In this blog post, we delve into the economic principles that can help quantify the value of data within an organisation. We'll explore various theories of value, such as the Subjective Theory of Value, the Cost-of-Production Theory, and the Power Theory of Value, and demonstrate how these concepts can be applied to assess the worth of data initiatives and specific data products. By understanding and implementing these economic frameworks, businesses can better align their data strategies with organisational goals, ensuring maximum return on investment and fostering a robust data culture.
Economic Value
Value, from an economics perspective, can fall into the following theories:
- Subjective theory of Value
- Cost-of-Production theory of Value
- Power theory of Value
- (many other theories exist)
Subjective theory of Value
This is the theory that value is decided by individual preferences and perceptions. It is central to the theory of marginal utility, which states that the value of a good or service is based on the added benefit that an individual gains from consuming one or more unit of it.
Applying this theory, the value of data is decided by the use case applied to it, whether it is used to increase revenue (for example, monetised by being sold) or to reduce costs.
For example, if we assume that the benefit use case for a data product results in the saving of an 0.5 FTE (full-time equivalent) employee by freeing up their time from manually transforming and reporting on data. If the role behind the FTE has an associated salary of £30,000 per annum, we can say that the value of the data product is £15,000.
Cost-of-Production theory of Value
The Cost-of-Production theory, or the Labour theory, is decided by the total amount of labour, and other associated costs, which go into producing value.
If we take the same data product and assume that it takes a member of the data team 200 hours to create, we can then take the associated annual salary of the data team member (£60,000 per annum), work out the hourly cost of that individual (£30 – work 8 hours a day for 250 days) and multiply the number of hours worked on the product to produce the value: £6,000.
But that’s just Capital Expenditure (CAPEX), there’s also Operating Expenditure (OPEX) to consider. For arguments sake, let us say that the OPEX for the data product in a cloud hosted environment is: £2,000 per annum.
Power theory of Value
In this theory, power and politics dictate the value of a commodity. The more power one has, the more one can decide what value a commodity has.
Let us assume that the department needing this data product is Marketing. Business and value case are submitted, and the CFO (Chief Financial Officer) looks at the case, realises that it was previously taking a 0.5 FTE to produce the data and starts questioning the value of that role. The role is made redundant, and the value of the data product is now £30,000.
Conversely, the CFO could also use their power to devalue the data product and deny it from being created in the first place.
Calculating Data Value
For this calculation, while interesting, we will not be applying Power Theory due to the volatility and need to understand the specific power dynamics in an organisation. However, it is worth taking Power Theory into consideration as it could have an impact on the overall value of data.
For this calculation, we will be applying both Subjective and Cost-of-Production theories together and we will look at how calculated value is not a one-off realisation.
Year 1 Value = Year 1 Subjective Value – Year 1 Cost-of-Production Value
Recurring Value = Year 2n Subjective Value – OPEX
Total Value = Year 1 Value + Recurring Value
The aim being to that all three value calculations are positive – although from an accounting perspective it would be OK if the Year 1 value were not positive.
Example Calculated Data Value
Using our example data product, it has a Year 1 Subjective Value of £15,000 with a Year 1 Cost-of Production value of £8,000 (£6,000 in labour costs, £2,000 in OPEX).
Year 1 Value = 15,000 – 8,000
Year 1 Value = £7,000
For our recurring value, we have an estimated OPEX of £2,000 per annum. This excludes any maintenance and BAU (Business as Usual), but we can assume that 20% of build time is used for maintenance and any improvements – being 40 hours across a year (£1,200).
Our recurring subjective value, in our example, is going to be our Year 1 Subjective Value (£15,000) we could add an accumulative inflation adjustment modifier (4%) to account for any pay adjustments the role that the value is based on would have received, but we’d also have to do this for the other cost-of-production side, which would balance out. We can apply this for the life of the data product – note we are not running this in perpetuity, otherwise our recurring value would, in effect, be infinite.
Let us assume that the life of the data product will span for 5 years, including year 1.
Recurring Value = (15,000 – 3,200) * 4
Recurring Value = £47,200
Therefore, the total value of the data product across its life is: £54,200.
Intrinsic & Intangible Value of Data
Economic value is great, but the discipline of economics has tried to condense complex systems into simplistic balancing equations – meaning that complexity is lost. Therefore, there are other considerations that we need to apply to defining and ascribing value to data, these are Intrinsic and Intangible attributes.
Data has intrinsic value in that data is needed to make decisions. The ability to make decisions informed and supported by data helps create a healthy data culture as organisations are decision factories – transforming the information input (data, gut feel, anecdotes) into decisions. The more individuals within organisations rely on data to inform decisions over gut feelings and anecdotal evidence, the more successful an organisation is as well as a more mature approach to data it has.
Some of this intrinsic value could and should be applied to the economic value to enhance the value of data, but sometimes a data initiative has no economic value beyond enabling data products, which do have economic value, to be produced. An example could be a data platform. A data platform, by itself, has no economic value and it has no intrinsic value either – but it has a huge wealth of intangible value as without it, the delivery of data products and other economic value is challenging and, potentially, ineffective.
Conclusion
The economic value of data can be complex to decide, but by applying established economic theories such as the Subjective Theory of Value and the Cost-of-Production Theory, we can arrive at a reasonable approximation of its worth. Through our example, we have shown that data products can provide substantial value by reducing operational costs and improving efficiency. This calculated value of £54,200 over five years shows the significant impact that well-utilised data can have on an organization.
However, it is important to note that the value of data is not static and can be influenced by various factors including changes in business needs, advancements in technology, and internal power dynamics within the organisation. Regular reassessment and alignment with business goals are essential to ensure continued value realisation from data initiatives.
As organisations increasingly recognise the value of data, it is imperative to implement structured frameworks for assessing and maximising this value. We encourage businesses to:
- Adopt a Comprehensive Valuation Framework: Use both Subjective and Cost-of-Production theories to regularly evaluate the value of data products.
- Align Data Initiatives with Business Goals: Ensure that data projects are directly linked to strategic objectives and operational efficiencies.
- Invest in Data Literacy and Tools: Equip teams with the knowledge and tools necessary to understand and use data effectively.
- Monitor and Reassess: Continuously monitor the performance and value of data products and be prepared to adjust strategies as needed.
- Consider Power Dynamics: Acknowledge and navigate internal power dynamics that could affect the valuation and implementation of data projects.
By taking these steps, organisations can unlock the full potential of their data, driving growth and innovation in an increasingly data-driven world.
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Author
Ust Oldfield