Jake's post — est. reading time: 9 minutes

Digital transformation is often mistaken for a technology programme. Enterprise software gets upgraded. Cloud services are introduced. Teams adopt new tools for collaboration, automation, and customer engagement. These moves are visible, budgeted, and easily tracked. Yet in many organisations, the underlying question goes unasked: what’s powering these tools? What’s informing the decisions? What’s guiding the automation? The answer, or rather the gap, is often data.

Transformation that prioritises software without a coherent data strategy risks being superficial. Tools may be modernised, but behaviours remain reactive. Decisions may be digitised, but not improved. Processes may be automated, but not optimised. The result is a surface-level change that gives the impression of progress—while legacy thinking continues to shape outcomes. In contrast, organisations that treat data as a strategic asset transform not just what they do, but how they think and learn.

Data is not simply a byproduct of operations. In a digitally mature business, data is infrastructure. It guides product design, customer experience, risk management, and innovation. It connects teams across silos, aligns decisions with reality, and makes it possible to scale insight across the organisation. It is what turns digital tools into strategic enablers—not just functional upgrades. And yet, too many transformation programmes treat data as an afterthought. Systems are deployed without a plan for integration. Dashboards are introduced without trust in the underlying inputs. AI pilots are launched without clear data pipelines or governance.

True digital transformation begins when organisations build on a data foundation—not just a software stack. This means investing in data quality, accessibility, architecture, and culture. It means treating data as a product, not a project. It means embedding data ownership into teams, so that insight and action are closely coupled. And it means moving from sporadic analytics to real-time, decision-grade intelligence. Without this shift, digital initiatives risk creating more noise than value.

What separates high-performing digital organisations is not simply their use of technology—it’s their ability to learn faster than the competition. Learning depends on feedback. Feedback depends on data. Organisations that can capture, interpret, and act on data in near real-time develop an adaptive advantage. They respond to customer signals more quickly. They optimise operations with precision. They test, iterate, and improve faster. This is the essence of digital transformation: turning data into feedback loops that continuously improve business performance.

To make this a reality, the organisation must reorient around data—not just tools. That begins with architecture. Legacy systems often trap data in silos—marketing, sales, finance, and operations each operating on different definitions, formats, and timelines. Modern data architecture breaks these silos. It creates a single source of truth, federated access, and clear lineage. It enables data to flow across domains securely, consistently, and in context. Without this infrastructure, even the best dashboards will mislead rather than inform.

But architecture alone is not enough. Data also needs governance—clear standards, definitions, and accountability. Many organisations launch data initiatives only to find that teams don’t trust the results. Fields are inconsistently defined. Metrics vary between departments. Historical data is patchy. To change this, data governance must move beyond compliance. It must support usability. That means engaging users in defining what matters, establishing stewardship roles, and building systems that validate, monitor, and clean data continuously.

Then there is the question of ownership. In traditional organisations, data is often seen as IT’s problem—or worse, no one’s problem. In digital organisations, data is owned where it is used. Product managers own the metrics that define success. Operational leads own the data that drives efficiency. Marketing owns the signals that guide campaigns. This decentralised model is only possible when the infrastructure supports it, the culture embraces it, and leadership empowers it.

Data maturity is not about collecting more—it’s about using what you have better. Many companies are awash with data but struggle to generate insight. The challenge is not volume, but relevance. This is where the concept of data as a product becomes essential. Like any product, data must be designed with a user in mind. It must be discoverable, usable, reliable, and supported. When data products are embedded into workflows, they stop being reports and start being capabilities. They become part of how people work, not just how they justify work.

Organisations that do this well develop a new rhythm. Decisions are anchored in evidence. Hypotheses are tested with data. Success is measured not just by delivery but by outcome. Teams start to ask better questions. Insights become shared assets. And a culture of curiosity begins to take root. This is the shift from reporting to intelligence. From hindsight to foresight. From acting on instinct to acting on insight.

But transformation through data is not just operational—it’s strategic. Businesses that harness data effectively often discover new revenue models, new customer segments, and new value propositions. They move beyond selling products to offering services. Beyond transactions to relationships. They use data to personalise, to predict, and to differentiate. In many cases, data becomes the most valuable asset the company holds—not just because of what it reveals, but because of what it enables.

For this to happen, the C-suite must lead. Data strategy is not a technical exercise—it is a leadership responsibility. It cuts across functions. It shapes investment decisions. It defines how the company sees itself and its market. Without executive sponsorship, data initiatives stall. Without alignment at the top, priorities conflict. And without a vision for what data makes possible, transformation remains incremental rather than exponential.

Executives must ask: are we building a business that learns? Do our systems talk to each other? Are our people confident in the data they use? Do our decisions improve over time? These questions go beyond digital tools. They touch on culture, structure, and capability. And they must be asked repeatedly, especially as transformation unfolds.

Many companies invest heavily in platforms—CRM, ERP, AI, automation—but miss the connective tissue. Data that’s trapped in one system can’t inform another. Models that aren’t continuously fed with fresh data degrade over time. Automation built on bad data accelerates bad decisions. To avoid this, organisations must prioritise integration. They must design for flow, not just storage. And they must recognise that the value of any application is only as strong as the data that powers it.

In practical terms, this means embedding data thinking into every stage of digital delivery. During discovery, what data do we need to validate assumptions? During design, what signals will shape the experience? During delivery, how will we track success in real time? During scaling, how will we ensure data remains clean, relevant, and connected? When data is part of the delivery conversation—not an afterthought—the outcomes are dramatically stronger.

Data literacy is also key. It’s not enough to have good data—people must be equipped to use it. This includes technical skills, yes, but also critical thinking. The ability to interpret trends, challenge assumptions, and make evidence-based decisions. Training should be tailored—not everyone needs to code or model. But everyone should understand what “good data” looks like, how to question it, and when to trust it. Data-driven organisations build this capability intentionally—through onboarding, through learning paths, and through day-to-day expectations.

All of this points to a final truth: if digital transformation is about being adaptive, responsive, and customer-centred, then data is the foundation. Not in the abstract—but in the way decisions are made, processes are designed, and value is delivered. Software alone won’t get you there. It can enable change, but it can’t direct it. Only data can do that. And only if it's treated as the strategic asset it truly is.

So the real question is this: Are you building a software-enabled business—or a data-driven one?