Five steps to meaningful data monetisation

Organisations have been collecting and collating data for years, but many have missed the opportunity to realise its true value. While technology has played its part in data monetisation – first cloud, and now AI – the failure to enable it effectively often lies in other areas such as business alignment, culture, and governance.

Which is why creating a robust strategy that considers an organisational-wide view, will pay dividends further down the track. The first move, however, is to decide what path your organisation will take to data monetisation. It can go in the direction of external monetisation, that is selling data products, or it can focus on internal monetisation - using data to improve customer value and boost business efficiency, in a way that offers a measurable return on investment.

As you might expect, at Spark, we favour the latter road – it can take longer, but it ultimately leads to greater riches. And the reason why is demonstrated in the development of a comprehensive AI and data programme at Spark. The first cab off the rank was known as BRAIN, which stands for Build Robust AI for Next-Best-Action. It is designed to help improve our product offering by sifting through a vast trove of data points. Our next major initiative at Spark is also well underway with the use of generative AI across the enterprise. In the era of generative AI, data isn’t just advantageous—it's vital to enable both predictive and generative AI use cases to deliver better customer experiences.

We began creating BRAIN four years ago to address the challenge that at any point in time, only 1-2% of the addressable market is shopping for a plan. The idea was that if we could predict who those people were, then we could send them the right offers at the right time.

Over the last three years, Spark has delivered a 17% efficiency gain in our marketing investment annually through our BRAIN capability. So how did we achieve this? We put it down to the following five steps – vision, organisation, governance, technology, and culture – which can also be followed by your organisation.

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1. Vision – defining a data product that supports your business strategy

It can be tough to know where to begin, which is why the first step to meaningful data monetisation is having a vision. And what we mean by this is twofold.

Firstly, it’s about having a very senior person, or people, be evangelists for data and AI in alignment with your business strategy. An organisation will struggle to progress a data monetisation strategy if it doesn’t have buy-in – and preferably enthusiasm – from the very top.

Secondly, it’s finding the right data product. This can be achieved either by going bottom-up – looking at the data you have to determine potential products, or top-down – identifying a business problem or opportunity that delivers on the business strategy and then finding or collecting the right data. It’s also about asking the age-old question – what problem will deliver the biggest return on investment. It’s critically important the problem has a clear link to the business strategy in order to deliver significant returns.

It can be useful to have an external data expert come in and help your team brainstorm new ideas and consider which are the most achievable. They will provide an unbiased perspective and a structured approach to identifying, prioritising and refining your data products. It’s also worth cultivating a test and learn process when experimenting with data monetisation, so you can fail fast until you discover the best idea to proceed with.

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In addition, from the outset, think about designing for a ‘system solution’ rather than a ‘point solution’. That is, create a technical capability that is scalable, rather than one that can only be used to solve a specific problem at a specific point in time.

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2. Organisation – gathering together the right people

The second step is to think carefully about who in your business you need on board to help design, create, deploy and adopt a data product effectively.

Look across the organisation and get those people on board early. It won’t all be technology folk either; if you are looking at improving efficiency in customer service, it is critically important to involve your people in customer-facing roles.

Breaking down silos in your organisation and getting people with diverse skillsets to work collaboratively will really pay off when it comes to building an enduring data product that can scale and deliver the envisioned benefits.

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3. Governance – getting the guardrails in place

The third step is to consider governance structures, because this will help you overcome one of the biggest impediments to data monetisation - fear. The idea that deploying a data and AI solution will unleash a whole lot of trouble, for example, a privacy breach or a cyber-attack. Putting the necessary governance structures and guardrails in place will help overcome this fear.

When designing data products, we did so in line with GDPR guidelines, which must be followed by any organisation doing business in the European Union and is arguably the most robust privacy and security law in the world. We also developed and published a set of AI Principles that guide the development of responsible and ethical data products.

Being confident that you have the right privacy and security measures in place is also hugely enabling because it forces you to think about things from the customer’s perspective. For example, most people will think it’s acceptable for Spark to know if a customer subscribes to Spotify and to act on that knowledge. They would not, however, like the idea of Spark knowing what music they listened to and then acting on that.

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4. Technology – cloud-first pays dividends

As you might expect, technology modernisation is a key step, because to enable a data monetisation strategy, you need the right foundation.

Cloud provides access to technology that improves the efficiency of internal teams, which gives them bandwidth to deliver new data products. Cloud also offers access to new technology that unlocks value from existing data; examples include new data science libraries or Large Language Models.

Many organisations will be grappling with some legacy technology, so it may not be easy to bring all the data that is contained in disparate systems together in one cloud solution.

If that’s the case, then think carefully about what data will be the most useful to create a data product and prioritise that when investing in systems and solutions.

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5. Culture – data is a team sport

If you have followed all the steps above, and created a product within a scalable system, then servicing your data product should be an ongoing commitment. It will evolve and change, and as it does, you will need many people within your organisation to champion its development.

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Creating a culture where your people are keen to learn and experiment with data and AI, not only pays off in terms of better products and services, but it also helps you to retain people in your organisation who are intellectually curious.

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These five steps established for BRAIN have also put Spark in good stead to move quickly in our adoption of generative AI. As an organisation, we have been exploring generative AI as a way to enable our people, including customer service teams, to be more productive and augment their skills.

One of our generative AI products is BRAVO, which stands for ‘Bold & Revolutionary Adoption of ChatGPT in Spark Via Open-AI’. BRAVO is our new search engine, which uses AI to surface up answers that help our customer service advisors with custom queries and decision making. This allows our people to answer customer questions promptly, comprehensively and accurately, with references, to provide a more positive experience for our staff and customers.

In the end, whatever product or service you create, your business will be good company.  After all, over half of those surveyed in McKinsey’s State of AI in 2023 have adopted AI.

According to McKinsey, those going ‘all in’ with AI are using the technology principally to create new business ideas and new sources of revenue, not for cost reduction. It’s on the rise in every sector. To join in, you just need to take the first step.

Need help with where to get started? Our team is here to support your business to map out the best path to meaningful data monetisation.

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ABOUT THE AUTHORS

Kallol Dutta leads the strategy, governance, and enterprise adoption of data, AI, ERP, and automation capabilities across the group. With over 20 years of experience leading consulting, IT, digital, and data teams, Kallol has a proven track record of developing and implementing IT and digital strategies that generate commercial and customer value, while evolving Spark into a data-driven organisation.

Evan Wilson is the Head of Customer and Advisory at Qrious, where he is responsible for the teams that help our customers imagine and design data-centric solutions to industry problems. Evan has extensive experience delivering strategies and pragmatic solutions to address business problems and unlock opportunities.

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