Spirit Airlines collapsed. It started auctioning its assets. What would you normally expect to see in an airline’s bankruptcy auction? Aircraft, equipment, software, maybe even airport slots too. But what made the headlines was their data. Not any data- their operational data, such as emails and Teams messages.
Google offered to pay $10M for it. Google wasn’t the only one bidding. Mercor, an AI-powered hiring platform, has offered $7.5M.
This wasn’t their customer base or even invoice data. According to the reports, it comprises 100 million emails, 500 million Teams messages, and 30 million lines of code. That’s their day-to-day operations and decision-making conversations. Imagine a dying company’s conversations are worth $10M.
Of course, the deal isn’t final yet. The airline staff has filed a petition against the deal over privacy concerns. But why did Google want that data? More importantly, how can you estimate the value of your data estate? What makes this estimation hard?
My company helps PE firms with commercial due diligence. Having spent 8 years here, I might be able to share some techniques to help you value your operational data.
What makes Spirit Airlines’ data so attractive?
Traditionally, structured data is perceived as high value. That’s because it’s easy to feed into analytical engines and train machine learning models. Converting unstructured data into a model-friendly format was cumbersome. Most companies didn’t think doing so had meaningful ROI.
But that perspective started to change recently. GenAI could extract structure from this massive unstructured data. What makes Spirit Airlines’ data more interesting is reinforcement learning.
Reinforcement learning involves an agent interacting with an environment and learning from the feedback it receives. Over time, with more interaction and feedback, the agent optimizes for a challenging task. It has been very effective in teaching AI how to code. Because thousands of public GitHub repositories already provide a meaningful starting point for the agent. Agents can also run the code in a sandbox and get instant feedback by testing it. This instant feedback loop makes reinforcement learning possible.
But coding is a special case. Creating a feedback environment is extremely easy there. But many other AI applications aren’t like that. And nothing is more lucrative than the airline industry. Engineers can create a simulated environment for the physical world to model aircraft operations. But decisions made during the airline’s day-to-day operations are invisible to the modeled environment.
Simply put, engineers can simulate a storm and learn to navigate it with reinforcement learning. But whether it should fly through the storm or return to the previous airport needs more than survival probability. An airline operator must assess various factors and expert opinions before deciding. A physical simulation can’t help here.
But what if engineers have access to decades of conversations that lead to such decisions? That’s what Spirit Airlines’ operational data means to Google. How did the airline survive the pandemic? What did it do during a sudden spike or drop in demand? How did it manage its supply chain issues? And many more questions that aren’t foreseeable or inferable by an outsider.
How to value your operational data assets
Unlike physical assets, data, more specifically operational data, doesn’t have a direct value. But we can still use valuation principles to value data assets.
But I want to make a distinction before moving on. The following section isn’t about valuing any data assets. I want to focus more on operational data, which has largely been overlooked because it didn’t have marketable value until recently.
We primarily use three approaches to value any asset.
The income approach
Assets generate income for the company. Wouldn’t it be wise to value assets based on their expected return over their lifetime? That’s the income approach. In general, we estimate the direct cash flow the asset generates, subtract the asset’s operating costs over an expected period, and discount those figures at the rate of return you could expect from the next-best alternative. This works perfectly for many asset classes, including physical, intangible, and even businesses. It works perfectly for your data too.
But for operational data, this approach struggles with two aspects. Operational data doesn’t directly generate cash. The email sent a year ago and software logs are helpful, but they don’t make money on their own. The second issue is that there’s no next-best-alternative rate to discount it. Yet, there are ways to think around them.
Operational data doesn’t generate cash directly. But it saves.
Think of emails; they don’t directly generate any cash. But the company wouldn’t have made revenue without them. Yet, emails that once helped generate cash aren’t as important as they once were. Also, not every email is worth the same. Client communication and important decisions carry more value than emails sent to inform staff about the year-end party. Yet these emails would have helped the company resolve issues faster and even prevented legal penalties.
The challenge is how we could assign a monetary value to these. The answer isn’t straightforward. But the following steps are helpful:
Step 1: Identify different data asset classes.
By that I don’t mean the type of data. Even within email, email threads with customers, insurance providers, and staff comms must all be treated as separate classes. Likewise, treat a system’s performance monitoring differently from application logs.
Step 2: Find future use cases for each data asset class
Each asset class has different future use cases. Think of performance monitoring logs. They help a team plan compute resources for the application. Application logs, on the other hand, will help explain customer queries and debug issues in the application logic.
Step 3: Estimate the revenue it brings or the cost it saves
Think of recent legal issues you or similar companies in the industry faced. How much did the company lose or would have lost? That’d give you an estimate of how valuable that specific use case of the asset class is.
Step 4: Estimate the probability or frequency of future use
One legal dispute doesn’t mean every client will dispute with you. Thus, the value of your client communication email can’t be the sum of your estimated losses. Compute the probability of these events. If you don’t have enough events to compute them, try a proxy organization or estimate at the industry level.
Step 5: Compute the value of each asset class
Now you have the use cases for every data asset class, the probability that it might be used, and the value when it is needed. Enough information to compute expected asset value. And then you can sum these use case values to get the value of the data asset class.
A finance professional would go further by distributing it across years and discounting them at a predetermined rate. This would give a slightly more meaningful estimate. But I don’t want to take the audience of this post into a financial engineering masterclass.
The cost approach
If you’ve bought a laptop for $1000, and I ask you what’s the value of the laptop, you would tell me $1000. Meanwhile, if you look up your model’s price today and it says $980, you may say $980. Both are correct answers. The former assumes the cost you’ve spent is the value, and the latter assumes the amount you’d have to pay to acquire the same asset today is its value. These are examples of cost-based valuation.
Most data assets can be valued using this approach. If you’ve purchased this data, or invested in data collection activities, you have the number. But operational data is different. You don’t acquire this data; you generate it. Every day, as your business operates, it generates this data.
But you can still use the cost approach to value operational data. I see two possibilities.
Use storage cost as the value of your data.
You choose to store this data for future needs and pay the storage cost. That also tells you how important this data is to you. Your organization’s retention policy would have accounted for it. You deem data useless after a certain amount of time. Until that time, you pay for the storage. Also, if the data is critical to you, you’d install replicas. That too costs.
Use the insurance cost.
If data is critical to your business operations, you’d have it insured. If not, ask for a quotation. You can deduct a small percentage of that value to account for the insurance company’s premium.
For operational data, the cost approach is perhaps the quickest way to put a dollar value on your data.
The market approach
How much can you get selling your operational data? That’s the market value.
Unfortunately, companies don’t go around and sell operational data. They protect it. Unless you find it in a bankruptcy auction, such as Spirit Airlines’ case, you can’t find it anywhere. But if you’re lucky to find one, that doesn’t mean your data is worth the same amount.
Once you’ve found the market value for a proxy company’s data asset, you must adjust the value for data quality, volume, and variety. Volume, perhaps, is a no-brainer. But if the proxy dataset scores high on completeness, correctness, timeliness, and granularity, your data might not be as valuable as theirs. Other things that matter most are the provenance and usage rights. You can’t sell a dataset you don’t own or don’t have the rights to sell. It is a frequent case for operational data. Who owns the email a customer sent you? Even if it’s you, could you sell it?
In fact, that’s what blocked Google’s Spirit Airlines data deal. The labor union thinks Spirit Airlines’ operational data contains elements that belong to the staff, not to the airline. Hence, they insist the airline can’t sell it, and doing so would be a privacy violation.
Final Thoughts
Unknowingly, you might be creating a million-dollar asset. Spirit Airlines’ bankruptcy auction proved it. Everyday conversations, software logs, and code written for internal efficiencies are valued by Google at $10M.
If a dying airline’s data is worth that much, how much would yours be worth? We can use some traditional valuation techniques to address this question. Of course, each method would put a different value on your data. But when more than one method converges in value, you get more confidence in the figure.
I’ve discussed how to use the income, cost, and market approach to estimate the value of any business’s operational data. But in practice, you’ll still need to tweak it. What would you change? I’d like to know.
Thanks for reading, friend! Say hi to me on LinkedIn and X, too!