What do we mean by value?

Assets have value. So logically we should be able to agree the value of that asset. Respected metrics to do are understood for finance, people and buildings. These metrics provide some of the framework to manage assets both in terms of their cost to an organisation, and the way that value is demonstrated – be that quantitively and quantitatively.

The data asset fails to ascribe to any of those criteria! There’s no industry standard or accredited approaches to assessing that value, nor a universal benchmark to cost it. This does raise the question of whether data should be considered in the same asset class as traditional assets, and, if not, how does that change our management of it. Both of these questions I’ll return to in future articles.

So, if there isn’t an internal or external framework to help us, what can we do? Start by applying common sense, organisational context and operational knowns. Some of these come from traditional asset management valuation, some are specific to data.
For both of these ‘lenses’, we’ll attempt to derive cost and benefit. So let’s dive in and see what might work.

Traditional metrics
Cost of managing the asset.
We should consider these collectively as ‘the cost of data in the information lifecycle’. To understand those costs, we need metrics or rigorous estimations for:

  • Cost of finding and collecting data
  • Cost of storing data
  • Cost to mitigate risks associated with data in the lifecycle
  • Cost of extract, transform and load processes
  • Cost of retention
  • Cost of disposal.

Even with this level of granularity, firm numbers are hard to calculate. Staff time and cost is the simplest. It’s also compelling as it shows the direct link between ‘more data’ and ‘higher cost of operation’. The thrust of these metrics are to help organisations understand that the management of data does not come for free.

Traditionally this has been focussed on data infrastructure – servers, disks, applications, etc., but there needs to be at least as much emphasis on high cost, high scarcity staff and external support.

Benefits of a well-managed asset.
These are more intangible. The most often quoted are:

  • What your data is worth to someone else
  • Expected current and future revenue

In Higher Education, this data tends to be around recruitment, market analysis and course pricing. The cost of commercial data needs to be factored into any calculations.

Bespoke data metrics
Cost of managing the asset.
These costs can be split between staff time, reputation, compliance and opportunity cost of poor decision making. They include:

  • Impact of data that cannot be found or is not available
  • Risks of poor data quality or data not fit for purpose
  • Costs to improve and maintain data quality.

These are additional to the cost of managing data in a lifecycle. Often that lifecycle ships data around an organisation without the context of what it is for. The costs above can be thought of as ‘the price of utility’ – making less data work harder.

Benefits of a well-managed asset.
The value of data question can be answered in a number of ways. It can be measured specifically by:

  • Combined benefit value of fit for purpose data quality.

Fit for purpose data can enable, enhance or create better outcomes for well understood outputs. For HE, these include student number planning; estate planning; financial planning, budgeting, forecasting and reporting; workforce and workload planning; research fund planning, teaching and research innovation, learner analytics and student personalisation.

Assigning a hard number to that value is tricky. The answer is to consider it in light of other organisational metrics.

Organisational alignment

The key to valuing data as an asset is to target that value at objectives the organisation has already subscribed too. These include:

  • Operational Key Performance Indicators (KPIs)
  • Management KPIs
  • Strategic KPIs
  • External metrics, league tables
  • Compliance and regulatory frameworks
  • Financial projections
  • Innovation
  • Community impact
  • New products and revenue streams.

All of these objectives have value and – crucially – a cost to create and maintain them. Depending on the culture of your organisation you can position the value of the data asset in terms of how it increases the value of these outputs. Or you can show how best practice data management and governance can reduce the cost to manage this asset.

What next?

As we can see valuing a data asset – either from a cost or benefit perspective– is both more difficult and more nuanced than traditional assets. Context, culture and alignment of your organisation should be your guide here.
There is no point attempting to measure an asset if no one else cares about the result.

However, once that data asset becomes a structural and mandatory component of something else, the cost and benefits move into plain sight. Nicola Askham and I will be running a series of webinars covering this and many related areas on ‘Data as your GPS’. These will start in early 2020.

If you’d like to get in touch for further details and some real world examples use the contact form.