From Strategy to Action: Building a Data Roadmap

Introduction

Before we delve into the next logical step — the data roadmap — a quick refresher on data strategy and how to adopt a bottom-up approach can be found in my previous blog post. A strategy is a concept; it becomes real when it is translated into a roadmap that clearly outlines when we will achieve our goals. Now that you have done the hard part of bringing everyone on board with your strategy, it is time to deliver. The blog post provides a guide on transforming a bottom-up data strategy into an actionable and adaptable data roadmap that aligns with business goals and facilitates strategic execution. So, let’s get into it.

What Is a Data Roadmap?

A data roadmap is a blueprint that outlines how an organization will implement its data strategy. This structured plan turns data strategy into action. The activities mentioned in the roadmap will have a time component associated with all deliverables. A final roadmap is only possible when we prioritize and align between the various activities. The data roadmap demonstrates how certain things will be executed, and most importantly, when.

Key Elements of a Data Roadmap

The key elements of any standard data roadmap should include the following:

· Business Alignment: This was already done as part of creating the bottom-up data strategy. We have aligned the business goals with the data goals, meaning that business alignment has been achieved.

· Data Capabilities: For any initiative to succeed, you need a strong foundation. Data capabilities such as data quality, metadata management, master data management, and data security are essential for achieving the long-term goals defined as part of the strategy. Key focus should be placed on building these foundations in the initial period to reap rewards later.

· Initiatives and Milestones: The proof of the pudding is in the eating. Initiatives and milestones clearly let users know which features they can expect and when. This excites most people, so be mindful to keep expectations realistic.

· People & Roles: Tasks will not complete themselves. They need people who perform certain duties as part of their roles, such as a data steward or data owner. These people help achieve the milestones we have set out to achieve. Without this important piece, we will not be able to demonstrate success.

· Technology enablers: In a tech-savvy world, we cannot exclude technology from any roadmap. Technology must be treated as an enabler that unlocks the full potential of people to achieve the target we have set out to achieve. These can be tools, platforms, or even infrastructure needed for the roadmap to be achieved.

· Process enablers: No real change can be achieved if we do not change the underlying processes responsible for running the organisation. Only by changing the processes will we be able to tackle many unnecessary data issues at the very beginning. Tackling the problem at its source is critical in reducing the overall overhead to the organization.

· Measurement Metrics: Demonstrating progress is as important as preparing a plan. We need to place certain metrics in place to demonstrate progress and highlight any impediments that hinder us from achieving success.

Phased Execution Plan

If you recall, the bottom-up data strategy focused on aligning with the direction in which the organization is heading in the short term, making smaller changes in the mid-term, and then making strategic data changes in the long term. There are multiple ways to create a roadmap, but what I recommend is having a consolidated roadmap, which can be segregated into roadmaps per strategic data pillar. The whole idea is that while we work on these pillars, we work on the underlying goals. However, it is very important that business stakeholders understand the roadmap and when their items will be delivered. This is value, and this is what gains their trust.

Making It Agile and Adaptable

A data strategy document is static, whereas a data roadmap is a living document. The document starts off as a plan with clear timelines; however, we need to be mindful that things are subject to change. For this reason, we need to have a constant feedback loop with our stakeholders. We may have changed business priorities, unforeseen incidents, regulatory scrutiny, etc., which impact the roadmap and deliverables. The whole premise of the bottom-up data strategy was being realistic, and we must apply the same concept to a roadmap. We need to be agile and adapt to changes. But this does not mean abandoning the strategy; we must reassess the path we need to take to reach the same goal, and this must be done repeatedly.

Common Pitfalls to Avoid

The most common mistake people make is committing a lot and delivering very little. This must be avoided. If you start everything at once, your resources and focus will be spread thin, delaying overall delivery. Remember, the key to gaining trust is by delivery. If you can deliver what you have promised, you will gain the trust of the organization. Keep the plan simple and try to avoid over-engineering. The first couple of months should be spent completely aligning with business and delivering value. Once you have done that and gained the trust of the organization, you can implement the next stage of your strategy which spans over the mid to long term.

Conclusion

A data strategy is just an idea; it only gets wings when we can translate it into actionable items on a roadmap with clear timelines. Remember, data works best when we enable, people, processes and technology as part of the roadmap. A data roadmap is not static, it a living document that needs to be updated based on circumstances. Once we have a strategy and a roadmap, we need to have data controls, but this topic is for later! How have your roadmaps evolved over time? Let me know in the comments!