Bridging Data Capabilities and Business Outcomes in Large Enterprises

Organisations today collect and generate unprecedented volumes of data from customer interactions, websites, sales channels, operations, and more. In fact, 90% of the world’s data has been produced in just the last two years.

Thanks to cloud technologies and modern data architectures, storing vast amounts of information is no longer the primary hurdle it once was. Data lakes, distributed storage, and scalable processing have made data abundance routine. Yet many enterprises find that amassing data is very different from harnessing it for business value.

The promise of data-driven decision-making to improve customer experiences, streamline operations, and spark innovation often remains frustratingly out of reach. This report examines why a gap persists between technical data capabilities and real business outcomes, and how data teams struggle to bridge that divide.

It will explore the current state of large organisations’ data estates (typically dispersed, fragmented, and siloed), explain why storage is no longer the challenge rather, making data actionable across departments is and delve into the organisational, cultural, and technical barriers that impede progress.

Throughout, we include perspectives from industry experts and leaders to illuminate the complexities data teams face in turning bytes into business value.

The Fragmented Data Estate in Large Organisations

In most large enterprises, data is scattered across siloed systems and departments. Each function marketing, sales, finance, operations, customer service may have its own applications and databases. Mergers and acquisitions leave legacy systems in place.

Front-end digital channels (websites, mobile apps, social media) generate streams of customer data that often reside separate from back-end transactional systems. The result is a patchwork of data “islands” that do not communicate with each other.

Anyone who has ever worked in a large organisation knows that information silos are a challenging fact of life “the left hand doesn’t always know what the right hand is doing, and employees who are supposed to be working in concert are out of sync”. These silos mean different parts of the business have incomplete, isolated views of what should be shared knowledge.

Multiple studies underscore how pervasive and problematic this silo effect is. A Harvard Business Review survey found 84% of executives report suffering from the negative effects of data silos.

Similarly, an American Management Association study found 83% of executives acknowledge silos in their companies and 97% believe silos have a negative impact on the business.

Data that is critical for one team may be trapped in another’s repository indeed, an estimated 40% of business-critical data is locked away in silos. These fragmented data estates lead to inconsistent information, duplicated efforts, and an inability to get a single source of truth.

For example, customer experience suffers when marketing, sales, and support platforms each hold bits of a customer’s history but no one has a holistic view.

It’s no surprise that in one survey 70% of customer experience leaders cited silo mentality as the biggest obstacle to improving customer service.

Beyond internal silos, data fragmentation can extend externally as well companies that should be sharing data with partners or suppliers often do not, hampering collaboration.

The cost of this fragmentation is significant: by one estimate, data silos cost the global economy $3.1 trillion annually in lost productivity and innovation. In short, large organisations today face a reality of dispersed, unintegrated data estates.

The modern enterprise is rich in data assets but poor in unified information. Storage capacity and data generation are abundant, yet making that data useful at an enterprise level remains a formidable challenge.

From Storage Abundance to Actionable Insights

Paradoxically, as data volumes have exploded and technology to store data has matured, organisations find themselves data-rich but insight-poor. The challenge has shifted from storage to utilisation.

Executives no longer wonder if they have the data they know it’s being collected in droves but rather how to unlock its value across the business. In the past, companies struggled to capture data due to limited storage or high costs.

Today, cloud data lakes and warehouses can economically hold petabytes, and distributed computing can process it. Storage is plentiful and cheap; the real bottleneck is turning data into actionable insights for decision-makers.

In other words, technical capacity is not the limiting factor – accessibility and usability are.

Most large firms have invested in modern data infrastructure perhaps implementing a Hadoop-based data lake or a cloud analytics platform with the expectation that these would break down silos and enable data-driven decisions.

However, many of these initiatives have failed to deliver the expected ROI. Data lakes often became “data swamps” when not coupled with proper governance and purpose.

A data lake can easily turn into an unorganised dumping ground of low-quality or context-less data, “making it difficult to extract useful insights and gain business value from the data.”

Without careful management, a lake meant to unify data can still end up segregated by lack of metadata or controls, effectively creating new silos of unusable information.

The lesson learned is that simply storing data is not enough it must be accessible, trusted, and relevant to the people who need to use it.

Executives are recognising that the frontier has moved to enabling data liquidity and cross-functional insight. As one Gartner analyst noted about big data projects, “the problem isn’t technology” when it comes to extracting value the technology exists and is often “table stakes” now “It’s [the people and processes].”

In fact, Gartner found that around 85% of big data analytics projects fail to deliver their intended results, primarily due to non-technical factors. Companies that poured resources into data technology sometimes found themselves with lots of data and dashboards, but little business impact.

A McKinsey case study described a large company that had hired dozens of data scientists and launched over 50 analytics pilots yet “not one of those pilots was successfully scaled across the company.”

The analytics team was “working on an island, with no connection to cross-functional business strategy, and, as a result, produced limited impact.” This “last mile” gap between analytical output and tangible business outcome has emerged as the new critical challenge.

Embedding data insights into core business workflows and decisions is often cited as the hardest part of data initiatives.

Nearly 90% of leading “data mature” companies’ analytics budgets are now aimed at this integration phase, precisely because “the biggest challenge companies face in extracting value from analytics [is] the last mile integrating insights into workflows and decision-making processes.”

Storage and computation have largely been solved in today’s enterprises. The pressing issue is enabling data to flow freely to those who need it, in a form they can act on. Organisations have more data than ever, but making that data actionable breaking it out of silos, refining it into insights, and delivering it to decision-makers at the right time – is the unmet goal.

Organisational and Cultural Barriers

If technology is not the primary hurdle, what is?

Organisational culture and structure are frequently the greatest barriers to deriving value from data. While executives enthusiastically espouse becoming “data-driven,” transforming ingrained ways of working is difficult.

A survey of senior data and technology executives in 2023 found that only 23.9% of organisations characterise themselves as truly data-driven, and just 20.6% say they have established a genuine data culture.

These low figures persist despite years of investments in analytics highlighting that technology deployment alone does not equal a data-driven organisation. In fact, nearly 80% of those executives cited organisational resistance to change and lack of business transformation as the biggest obstacle to realising value from data.

Culture eats strategy for breakfast, as the saying goes and in the realm of data, a culture that does not embrace data-driven decision-making will stymie even the best tools.

Building a data-driven culture is easier said than done. MIT Sloan Management Review notes that more than 57% of companies struggle to create a data-driven culture. Leaders may fully believe in the power of data and invest heavily in AI and analytics, “but their organisations still aren’t getting the real benefits.” Why?

Because fostering an environment where employees instinctively turn to data for decisions requires changing mindsets and behaviours across the entire enterprise. “The challenge is not buying advanced analytics tools or building accurate technical solutions,” the MIT authors write.

Such cultural change is neither quick nor easy – it demands sustained leadership emphasis, incentives aligned to data-informed outcomes, and often a reevaluation of power dynamics in the organisation.

One common cultural barrier is the “silo mentality” a tendency for departments to guard their data or operate in isolation. In siloed organisations, data is seen as a source of power or turf, not a shared asset.

This mindset leads to lack of collaboration: teams may be reluctant to trust data coming from other groups, or they may simply never communicate enough to realise that insights in one area could benefit another. Internal politics and management resistance can arise when data initiatives threaten to expose inefficiencies or contradict gut instincts.

Gartner’s research into failed analytics projects found that beyond technical integration issues, “management resistance and internal politics” were primary causes of failure. Senior managers might resist data findings that challenge the status quo or their autonomy. In some cases, there is a fear of transparency data shining a light on performance can be uncomfortable in a blame-oriented culture.

Another major barrier is the lack of data literacy and cross-functional understanding. Many non-technical departments struggle to interpret analytics or to see how data applies to their day-to-day decisions. Data science teams, on the other hand, may not fully grasp the business context or communicate insights in the language of business value.

This gap often results in sophisticated analysis that business users ignore or misinterpret. Katia Walsh, Chief Strategy and AI Officer at Levi Strauss & Co., emphasises the importance of upskilling the entire organisation:

Without broad data literacy, analytics efforts become confined to specialists and fail to penetrate the organisational fabric. Likewise, if there is no culture of data-driven decision making where employees from the C-suite to the front line expect to see evidence and insights guiding plans then analytics projects will continually run into apathy or skepticism.

It is telling that in one survey, 93% of executives admitted that cultural factors – people and processes – were a greater obstacle to business success with data than technology was.

Leadership and organisational structure also play pivotal roles. Strong executive support is critical to break down silos and encourage cross-department data sharing.

Companies that succeed with analytics are far more likely to have top management alignment on a clear data vision. McKinsey research shows that “breakaway” data leaders are twice as likely as others to report that their leadership team is completely aligned on an enterprise analytics strategy, and they aggressively integrate analytics across all business units.

By contrast, in many firms the Chief Data Officer or analytics leader operates without real clout perhaps viewed as a back-office function rather than a core business partner. One study observed that Chief Analytics Officers often lack a true seat at the table and have limited authority; they “typically do not have profit-and-loss accountability… placing them at a disadvantage when trying to obtain adequate funding or resources” for data initiatives.

When the data leader is isolated or low in the hierarchy, it signals that data strategy is not fully integrated into business strategy. Leading organisations increasingly recognise that data leadership must combine technical savvy with business acumen.

“The role of the CDO is evolving from just governing data to being a strategic business leader focused on using data to drive value,” says Caroline Carruthers, former CDO and author of The Chief Data Officer’s Playbook.

Similarly, Linda Avery, Chief Data Officer of the Federal Reserve Bank of New York, notes that a successful data leader needs to balance technical skills with strategy, “acting as a bridge between data and the broader organisational goals.”

In short, embedding a data-driven culture requires visible leadership commitment, organisational structures that encourage collaboration, and a workforce enabled to understand and trust data.

Technical and Data Integration Challenges

Alongside cultural issues, there remain significant technical barriers to making data usable across an enterprise. Foremost among these is the complexity of integrating data from numerous disparate sources.

Large organisations often have a convoluted IT landscape from on-premises legacy databases to modern cloud platforms and data residing in incompatible formats or schemas. Data silos occur due to disparate systems, inconsistent data formats, and a lack of integration between them.

Customer records might be structured differently in a sales system versus a support system; product data might live in a supply chain database that doesn’t talk to the marketing analytics tool. Integrating these into a unified view is technically challenging, requiring significant ETL (extract, transform, load) work, master data management, and sometimes re-engineering of systems.

It is not glamorous work in fact, data teams famously spend the majority of their time (some estimates say 80%) on data cleaning and preparation, rather than on advanced analysis. This heavy data janitorial workload slows the time to insight.

Even when integration is achieved, data quality and governance issues can undermine actionability. Data from different silos often comes with mismatched definitions (what one department calls a “customer” might differ from another’s definition), duplicates, or errors. Without robust governance clear ownership of data domains, data quality controls, and common standards a unified data lake can turn into a messy repository where users don’t trust the findings.

It’s notable that companies leading in analytics are 2.5 times more likely to have a strong data governance framework in place. Lack of governance not only impedes insight but can also recreate silos: if data in the lake isn’t well documented or catalogued, only a few experts will know how to use it, effectively siloing the data from everyone else.

As one engineering publication quipped, “data swamps don’t have metatags” people may suspect useful information is in the data lake, but they “have no idea how to find it”. Thus, technical debt and poor data management practices directly impact the accessibility of data to the wider business.

Legacy systems pose another challenge. Many core business applications (ERP systems, mainframe databases, etc.) were not designed to share data easily. Extracting data from them or making them interoperate in real-time can be difficult and costly.

This is why data teams often must stitch together multiple data sources and pipelines, acting as plumbers connecting one system to another. McKinsey observes that analytics leaders must frequently “navigate long-standing processes, stitch together data silos, and challenge legacy power structures” that keep data locked in departmental vaults.

This integration work is ongoing – as new data sources appear (e.g. IoT device feeds, third-party data, etc.), the complexity grows. Modern data architecture concepts like data fabric or data virtualisation are emerging to address this, but they are still maturing.

Additionally, privacy, security, and compliance requirements can slow down efforts to democratise data. In an era of strict data protection regulations (GDPR, CCPA, etc.), data cannot always be freely shared across departments or borders. Controls are needed to anonymise or restrict sensitive information, adding friction to data flows.

One analytics leader noted that nearly every analytics project his company undertook encountered delays due to new data security and privacy requirements cropping up during implementation. Cybersecurity concerns also mean that IT might sandbox certain data, making access cumbersome for analysts or business users. While these safeguards are necessary, they add to the technical challenge of providing frictionless yet governed data access enterprise-wide.

Finally, we must acknowledge that tools and technology, while advanced, are not plug-and-play when it comes to delivering insights. Business intelligence dashboards, self-service analytics platforms, AI algorithms all promise value, but only if aligned with the right data and used correctly.

Often, companies implement sophisticated analytics software but see low adoption because the tools were not tailored to users’ needs or the data context wasn’t right.

Thus, the technical challenge is not merely one of engineering, but of usability: providing data in a format and interface that non-technical users can readily interpret and act upon. Without collaboration between technical teams and business teams in designing data solutions, even the best technology can sit idle.

Technical issues – from integrating siloed systems and ensuring data quality, to managing security – present major obstacles to making data broadly usable.

These are solvable problems, but they require investment, the right talent (data engineers, architects, etc.), and close alignment with business objectives so that integration efforts focus on the most valuable data.

The technology exists; the difficulty lies in applying it across a sprawling enterprise in a coherent, business-focused way.

The Data Team’s Challenge in Bridging the Gap

Sitting at the nexus of these organisational and technical barriers are the data teams themselves the data engineers, analysts, data scientists, and Chief Data Officers tasked with turning data into value.

Data teams often find themselves as translators and intermediaries in their organisations, trying to connect two worlds that don’t always understand each other: the world of business needs and domain knowledge, and the world of data infrastructure and analytics models. This role is not easy. They face pressures on multiple fronts:

In essence, data teams are tasked with bridging a dual gap the technical gap between incompatible systems, and the organisational gap between data and decision-making.

They are change agents in a company, trying to introduce an evidence-based approach in areas that may be historically driven by experience or hierarchy.

This is complex, interdisciplinary work. It requires not only technical data skills, but also project management, communication, domain knowledge, and diplomacy.

As Bruno Aziza (Head of Data & Analytics at Google Cloud) has said, the CDO (and by extension the data team) must act as a strategic advisor to the business, “translating complex data insights into actionable business strategy.”

The difficulties data teams face are a reminder that successful data-driven transformation is as much about people and processes as bits and bytes. They operate in a context where success depends on organisational readiness to listen to data. When that alignment is absent, data teams often bear the brunt of unrealised expectations.

Towards Actionable Data and Business Value

Bridging the gap between technical data capabilities and meaningful business outcomes is one of the defining challenges for enterprises in the modern era. Large organisations have no shortage of data, nor a shortage of technology for handling data.

The crux of the issue lies in liberating that data from silos, refining it into useful knowledge, and infusing it into decision-making at every level of the business. This report has highlighted that storage or technology per se is rarely the roadblock the harder obstacles are organisational silos, cultural resistance, and the “last mile” of implementation.

Overcoming these hurdles requires a holistic approach: strong leadership and vision that treats data as a strategic asset, cultural change programs to foster data-driven thinking, investment in data literacy and talent, and robust data governance and integration efforts to unify the data estate. It is a complex journey, but the prize is significant.

Companies that manage to bridge this gap can unlock enormous value improving efficiency, delighting customers with personalised experiences, and innovating new products and services all fuelled by insights that were previously inaccessible.

Perhaps the metaphor coined by British mathematician Clive Humby captures it best:

In other words, raw data in a vault like crude oil in the ground is potential value, not actual value. The refining process for data is exactly what many organisations are struggling with today.

It involves breaking down silos, cleaning and standardising data, analysing it to extract insights, and crucially, connecting those insights to action. Bridging the gap between data capabilities and business outcomes means building that refinery within the organisation: aligning technology, people, and processes to turn raw data into strategic decisions and tangible results.

For executive leaders, the implications are clear. Success in the next phase of digital competition will belong to those organisations that can truly weave data into their fabric not just in isolated pockets, but across the enterprise.

This is not about chasing the latest technology buzzwords; it is about ensuring that your organisation can actually use the data it collects to drive better outcomes.

It means fostering collaboration across silos, championing a culture where evidence trumps intuition, and continually asking: how does this data project link to our business goals? It also means being realistic about the challenges acknowledging that bridging this gap is a multifaceted transformation, not a simple IT project.

But with perseverance, the gap can be closed. Data teams, empowered and integrated with the business, can become catalysts for innovation and efficiency rather than back-office report generators.

When technical data capability meets organisational capability, data stops being a by-product of operations and becomes a core driver of value.

In closing, the journey to bridge data and business is difficult but vital. The competitive advantage of the future will accrue to those who master it.

As we have discussed, the path forward lies in breaking down both the technical and human barriers connecting the silos, aligning the teams, and translating data into action.

The organisations that achieve this will not only see better business outcomes; they will cultivate an adaptive, insight-rich culture that can continually reinvent itself in a data-driven world. The message to the C-suite is clear: invest in not just the data technology, but in the ecosystem of people, culture, and processes that turn data into business gold.

The companies that do so are already reaping the rewards, and those that don’t will increasingly fall behind. Bridging the data-value gap is no longer a technical problem – it is a strategic and organisational imperative for any business that aims to thrive in the information age.