Data on the Balance Sheet

Andrew Bilsdon, Delivery Lead – Altis Sydney

Let’s agree that the cliche of ‘data is an asset’ is not disputed, the question is really, how much of an asset. Before we go too far, some ground rules:

  • The terms Data and Information are used interchangeably as is the case in general conversation, particularly with non-IT colleagues.
  • The terms Value and Fair Value are synonymous.
  • Each country will have different accounting practises, particularly with regards to the creation of intangible assets.

So let’s look at a few examples where I have seen a dollar value/fair value placed on Data.

  • Enterprise Data Warehouse
    • A Mortgage Lender sold their book of loans and the EDW was an asset included in the transaction.
    • A Bank was taken over and their data was considered as an asset in the final agreed fair value of the Bank.
    • A Telco built an EDW and it was considered an asset without a sale having taken place.
  • Data Set
      • A data scientist ‘created’ a dataset to predict future behaviour and they priced the value of the dataset so they could sell it.

These nicely describe two distinct scenarios, they are:

    • A data repository/set was created by a business where the intent was to support the business operation, not to create a product. This is the case with the EDW, it was not built to be sold, even though it ultimately was.
    • A data repository/set was created by a business with the sole intent of selling it. This is the case with the data science example, it was not built to support the operation, it is effectively a product.

Given that getting and storing data these days is relatively easy, we are now effectively in a position of potentially creating an asset each time we build a new Data Lake, EDW, dataset or similar. Before we dig further into the notion of assets, some definitions are necessary. The following are examples of different types of assets in the world of accounting:

    • Cash and cash equivalents
    • Inventory
    • Investments
    • Property, Plant, and Equipment
    • Vehicles
    • Furniture
    • Intangible Asset
        • Goodwill
        • Patents
        • Brand
        • Copyrights
        • Customer lists
    • Stock

Data is a candidate for the Intangible Asset category, providing the applicable accounting standards are met, example tests include the ability for a company to demonstrate:

    • The fact that the Asset is not physical.
    • The technical feasibility of completing the intangible asset so that it will be available for use or sale.
    • Its intention to complete the intangible asset and use or sell it.
    • Its ability to use or sell the intangible asset.
    • How the intangible asset will generate probable future economic benefits.
    • The availability of adequate technical, financial and other resources to complete the development and to use or sell the intangible asset; and
    • Its ability to measure reliably the expenditure attributable to the intangible asset during its development.

Given we have an accounting bucket for data to reside in and a test to see if it is eligible to do so. The biggest question now is how do we value it? As you would expect there are existing approaches to do this for Intangible Assets, they include Cost model, Revaluation model, Market to net book value, calculated intangible value (CIV) and several others. These, however, are not nuanced enough to deal with the challenges of valuing or revaluing data.

Dimensions of Value

The term ‘intangible’ will strike fear into the hearts of Information Technology (IT) types, less so for a Financial Analyst. To put IT at rest. I propose the following dimensions to assist us in having a meaningful conversation on fair value:

Age

How old is the data? When was it captured and when was it last updated? Remember that beyond a certain point, as data ages, it becomes less valuable.

This does not mean the data has no value, it simply means that knowledge about the data, how it was collected and the governance that was applied becomes less well understood.

Consumers

Is anyone actually using the data? It is all well and good to have a beautiful repository of high-quality data, but if no one is using it then it is really of little, or no value to others.

Cost

How much did you pay to create the data? If a project delivered the data, then you can reasonably assume that the data is worth at least as much as the cost of the project that delivered it. You would in fact hope that the value is in fact greater than the cost of creating it.

Efficiency

Does the presence of the data mean that your organisation is more efficient? An example might be the ability to reduce your headcount, as the data means you need fewer staff to manage a process (e.g. evaluating a load application).

Perception

Does the market place, or potential buyer, perceive it to be valuable? Does it really only matter to you and your organisation? If this is the case then it still has value, however, less than a rich customer list for example.

Prediction

Does the dataset and the information within it predict an outcome or report on a past event? We shouldn’t underestimate the value of knowing what has happened, after all much of the operational reporting we still deliver is based on this, however, compared to the value of predicting a future event there is a large difference in value.

Quality

Is the quality of the data appropriate for the use intended?

Saleable

Do the data privacy rules under which you operate prevent sale of the data? Was it collected under a set of terms and conditions that allows for the data to be used or commercialised?

Sources

Is the data a consolidation of more than one data sources? The more complete the data is in describing a domain then more valuable it is. A single view of a customer is a great example of this.

Uniqueness

Are you using Open Data that is already widely available to a potential buyer? If this is the case and you have not added any additional features or data sources to it, the value would be low.

Now, obviously not all of these dimensions will have the same weighting, nor will they all be measurable, the stand out is Perception. However, with the assistance of our Finance colleagues, we are at least now in a position to describe an Intangible Data Asset with supporting evidence.

Conclusion

  • Be aware that you will quickly be out of your depth if you start this conversation with your CFO without first gauging their appetite/company direction. After all, we are data specialists, not accountants. This exercise needs to be led by Finance with close support from IT.
  • You can only sell data in accordance with the data privacy legislation that you are operating under.  Removal of the PPI, PII data will devalue the asset and this needs to be considered.
  • If we can place a dollar value on data we move the conversation with business users from the abstract and towards the balance sheet. How many times have we had a conversation with a business manager about a data quality issue to be told that “it’s good enough for what they are using it for” or “data quality is not their issue”.
  • The ability to monetise data effectively can be a source of competitive advantage.
  • Any suggestions within this blog need to be evaluated against the accounting laws that are applicable in your region and the internal objectives of your organisation/client.
  • Valuing data, like other intangible assets, is more of an art than a science, IT can help but final word needs to reside with Finance

 

Check out some of our similar blog posts:

Cost-effective XML storage and querying at scale

Data Visualisation Tips – Bullet Chart, an alternate to Gauge Chart

Import your Salesforce data into Google BigQuery in minutes

Join the conversation

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.

Comments

Post has no comments.