When a company like LinkedIn or Tumblr gets purchased, or when Snapchat or Twitter go public, it's no secret that it's not just the snaps and tweets that make them so lucrative. It's their data that can boost their perceived worth. Data is rapidly becoming a strategic asset with just as much value as a company's finances. Gartner even says that within five years, organizations will be valued on their information portfolios.
Why isn't data yet found on the balance sheets? Part of the reason is that data valuation is tricky business. Although it's one of the most lucrative byproducts of the modern age, appraising the plethora of data that's collected is murky — what types of data does a company collect? How does it help their (and other) businesses? Does anyone else have access to the same data?
Thinking through these questions to evaluate your own data is no small task. But when your data is a powerful asset of your business, it's important to understand the thinking that underlies data valuation. Here we'll discuss a rubric that can help you understand what your data is worth.
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What type of data is it?
Not all data is created equal — especially where valuation is concerned. We can think of data being broken down on two lines: exclusivity and specificity. Let's go over what this means, in broad strokes.
In data, like anywhere else, the more exclusive, the more lucrative. So if your company has data that nobody else has, it will generally be more valuable than data that is widely available to everyone. For example, if you use data available through the census report, that might add value to your company because of the way you utilize it, but it doesn't add as much raw value to your data, because it is available to everyone.
Of course, the way you use data — public or private — is a huge caveat in the way that exclusivity shapes valuation. If you are able to pull publicly available data in a way that creates real value, that can and will drive up the value of your data and your data processes.
The more granular your data, the more value it has, generally. Take customer data as an easy example. You might know their address and their order history. Do you know their income bracket? Do you know the number of people in their household? Do you know what field they work in? These are valuable pieces of information that are much more specific pieces of data than just address and order history.
Another reason why specific data is so valuable is that many pieces of granular data can be combined to form an overview, but broader datasets can't generally be broken down. You may know, for example, the population of every county, from which you could easily find the population of every state. But if just given the population of every state, you would not be able to find the population of every county.
Of course, this is a simplification of how data works in the real world. For example, rarely does any one company hold exclusive collection of data except for the user data for their product. Rather, different companies collect different types of data on different industries and demographics, and they might combine this with publicly available data and their own user data to create their pool.
But thinking about the exclusivity and specificity of your data is a good starting point for understanding its value. It is likely that you will use data that fall into every category somewhere throughout your company, and breaking down all the data you use into general types should be seen as a springboard.
How workable is it?
Collect all the data you want, but if you can't use it, its value drops. That doesn't just mean collecting relevant data, it also means ensuring that everyone can use your data.
Think of it this way. You're a company that's looking to acquire a smaller company. One of the reasons you're interested in them is that they have a huge cache of user data that could be very valuable to your company's efforts. But when you find out more about how they're using their data, it turns out that they funnel all their requests through a pair of data scientists who spend all their time scrubbing and formatting data.
Is that company as valuable to you knowing that you'll have to go through and redo their data collection and distribution to open up access to the whole team? Is that data as valuable when you know any given set has to be cleaned every time you want to pull from it?
In this way, workability of data really affects its valuation. Besides the benefits that a clear, open data infrastructure has for your business internally, you really need to be running a clear, accessible data framework to maximize your data's worth.
Often, this means data tools are a necessary part of the equation. They can provide a better, more centralized, more accessible way to work with your data. Most businesses will want a central platform, but will integrate data and analytics from any number of sources — APIs, identity management services, marketing platforms, etc.
Does your data drive business?
In the first section, we mentioned that how you use your data can make a big difference in how valuable it is. Where census data might be free and available to everyone, your insights from that data might drive your business to be much more profitable. Conversely, you could collect the most granular data imaginable on every single user you have and not use it to better your business.
In both of these cases, it's not what you're collecting that matters. It's how your data relates to your business.
This matters not just for what you can do with your data, but what other people can do with it. Again, if we think about a company looking to acquire, it would matter what they could do with your data (and how easy it would be for them to utilize it), not just what you're doing with it now.
Take Apple for example. Their acquisitions of companies like Tuplejump, which specialized in machine learning and data management, show that companies using data in a way that creates better businesses are desirable.
The fact of the matter is that we value raw data because of its potential, and understanding how your data drives business is one crucial way that potential manifests itself. What your data is and how you use it are two key components to this, and how your business benefits from your data is inextricable from these threads.
This obligatory interlinking is one reason that the valuation of data is so difficult. If you're trying to understand how valuable your data is to your company, your best bet is to look at how your data is improving your internal benchmarks:
- Did that new onboarding boost retention?
- How has your churn slowed since you triggered your new set of reminder emails?
- What were the results of your latest targeted marketing campaign?
Looking at the answers to questions like these will help you better understand the value that your data helps you build internally. And if you find there are chunks of data that you're not putting to use, it's a great time to start considering how to become better with working data — or how to start collecting more relevant data to work with.
Long story short: there is no easy answer
Valuation of data is truly a never-ending task for the modern age. We may never pin down exactly how to determine what data is worth in any given scenario.
But one thing's for sure: data is truly one of the byproducts of how we live today, and how we think about it matters. We've laid out some basic framework tools to help you understand how valuable your own data is. But it's just the tip of the iceberg. There will continue to be thought and discussion on how valuable the data we collect as companies is until, well, the end of companies themselves.