Data Valuation | Why It Matters & How It’s Done

A guide to the methodologies that help you better understand the value of your data assets.
Graphic of a team doing data valuation

An Introduction To Data Valuation

To manage something, you need to measure it. How can you truly manage your data without having a full understanding of the value of it? And specifically, which data assets in your data portfolio are more valuable, and therefore should receive more attention, than others?

All 2021 data trends, and indeed past year trends, point towards data’s role becoming increasingly more valuable in business, especially as more companies turn towards data monetization. To get the most out of anything, including data, means it has to be managed well.

The data growth statistics of 2020 show why this is an increasing challenge. The volume of data continues to grow exponentially. With so much data, the need to focus and prioritise becomes increasingly more important. This is where data valuation comes in. 

By valuing your data based on the role it plays in creating value, it becomes clear where to focus limited resources to get the best ROI from data. For businesses that existed before the digital age, monetary data valuation is the foundation needed to build a data business.  

It’s more than just knowing the hard numbers of your data asset values. A comprehensive data valuation provides a host of advantages for your company. 

We’ve broken down the main ways a successful data strategy benefits from data valuation. Given the sums invested in digital transformation to become a data-driven business, making sure those investments are focused on the most valuable data is the best way to get the most ROI.

Why Data Valuation Matters

More and more company leaders agree that data is a central part of their business. They also admit they don’t have a good handle on their own company’s data and efficient usage. Understanding the value of data is central to becoming a data-driven business, here are five reasons why:

1. Data creates value by informing decisions with valuable insights. These insights come from a successful data analytics strategy. Your data analytics strategy will be far more successful if it’s focused on your most valuable data assets, rather than the least valuable.

2. Knowing what your most valuable data assets are mean you can focus your limited resources on the areas that will create the most value. It makes the choice of where to focus much simpler, and choice is the root of any good strategy, especially a data strategy.

3. Data valuation puts a number on your data assets. A monetary value of data. This is a language the business understands, which means they understand the value of data. Especially because this number makes the value of data comparable with other assets in the business.

4. Having a value on data makes it far easier to create a data culture, because instead of telling and convincing people data is valuable, the monetary value does that for them. No self-respecting businessperson is going to ignore an asset that’s valued in the millions or billions.

5. Having a clear monetary value also boosts the morale of the data team. Instead of being seen as the data geeks, they are transformed into the custodians of, and experts on, some of the company’s most valuable assets.

How To Calculate The Value Of Data

Watch this to learn about the benefits of data valuation, the detail around getting data onto the balance sheet, and the pros and cons of different data valuation methodologies.
  • Intro
  • 00:35 – Overview
  • 01:54 – Intangibles now make up 90% of S&P 500 
  • 03:11 – Organisations aren’t making the right investments in data
  • 04:36 – Data is a new kind of asset
  • 05:58 – Why is data not on the balance sheet?
  • 08:38 – What’s been going on in data?
  • 11:17 – Leaders say data’s important, but it’s not managed as such
  • 14:51 – Data asset management and data valuation are critical
  • 16:56 – Different data valuation methodologies
  • 20:53 – Data valuation in action

Different Data Valuation Methodologies

There are many different methods for determining the value of data. Regardless of which method you choose to determine the value of your data, the first step will always be to understand what your data assets are. From there, the next step is understanding how data drives value. Neither of these steps are easy. We wish they were, but they aren’t.

When deciding how to calculate the value of data you will need to choose the right methodology for your company. Here is a rundown of the major methods, as well as the pros and cons of different data valuation techniques. 

The cost value method

This method is based on the cost to produce and store data, as well as the cost to replace lost data and what the impact on cash flow would be. Some of the data valuation techniques that use this method include:

One of the main advantages to this overall method are that it is easier to execute than many of the other methods. Additionally, it provides part of the answer to quantifying the return on data, because it captures some of the costs.

However, this method is extremely subjective. While it does allow an organisation to conceptualise the value of their data, in part, it falls short of providing a reliable economic picture. Put simply, cost-based methods of data valuation will always undervalue data because it is only concerned with one aspect of value, it ignores the question of how does data becomes business value. There’s much more to the question of what is data valuation, than just cost.

The market value approach

This approach is based on what others pay for comparable data on the open market, by observing those selling data (thus drawing on an example of data value) and calculating the data selling price. 

While simple to calculate, it has some significant drawbacks. Some data is simply not traded – there may be no comparable examples of business data – either because others are not interested at this time, or because a company is keeping its data to preserve a competitive edge. 

Additionally, some data is one-of-a-kind, so there will be no comparable examples to study. Getting a true price of the data relies on there being an efficient market, which at present, there isn’t. 

Alongside this, users of this method must understand that price is not the same as value.

The economic value approach

On the economic value approaches, there are two key methods.

The first is income or utility valuation, which tracks the impact of data on the business’ bottom line, therefore it can identify value added to the business by data and can be used to identify value add for specific business functions or use cases.

However, this is hard to measure, particularly distinguishing value added by data from value added more broadly. Much like the other approaches, a lot of this is subjective and it is incredibly hard to predict the future value of data.

The second approach is around use case valuation – and there are two separate techniques here. 

The first is the business model maturity index (Internet of Water), which calculates the value of data by identifying a number of business use cases, estimating the value of each of these use cases, and calculating how much of this value is contributed by data. 

The benefit of this approach is that it values the data based on a thorough analysis of multiple use cases within the business, and ties it to real business outcomes. However, it is one of the most subjective as the contribution of data assigned to each use case is through surveying, based on hypothetical scenarios rather than real use cases. The margin for error is large.

The decision-based valuation method is similar but has an increased degree of sophistication as it models frequency of data collection, accuracy and how fit for purpose the data is. However, once again there is a degree of subjective estimation. It is also a complex model to apply for data assets as it requires the ability to conceive and project use cases. 

There is also an issue with ‘unknown unknowns’ – in other words, using this method businesses can only model use cases and desired outcomes that can be thought of from inside of the business. 

This relates back to the importance of what question a business is asking – sometimes if it is too specific, and if the data set is also very specific, a business will get the answers it wants, but this discounts many of the other factors and unknowns.

The stakeholder value approach

Value is in the eye of the beholder. The stakeholder value approach goes right to the source of value, by measuring the economic value created for each stakeholder. Not just shareholders, but customers, employees, suppliers, communities and the environment. 

This makes it a more modern approach, aligned with the shift from shareholder to stakeholder capitalism, much discussed at the World Economic Forum 2020, and mirrored by the growth of environmental, societal and governance (ESG) factors in investing. And yes, it’s our approach, but we won’t be all salesy about it. It’s not perfect, but it does overcome many of the problems of previous data valuation methodologies.

While other data valuation methodologies race towards data monetisation, they ignore the broader context, to focus on data in use, or not. The stakeholder method works from an understanding of the total economic value the organisation creates for its stakeholders. Valuation isn’t an end in itself, it’s a means to achieve better management and decisions. 

Decisions are never taken out of context, so data valuation shouldn’t be either.

The most difficult part of this methodology is attributing the right portion of the organisation’s total value to specific activities, and from there, into the data that underpins them. 

It’s only possible with well-trained, intelligent technologies. This is the main challenge for this method. It’s hard. Very hard. And like all the other data valuation methodologies, does not give a true, definite measure of value, but then monetary value has only ever been a subjective construct anyway. 

One historian Yuval Noah Harari explains, the idea of monetary value exists to enable mass cooperation. Instead of having to get to know people intimately to trust them enough to work with them, we just need to trust that the monetary value is something others believe in, because then they will act accordingly. 

Data valuation achieves the same end – because we all believe money is a measure of value, instead of just repeatedly saying data is valuable, expressing the value of data in monetary form communicates its value far more powerfully than any video, case study or well written marketing message.

For better cooperation to be achieved, trust and belief in the methodology is critical. In our opinion, it’s the combination of complexity with simple logic that makes the stakeholder method the best. At the end of it, you can clearly explain how the organisation creates value and data’s role in that. A simple story, based on strong evidence, producing a monetary measure of data that is anchored to the real value an organisation creates.

So, in summary, here’s how this methodology works:

1. Calculate the total economic value of the organisation.

2. Reveal which of the organisation’s activities create value for its different stakeholders, and what portion of the total economic value is attributed to each activity.

3. Identify how data dependent each of those activities are, and apportion the value accordingly.

Value of Data In Business

Looking back at the trends from 2020, and looking forward into 2021, we can see some distinct differences that show where the world of data is heading, and how central understanding the quantified value of data will become. 

A note on the trends. The vast majority of the data world is focused downstream, at the analytics end of the data value chain, despite the business value of data analytics depending on the value of the data it’s analysing. This includes the trends work, which is why they’re mentioned below. 

Yet even here, we can detect a shift to focus more upstream, towards understanding which data assets in an organisation’s data portfolio are the most valuable, and then honing the focus on them.

Data Analytics Trends 2020

In 2020 the world reeled from COVID. Many operations had to immediately transition to functioning entirely online, while others cobbled together a hybrid existence. 

As people wrapped up the year and reviewed the top data analytics trend of a world in shock, they focused heavily on technology. Some of the top trends included the use of commercial machine learning and AI, data analysis automation and data security and privacy. 

While these trends show a preoccupation with the nuts and bolts of data analytics, predictions for a data-driven culture hinted at the importance of value. Big data growth statistics from 2020 show that the value of data in business is only going to grow as the central importance of data increases.

Data Analytics Trends 2021

While there is still a major focus on technology, particularly AI, we also see the value of data becoming more of a priority. For example, there is more emphasis on aligning data to business strategy and a greater emphasis on efficiency in data management, both of which point squarely to understanding the value of data across an organisation’s data portfolio.

The Value of Data Valuation

Data valuation is not about getting a dollar or pound figure to put on your data. Yes, that’s an output, but the outcomes which that output drives are many and, in themselves valuable. They solve problems of focus, of understanding, of making sure appropriate amounts are invested in the areas of your data that will give the business the biggest return and the Chief Data Officer the most success.

Data Valuation Resources

£60bn Data Valuation Case Study
Learn how one Chief Data Officer applied data valuation and data asset management and now delivers £2.70 for every £1 invested in data and has a roadmap to deliver £1.2 billion in savings.

Data Leadership Report: Data assets and their (mis)management

86 data leaders. $1 trillion in combined revenues. Find out what they had to say about data assets, how they’re seen, invested in, managed (or not) and what experts say can be done to tackle the challenges.

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