Most organisations already recognise the potential within their data. The challenge is turning that potential into better decisions, more efficient services and measurable organisational value. Having more data or investing in a new platform does not automatically deliver those outcomes. The difference often lies in having a clear data strategy roadmap.
A good roadmap establishes where you are today, where you want to get to, and, crucially, why getting there matters. It enables you to identify the opportunities worth pursuing, understand what needs to change and prioritise investment around real organisational value.
We spoke to Ian Bobbett, Chief Data Officer at Crimson, about how organisations can take a more disciplined approach to data strategy.
His starting point is refreshingly simple: don't begin with the technology. Begin with the outcome.
"Your data strategy should be based on what your business outcomes need to be."
Ian Bobbett, Chief Data Officer at Crimson
A data strategy sets out what your organisation wants to achieve through data and the principles required to do it effectively. A data strategy roadmap takes that ambition and turns it into a structured plan for getting there.
According to Ian, the strategy begins with your data vision. What do you want data to enable within your organisation? Where is the business going? Where are the opportunities? Where could data help you improve efficiency or deliver better outcomes?
The strategy then needs strong foundations, including data governance, compliance, security, ethics, and data quality. The roadmap formalises how you intend to deliver that strategy.
This distinction matters because a data strategy should not begin with "we need a new data platform."
Technology is an enabler, but the desired outcome should determine both the destination and the route towards it.
It is tempting to assume that newer technology will automatically mean better data.
Ian has seen organisations move information from an older database to a modern data platform without addressing problems in the data itself. The result is essentially the same problems sitting inside newer technology.
His analogy is simple: if an application is the engine, data is the fuel, and even the best engine will struggle to perform when the quality of that fuel is poor.
The same principle applies when organisations start with a requirement such as better reporting and immediately jump to procuring a data platform. Before investing, leadership teams need to ask whether the proposed project solves a valuable business problem and whether the outcome justifies the investment.
That is where a data strategy roadmap introduces discipline.
It creates a link between a business challenge, the data required to address it, and the investment needed to achieve the desired outcome.
If you're asking what a data strategy should include, start by thinking beyond technology.
Ian highlights governance, compliance, security, ethics and data quality as important foundations, while also stressing the need for the strategy to support defined business outcomes.
Ian explores this further in his article on the role of a data governance strategy, including how trusted, well-governed information can support more confident day-to-day decisions.
Crimson's wider Data & AI approach considers four interconnected areas:
This is important because data transformation rarely succeeds as an isolated IT exercise. It needs people to own it, processes to support it and leaders who understand what success should look like.
A useful way to approach writing a data strategy is to move progressively from discovery to prioritisation.
Crimson's data framework starts by understanding current capability and establishing target outcomes. It then evaluates potential opportunities before creating the prioritised roadmap.
Before deciding where to go next, establish your starting point.
Ian recommends understanding current capabilities across data quality, systems, internal skills and processes. That exercise can reveal a difference between where stakeholders believe the organisation is today and its actual capability. From there, the organisation can establish its desired future or "north star" position.
Crimson's Discover framework formalises this through stakeholder interviews, target outcomes, a technology review, data audit, delivery-team interviews, review of ways of working, and reporting and insight review.
This Discovery phase provides the evidence needed to make the rest of the roadmap realistic, achievable and aligned with the organisation's priorities.
Once you understand your starting point and destination, move from broad ambition to specific use cases.
Where can data improve an outcome that matters?
Perhaps the priority is improving customer experience. For a university, it could be student retention, operational efficiency or another strategic objective.
Ian makes an important point here: value does not always have to be financial. Different stakeholders and organisations define value differently.
Crimson's framework builds use cases from the discovery phase and identifies their dependencies and critical path.
A technically possible use case is not necessarily one worth pursuing; the next question is whether the proposed outcome justifies the investment.
Crimson's framework works with business and finance teams to estimate the potential benefit of individual use cases based on agreed assumptions.
Ian explores this further in The Real Reasons Organisations Invest in Data & AI, including how investment can improve customer experiences, unlock operational efficiencies and support more agile decision-making.
This provides an important filter, moving the conversation away from "Could we do this?" towards the more valuable question: "Should we do this?"
Once a use case has potential, determine whether your organisation currently has the capability to deliver it.
The next task is to identify where the gaps lie across data, technology, people, and processes. Crimson's Evaluate stage compares current capability with the proposed business cases and considers the costs required to close those gaps. This is where ambition becomes an achievable plan, giving leaders a clearer understanding of what must change before an initiative can deliver its intended outcome.
One of Ian’s strongest messages is not to try to solve everything at once.
His recommendation is to avoid trying to "boil the ocean" and instead adopt a domain-oriented approach. Pick an area, fulfil the requirement and demonstrate value before expanding.
Prioritisation does not need to become unnecessarily complicated either. Ian keeps returning to two fundamental considerations: the value a solution could provide and how difficult it will be to deliver.
Crimson's framework then adds wider considerations, including investment, dependencies, other projects and business context, before agreeing on the proposed roadmap with stakeholders.
Crimson's data framework connects strategy with delivery, adoption and value realisation across technology, data, people and process.
For another perspective on structuring the journey, watch Ian Bobbett explain a proven framework for building an AI and data roadmap.
One reason organisations can become stuck when considering how to create a data strategy is the belief that every foundation must be perfect before anything useful can happen. In practice, organisations do not need to build fully fledged solutions from day one. An initial solution can draw on a manageable number of relevant data sources, then develop iteratively as the organisation learns more. This principle is especially useful for large organisations with significant volumes of information.
A smaller, outcome-focused starting point lets you prove whether the use case works, understand how people respond to it and then introduce additional data and capability over time.
The roadmap provides direction without locking the organisation into trying to transform everything simultaneously.
Data quality is an essential part of a successful data strategy roadmap, but waiting for every piece of organisational data to be perfect can create another barrier to progress.
Ian Bobbett's advice is more pragmatic:
"If you want to improve data quality, start using data."
Using data helps expose what is working, what isn't and where information needs to be repaired. Ian also describes how bringing information from different sources into a data platform can expose discrepancies and create opportunities to improve information quality across the wider estate.
That makes data quality an active part of the journey rather than simply a hurdle at the starting line.
And its importance continues to grow as organisations explore AI. Ian argues that greater awareness of the quality of information underpinning AI has helped organisations recognise the importance of information and data quality more broadly.
Once trusted data foundations are in place, organisations can begin moving from historical reporting towards forward-looking insight. Ian explores this further in From Insight to Impact: Building an Effective Predictive Analytics Strategy.
A data strategy may involve technology, but it should not be the sole responsibility of IT.
Ian recommends having a committed senior sponsor, ideally at the executive level, while involving other senior stakeholders early in Discovery and Evaluation.
The objective is to give leaders ownership of the process, help them understand what the roadmap could mean for their teams and avoid presenting them with a solution they do not feel part of.
That aligns closely with Crimson's approach. Its framework includes stakeholder interviews during Discovery and works with internal stakeholders to build and agree on the proposed roadmap.
A roadmap developed with the business has a much stronger connection to organisational priorities than one created solely around technical requirements.
Perhaps you know your organisation could do more with its data, but you do not yet know which opportunities are of the highest value.
That is a perfectly reasonable place to begin.
Ian recommends exploring the art of the possible. Rather than somebody arriving with a predetermined answer, these conversations examine the organisation, its challenges and where data could potentially make a difference. From there, leaders can start identifying the value opportunities worth investigating further.
Crimson's approach follows the same principle. Its data consultancy services are designed to establish current capability and the desired end state, identify value opportunities, understand risks and costs, and prioritise a roadmap towards the selected outcomes.
You do not need to know the finished answer before you start. You need enough clarity to ask the right questions and identify the opportunities worth exploring.
Ultimately, a successful data strategy roadmap is not a shopping list of technologies; it is a plan for turning data into outcomes.
Start with the business vision, understand your existing capabilities and identify meaningful use cases. Test the business case, assess the gaps, and prioritise based on value and feasibility. From there, create a roadmap that provides everyone with a clear and realistic path forward.
Most importantly, resist the temptation to do everything at once.
As Ian explains, a disciplined sequence of use cases, business cases, gap analysis, prioritisation and roadmap development keeps organisations focused and prevents data programmes from becoming overwhelming.
The technology will inevitably evolve. Your roadmap should too.
What should remain constant is the reason you are investing in data in the first place: to make better decisions, improve outcomes and create measurable value.
Knowing that data could create value for your organisation is one thing. Knowing where to focus first, what is feasible and which opportunities justify investment is another.
Crimson's Data & AI Discovery Workshop helps senior leaders examine their current data and technology landscape, identify the barriers limiting progress and uncover practical opportunities where data and AI could improve efficiency, decision-making or service outcomes.
Working with Crimson's data and AI specialists, you will assess where your organisation stands today, identify opportunities grounded in value and feasibility, and leave with clearer priorities and recommended next steps.