Despite billions invested in data and AI infrastructure, most financial institutions have yet to unlock the true value of their data assets. According to recent studies by Gartner and Forrester, more than 80% of financial organisations in the UK have yet to achieve measurable ROI from their data transformation efforts. Meanwhile, IT spending continues to surge upward by nearly 10%, with financial services accounting for more than 40% of Europe's technology investment. This paradox reveals a fundamental disconnect between investment and outcome, highlighting what we believe is the missing link in digital transformation strategy.
I believe the answer lies in a clear strategic shift from digital enablement to data monetisation. The future of finance is not just data-informed but data-native. Those who will dominate the next decade won't merely store and analyse data, they will generate entirely new business models, products, and revenue streams from the information assets they already possess. This transformation demands not only sophisticated technology but a fundamental change in organisational mindset about what data represents and how it can create value.
Reframing the IT function as a Revenue Engine
Consider the traditional IT office, long viewed as a necessary cost centre focused on compliance, reporting, and risk management. Today's CIO faces an unprecedented opportunity to transform this perception and become a genuine revenue enabler. The path forward involves building internal data marketplaces where different business units can access, consume, and pay for data services based on actual usage and value creation. Even more compelling is the opportunity to create anonymised, API-based data services that can be monetised externally while maintaining strict compliance with regulatory requirements.
This transformation is already happening in practice. At one major UK financial institution, a Keyrus-led data monetisation initiative leveraging Snowflake's cloud data platform and Alteryx's advanced analytics capabilities delivered a remarkable 15% revenue uplift within just 18 months. The secret to this success wasn't purely technological; it was the combination of scalable data governance frameworks with intelligent automation, all while empowering teams with what we call augmented intelligence. This approach ensures that AI supports and enhances human decision-making rather than replacing the critical thinking and contextual understanding that business professionals bring to their roles.
The initiative began with a comprehensive audit of existing data assets, identifying dormant datasets that contained valuable insights about customer behaviour, market trends, and operational efficiency. Rather than treating this information as a byproduct of business operations, the organisation began viewing it as inventory that could be packaged, refined, and sold both internally and externally. The team developed internal pricing models for data consumption, creating accountability across business units while generating measurable revenue from previously unutilised assets.
Three Comprehensive Monetisation Models Transforming IT Services
The first model, internal monetisation, represents a fundamental shift in how organisations approach cost allocation and resource management. CIOs are discovering they can reallocate costs based on real-time usage data, dramatically boosting profitability by increasing visibility and accountability across the organisation. This approach goes beyond traditional cost accounting by creating dynamic pricing models that reflect actual data consumption, processing requirements, and value creation. For example, a commercial banking division might pay for access to sophisticated risk modelling data, while the retail banking arm purchases customer behaviour analytics to improve product targeting. It will also change the way to get the budget approved.
One practical implementation involved creating an internal data marketplace where marketing teams could access customer transaction patterns, risk teams could consume credit scoring algorithms, and product development teams could purchase market trend analyses. Each transaction was tracked, priced, and billed back to the consuming department, creating both revenue for the IT function and clear visibility into the true cost and value of data-driven decision making. This model transformed the finance team from a cost centre into a profit-generating division while improving data quality and governance across the entire organisation.
The second model, external Data-as-a-Service offerings, represents perhaps the most significant opportunity for financial institutions. Banks and other financial services firms possess extraordinary datasets about economic trends, consumer behaviour, and market dynamics. When properly anonymised and packaged, this information becomes incredibly valuable to insurers, credit agencies, fintech companies, and other market participants. The key lies in creating compliant, secure methods for sharing insights without compromising customer privacy or regulatory requirements.
A leading UK retail bank recently launched a successful Data-as-a-Service platform that provides anonymised spending pattern insights to insurance companies looking to better understand risk profiles. The bank's finance team worked with compliance and technology partners to create APIs that deliver real-time insights about consumer behaviour in specific sectors, retail, hospitality, and transportation, without exposing individual customer information. This service now generates millions in annual revenue while helping the bank's partners make more informed underwriting and pricing decisions. The success of this initiative has led to expansion into other areas, including providing economic indicators to government agencies and offering market sentiment data to investment firms.
The third model leverages AI-powered compliance and risk management capabilities to create billable services for external partners. Agentic AI architectures are revolutionising fraud detection, Know Your Customer checks, and risk monitoring processes. Rather than viewing these capabilities as purely internal tools, forward-thinking financial institutions are packaging them as services that can be offered to smaller banks, fintech startups, and other financial services companies that lack the resources to develop such sophisticated systems internally.
One compelling example involves a major European bank that developed an advanced machine learning system for detecting suspicious transactions and money laundering activities. After implementing the system internally and achieving remarkable results in reducing false positives while improving detection rates, the bank recognised an opportunity to offer this capability as a service to smaller financial institutions. They created a secure, cloud-based platform that allows community banks and credit unions to access enterprise-grade fraud detection capabilities on a subscription basis. This service not only generates significant revenue but also strengthens the overall financial ecosystem by improving security standards across smaller institutions that might otherwise be vulnerable to sophisticated fraud schemes.
The Strategic Power of Ecosystem Partnerships
The complexity of data monetisation in financial services cannot be overstated, and successful implementation requires sophisticated partnerships with technology providers who understand both the technical requirements and regulatory constraints of the industry. Keyrus has developed strategic relationships with leading technology partners, including Snowflake, Qlik, Alteryx, and Amazon Web Services (AWS), and many others to create cloud-native, secure monetisation engines specifically tailored to the unique challenges of the financial sector.
Snowflake's data cloud platform provides the foundation for secure data sharing and monetisation, enabling financial institutions to create controlled environments where data can be accessed, processed, and consumed without compromising security or compliance requirements. The platform's native applications framework allows organisations to build sophisticated data products that can be deployed across multiple environments while maintaining strict governance controls. This capability is essential for financial institutions that must balance the desire to monetise data with the absolute requirement to protect customer information and maintain regulatory compliance.
Qlik's embedded analytics capabilities transform traditional reporting into interactive, monetisable insight pipelines. Rather than producing static dashboards that simply display historical information, financial institutions can create dynamic, real-time analytics services that provide actionable insights to internal stakeholders and external customers. These services can be priced based on usage, complexity, and value delivered, creating sustainable revenue streams while improving decision-making across the organisation.
Alteryx's advanced analytics platform enables financial institutions to automate complex data preparation and analysis processes, reducing the cost and time required to create valuable insights while improving accuracy and consistency. This automation is crucial for scaling data monetisation efforts, as it allows organisations to process vast amounts of information and create standardised data products that can be reliably delivered to customers.
AWS provides the underlying cloud infrastructure that enables secure, scalable data sharing across organisational boundaries. The platform's comprehensive security features and compliance certifications give financial institutions the confidence to share data externally while meeting stringent regulatory requirements. AWS's data exchange capabilities facilitate the creation of external data marketplaces where financial institutions can offer their data products to other organisations in controlled, secure environments.
Market Momentum and Strategic Timing
The current market environment presents an unprecedented opportunity for financial institutions to embrace data monetisation strategies. UK firms are showing early returns on data monetisation initiatives, consistently outperforming their global peers according to recent TechRadar analysis. This performance advantage stems from several factors, including regulatory clarity around data usage, sophisticated technology infrastructure, and a mature financial services ecosystem that facilitates collaboration and innovation.
Gartner's latest research predicts a 9.8% increase in IT spending across financial services, with the majority of this investment focused on analytics and AI capabilities. This investment surge reflects growing recognition that data and AI are not just operational tools but strategic assets that can drive competitive advantage and revenue growth. However, the key to success lies not in the technology itself but in how organisations leverage these capabilities to create new value propositions and business models.
Forrester's comprehensive analysis reveals that insights-driven businesses are 8.5 times more likely to exceed 20% annual growth compared to organisations that rely primarily on traditional decision-making approaches. This statistic underscores the competitive advantage that comes from effectively monetising data assets. Financial institutions that can transform their data into actionable insights and valuable services will not only improve their performance but will also be able to help their customers and partners achieve better outcomes.
BARC 's recent study shows that financial planning is increasingly becoming AI-powered, with organisations using machine learning and predictive analytics to improve forecasting accuracy and strategic decision-making. However, the research also highlights that security and governance remain top-of-mind concerns for financial services leaders. This tension between innovation and risk management creates both challenges and opportunities for organisations that can develop secure, compliant approaches to data monetisation.
The market research consistently points to a critical insight: the organisations that will succeed in the next phase of digital transformation are those that can effectively balance innovation with security, compliance, and governance. Financial institutions have a unique opportunity to lead this evolution, given their existing expertise in risk management and regulatory compliance, combined with their access to valuable data assets.
A CEO Perspective on Augmented Intelligence
Throughout my career leading digital transformation initiatives across Europe, I have consistently advocated for an augmented rather than automated approach to AI implementation. This philosophy is particularly relevant in the context of data monetisation, where human expertise, contextual understanding, and strategic thinking remain irreplaceable elements of success.
At Keyrus, our conviction is straightforward yet profound: monetising data is not primarily a technical project but a strategic transformation that must begin with clear business outcomes and measurable value creation. This perspective shapes every engagement we undertake and every solution we develop. Rather than focusing on experimental AI implementations or proof-of-concept projects that rarely scale, we concentrate on delivering measurable ROI, ensuring regulatory compliance, and empowering human teams to achieve more than they could with traditional tools and processes.
The augmented intelligence approach recognises that AI excels at processing vast amounts of data, identifying patterns, and executing repetitive tasks with speed and accuracy. However, humans remain superior at understanding context, making nuanced judgments, managing relationships, and developing creative solutions to complex problems. The most successful data monetisation initiatives combine these complementary strengths, using AI to handle data processing and pattern recognition while relying on human expertise for strategic direction, relationship management, and creative problem-solving.
This approach has proven particularly effective in financial services, where regulatory requirements, customer relationships, and risk management considerations require sophisticated human judgment. AI can identify potential compliance issues or fraud patterns, but human experts must evaluate the context, make final decisions, and manage the consequences. Similarly, AI can analyse customer data to identify monetisation opportunities, but human professionals must develop and execute the strategies to realise those opportunities while maintaining customer trust and regulatory compliance.
The emphasis on business outcomes rather than technological capabilities represents a fundamental shift in how organisations approach AI and data monetisation. Instead of asking what AI can do, successful organisations ask what business problems they need to solve and what value they want to create. This outcome-focused approach ensures that technology investments align with strategic objectives and deliver measurable results.
The Imperative for Action
To the finance leaders reading this analysis, the message is clear and urgent: the window of opportunity for leading the data monetisation revolution is rapidly narrowing. While your competitors are still focused on basic analytics and reporting, you have the chance to transform your organisation into a data-native business that generates revenue from information assets.
The transformation requires courage to move beyond comfortable, traditional approaches and embrace new ways of thinking about data, technology, and business models. It demands investment in both technology and talent, as well as a commitment to developing new organisational capabilities around data governance, product development, and customer engagement.
However, the rewards for early movers are substantial and sustainable. Organisations that successfully monetise their data assets will not only generate new revenue streams but will also develop competitive advantages that are difficult for competitors to replicate. They will build deeper relationships with customers and partners, develop superior insights about market trends and opportunities, and create organisational capabilities that drive continued innovation and growth.
The choice facing finance leaders today is not whether to embrace data monetisation, market forces and competitive pressures will eventually require all organisations to develop these capabilities. The choice is whether to lead the transformation and capture first-mover advantages or to follow competitors and accept a secondary position in the evolving market landscape.
At Keyrus, we stand ready to partner with organisations that are prepared to make this strategic leap. Our experience, expertise, and technology partnerships position us to help financial institutions navigate the complexities of data monetisation while delivering measurable results and maintaining the highest standards of security and compliance.
The future belongs to organisations that can transform data into value. The question is not whether this transformation will happen, but whether your organisation will lead it or be left behind by those who act with greater speed and strategic clarity.
Let's make data matter, not just as an operational asset, but as a strategic differentiator that drives sustainable competitive advantage and measurable business growth.
Further Reading
White Paper: The Enterprise Data Marketplace Revolution: Transforming Data Assets into Revenue Streams
Connect with Bruno Dehouck
Sources used in the Blog:
Gartner (2025): Forecasting a 9.8% increase in global IT investment for 2025, led by analytics, AI, and cloud.
Forrester (2025): Insights-driven businesses are 8.5× more likely to report revenue growth of 20%+; the finance sector is to account for 40%+ of European tech spend.
TechRadar (2025): UK CFOs report higher-than-global-average returns from AI/data monetisation initiatives.
Qlik: Data Monetisation whitepapers and webinars outlining embedded analytics and monetisation models.
Snowflake: Native apps and secure data marketplace capabilities to enable financial services data monetisation.
AWS: Partner stories and secure data sharing infrastructure via AWS Marketplace and analytics services.