Data monetisation brings rewards for good governance

Companies that have gone through the pain of a data governance journey find they are now ideally situated to take the next step with their data.
Veemal Kalanjee
By Veemal Kalanjee, MD of Infoflow.
Johannesburg, 17 Feb 2023

Good data management has long been driven by regulatory and governance requirements. This has made it something of a grudge investment for some time.

However, those organisations that have gone through the pain of a data governance journey are discovering they are now in a prime position to take the next step with their data – moving to data monetisation.

Data monetisation offers the compelling prospect of benefiting from something the organisation already has in abundance. But data monetisation seldom refers to actually selling the data as a revenue stream, unless it is in the form of aggregated anonymised data.

Gartner defines data monetisation as “the process of using data to obtain quantifiable economic benefit. Internal or indirect methods include using data to make measurable business performance improvements and inform decisions. External or direct methods include data sharing to gain beneficial terms or conditions from business partners, information bartering, selling data outright (via a data broker or independently), or offering information products and services (for example, including information as a value-added component of an existing offering).”

Data monetisation offers the compelling prospect of benefiting from something the organisation already has in abundance.

In general, the real data monetisation opportunity is to derive value through improving revenues or reducing costs.

Many organisations have been so focused on governance that they have overlooked the fact that there is real value inherent in data: every business decision is based on reliable data. It can be used to reduce costs or increase revenues.

Data’s potential value can be difficult to quantify, until the organisation has identified the value it is trying to achieve. Once it has identified a value, such as cost saving, innovation or competitive advantage, it can start putting down measures around areas such as customer retention or reducing customer churn, which inform a baseline for measuring the value of the data.

Foundations of data monetisation

In order to achieve the benefits within this definition, some fundamental building blocks need to be in place from a data management perspective.

Access to data: Inaccessible data means no value or inability to utilise the data to derive value.

Ensuring trustworthy data: You want reliable insights and so do your data customers. Confidence in data trustworthiness makes monetising the data that much easier.

Data traceability: This enables the company to provide context to the data and its origin, as well as relevance to the monetisation drive.

Once these principles are in place, the organisation is better positioned to strategise data monetisation.

However, addressing just the data principles is never enough as this takes care of the data itself. There is much more needed to ensure the value derived results in tangible commercial benefit.

Organisations need a comprehensive monetisation strategy, driven by owners of the strategy and the environment.

It should be noted that an effective data monetisation strategy requires more than data and analytics skills – it also needs business acumen, with experience in the niche department or sector the data must deliver value in.

The company needs to understand the context in which the data is created, categorised and destroyed. That’s where subject matter experts come in as a bridge between business and data teams.

They put the available data into context and evangelise the monetisation drive – saying ‘this is the data we have available, it is trustworthy, and this is what value it can bring’.

Subject matter skills bridging data and business are hard to come by, however. To overcome skills gaps, organisations could look to upskilling staff, working with external experts, or foster close collaboration between data and business teams.

With the data management and governance fundamentals in place, it is often best to start with a small use case, identify the data, stakeholders, the value and then work on that use case to show the value.

Organisations that have been taking the same approach with their data governance – in manageable ‘chunks’ − are now positioned to move to the next level of maturity.

With data governance in place, and when the organisation understands the impact points and use cases data could have, data moves from a grudge governance project to an investment that will benefit the organisation.