Informatica Perspectives

As a follow up to my previous post Data As An Asset Part 1 – Should Data be on your Balance Sheet?, let’s get back to the balance sheet question and ask it differently – how COULD an organization go about formally valuing their information assets?

There is a relatively straight-forward way to establish the initial value of data assets by simply adding up the costs associated with creating the data. In other words, we could quite easily figure out what it costs to initially create the data – the acquisition cost. Once we have the initial value established, we could use standard depreciation models such as straight-line or accelerated depreciation to account for the deterioration in the value over time.

On the other hand, the data may be worth more than the cost to create it so we should find a way to measure the return (i.e. profit) that is generated by the asset. Furthermore, the data may continue to become more valuable over time, so it would be nice to have a way to re-value the assets from time to time (Mark-to-market pricing or fair value accounting). For example, a building may have cost $100,000 to build 30 years ago and is now fully depreciated, but it could be worth $10M now. The same holds true for data assets. If you are a bank and have 20 years of customer transaction history, your data may say more about individual customer credit risk than external data such as a credit score.

There are basically three ways to value an asset. The first way is based on what you paid for it (the acquisition cost). You might value your car based on what you paid for it – and possibly apply a depreciation schedule based on the useful life of the asset. Similarly, you could value your data assets based on the sum of discretionary investment costs involved in creating the asset. For example, if you implement a CRM system or an MDM system in order to create a consolidated 360-degree view of your customers, you could add up the cost of all the projects that created the system and use that as the basis for valuing your customer information. You can also use this approach for valuing existing data assets by creating a model which estimates the cost to re-create the asset if you had to do it today just as you could value a car or a house based on the replacement cost of an equivalent product.

The second approach is to value the earning potential of an asset using a DCF (discounted cash flow) model. This is a common method that is frequently used for developing business cases and determining the ROI for a given investment. For example, your customer MDM implementation might require a $1M investment plus an annual operating cost of $500K, but once in place it could generate an incremental revenue stream of $2M per year due to more effective cross-selling with a 25% margin which results in $500K profit, plus reduce customer servicing costs by consolidating call centers which saves $500K per year, plus reduce average outstanding receivables by $1M through more effective collections which represents an opportunity cost of $100K annually based on a 10% cost of capital. If you add up all these earning streams for the next five years, subtract the MDM operating cost, discount the net difference at 10%, the Net Present Value works out to be $1.93M. Using the cost-based approach the MDM asset is valued at $1M, but using the DCF method the asset is valued at $1.93M. Once you have established the metrics, you can recalculate the value of the asset each year by looking at historical actual results or by updating the future year projections. After several years you would have enough data to see a trend and know if your data asset is increasing or decreasing in value.

In upcoming postings I will tackle more complex topics such as how to apply managerial accounting practices and how to value data assets based on “market values”.  Stay tuned – and keep those comments coming.