Most organisations already understand the importance of treating data as an asset but it would appear that few are applying formal asset management principles.

For larger organisations, especially, it can be quite a struggle to identify the data assets they have, without even beginning to understand their actual and potential value to the business.

Data is now more important than ever

The ‘new normal’ post COVID-19, will see customer behaviour and business models changing dramatically and in some cases permanently.

Therefore, having access to high value data assets is of critical importance. It will enable organisations to understand the behaviour of customers, suppliers and competitors as well as assess their own business performance and resilience.

Understanding, improving and harnessing data across a large organisation can seem daunting, and that’s often why there is a disjointed approach to data initiatives.

In larger organisations, data is dispersed across multiple systems and geographic regions and used to support a variety of business processes.

That feeds a wide range of reports and analytics which are frequently fragmented, meaning result, data quality, availability and usability are low.

Giving your data a notional value

So how can you manage your data as an asset more effectively?

As a first step, companies should look to put 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 you should be looking to take the approach that emphasises intuitive transparency over sophistication.

Define 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).

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.

What are the benefits?

Having a notionally valued data asset register promotes transparent alignment of data initiatives with business value generation, 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 assess whether it should clean its supplier master data or its customer data first to get the quickest and best return.

Another benefit is in presenting a clearer picture of the value of data assets to potential investors, and it appears that many equity analysts are already taking data assets into account.

In future, the ability to show a structured approach to data asset valuation may have a direct, favourable effect on company valuations.

5 steps to accelerating the process

Putting in place effective enterprise data asset management is not a quick win and will take months rather than weeks for most organisations.

However, with data specialists executing the following practical steps, systems architects and business analysts can accelerate the process:

  • Perform a rapid first pass at creating the data asset register, including key data domains and entities.
  • Perform a high-level review of data quality across domains at data value chain level.
  • Attempt a first pass at notional book values.
  • Collaborate with the business to begin to define a roadmap of data initiatives in which priorities are guided by notional asset values.
  • Put in place a process to refine and periodically re-assess notional book values.

Leave a Reply

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