Have you developed your data strategy but haven’t made much progress on reaching your key business objectives? Are you struggling to prioritize data initiatives or convince stakeholders to buy in? A data strategy roadmap may be the missing link in your strategy, and in this blog, we’ll discuss what it is and why you need one. By the end, you’ll have a clear idea on how to effectively build a data strategy roadmap.

Creating a data strategy is a huge first step—don’t let the time and effort you put into it go to waste. You need a documented and actionable plan that provides you with a clear path forward and the specific details for how you will actually execute your data strategy. Without a data strategy roadmap in place, it can be difficult to allocate the necessary resources, establish policies and processes, build or improve data infrastructure, train staff on data literacy or new platforms, and even gain critical buy-in to start any other initiative.

A data strategy roadmap will ensure the success of your data strategy.

What is a Data Strategy Roadmap and Why Do You Need One?

A data strategy roadmap is a plan that outlines the implementation process for how an organization will effectively manage, analyze, and utilize data to achieve its business goals. It includes objectives, necessary resources, and a timeline.

A data strategy roadmap helps organizations align data initiatives with business objectives, track progress, measure success, and allocate resources effectively. It also helps to ensure that all necessary people and departments are involved—and it serves as a tool for meeting milestones and staying organized. By following a data strategy roadmap, organizations can use data to drive business growth and make decisions more effectively.

When Should You Build a Data Strategy Roadmap?

A data strategy roadmap should be developed whenever an organization is working on its data strategy—whether it’s at the start of a data initiative, as part of a broader business strategy, or at any other time when the organization is looking to optimize its use of data.

There are several situations where building a data strategy roadmap may be particularly beneficial. For example:

  • When stuck in a progress rut: A data strategy roadmap can fix any prioritization and support issues and help you make progress toward more valuable and sustainable objectives.
  • When starting a new data initiative: A data strategy roadmap can help you plan and prioritize data initiatives and ensure that they are aligned with business goals and have the necessary resources and support.
  • When launching a new product or service: A data strategy roadmap can help you understand customer needs and use data to inform the development and marketing of the new offering.
  • When entering a new market: A data strategy roadmap can help you gather and analyze market data to inform expansion plans and identify potential opportunities and challenges.
  • When looking to improve operational efficiency: A data strategy roadmap can help you identify areas where data can optimize processes and improve efficiency.
  • When seeking to drive innovation: A data strategy roadmap can help you identify trends and patterns in data that can inform new ideas.
Black text listed next five vertically lined orange graphics: lock, lightbulb, rocket ship, arrow and innovative light bulb. Text: When to Build a Data Strategy Roadmap above graphics

It’s a good idea to build a data strategy roadmap whenever you are working on your data strategy—regardless of what initiates it.

Step 1: A data strategy assessment is a good starting point to building an effective data strategy roadmap.

The Five Steps to Building a Data Strategy Roadmap

To build a data strategy roadmap, identify quick wins and critical initiatives, set high-level milestones based on business goals, add initiatives and details to the timeline, and plan communication with the company to use data to drive business growth.

Light grey curved roadmap with orange pinpoints and numbers from one to five placed scattered on the road with black text under each. Text states five elements of a data strategy roadmap.

A data strategy roadmap should include quick wins and highly critical initiatives, high-level milestones, timeline with initiatives, additional details and dependencies, and a plan to communicate the timeline with the company.

Step 1: Identify quick wins and highly critical/urgent initiatives.

The first step in building a data strategy roadmap is to identify which data and analytics initiatives should be prioritized. This includes identifying low hanging fruit that can be easily implemented, as well as highly critical or urgent initiatives that must be addressed to meet business goals.

Determining priorities allows you to:

  • Clarify the goals and objectives and enable the team to focus on the most important aspects of the data strategy roadmap as well as allocate resources accordingly.
  • Identify potential risks and challenges and put contingency plans in place to mitigate those risks.
  • Set realistic expectations and communicate them clearly to stakeholders.
  • Align the roadmap with the overall business strategy and support the achievement of the business’ goals.

Step 2: Identify high-level milestones based on business goals or expected changes to the business.

The next step is to identify high-level milestones based on business goals or expected changes to the business. These milestones will help to determine the required pace for completing data initiatives that will support business goals within the desired timeframe.

Ask questions such as:

  • What are my company’s big picture goals or the “big rocks” and what timeframe are those goals set at?
  • What important company events or seasonal factors need to be on the timeline?
  • Are there any known critical changes to the company that should be included on the timeline such as a new product launch, planned acquisition, or a new ERP system go-live date?
  • What level will our data and analytics maturity need to be at each of these milestones on the timeline?

Step 3: Fill in the timeline with initiatives.

With the high-level milestones in place, the next step is to fill in the timeline with the initiatives that will be required to build out the data architecture and other requirements in iterations that will incrementally increase data and analytics maturity while also providing value. In the first iteration, ask questions such as:

  • Do we need to set up new tools or technologies? If so, do we have an implementation partner or do we need to hire one?
  • Will we need to create new data pipelines? Do we have the in-house talent to do this?
  • Are our definitions consistent across the organization? Do we have a data governance program in place to address this issue going forward?

Remember to account for all the steps required for the first iteration, even if they seem minor. Skipping a small task early on may result in having to go back and reengage someone who now has other priorities. It is important to consider how each tool, technology, or process fits into the broader data architecture and to approach implementation in a phased manner.

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Step 4: Add additional details and dependencies.

To ensure that the data strategy roadmap is actionable and achievable, it is important to add additional details and dependencies. This includes:

  • Identifying the people and resources that will be required to complete the initiatives, including any external partners or vendors that may be involved.
  • Identifying any dependencies or interdependencies that may impact the timeline, such as the completion of certain initiatives that are required for others to move forward.
  • Creating a resource plan that outlines the required roles and responsibilities of each team member, as well as the estimated time and budget required for each initiative. This helps to ensure that everyone is clear on their roles and responsibilities and that the team has the necessary resources to complete the initiatives as planned.
  • Considering any constraints or limitations that may impact the timeline or budget, such as regulatory requirements, data privacy considerations, or technical limitations. These factors may require additional planning and resources to address, so it is important to factor them into the roadmap.

Step 5: Include a plan to communicate the timeline with the company.

Once the data strategy roadmap has been developed, it is important to communicate it to the company to build buy-in and ensure that everyone is aligned and working toward the same goals. To effectively communicate:

  • Clearly and concisely communicating the vision and goals of the data strategy roadmap, highlighting how it will support the achievement of key business goals and align with the overall business strategy.
  • Sharing the timeline and milestones with the company and clearly explaining the purpose and significance of each initiative.
  • Regularly engaging with stakeholders to gather feedback and input on the data strategy roadmap, ensuring that it is realistic and addresses the needs and concerns of all relevant parties.
  • Providing updates on the progress of the roadmap, addressing any issues or challenges that arise, and adjusting as needed to reflect changing priorities or circumstances.
  • Ensuring that communication is transparent and open, encouraging collaboration and buy-in from all relevant parties.

Review, Revise, and Refocus Your Data Strategy Roadmap

A data strategy roadmap is not a static document, and it is important to review and revise it regularly to ensure that it remains relevant and aligned with business objectives. This may involve revising priorities, updating resources, and adjusting timelines as needed. Remember to involve key stakeholders in the review process, as they can provide valuable insights and perspectives on the effectiveness of the data strategy roadmap. By staying agile and adaptable, organizations can effectively use data to drive business growth and decision-making.

Rebecca Zeni Rebecca is a data strategy consultant who specializes in executing data strategy engagements. Having led her own company for 10 years, she understands the importance of a holistic approach to data strategy, gaining insights, and providing business value.
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