Functional Practice

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Level 1: Performed

Data management objectives, priorities, and scope reflect stakeholder business objectives within projects.

For a specific project, the business sponsor, business representatives, and project team define the priorities and objectives for the data set within scope. This may include identifying data scope and requirements, mapping to business process steps which create, read, update, and delete data, applying standards for management of supplier and consumer data, instituting data quality checks, etc.

Data Lifecycle Management addresses alignment between business needs and processes and data management priorities.

Example Work Products

Documented business objectives
Data management objectives, priorities, and scope for a project

Report on data management outcomes versus objectives for a project

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Level 2: Managed

Data management objectives, priorities, and scope are defined and approved.

The subject areas within scope, that are relevant to a business unit or cross- cutting initiative (for example, reference data used by multiple business areas) are defined and approved by all stakeholders, including IT and lines of business. When multiple business units are impacted, data governance should be engaged. In addition, the scope should address requirements such as externally procured data vital to business processes, regulatory requirements, etc.

2. 2.2  Data management objectives and priorities are aligned with business objec- tives.The subject areas within scope are mapped to business unit objectives, functions, and processes. Data management priorities and objectives are reviewed and modified with input from business unit stakeholders.
3. 2.3  A process for prioritizing projects across a business unit from a data perspective, as well as traceability to business objectives, is established and followed.Projects are prioritized based on factors important to the business unit, such as business value, time-criticality, level of e ort, and process and data dependencies.
4. 2.4  A tactical plan for addressing data management objectives and priorities across the business unit is established and maintained.The business unit (or cross-cutting project) has developed a plan that typically includes key milestones (e.g., component projects), major activities, dependencies, resources, and identification of stakeholders.
5. 2.5  Metrics are used to assess the achievement of objectives for data management.Example Work ProductsData management objectives and corresponding metrics
   Data management scope definition
   Subject area mapping to functions that create, update, and delete data Approved list of data management priorities
   Data management priorities mapped to business objectives
   Project prioritization list
   Capability enablement sequence plan

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Level 3: Defined

A data management strategy representing an organization-wide scope is established, approved, promulgated, and maintained.

The data management strategy describes the approach for implementing a new (or enhanced) multi-year data management program organization-wide.

The data management strategy typically addresses, at a minimum:

A vision statement, which includes core operating principles; goals and objectives; priorities, based on a synthesis of factors important to the organization, such as level of eort, business value, dependencies, and stakeholder support for strategic initiatives

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)

Business benefits
The selected data management framework and how it will be used High-level roles and responsibilities
Governance needs
Data management program development approach
Compliance approach and measures

High-level sequence plan (roadmap)

The data management strategy integrates the organization’s key objectives and business priorities for its data assets, and the processes that establish, build, and improve them. It is developed to increase understanding and awareness of data as a critical corporate asset, and to gain approval and commitment from all relevant stakeholders and decision makers. Active and sustained buy-in is the key to adoption and success over time.

• 3.2  Data management objectives for the organization are evaluated and prioritized against business drivers and goals, and aligned with the business strategy.A recommended approach is to begin the development of the data management strategy with reference to the organization’s strategic business plan with its goals and objectives to determine the primary drivers for improvements to data management and architecture.
• 3.3  Business and technology collaboratively develop the organization’s data management strategy.The scope of the data management program aligns with the enterprise architecture, the target data architecture, and the existing and future infrastructure. Typically, the data management function and governance bodies play a significant role in orchestrating collaborative development and ensuring a comprehensive strategy.
  Architectural Standards provides guidance to ensure that business strategy and architectural standards are aligned and consistent with business needs.
  • 3.4  The sequence plan for implementation of the data management strategy is monitored and updated, based upon progress reviews.A successful sequence plan includes high-payback quick wins to generate and sustain momentum, as well as longer-term and important strategic initiatives. It is important to identify major dependencies—impacting projects underway, organization formation, etc.Reviews of the sequence plan for implementation of the data management program should take into consideration:Progress against milestones
    Risk identification
    Resource usage
    Alignment with data management priorities Achievement of data management objectivesBusiness Case describes activities associated with data management prior- ities and its alignment with business needs.
  • 3.5  The organization’s data management strategy is documented, maintained, reviewed, and communicated according to the organization’s defined standard process.The data management function and data governance bodies are responsible for ensuring e ective communication of the data management strategy.
    As business needs and strategy changes occur, the governance body representing all business stakeholders is typically responsible for regularly reviewing the priorities, objectives, and roadmap, as well as updating them and following a defined process for approval.Communications provides additional guidance developing and ensuring e ective communications for data management.
  • 3.6  The organization’s data management strategy is consistent with data management policies.As needed, policies are reviewed with respect to the strategy to evaluate the need for new policies or enhancements to existing policies. Policies provide the foundation for organization-wide compliance.The strategy may also identify the need for new policies, which should be included in the sequence plan.
  • 3.7  Metrics are used to assess and monitor achievement of data management objectives.Program metrics may include measurements of the sequence plan milestone progress and evaluation of costs and benefits. Program-level metrics should be accessible by all stakeholders (e.g., through a dashboard). Measures should be developed employing standard best practice techniques, such as Goal Question Indicator Metric.Measurement and Analysis provides a systematic approach for establishing and analyzing metrics.

    Example Work Products

    • Data management strategy
    • List of data management objectives and priorities
    • Data management policies
    • Stakeholder participation and approval documents
    • Data management program scope documentation (e.g., subject areas, business areas, key data elements, key disciplines, etc.)
    • Data management strategy sequence plan Data management program metrics Program cost-benefit analysis results Data management program reviews Data management strategy dashboard

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    Level 4: Measured

    Statistical and other quantitative techniques are used to evaluate the effectiveness of strategic data management objectives in achieving business objectives, and modifications are made based on metrics.

    Changes made to the process for developing strategy and objectives typically include the following:

    Improving the process employed for developing data management objectives, priorities, and scope needed to achieve business objectives

    Adjusting the statistical evaluation or data collection method

    4.2 The organization researches innovative business processes and emerging regulatory requirements to ensure that the data management program is compatible with future business needs.

    Example Work Products

    • Metrics-based data management program reports
    • Plan and documentation for monitoring emerging industry or regulatory requirements
    • Data management policies Level 5: Optimized
    • The organization researches and adopts selected industry best practices for strategy and objectives development.
    • Contributions are made to industry best practices for data management strategy development and implementation.
    • Example Work Products
    • External publications and presentations about best practices at industry conferences
    • Comparative analysis reports of best practices

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