Best practices for aligning business strategy with data management

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Data is the engine that supports both strategic and operational decisions; these decisions depend on what you know and what you know depends on the information provided by the data.

It is known that data evolves as the business evolves. The value of data is tied with the organization’s ability to manage their data through identifying, accessing, integrating and synchronizing data from multiple internal and external sources and then effectively govern that data over time by implementing principles, policies, procedures, and standards for the effective use of data.

Data management initiatives are not all about infrastructure & technology change and delivery but it is a transformation ensuring the right processes, people, and technology are in place which requires process change, operational change, HR changes, market change and even environmental change.

Why Data Management initiatives could fail?

  • Alignment between data management strategy and execution is unclear
  • Data management initiatives are not well managed under one data management program
  • Data management projects are extremely complex
  • Lack of clear identification and agreement on the dependencies and interfaces
  • Lack of business and IT alignment for the business problems they need to solve and tackling it from a technology perspective only
  • Restricted access to data sets needed to find answers for business data issues
  • Scope creep and failure to accurately predict results
  • Risks not well understood and managed
  • Lack of resources and available skill sets don’t match required data management needs
  • Lack of management commitment and executive support
  • Difficult to manage and align between different vendors and partners involved in the data management projects
  • Difficult to manage without authority over people who are on multiple projects, not functional reports
  • Difficult to manage people resistant for the change

Seeking better alignment between data management strategy, objectives, and organizational capabilities, and execution is critical for successful data management initiatives implementation

Data Management Strategy Setting

Current State

Assessment for the data management maturity in an organization and what needs to be achieved and how it creates a business value is representing the foundation for data management initiatives and strategy as it is wise to know where we are today before setting a path for the future.

The most effective organizations assess the current business pains to understand and cultivate a sense of needed data management initiatives and strategy.

Understanding the organization’s culture and structure describes how things are done or not done in the organization, how people will behave and respond to the change, and determining authority and power of the stakeholders to be managed is an essential step.

Future Vision

Vision is all about setting goals, and setting standards for meeting them (metrics)

Goals should be “SMART” specific, measureable, achievable, resourced an, time bound:

  • Specific – precise statement on what needs to be accomplished
  • Measurable - show progress in completing the strategy and moving towards the vision
  • Achievable – results that can be realistically achieved, given available resources and organizational capabilities
  • Relevant – to the future vision
  • Time-bounded – specific planned dates for when results will be achieved


Strategy is the roadmap or the path chosen to move towards the future vision.

Data management strategy should be easily broken down into discrete projects and initiatives highlighting the needed change in infrastructure & technology, process change, operational change, HR changes, market change and environmental change with estimates on resources, time, and funding requirements.

Data management strategy should highlight the right projects and priorities (Which project should be funded first to focus on the right efforts for the right time and business benefits).

Data management projects and initiatives priority can be determined according to different criteria like:

  • Value to the organization; How much value will be delivered to the organization like increase efficiency or reduce costs and value to its customers
  • Market differentiation
  • Market share
  • Financial return
  • Comply with legal requirements
  • Effort of implementation; How ready the organization is to acquire, develop and implement the solutions provided by the project
  • Time to market
  • Project risks

Project managers have both a tactical role in executing projects and a strategic role in participating in the projects selection process.

Data Management Strategy Execution

Organization’s ability to execute the strategy is the key for delivering successful data management initiatives.

Executing data management strategy is becoming increasing difficult for a number of reasons not the least of which include the changes to current business processes, new roles of data stewardship, poor data quality, and the complex part of people management and their resistant to the change.

People have different personalities, viewpoints, experiences, and expectations that need the project manager soft skills;

  • Are they unmotivated or afraid from the change? Touch their heart by building trust, and showing the benefits for all of us working together as a team.
  • Are they working really hard, but their efforts are misplaced? Address their head by clarifying the future vision and objectives, ‘what’ and ‘why’ of the change.
  • Are they stuck and handicapped? Sounds like they need a close hand by providing plans, processes, and skills building to guide their efforts through the change.


Move the results of your projects into the main stream of the organization’s operation and business as usual (BAU).
Planning for operational readiness or service transition or production readiness is a critical component of data management initiatives success; three recommended components of operational readiness are:

  • 1. Clearly defined post project support process
  • 2. Training
  • 3. Handover of project material (like user manuals, issue logs, product overviews)
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