With data considered the new currency, why should we pay attention to data monetisation? (Part 1)
We have all become very familiar with the old clichés of “data is the new gold”, “data is the new oil”, “discovering diamonds in the data”, and more recently “data being the new currency”. It is therefore not surprising that the concept of “data monetisation has become a much-published topic by research and consulting firms as well as some cloud-based data services organisations. A novice can probably be forgiven for suspecting a potential relationship between big data, data vaults, and… the potential promise of data monetisation.
So, what is data monetisation?
Data monetisation is ultimately about gaining tangible economic benefit from data. Considering the post-pandemic efforts of organisations to reposition themselves through digital transformation initiatives, it is well-known that data and analytics have become critical success factors in achieving this. Technological enablers like the Internet of Things (IoT), 5G, edge computing, cloud- platforms and services, and the ongoing evolution of artificial intelligence (AI), have significantly contributed to – more data. The value of data is beyond question – monetising it, a logical progression.
There are different methods of data monetisation. Direct monetisation typically relates to actual data being sold. Indirect monetisation can refer to potential operational efficiencies or process improvements that are quantifiable.
Some data monetisation use cases
With direct monetisation, the opportunity implies the actual selling of data once-off, or as part of a subscription – think “Data as a Service”. Alternatively, the “product” could be packaged by enhancing the data with analytics as part of a data visualisation solution: “Insights as a Service”, or reporting.
Notably, as with any business case, it is important to best determine the feasibility of the initiative by carefully considering the value-add of the intended “product”, the projected income, associated expenses, ongoing maintenance and support, pros and cons, regulatory compliance, organisational change, etc. These will be specific and unique to the organisation.
Additionally, some use cases of internal indirect data monetisation are cross-selling through marketing campaigns, or staff retention because of advanced analytics. And the provisioning of organisational structure, or key role-player information, is an example of external indirect data monetisation. Sharing research results is another familiar case in point where organisations are paying for information. There are many more examples of data assets for which there is a demand across industries – it just needs to be identified and quantified.
Key considerations for data monetisation
Embarking on a data monetisation initiative is probably for most organisations a journey into uncharted territory and requires a sound approach based on key considerations, some of which include:
- Understand the potential demand for the data, determine the business case and quantify the value.
- Ensure a reliable process to obtain, quality assure, govern, and maintain the data. The data asset must be of a high quality and remain relevant.
- Access, security, and regulatory compliance aspects are critical considerations in determining the practical implications and minimising any risk to the organisation. Do not underestimate any relevant privacy policies and legalities of data sharing.
- Organisational change management and the associated buy-in, education and communication remains important for an initiative of this nature.
- Determine any supporting technology that is required to process, enhance, and visually represent the data or insights.
Getting started: Data for sale!
As data monetisation is not the main revenue stream for most organisations, it probably does not receive the same focus as the typical “product development” would and is usually not supported by the appropriate functional structures. The target market might differ entirely from the existing clientele, and it is unlikely that the “data product” can be distributed via the organisation’s current channels. An entirely different go-to-market approach will have to be adopted.
Before putting up that “Data for sale” board, let’s explore and discuss such an approach, data monetisation’s role in analytics, the necessity for potential partnerships, and … getting started – in the 2nd part of this blog. Until then.
We have all become very familiar with the old clichés of “data is the new gold”, “data is the new oil”, “discovering diamonds in the data”, and more recently “data being the new currency”. It is therefore not surprising that the concept of “data monetisation has become a much-published topic by research and consulting firms as well as some cloud-based data services organisations. A novice can probably be forgiven for suspecting a potential relationship between big data, data vaults, and… the potential promise of data monetisation.
So, what is data monetisation?
Data monetisation is ultimately about gaining tangible economic benefit from data. Considering the post-pandemic efforts of organisations to reposition themselves through digital transformation initiatives, it is well-known that data and analytics have become critical success factors in achieving this. Technological enablers like the Internet of Things (IoT), 5G, edge computing, cloud- platforms and services, and the ongoing evolution of artificial intelligence (AI), have significantly contributed to – more data. The value of data is beyond question – monetising it, a logical progression.
There are different methods of data monetisation. Direct monetisation typically relates to actual data being sold. Indirect monetisation can refer to potential operational efficiencies or process improvements that are quantifiable.
Some data monetisation use cases
With direct monetisation, the opportunity implies the actual selling of data once-off, or as part of a subscription – think “Data as a Service”. Alternatively, the “product” could be packaged by enhancing the data with analytics as part of a data visualisation solution: “Insights as a Service”, or reporting.
Notably, as with any business case, it is important to best determine the feasibility of the initiative by carefully considering the value-add of the intended “product”, the projected income, associated expenses, ongoing maintenance and support, pros and cons, regulatory compliance, organisational change, etc. These will be specific and unique to the organisation.
Additionally, some use cases of internal indirect data monetisation are cross-selling through marketing campaigns, or staff retention because of advanced analytics. And the provisioning of organisational structure, or key role-player information, is an example of external indirect data monetisation. Sharing research results is another familiar case in point where organisations are paying for information. There are many more examples of data assets for which there is a demand across industries – it just needs to be identified and quantified.
Key considerations for data monetisation
Embarking on a data monetisation initiative is probably for most organisations a journey into uncharted territory and requires a sound approach based on key considerations, some of which include:
- Understand the potential demand for the data, determine the business case and quantify the value.
- Ensure a reliable process to obtain, quality assure, govern, and maintain the data. The data asset must be of a high quality and remain relevant.
- Access, security, and regulatory compliance aspects are critical considerations in determining the practical implications and minimising any risk to the organisation. Do not underestimate any relevant privacy policies and legalities of data sharing.
- Organisational change management and the associated buy-in, education and communication remains important for an initiative of this nature.
- Determine any supporting technology that is required to process, enhance, and visually represent the data or insights.
Getting started: Data for sale!
As data monetisation is not the main revenue stream for most organisations, it probably does not receive the same focus as the typical “product development” would and is usually not supported by the appropriate functional structures. The target market might differ entirely from the existing clientele, and it is unlikely that the “data product” can be distributed via the organisation’s current channels. An entirely different go-to-market approach will have to be adopted.
Before putting up that “Data for sale” board, let’s explore and discuss such an approach, data monetisation’s role in analytics, the necessity for potential partnerships, and … getting started – in the 2nd part of this blog. Until then.
