow the proven implementation steps that lead to successful data management for any organization.

What is an Effective Data Strategy?

A data strategy is the development, administration, and maintenance of a single, enterprise-wide plan for the use and control of corporate data for strategic and operational decision-making. An effective data strategy is based on the enterprise’s adoption of an enterprise data / information management framework that guides its understanding and implementation of all the components of enterprise data management.  A data strategy is designed to support and align with the organization’s mission and business strategy.  Major components of a data strategy include:


An effective data strategy is dependent on the business users and IT working together and adhering to the accepted data strategy and the resulting plans.  This happens when data ownership and data stewardship are embraced by the business unit that created the data. However, data storage and integration are controlled by the IT department.

Strong awareness of data as a vital corporate asset must permeate the organization.  In addition, business management must possess the awareness / knowledge of the data challenges facing the company, and understand that data is truly a corporate asset that needs to be managed and protected like the other assets the company possesses.  Finally, each data initiative has to align with—and support—the corporate mission, strategy, and key initiatives.

Steps to Develop a Corporate Data Strategy

The basic and essential steps for the development of a corporate data strategy are:

  1. Assessment of the organization’s existing data environment involving the four components outlined above: people, processes, technologies, and organizational culture. This exercise provides the baseline information to understand the existing competencies of the organization for managing and controlling its data.
  2. Alignment of data initiatives and requirements to the organization’s strategic vision and supporting initiatives. As an example:
  • The organization identifies financial growth as a business driver or strategic goal.
  • To address this business driver the organization then pinpoints areas of focus that supports strategic goal:  e.g., creation of new customers to increase revenue.
  • Then, the organization chooses initiatives to support these areas of focus to create new customers to increase revenue.  These initiatives might include:
    • Developing new products and services to specific individual customer segments
    • Gaining a fuller understanding or view of their customers
    • Establishing quantifiable metrics for each initiative to define success
  1. Defining how the business will use the data to deliver or support the strategies and business drivers and these set initiatives. This effort might involve:
  • Leveraging analytics to increase relevant personalization and contact strategies to influence customer behavior
  • Development of a more personalized view/understanding of customers by demographic area
  • Increasing the ability to react to behaviors in real time as the customer is engaging with you
  1. Identifying the specific data that will be required to achieve these corporate goals/strategies. Linking back to the overall business vision will allow the business to assess which information is a priority.
  • Data items that do not affect any of the overall objectives can be disregarded at this point.
  • Data that is required but not available either can be harvested from an external seller of data or placed on a wish list to be acquired from various sources.
  1. Prioritizing data initiatives to align with business priorities. One of the keys to success is establishing the proper enterprise data management foundations, such as a data governance committee, data models, a business glossary, metadata, and the people skills and necessary processes.
  2. Maintaining executive support for enterprise data management. Ongoing ownership at a high level is critical.  The data strategy must include high-level executive sponsorship and continuing commitment to ensure all business decisions are supported by accurate and timely data.  Data continues to evolve along with the business’ strategy and objectives.  This means the data strategy should be seen as a “living document” and that it is adjusted with the times and the needs of the organization.

Data Management Components in a Data Strategy

Data governance becomes a key process for managing and controlling data, and it is based on the results of the data strategy.  Data governance is the foundational component of enterprise data management, so it is one of the most important aspects in an enterprise data strategy.  In the organizational area of the data strategy, the team will identify how to create the all-important data governance committee.  The data governance committee monitors the policies and processes to manage the company’s critical data; these policies are developed by data governance professionals.  A data governance framework defines the guiding principles, goals, policies, roles, accountabilities, metrics, and oversight of the company’s data and communicates these to management and users.

Data quality is also viewed as a critical component of an organization’s data strategy where data quality is measured, monitored and embedded into the company’s processes and performance culture.  Every organization needs a data quality program that is aligned to the enterprise data strategy and the goals established for the value of data and information as assets.

Linking business objectives to data initiatives is crucial to a solid and effective data strategy that will gain executive level awareness and support as well as achieving business acceptance, collaboration and participation.  Not to mention gaining a competitive advantage over those organizations who “don’t know where they’re going…”

Lastly, developing and implementing an enterprise data strategy will allow the company to use the data it collects more effectively and efficiently, to make better management decisions and provide the competitive means for achieving its vision, mission, and goals.


Developing and implementing an enterprise data strategy will allow any company to use the data it collects more effectively and efficiently, to make better management decisions and to improve its position in its industry or market.


William B. Jenkins

William B. Jenkins is an accomplished data management and analytics executive and consultant, primarily in insurance and financial services. Bill has held a number of insurance executive level positions including CIO and COO, VP of Human Resources, senior Data Management and Project Management positions, and president of an IT/management consulting firm. Also, he has been appointed to several advisory boards in leadership roles, including ACORD, IDP, OMG, and A.M. Best Rating Services. Bill earned degrees from Temple, Drexel, and St. Joseph’s universities and is a Certified Property and Casualty Underwriter ( CPCU).

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