Drive transformation through a mission focused data and analytics strategy that can influence the enterprise-wide business strategy and answer key agency questions.
Focus on mission value creation and Key Performance Indicators (KPIs) to develop a modern data and analytics strategy that delivers tangible outcomes and value in the immediate and long-term while accounting for organizational, cultural, and technical constraints. A holistic data and analytics strategy also properly plans for budget and resource allocations for mission critical needs. Connecting mission value to your data specific investments and identifying resources that will allow your organization to maximize data for decision making and mission enabling purposes, will help move your organization forward even with a limited budget. Make sure your agency’s data and analytics strategy is more than just complementary to your agency’s mission and enterprise-wide business strategy. Your data strategy should focus on how to transform the organization and in turn influence and transform your mission objectives and business strategy.
4.1 Focus on use cases, pain points, and transformation/innovation opportunities when interviewing key stakeholders to assess your agency’s current data landscape. This assessment will present where the existing data sources and silos are and opportunities for data to accelerate change and improve existing KPIs.
4.2 Identify any shared goals, consistent themes, and parallel findings from the stakeholder interviews.
4.3 Understand your agency’s budget process including the IT investment management processes to ensure that appropriate resources are planned for in future budgets as required under the Federal Data Strategy 2021 Action Plan - Action 3. Having this knowledge will help provide an agency-wide lens when incorporating budget considerations into your data and analytics strategy.
4.4 Connect your business problems to the key agency questions that data can help answer to prioritize transformational opportunities.
4.5 Conduct an inventory of your data tools and analytics platforms to see how well they are integrated into your agency’s business processes.
4.6 Understand the value of predictive analytics to create a future-focused organization.
4.7 Develop a strategy framework that moves the agency to a “future-state” focus and positions you as a strategic change agent.
4.8 Create a working group with stakeholders across various agency offices to assist with any strategic planning activities.
4.9 Identify the strategic goals, subgoals, and key objectives for your data and analytics strategy.
4.10 Link data specific resource allocations to the key business problems and objectives that are core to your data and analytics strategy.
4.11 Create vision statements on ways to transform data stewardship, analytics, data technology/tools, data literacy, and data governance.
4.12 Develop a roadmap outlining a threeto-five-year strategic plan to address key objectives and strategic goals outlined in the action plan.
4.13 Analyze any existing and planned data investments to see if they are directly addressing your key business problems, compatible with your agency’s mission, and aligned with your target data architecture. Consult your agency’s CPIC or budget processes for further guidance on this step.
4.14 Review any set strategic goals and key objectives to ensure they have clear timelines and metrics.
4.15 Create an action plan that outlines key objectives and strategic goals with assignments to key stakeholders (e.g., data stewards.).
4.16 Identify and prioritize the projects within your agency that can accelerate progress while building critical capabilities.
4.17 When interacting with key stakeholders to advocate for any data and analytics investments, emphasize the importance of these investments as integral to both the agency’s IT strategy and data and analytics strategy.
4.18 Audit the action plan on a yearly basis to ensure it is aligned with changing priorities and new iterations of the Federal Data Strategy.
4.19 Measure the progress of your data and analytics strategy including your strategy framework, action plan, and critical projects.
4.20 Continuously communicate the value and measured progress of your data and analytics strategy’s action plan and critical projects to external and internal stakeholders.
4.21 Consider using open and interoperable tools as a sustainable and more cost-effective resource to address your mission needs.
4.22 Prioritize centralized data analytics, Machine Learning (ML), and Artificial Intelligence (AI) projects in your data and analytics strategy and investment plans.
Identify the pain points from key stakeholders and where they see near term opportunities to transform the organization.
Develop a data and analytics strategy which considers perspectives from across the organization with the goal of influencing the overall business strategy.
Discover points of intersection between stakeholder groups to connect priorities for future collaboration opportunities.
Create a data and analytics strategy needs to be enterprise focused and not centered on any single agency office or entity.
Address the overlapping priorities of agency staff across the organization and creating solutions that emphasize data sharing and shared services.
Gain an understanding of the IT investment management processes (e.g., Capital Planning and Investment Control or CPIC) to help guide future decision making and get integrated into the federal budget process.
Ensure your budgeting and resource allocation is aligned with the CIO Office and established federal-wide policies and practices in the future.
Conduct a deep dive into your core business problems to figure out its origins to better identify solutions that the data and analytics strategy can address.
Create a data and analytics strategy that clearly outlines and demonstrates your understanding of the core business problems within your organization. Only then can you specifically link these core business problems and their root causes to innovative solutions that answer your key agency questions.
Connect your existing data investments to your business processes helps you identify if these investments are still needed and are providing value to your organization.
Catalog all your existing tools and platforms to create an inventory that documents all your existing investments in one place.
Identify opportunities to gain a quick win through predictive analytics tools and platforms.
Incorporate predictive analytics and its ability to identify future trends to get your organization ahead. Predictive analytics tools and platforms can cut costs, rapidly identify solutions to future problems, and create a culture of preventing problems, not just reacting to them within your organization.
Investigate the power of predictive analytics tools to transform your organization to demonstrate your vision.
Link your business problems and agency mission to transformative solutions that are driven by greater data utilization, established data practices, and defined data management standards.
Establish a framework that considers the agency’s size, data priorities, and data architecture.
Use this framework to highlight your plans to progress the data culture away from the current state and influence the enterprise-wide business strategy.
Create working relationships between key agency employees for information sharing and strategy building purposes.
Build a holistic enterprise data and analytics strategy that brings these groups together to connect priorities across agency offices.
Define the key markers for success within your data and analytics strategy and document them in a clear and concise format.
Identify goals, sub-goals, and key objectives that clearly illustrate your plans to leverage data as a strategic asset while also centering your agency’s mission and data customer’s priorities.
Connect and explain how any new data specific technologies, programs, etc. will address your agency’s key challenges.
Understand the problems your agency is facing and your core mission objectives before you submit plans to invest in data specific resources.
Include resource allocations and investment plans that provides a solution to a business problem and further your agency’s set strategic goals.
Align the messaging in your data and analytics strategy to the future state, moving beyond the current data environment and prioritizing the projects and activities that will transform your agency.
Track and account for existing data-specific budget allocations and resources when advocating for transformation activities. Also, anticipate impacts to other strategies at the agency level including the agency strategic plan, the IT strategy, etc.
Use the strategic roadmap to demonstrate your ability to set goals that are attainable and map out longer timelines for projects that are five to ten years into the future.
Provide an actionable timeline that features each marker (e.g., goals, subgoals, objectives, pilot projects) within your data and analytics strategy.
Prioritize goals and projects to tackle first, such as the high impact projects with shorter timelines that can demonstrate value early and help you gain influence.
Create alignment between your mission needs and your existing data resources.
Identify any inefficiencies that exist within your organization to help you consider different resource options or suggest replacing certain budget requests that are not connecting directly to your core business problems.
Set clear timelines and metrics to track progress and account for any roadblocks to your data and analytics strategy that may emerge.
Provide an opportunity to revise your strategy periodically based on goals or objectives that are not consistently achieving the required standards linked to mission KPIs.
Ensure accountability for each set objective and goal and a key point person who can manage its progress.
Develop measurable objectives and strategic goals that are managed by individuals across your organization sets your data and analytics strategy’s action plan up for success. For example, data stewards can oversee a specific portion of the strategy that best aligns to their strengths and job duties.
Work with data stewards to define future success factors for their assignments as the action plan progresses.
Prioritize projects which demonstrate near term value to gain trust from your key stakeholders and buy-in for future projects with longer implementation timelines and more challenging projects.
Include pilot projects that can demonstrate the value data can bring to programs and build a business case for future investments that support the enterprise.
Emphasize the CDO role as integral across business units and interconnected with other priorities of the organization.
Ensure stakeholder groups understand the mission enabling value of data investments as equally critical to IT investments. For example, work closely with the CIO and other key stakeholders to develop a case for change to advocate for a target data architecture plan.
Ensure your data and analytics strategy connects to the larger priorities of the federal government and CDO community.
Keep your agency’s data and analytics strategy moving forward by incorporating new visions and priorities, and emerging technologies that could be used to solve business problems as your organization progresses in data maturity.
Keep your data and analytics strategy updated and consistent with changing priorities, mission KPIs, and lessons learned.
Look for any set objectives that are not demonstrating success factors to locate potential roadblocks and identify a different path.
Gain influence and create awareness of your agency’s success by reiterating the success factors of your data and analytics strategy to key stakeholder groups.
Find opportunities to demonstrate the value of projects and initiatives that have been implemented, and the need for future investments to further grow progress in those avenues.
Demonstrate your ability to build and grow trust within your stakeholder relationships by demonstrating how your agency is advancing.
Provide a low or no-cost option that fosters greater collaboration and integrated decision-making opportunities.
Maximize your data use, as directed in the Evidence Act and ease barriers to access for internal users. Unlike software that require licenses, these types of tools provide no cost options to users.
When a need or problem is clearly defined, ML and AI has the potential to recommend and/or automate decisions and actions, increasing efficiency for agency offices.
Implement a methodical approach to build ML and AI pipelines.
Develop ML and AI models that provide both the functionality and the procedures for how these models make decisions.
Communicate through your strategy, the tangible benefits of using ML and AI to support your agency’s mission.
Understand the decision-making style (e.g., relationship-based, memo, or directive driven) and motivating factors (e.g., OMB, Government Accountability Office (GAO), or industry research) of your agency and how that plays into strategy building.
Create a standard definition for how your data and analytics strategy will return value to the agency so there is a common understanding on what value creation means.
Consider leveraging different types of team environments (e.g., time bound working groups, project specific teams, enduring working groups) when bringing together stakeholders for strategy building.
Consider the commitment, focus, and priorities of senior leadership when identifying projects that can provide you with quick wins and show nearterm value.
Consider the scalability of specific resources, tools, and investments, choose options that allow better accessibility and sharing between your data consumers.
Department of Veteran Affairs Data Strategy
U.S. Food and Drug Administration Data Modernization Action Plan
Department of Defense Data Strategy
Department of Commerce Data Strategy