Data Valuation | How It’s Done & Why It Matters
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?
An Introduction To Data Valuation
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 monetisation. 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, knowing where to focus limited resources to get the best ROI from data becomes clear. 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:
- 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.
- 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.
- 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.
- 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.
- 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.
This case study shows what happens when a company values its data.
How To Calculate The Value Of Data
There are many different methods of how to determine 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:
- Daniel Moody’s 7 Laws of Information
- Glue Reply Valuation Technique
- Relief from Royalty Method
- Data Hub Valuation Technique
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
This can be broken into two main categories, income or utility valuation and use case valuation. In the first instance – income, or utility valuation – the impact of data on the business’ bottom line is calculated. This provides information about specific value adds for different business functions and use cases, and can do a great job of showing the value added by data. However, this is extremely difficult to measure, falls to subjectivity too often and does a poor job of predicting the future value of data.
With use case valuation, there are two data valuation techniques. The first uses several business use cases and calculates the value of each and estimates how much of the value is created by data. This can provide a thorough analysis of business use cases, and ties the approach to a true business outcome, provided all use cases can be tracked and that there is a singular pathway from data to use case to value. The real world rarely works like this. The downside is that once again it is highly subjective, relying on hypothetical situations, or a single snapshot in time. While machine learning and other technologies can be employed here, it’s worth remembering that these technologies are only as good as the data they are provided with.
Both approaches here also require a subjective judgement call to be made on where to start. Identifying the areas that it’s assumed are the most valuable and then zooming in to understand the data and use cases.
The stakeholder value approach
Value itself is subjective, something the stakeholder value approach acknowledges in looking at how value is calculated for different stakeholders in the business. This goes beyond just shareholders to include customers, employees, suppliers, the community and environment. It is not a simple approach that can be done with Excel. It requires specially trained data valuation machine learning algorithms. This outside-in approach, which we practice, does not require a judgement call on where to start because it builds a framework of how a business creates value. This framework then shows an organisation where to focus to find their most valuable data – it becomes a data valuation framework. This removes the need to map every use case, a task which would require huge resources to complete, and once complete would need to be redone because it would be out of date.
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.
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