What's the difference between data valuation and data evaluation?

A couple of weeks ago, I participated in the UK Government's Data Connect conference. Bringing together data experts from across the government, this week-long virtual conference featured over 100 speakers from 40 different organizations, including the Ada Lovelace Institute, Alan Turing Institute, GCHQ, Met Office, Ordnance Survey, Cambridgeshire County Council, Greater Manchester Combined Authority, Greater London Authority, Transport for London, and of course, Anmut!

In his opening address, Peter Kyle, Secretary of State for Science, Innovation and Technology, proclaimed that "for too long, public sector data has been undervalued and underused." Why is this? Why, for too, long has public - and arguably private - sector data been undervalued and underused?

In unpacking this question there are three fundamental issues to address - (i) data valuation, (ii) data evaluation and (iii) data environment. The first "data valuation" speaks to Peter Kyle's "data is undervalued" comment. The second and third - data evaluation and data environment - are at the heart of his "data is underused" comment. Let me explain why.

What is Data Valuation?

Data valuation refers to the process of assessing the economic or intrinsic value of data. It’s a financial perspective where organizations assign a tangible value to their data based on at least one of three factors:

• The data's usefulness in enabling the organization and/or its stakeholders to achieve their goals.
• The data's potential to be monetized and generate revenue.
• The data's potential to enable organizational and supply chain efficiencies, which result in cost savings.

Leading organisations are looking to value their data by asking how data enables them to deliver the organisational outcomes they need to create maximum value for their stakeholders. This approach allows you to identify and prioritise the most important data assets - those which really enable you to deliver your organisational outcomes.?

What is Data Evaluation?

Data evaluation asks two questions. Is our data fit for purpose - is it trusted decision-grade data? And does our organisation provide the right context to ensure our data will continue to be fit for purpose in the future.

Addressing the first question involves assessing the quality, relevance, and usefulness of a data asset for a specific purpose. It focuses on understanding whether the data is accurate, consistent, and fit for use in analyses, reporting, and decision-making.

Assessing the data environment involves asking broader questions about the organisation's approach to data. What governance structures are in place for managing data? What technological architecture do we have? Is it fit for purpose? Does it help us make best use of our data? Is the culture in place to ensure we use data appropriately?

Key Differences Between Data Valuation and Data Evaluation

Though both data valuation and data evaluation revolve around data, the purposes and approaches of valuation and evaluation are different.

Why Both Data Valuation and Data Evaluation Matter

Both data valuation and data evaluation play crucial roles in modern organizations, and it’s essential to recognize when to focus on each.

• Data Valuation?helps businesses recognize the potential value hidden within their data, guiding investment decisions, shaping strategies, and aiding in mergers or acquisitions. Organizations with a clear understanding of their data’s worth can better leverage it as an asset.
• Data Evaluation?ensures that the data assets used for business operations, reporting, and analytics are of high quality and reliable. Poor-quality data assets can lead to flawed insights, bad decisions, and costly mistakes, making evaluation an essential step for data-driven decision-making.

Critically, bringing together data valuation and data evaluation allows organizations to prioritize their data investments. When you understand the value and condition of data assets, you can concentrate your data investment on assets that are in poor condition, yet potentially valuable. When you’re drowning in data and struggling to decide what to do next, this prioritization of data investment can be invaluable.

As I said at the end of my presentation at the Data Connect conference: If you only take away two things, take away this—"data valuation" and "data evaluation".

• You need to know what your data's worth (data valuation), and you need to know what condition it’s in (data evaluation).
• With those two pieces of information, you can prioritize, focus, and make sure you are concentrating on the data that will make a real difference in how your business performs.

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#dataconnect #datavaluation #dataevaluation #dataenvironment