Most organisations I talk with already understand the importance of treating data as an asset – nothing new there. But when we get into deeper conversations about what that means, few are applying formal asset management principles. For large organisations, especially, it can be a struggle just to identify the data assets they have, let alone understand their quality and their actual (and potential) value to the business.
As we emerge into a new reality post COVID-19, customer behaviour and business models will have changed dramatically – in some cases permanently. That’s going to make it more important than ever to have access to high value data assets. It’s data that will enable organisations to understand the behaviour of customers, suppliers and competitors – as well as assess their own business performance and resilience – so that better decisions can be made in pursuit of business value.
But the task of understanding, improving and harnessing data across a large organisation can seem daunting. There’s often a disjointed approach to data initiatives, be they data management improvements, data governance or the acquisition and utilisation of data for analytic purposes. In large organisations, data is often dispersed across multiple systems and geographic regions. It is often used to support a variety of business processes and feeds a wide range of reporting and analytics that are frequently fragmented. As a result, data quality, availability and usability are often low.
So how can you manage your data as an asset more effectively?
As a first step, I recommend putting in place an agreed framework for scoring the relative business value (current plus potential) of data entities within the data asset base. There is a wide range of data valuation models, but here I’m suggesting an approach that emphasises intuitive transparency over sophistication. So, to start, I suggest defining a relative score based on a blend of weighted business value factors. These include the data’s role in supporting current processes and its potential to drive increased revenue, reductions in cost and improvements in risk management.
The next step is to score data quality for the data entities within the data asset base against agreed data standards for completeness, accuracy, timeliness, uniqueness, validity and consistency.
These scores enable you to calculate notional book values for data calculated at data entity level: Notional Book Value = f1 (relative business value) x f2 (data quality), where organisations individually define f1 and f2. These values should then be recorded within a data asset register, containing the definitions of key data domains, the data entities within them and other business and technical metadata.
Having a notionally valued data asset register promotes transparent alignment of data initiatives with business value generation. It highlights data assets with a high notional book value that should be being put to better use. And where relative business value is high but notional book value is low, it highlights the need for initiatives to improve data quality.
The register also enables an organisation to, for example, start assessing whether it should clean its supplier master data or its customer data first to get the quickest and best return. Of course, while notional data asset value can provide a useful guide to priorities, individual investment decisions should also consider cost and implementation risk.
Another benefit is in presenting a clearer picture of the value of data assets to potential investors. Yes, there’s a continuing debate on the impact of data assets on investors’ valuations of companies and around the formal capitalisation of data assets. But it appears that many equity analysts are already taking data assets into account. And, in the future, the ability to show a structured approach to data asset valuation may have a direct, favourable effect on company valuations.
Finally, measuring improvements in notional data asset value over time also provides a key performance indicator for chief data officers or their equivalents.
I’ve seen through experience that putting in place effective enterprise data asset management is not a quick win – it’s probably going to take months rather than weeks for most organisations. But the following practical steps, executed by a small team that includes data specialists, systems architects and business analysts can accelerate the process:
If you’re interested to read more about how you can work your data assets harder, I can recommend Nick Whitfeld’s article, ‘Wrestling your data into shape - managing the Enterprise Data Lifecycle’.
If you’d like to discuss this topic further, please drop me a message and we can arrange a virtual coffee.