Build a Robust and Comprehensive Data Strategy

Key to building and fostering a data-driven culture.

Onsite Workshop

The absence of an enterprise data strategy leads to issues such as:

  • The inability to effectively and strategically leverage the vast (and growing) volumes and varieties of data being ingested and collected by the organization.
  • Dissatisfaction with and a lack trust in data from executives, key decision makers, and other data consumers.
  • Data becoming a liability rather than the (corporate) asset it should be.
  • A hasty response to changing demands, disruptors, and trends that results in a costly, ineffective, and piecemeal approach that misses the mark in delivering incremental value from the organization’s data assets.

A well-formulated and robust data strategy will:

  • Serve as the mechanism for making high-quality and well-governed data readily available to fuel decision making, manage risk, meet regulatory and compliance requirements, and deliver on your organizational mandate.
  • Allow you to go beyond platitudes such as “data is an asset” and get to actually treating, managing, and leveraging data as the strategic asset it is for gaining or maintaining your competitive edge and for supporting organizational innovation and transformation.
  • Ensure that your data investments bring you the returns by meeting your organization’s strategic objectives.

Module 1: Establish Business Context

The Purpose

  • Establish the business context for the business strategy.

Key Benefits Achieved

  • Substantiates the “why” of the data strategy.
  • Highlights the organization’s goals, objectives, and strategic direction the data must align with.

Activities: Outputs:
1.1 Advisory kick-off session with the data strategy sponsor (such as the Chief Data Officer [CDO], Chief Architect, Digital Transformation Leader, CIO).
  • Business context and strategic drivers
1.2 Executive and senior business stakeholder interviews, one-on-one and small groups, to understand stakeholders’ strategic priorities and the alignment with data, discuss the vision, mission, goals, and principles of the data strategy, and understand the organization’s data culture.
  • Defined vision and mission
  • Defined principles and goals
  • Data Culture Survey diagnostic results analysis

Module 2: Ensure You Have a Solid Data Foundation

The Purpose

  • Build use cases of demonstrable value and understand the current environment.

Key Benefits Achieved

  • An understanding of the current maturity level of key capabilities.
  • Use cases that represent areas of concern and/or high value and therefore need to be addressed.

Activities: Outputs:
2.1 Build use cases of demonstrable value: Drivers, challenges, and opportunities.
  • High-value use cases
2.2 Understand the current data environment: Data management enablers.
  • High-level evaluation of the current environment
2.3 Understand the current data environment: People and organizational structure – key roles and skill sets.

Module 3: Build Your Future State Plan and Initialize the Roadmap

The Purpose

  • Build out a future state plan that is aimed at filling prioritized gaps and that informs a scalable roadmap for moving forward on treating data as an asset.

Key Benefits Achieved

  • A target state plan, formulated with input from key stakeholders, for addressing gaps and for maturing capabilities necessary to strategically manage data.

Activities: Outputs:
3.1 Target state plotting: Gap analysis and roadmap planning.
  • Target state plan – high-level roadmap
3.2 People and organizational structure planning: Key roles, and skill sets.
  • High-level RACI for key functional areas

Module 4: Formulate Your Data Strategy

The Purpose

  • Consolidate business and data needs with consideration of external factors as well as internal barriers and enablers to the success of the data strategy. Bring all the outputs together for crafting a robust and comprehensive data strategy.

Key Benefits Achieved

  • A consolidated view of business and data needs and the environment in which the data strategy will be operationalized.
  • An analysis of the feasibility and potential risks to the success of the data strategy.

Activities: Outputs:
4.1 Build your business-data-needs model.
  • Business-data-needs model
4.2 Risk and feasibility analysis: Conduct a SWOT analysis.
  • SWOT analysis
4.3 Initialize the organization’s data strategy.
  • Initialized data strategy document

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