Data Monetisation: A revenue stream that’s easily overlooked

Business leaders today are facing an innovation conundrum. On the one hand, they recognise the critical importance of business model innovation as a driver of long-term growth. They know that designing new ways to create, deliver and capture value are vital for maintaining customer relevance and long-term competitiveness.

But at the same time, the combination of inflation, a sluggish economy and market volatility is eroding margins and dulling their appetite for risk. 

With traditional sources of growth under increasing strain, where should companies look next for innovation? The answer could be hidden in plain view. Many businesses overlook one of their most valuable untapped assets – namely, their own data. Most organisations are data-rich but value-poor because they haven’t yet recognised what data they have and who would pay for it.

Despite considerable progress in information capture and analysis – and the potential of AI as an accelerator – there is still a lingering sense of frustration around data monetisation. A third of global executives believe their companies’ data assets have unrealised potential.  Most companies sit on significant unused data. In fact, according to Forrester, two-thirds of organisations say at least half of their data goes unused. 

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Show me the money: how to spin data into gold

Successful data monetisation relies on two fundamental approaches: identifying the potent data in the business and then converting it into financial gain. The first step involves harvesting data and analysing it to reveal insights, patterns and potential use cases. Armed with this intelligence, companies can then set about using it to their advantage. 

The focus of this first approach typically remains business-oriented by using internal data to: 

1. Optimise internal operations for cost efficiencies – data analysis can identify high-cost areas, help streamline or automate process, manage resource allocation, forecast demand and implement predictive maintenance. 
2. Improve customers’ experiences with data-driven insights - customer data can help to unlock more personalised touchpoints, identify pain points and proactively address needs across the entire customer journey. 
3. Take a data-centric approach to developing new propositions – an insight-led strategy helps curb the intrusion of assumption, intuition and optimism bias. Teams can create, develop, test and measure concepts against predefined metrics such as KPIs. 

These lower-hanging data fruits are worth pursuing and should already be in reach of all businesses. 

The alternative route to data monetisation is by selling data products that solve business problems, meet new customer needs and provide actionable insights for other organisations. Here, proprietary data is used to power entirely new business models that generate revenue in ways that are far removed from company’s founding mission.  

From by-product to the product itself

Organisations hold vast stores of commercial, customer and operational information, yet few have transformed it into marketable products or services. Those that do discover that data is not a by-product of business – it is the product.

And the usual rules of product innovation apply. How big is the market? Does the opportunity stretch beyond immediate customers to include adjacent businesses, suppliers, regulators and seemingly unrelated industries? 

Having gauged the size of the prize, what is the effort needed – in terms of resources, capabilities and operational complexity – to turn a raw data asset into a revenue-generating product? 

Assets should be distinctive and attractive in the eyes of the target customer. To minimise the risk of failure, the proposition should be torture tested for its desirability, viability and feasibility, and forced to pass stringent go/no-go checks that will maximise its chance of success.

The skill lies in matching the right data with the right customer need. Having a good handle of data is only one half of the equation, as it is still only as valuable as the problem it solves for a customer. After all, customers don’t buy data; they buy the ability to make better decisions.

Depending on what type it is, data has the ability to help potential customers with a wide range of business challenges. 

• Commercial data: identify trends, competitor understanding, demand forecasting, supply chain planning
• Customer data: customer segmentation, geographic distribution, customer loyalty and purchasing habits
• Operational data: performance effectiveness, industry efficiency, predictive maintenance and logistics planning 

Vendors might choose to sell raw or aggregated data like transaction history; license data for specific uses; or provide data-driven services like personalised advertising or financial analytics. Companies can also sell anonymised data to partners for market research, to aid their operational efficiency or boost their product development. 

Here are 5 examples of data monetisation in action:

1. Online fashion retailer Zalando has used insights from 52 million customers to offer marketing and logistics solutions to other brands.
2. Mastercard’s SpendingPulsesells aggregated and anonymised transaction data to banks, retailers, investors and governments to assist with fraud detection, marketing strategies and regulatory policies.
3. A number of healthtech and insurance companies provide deidentified, patient-level data (Real-World Data) to researchers for medical and pharmaceutical research, which helps in developing new treatments and improving clinical trial design. 
4. In the UK, National Highways sells traffic and road condition data to delivery services and navigation apps. The body has also partnered with Ordnance Survey to use its trusted road network data to enhance the efficient movement of people, goods and suppliers across the country’s busiest routes. 
5. Authoritative voices such as Nielsen and IBM sell their data analytics and market research reports to organisations that are hungry for customer-based insights that will drive their strategy and product development.

Data-as-a-Service or Direct Monetisation?

Choosing the right strategy for monetising data is a critical next step. For example, a Data-as-a-Service model will provide subscribers with continuously updated datasets and insights. While this brings the potential to create greater, recurring revenue streams; it’s harder to get right. Not every business has the volume of data nor the sales infrastructure to offer data products and services at the scale of a Mastercard, IBM or Zalando.

A more easily attainable approach could be selling raw or processed data as standalone products – known as direct monetisation. There are a growing number of data marketplaces and data collaboration platforms that will streamline the process and connect vendors with a wide network of potential buyers. 

Three Reasons to Act Now

1. In turbulent economic times, diversifying revenue streams is a key strategy to build resilience.
2. Developments in AI are making the standardisation of data increasingly affordable, bringing the digital transformation required to sell data within reach.
3. The market for data monetisation across industry verticals will become increasingly crowded, with forecasts showing growth of $4.05 billion in 2025 to $12.62 billion by 2032. Gaining first-mover advantage will be key to establishing credibility and a strong presence. 

Companies are increasingly recognising their data as a strategic asset, using AI analytics and data marketplaces to unlock economic and operational value. Many are moving from data collection to value creation. 

Fortune favours the bold. Data has become the currency of the modern economy, powering a new era of growth, efficiency and innovation. Those that succeed will not only uncover hidden value but build entirely new lines of business powered by information.