Is your data strategy driving business value?

Most organisations have a data strategy. Many have invested significantly in creating comprehensive documents that outline governance frameworks, technology roadmaps, and ambitious transformation programmes. Yet when leadership asks what tangible value these strategies have delivered, the answers often become vague.

In 2025, 82% of organisations plan to increase funding in business intelligence and data analytics. But here’s the uncomfortable truth: having a strategy document doesn’t mean you’re delivering business value. Data-driven organisations are 23 times more likely to acquire customers, six times as likely to retain them, and 19 times more likely to be profitable. Yet the gap between those who have strategies and those who execute them successfully is widening.

 

The execution gap

 

The problem isn’t that organisations lack strategy – it’s that they’ve confused strategic planning with strategic execution. Walk into most mid-sized organisations and you’ll find data strategies that tick every conventional box:

– Governance structures with clearly defined roles
– Detailed technology roadmaps spanning multiple years
– Ambitious transformation programmes involving every department
– Impressive architectural diagrams showing future-state environments

 

What’s often missing? A clear connection between these elements and the commercial outcomes that justify their existence.

68% of business leaders in 2025 cite data silos as their top concern, up 7% from the previous year. This disconnect appears in recognisable patterns: IT teams build complex platforms that business users don’t understand, governance committees refine policies nobody consistently follows, and dashboard projects deliver visualisations that don’t answer the questions decision-makers actually need resolved.

 

What business value actually means

 

Companies using big data see an 8% increase in profit and a 10% reduction in cost. But business value isn’t just about cost reduction, it’s tangible improvement across three critical dimensions:

 

Revenue growth:

– Better customer targeting improving conversion rates
– Pricing optimisation capturing additional margin
– Product development insights accelerating time to market

Operational efficiency:

– Preventing quality issues before expensive remediation
– Optimising resource allocation to reduce waste
– Improving forecasting accuracy to avoid stock issues

Risk mitigation:

– Earlier detection of emerging issues
– Better understanding of exposure across the organisation
– More informed decision-making during uncertainty

The critical question isn’t whether your data strategy could theoretically deliver these outcomes. It’s whether it is delivering them right now.

 

Common patterns that undermine value

 

47% of digital workers struggle to find the information they need to perform their jobs, while 32% admit making incorrect decisions due to lack of awareness that relevant data sources exist. Most data strategies fail not in conception but in execution. Several patterns consistently appear:

Misaligned priorities: Data teams optimise for technical excellence whilst business teams need rapid answers. 40% of CIOs in 2025 prioritise fostering a data-driven culture, but this cultural shift requires more than executive mandate.

Resource fragmentation: Spreading limited capacity across too many initiatives creates a portfolio of incomplete projects. Organisations average 897 applications but only 29% are integrated, creating data silos that prevent unified analytics.

Insufficient business engagement: Technical teams make assumptions about requirements that prove incorrect when capabilities reach production. Solutions address questions nobody’s asking whilst overlooking genuinely important issues.

Inadequate change management: Companies with strong integration achieve 10.3x ROI from AI initiatives versus 3.7x for those with poor connectivity. Sophisticated capabilities remain unused because surrounding processes, skills, and incentives haven’t adapted.

 

Building strategies that deliver

 

Effective data strategies share several characteristics that distinguish them from comprehensive documents gathering dust:

They start with business outcomes

– Identify which business improvements would create most value
– Determine what data capabilities those improvements require
– Connect every initiative directly to commercial impact
– They prioritise effectively

– Focus resources on initiatives with clearest path to significant value
– Create momentum through visible success rather than diffusing effort
– Implementation of a BI solution can result in 127% ROI in three years
– They deliver incrementally

– Implement targeted capabilities delivering benefits within months
– Each increment proves value and provides learning for subsequent phases
– Reduce risk whilst accelerating time to value
– They measure consistently

– Establish explicit success metrics at the outset
– Track throughout execution focusing on business outcomes
– 69% of companies cite better strategic decisions as a key benefit of using big data

 

Practical starting points

 

For organisations recognising their current data strategy isn’t driving adequate business value:

Audit current initiatives:

– Review every active data project against business outcomes
– Ask what specific improvement it delivers and how it’s measured
– Reconsider projects that can’t answer these questions clearly

Identify quick wins:

– Look for modest improvements delivering disproportionate impact
– Perhaps sales teams wasting hours on manual reporting and forecasting
– Or operational decisions reliant on outdated assumptions

Establish business-IT partnerships:

– Ensure stakeholders remain engaged throughout initiatives
– Not merely consulted during requirements gathering
– Co-locate teams or establish regular working sessions

Simplify governance:

– Strip back to essential elements only
– Clear ownership, basic quality standards, appropriate access
– Lightweight processes that facilitate rather than obstruct

Build measurement discipline:

– Track leading indicators (adoption rates, data quality, cycle times)
– Monitor lagging indicators (revenue impact, cost reduction, risk mitigation)
– Regular reporting creates visibility and maintains focus.

 

The competitive reality

 

By 2027, more than half of Chief Data Officers will secure funding for data literacy and AI literacy programmes, driven by failure to realise generative AI value. The gap between organisations with effective data strategies and those without continues widening.

Those successfully connecting data capabilities to business outcomes gain multiplied advantages – better decisions lead to better results, justifying further investment in capabilities, enabling even better decisions. Meanwhile, organisations where strategies remain disconnected from business value face mounting pressure as investments don’t deliver promised returns.

Gartner research indicates that data and analytics has become a primary driver of business success, with the potential for data-driven business strategies greater than ever. This arrives from choices about how strategy gets developed and executed. Organisations that treat strategy as a connection between capabilities and outcomes, measured relentlessly against business impact, succeed.

The question isn’t whether your organisation has a data strategy. It’s whether that strategy is driving business value right now, with clear evidence justifying continued investment. If the answer isn’t an unambiguous yes, the time for adjustment has arrived.