Case Study

Enterprise Data Strategy Enhances Data-Driven Diplomacy

Developing a Data-Centric Approach


An executive department of the U.S. federal government wanted to develop an enterprise data strategy (EDS) to strengthen capabilities and accelerate response time in an evolving geopolitical landscape. Interviews with stakeholders across 20+ departments highlighted opportunities to increase access to data-driven insights to support diplomacy and improve decision-making and security. An enterprise data strategy would enable the department to improve service delivery, empower their professionals, and enable trusted data to support decision-making and policy setting.



Guidehouse was engaged to help develop, create, and publish the department’s first-ever enterprise data strategy, a framework for enhancing the department’s enterprise data and analytics capabilities to foster cross-functional insights, timely and transparent reporting, and maximize data and evidence-based decision-making. 


Current-state Assessment 

Guidehouse consultants collaborated with the department and various stakeholders to select a data maturity model to assess data maturity across the department. The Open Data Maturity Model was used as a baseline and then tailored using the Capability Maturity Model Integration (CMMI) Data Maturity Model, Federal Data Strategy practices, the Geospatial Data Act of 2018, and the requirements from the Foundations for Evidence-Based Policymaking Act of 2018. Guidehouse helped the department assess its data maturity by completing a series of interviews and working sessions with leaders and staff and identified areas for improvement and strengths related to managing and using data.


Future-state and Roadmap Definition

Guidehouse facilitated numerous interviews and working sessions with over 20+ departments across the enterprise and collaborated with the enterprise data governance body to defined multiple goals, supporting objectives, and guiding principles. In addition, we defined target maturity levels across 6 data dimensions ranging from data management oversights, analytics, strategic oversight, and others; and performed a gap analysis relative to current state to determine what new people, process, and technology capabilities would be needed to close the gap. We also supported the creation of a multi-year roadmap to enable the future-state. Lastly, we led numerous stakeholder engagement and communication efforts to build awareness and generate excitement across the department for the new data strategy. This roadmap guides the execution of multi-year improvement efforts across the department to become a more data-driven organization.


Enterprise Data Governance 

Guidehouse worked with the department to create a data governance body to provide executive oversight, strategic direction, and decision-making regarding critical data assets to maximize the use of data-informed insights and analysis. This governance body is a direct result of requirements from the Federal Data Strategy and the 2018 Evidence-based Policymaking Act. 

Guidehouse engaged with the department’s Chief Data Officer (CDO) and 20+ senior stakeholders from across the department to draft and approve a data charter to legitimize the enterprise data governance body to make executive-level data decisions for the enterprise. Given the decentralized nature of the department, effective stakeholder engagement and management was critical to success of defining the charter and constructing the EDC. 

At the onset of the project, Guidehouse identified the relevant stakeholders to provide feedback, when they should be engaged, and how they should provide feedback to the data charter. Stakeholders included members who represent the business of the department and make strategic decisions regarding data, advisors, and subject matter experts of existing data workflows to inform decision-making, and Evidence Act officials that included both statistical and evaluation officers.

Guidehouse wrote an initial draft of the data charter and reviewed it with a core set of stakeholders, including the CDO, and incorporated feedback. Prior to conducting the working sessions with the stakeholders from across the department, we reviewed the draft charter in one-on-one sessions with a few key stakeholders to obtain early-on feedback, which improved buy-in. Following these review sessions, we conducted two working sessions with the stakeholders and captured, tracked, and incorporated feedback from these sessions. In some instances, we held follow-up meetings with individuals to work through specific concerns and provided the final draft version for review and approval to the EDC.

Guidehouse supports the EDC by working with stakeholders to develop bi-monthly presentations, documenting, and closing out action items, coordinating meeting logistics, and distributing meeting minutes. Guidehouse manages updates to the EDC’s charter based on stakeholder feedback.

In addition, Guidehouse worked across the department to create an enterprise data management policy, which will improve the data sharing capabilities of the department to ensure time-sensitive issues are not hindered by data sharing. Guidehouse has worked with stakeholders on developing policies that enable and promote sharing, while ensuring risk-based controls are in place. More specifically, we engaged with 60+ stakeholders, from 20+ departments, over 10+ facilitated sessions to create the draft data policy. The core team created working teams that covered the 9 policy areas: onboarding, preparation and storage, use, sharing, retention, privacy, security, stewardship, and access. The raw stakeholder feedback was then utilized to develop a policy document that will be published and iteratively updated.  



Guidehouse helped lay the foundation for the department's first-ever enterprise data strategy to enhance data-driven diplomacy across the enterprise. Our data governance approach and supplemental implementation guidance provides a long-term roadmap for the department to make data more manageable, accessible, and interoperable across the varied activities, contexts, and evolving data classification models. 



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