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An effective Data Analytics (DA) capability seeks to enable the organization to:

• Empower the organization to develop, articulate, communicate, promote, and execute its data analytics' strategy.
• Work with enterprise architecture to develop architectural guidance and implementation roadmaps for the data analytics capability.
• Conduct analysis of individual or combined data sets including relevant external data to identify revenue, operational, and strategic opportunities.
• Elicit exploitable insights from the data sets readily available to the organization.
• Identify and leverage cost effective external data sources (e.g. census data, public open data sources).
• Enable a data-driven culture and informed decision-making.
• Protect business operations from data analytics activities.
• Facilitate and enable data analytics for problem solving and diagnostics purposes (i.e. going beyond correlation to an understanding of causation).
• Support experimentation and learning to identify the best options for improvement consideration.
• Support portfolio analysis to determine the best product and service mixes, programme and project levels, investment portfolios, and so forth.

The Data Analytics (DA) capability is the ability to specify analytical objectives, to identify data sets likely to enable those objectives, to apply analytical methods and techniques appropriate to those objectives, and to interpret, communicate, and exploit the analytical results to deliver value for the organization.

Representative POMs are described for DA at each level of maturity.

2Basic

• Practice

  Based on the chosen processes, select and train staff for roles on selected tools, techniques, and processes.

  Outcome

  Trained staff are enabled to deliver an analytics service via supplied processes and tools.

  Metric

  # of trained staff with access to analytical tools and data.

• Practice

  Decide on the governance and organization structures and appoint staff to oversee data analytics.

  Outcome

  The data analytics board oversees general data management and looks at the ethical uses of data.

  Metrics

  • % of projects with ethical complaints upheld.
  • # of business systems impairments caused by analytics.

• Practice

  Promote a data-driven culture.

  Outcome

  There is improved acceptance of data-based decision-making.

  Metric

  # of decision processes mandating analytics.

• Practice

  Limit project complexity initially to ensure success.

  Outcome

  Data analytics support builds on some manifest wins.

  Metric

  # of successful analytics projects.

3Intermediate

• Practice

  Use standard data analytical tools and processes, and ensure that staff are competent in their use.

  Outcome

  The organization's data analytics function is insightful and supports business decisions and diagnosis.

  Metric

  # of decisions taken based on routine use of data.

• Practice

  Involve key stakeholders in governance and data analytics planning.

  Outcome

  Shared ownership of data analytics plans promotes usage.

  Metric

  Satisfaction ratings from key stakeholders.

• Practice

  Fund the strategic development of analytics and enable it with agile organization structures and governance.

  Outcome

  Data control and exploitation is balanced with funding, organization structures, and governance.

  Metric

  End-to-end elapsed time and approval cycle times for analytics projects and stages within those projects.

• Practice

  Leverage embedded analytics and make available bias-free high-quality data for analytical processing and use.

  Outcome

  Data quality programmes provide useful results from vendor-supplied and in-house analytics.

  Metric

  # of data errors and bias issues affecting analytical analysis and results reporting.

4Advanced

• Practice

  Use advanced data analytics tools and processes and enable their use with training and education to master's level and careers to higher professional grades.

  Outcome

  Highly competent staff enhance the organization's reputation and deliver advanced analytics objectives.

  Metric

  # of advanced analytical objectives delivered.

• Practice

  Involve all stakeholder groups in governance and data analytics planning.

  Outcome

  All data analytics requirements are identified and shared ownership of the data analytics plan leads to higher success rates.

  Metric

  % of data analytics projects that are successful.

• Practice

  Use advanced analytical preparation, processing techniques, and automation; and use alternative stakeholder perspectives to glean insights.

  Outcome

  High quality data can be safely analysed to yield exciting insights.

  Metric

  # of business performance degradations caused by analytics activities.

• Practice

  Hold multilateral stakeholder group meetings on data.

  Outcome

  Stakeholders bounce ideas off one another to support innovation from multiple perspectives.

  Metric

  # of innovative ideas arising from data analytics and data reviews.

5Optimized

• Practice

  Let objectives, experimentation, and research guide the development of processes, tools, and people.

  Outcome

  Processes, tools, and people are leading-edge and capable of serving the data analytical needs.

  Metric

  # of data analytics objectives delayed pending process changes, tools, or staff training.

• Practice

  Fund the data analytics strategy to be successful in supportive organization structures and cultures.

  Outcome

  Supportive organization structures and governance enable a funded data analytics capability to succeed.

  Metric

  % of data analytics objectives successfully delivered.

• Practice

  Execute data analytics using sustained leading-edge approaches to deliver exploitable insights.

  Outcome

  Planned continuous improvement in data analytics ensures that beneficial insights are available.

  Metric

  Count and value of beneficial insights acted upon by the organization (i.e. realized benefits).

This capability was introduced in Revision 18.10 as a new critical capability.

The following CCs cover topics that are related to DA in some way:

EAMEnterprise Architecture Management

Business and technical architecture.

EAMEnterprise Architecture Management

Technology roadmap development.

EIMEnterprise Information Management

Data management governance and guidance on Provisioning of data for analytics purposes.

ISMInformation Security Management

Data and information security.

OCMOrganizational Change Management

Management of the organization's culture.

PDPPersonal Data Protection

Data and information privacy.

SPStrategic Planning

Strategy formation for the organization.

SRCSourcing

Procurement specification, user acceptance, and solutions deployment.

TIMTechnical Infrastructure Management

Provisioning of data for analytics purposes.