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If you are building a data strategy, you’ll no doubt be looking to create a data strategy roadmap at some point. UDig regularly includes a data strategy roadmap in client deliverables to help communicate plans and approaches to key stakeholders. We’ve even gone so far as to create roadmaps as a data strategy template we can use as an accelerator in building a data strategy.
In this article, we will cover:
Well-structured views of the data strategy delivery plan are essential in conveying the infrastructure, architecture, capabilities, talent, and processes needed to drive success. Building the data strategy roadmap is a great way to test assumptions around deliverables and see how things play out when everything is on the same page. A data roadmap is similar to a technology roadmap, except that it focuses specifically on an organization’s data. We find roadmaps to be a fantastic tool to convey all of the components involved in the transformation. It becomes a valuable document to provide governance when executing the initiatives that will deliver the data foundation, as well as a way for other projects to make assessments of any impacts on their initiatives and how they might impact the data strategy. This clarity can be useful to accelerate the journey.
Without a clear plan, organizations may find themselves with a disjointed and ineffective approach to data management, which can lead to wasted resources and missed opportunities.
A data strategy roadmap helps an organization to:
Having a data strategy roadmap can also help an organization improve its decision-making processes, increase efficiency and productivity, and gain a competitive advantage. Additionally, a data strategy roadmap can support the implementation of compliance and regulatory requirements, such as GDPR and HIPAA.
There are many different ways to convey the implementation plan via the data strategy roadmap. We like to create a cascading view with increasingly detailed views as you drill down. At its highest level, the data strategy roadmap is an executive summary of the key transformation initiatives allowing stakeholders to get a single-page overview of the entire implementation plan.
The next level in the data strategy roadmap outlines how each of the development activities on the first page are interrelated. It shows several key things:
The way we’ve built this roadmap, there are visual signals between the executive summary and the Gantt chart to tie concepts together. The color coding of the elements on the executive summary will correlate with the color coding of the initiatives on the Gantt chart.
The final layer of our enterprise data strategy roadmap template is a one-page summary of each of the deliverables in the plan. This enables communication of detailed aspects of the plan in a streamlined and efficient way. The summaries are the foundation for a deep understanding of each delivery element in the overall data program. They will relate to the executive summary with color coding and initiative abbreviations. These colors and abbreviations tie the one-page summaries to the visual roadmap and the executive summary to provide a comprehensive view of the overall roadmap.
On the one-page summary, we include the following elements to aid in understanding each initiative and how it fits into the big picture:
A data strategy roadmap is vital for any organization to manage and utilize its data effectively. It serves as a clear and actionable plan for data management efforts, outlining the steps the organization will take to collect, manage, and utilize data to achieve its business objectives.
This roadmap not only provides direction and focus for data-related initiatives and projects but also serves several vital purposes:
Aligning Data Efforts with Business ObjectivesImproving Decision-MakingIncreasing Efficiency & ProductivityProviding a Competitive Advantage
ComplianceMonitoring PerformanceFuture-Proofing
By clearly defining the organization’s data objectives and goals, a data strategy roadmap ensures that all data management efforts align with the overall business strategy. This helps to ensure the organization is making the most of its data and utilizing it to its fullest potential.
A data strategy roadmap provides a framework for collecting, managing, and analyzing data. This enables organizations to make more informed decisions based on data, giving them a better understanding of their operations and customers.
By optimizing data management processes and making more effective use of resources, a data strategy roadmap can help organizations increase their efficiency and productivity.
Leveraging data to make better decisions and improve operations can give organizations a competitive edge over their peers.
A data strategy roadmap can assist in implementing compliance and regulatory requirements such as GDPR and HIPAA.
The roadmap can help organizations set key performance indicators (KPIs) and continuously monitor and measure performance, enabling them to identify areas for improvement and make adjustments as needed.
A data strategy roadmap can help organizations stay up-to-date with industry trends and developments and adapt to changes in the data landscape over time, ensuring that the organization is well-positioned for the future.
Data strategy roadmaps provide a clear plan for an organization’s data management efforts. The process of creating a data strategy roadmap typically involves several steps:
Define Organization’s Data Objectives & GoalsAssess Current Data LandscapeIdentify Key Performance Indicators (KPIs)Prioritize Initiatives & Projects
Develop Timeline for ImplementationAllocate Resources & BudgetImplement, Optimize & AdaptReview & Update
This step involves identifying the specific business objectives that the organization wants to achieve through its data management efforts. This could include improving customer engagement, increasing operational efficiency, or identifying new revenue streams. We cover this in great depth in our post on How You Build a Data Strategy.
This step involves taking inventory of the organization’s current data assets, including the types of data being collected, the systems and tools used to store and manage data, and the processes used to analyze and make decisions based on data. We often do this as a data maturity assessment, where we rate an organization’s maturity on around six dimensions and relate it to different capability levels. This becomes a starting point, and the desired capabilities become the target. Together, these highlight the elements that the roadmap needs to address.
This step involves determining the metrics that will be used to measure the success of the organization’s data management efforts. These KPIs will be used to evaluate the effectiveness of the data strategy roadmap and make adjustments as needed. These might include the percentage of scope completed, monthly engaged data users, data value enabled, or employee satisfaction with data being easy to use. The process is very analytical, and the key is to think about what metrics are needed to evaluate program success.
Once the organization’s data objectives and current data landscape have been identified and assessed, the next step is to prioritize the initiatives and projects that will be undertaken to achieve these objectives. This is where you will have to think about the work it will take to deliver the needed capabilities. Think about the technical infrastructure, software, talent, culture, communication, and processes required to be successful. A common mistake is to forget about talent, culture, communication, and processes and only include the technical aspects of the roadmap. A business case template can help you evaluate the return on investment (ROI) of a project.
This step involves creating a detailed plan for how and when the initiatives and projects will be implemented. The timeline should include specific milestones and deadlines to ensure that progress is tracked and the plan stays on schedule. This is where you’ll need to think hard about the skills in the organization, how long projects might take, and what dependencies they might have on other projects. The most common mistakes at this stage are being too optimistic about time frames and missing key dependencies in how work is sequenced.
This step involves determining the resources and budget needed to implement the initiatives and projects. This includes identifying the personnel, technology, and other resources required and budgeting for any data management costs. At this point, it is a good idea to identify a delivery governance process to ensure each project stays on track or to give you an early indication of any initiatives that might be falling behind.
Once the plan is in place, the next step is to implement it. This includes building and maintaining data infrastructure, collecting, storing and managing data, and analyzing and leveraging data for decision-making. It also includes assessing roles and talent, filling gaps where needed, building governance processes, defining an operating model, and developing a communication plan. It’s essential to continuously monitor and measure performance, identify improvement areas, and make necessary adjustments.
Data strategy roadmaps should be regularly reviewed and updated to ensure they stay relevant and support the organization’s business objectives. This could include incorporating new technologies, adjusting for changes in the organization’s data landscape, or modifying the plan based on lessons learned.
If this sounds like a lot of work, it can be, particularly if you’ve never done it before. If you are evaluating whether you need a data strategy roadmap or a refreshed roadmap, check out our Data Strategy Accelerators. Our experience and accelerators can improve your chances of success and avoid wasted investment along the way.
Reid, SVP of Data and Analytics at UDig, is a long-time data professional with experience at multiple Fortune 500 companies. Most recently, he was the Chief Data and Analytics Officer at Markel. Prior to that he held multiple roles at Capital One including VP of Data Engineering.
