What I share with my clients is that trying to place a monetary value on data and information itself is a red herring and an effort that I highly recommend all avoid – unless you enjoy philosophical exercises that don’t translate to actual business value (Apologies to those that fit in this camp – have fun!).
The “data is an asset” rhetoric doesn’t translate to putting a monetary value on a customer record, as an example, because data in and of itself has no value! The only value data/information has to offer – and the reason I do still consider it an “asset” at all – is in the context of the business processes, decisions, customer experiences, and competitive differentiators it can enable.
For example, a customer record doesn’t have value unless you can sell, market or service that customer. So for each customer record many customer intelligence analysts calculate lifetime value scores, the potential share of wallet available, the customer’s propensity to buy certain products and services and even the cost of servicing the customer. But that doesn’t put a value on the customer record itself, it places the value based on the sales, marketing and service processes the data supports. And that’s where the data value should live – in the consuming processes.
That’s why my process data management approach recommends that all data management, data governance, data quality and MDM efforts are put into context of the most critical business processes that consume and depend upon trusted data. The alternative is attempting to boil the ocean and trying to solve Customer, Product, or Financial data for all processes and decisions across the whole organization – too big an effort destined to fail before it starts.
Some organizations of course do place an accounting value for things such as acquired customer lists under “intangible assets” on their balance sheets. But the value of those customer lists are most commonly defined based on the value of the projected cash flow and revenue based on complex models with plenty of caveats and assumptions that act as necessary filler in quarterly and annual securities filings. So of course the customer records on the list are not what are being valued – it’s the future anticipated revenue net the future anticipated account maintenance costs that define the value.
And that’s where we should all be focusing our energies – on the business-value generating processes consuming the data – not the data itself.
Would love to hear any thoughts on use cases I may have missed where calculating a value on data itself is actually delivering value!
Great article!
Your example starts with CUSTOMER. What data supports your concept of CUSTOMER? AS you point out there is a hierarchy of data, information and knowledge. You are starting at the information level and ignoring the foundation you think you are standing on (I see you talking about your view from the fifth floor assuming that the first four floors have been planned, built, inspected,as well as, the rest of them building).
Your process approach works well if you do not have to integrate any data. That is, your sales, marketing and service either one monolithic system or in a modern well defined SOA set of services. The rest of us who have deal with required data integration start to rack up COSTS! Where is the data definitions? How is the data definitions reconciled between the system? How are the business definitions about the data reconciled between the business units? Who knows about the data and are they still around?
The operational costs are only some of the easy present day costs that are associated with data. What is the marginal cost of having 30 years of quality data on a customer?
Your process data management will fail since it does not have a data custodianship (look up the definition) component. A data custodian has an role of accountability and responsible for the data. What you need is to identify that business owner that is accountable for the data, so that can put any process or process discussion into play so you can maximize the monetary value of the information coming from that data.