Companies are amassing tremendous volumes of data, which they consider their greatest asset, or at least one of their greatest assets. Yet, few business leaders can articulate what their company's data is worth.
Successful data-driven digital natives understand the value of their data and their valuations depend on sound applications of that data. Increasingly venture capitalists, financial analysts and board members will expect startup, public company and other organizational leaders to explain the value of their data in terms of opportunities, top-line growth, bottom line improvement and risks.
For example, venture capital firm Mercury Fund recently analyzed SaaS startup valuations based on market data that its team has observed. According to Managing Director Aziz Gilani, the team confirmed that SaaS company valuations, which range from 5x to 11x revenue, depend on the underlying metrics of the company. The variable that determines whether those companies land in the top or bottom half of the spectrum is the company’s annual recurring revenue (ARR) growth rate, which reflects how well a company understands its customers.
Mercury Fund's most successful companies scrutinize their unit economics "under a microscope" to optimize customer interactions in a capital-efficient manner and maximize their revenue growth rates.
For other companies, the calculus is not so straightforward and, in fact, it's very complicated.
When business leaders and managers ponder the value of data, their first thought is direct monetization which means selling data they have.
"[I]t's a question of the holy grail because we know we have a lot of data," said David Schatsky, managing director at Deloitte. "[The first thought is] let's go off and monetize it, but they have to ask themselves the fundamental questions right now of how they're going to use it: How much data do they have? Can they get at it? And, can they use it in the way they have in mind?"
Data-driven digital natives have a better handle on the value of their data than the typical enterprise because their business models depend on collecting data, analyzing that data and then monetizing it. Usually, considerable testing is involved to understand the market's perception of value, although a shortcut is to observe how similar companies are pricing their data.
"As best as I can tell, there's no manual on how to value data but there are indirect methods. For example, if you're doing deep learning and you need labeled training data, you might go to a company like CrowdFlower and they'd create the labeled dataset and then you'd get some idea of how much that type of data is worth," said Ben Lorica, chief data officer at O'Reilly Media. "The other thing to look at is the valuation of startups that are valued highly because of their data."
Observation can be especially misleading for those who fail to consider the differences between their organization and the organizations they're observing. The business models may differ, the audiences may differ, and the amount of data the organization has and the usefulness of that data may differ. Yet, a common mistake is to assume that because Facebook or Amazon did something, what they did is a generally-applicable template for success.
However, there's no one magic formula for valuing data because not all data is equally valuable, usable or available.
"The first thing I look at is the data [a client has] that could be turned into data-as-a-service and if they did that, what is the opportunity the value [offers] for that business," said Sanjay Srivastava, chief digital officer at global professional services firm Genpact.
More rote and repeatable tasks are being automated using chatbots, robotic process automation (RPA) and AI. The question is, what is the value of the work employees do in the absence of automation and what would the value of their work be if parts of their jobs were automated and they had more time to do higher-value tasks?
"That's another that's a shortcut to valuing that data that you already have," said O'Reilly's Lorica.
Genpact also advances the concept of "derivative opportunity value" which means creating an opportunity or an entirely new business model by combining a company's data with external data.
For example, weather data by zip code can be combined with data about prevalent weeds by zip code and the available core seed attributes by zip codes. Agri-food companies use such data to determine which pesticides to use and to optimize crops in a specific region.
"The idea is it's not just selling weather data as a service, that's a direct opportunity," said Srivastava. "The derivative opportunity value is about enhancing the value of agriculture and what value we can drive."
It is also possible to do an A/B test with and without a new dataset to determine the value before and after the new data was added to the mix.
Netflix and Amazon use recommendation engines to drive value. For example, Netflix increases its revenue and stickiness by matching content with a customer's tastes and viewing habits. Similarly, Amazon recommends products, including those that others have also viewed or purchased. In doing so, Amazon successfully increases average order values through cross-selling and upselling.
"Algorithmic value modeling is the most exciting," said Srivastava. "For example, the more labeled data I can provide on rooftops that have been damaged by Florida hurricanes, the more pictures I have of the damage caused by the hurricanes and the more information I have about claim settlements, the better my data engine will be."
For that use case, the trained AI system can automatically provide an insurance claim value based on a photograph associated with a particular claim.
If a company using an external data source were to lose access to that data source, what economic impact would it have? Further, given the very real possibility of cyberattacks and cyberterrorism, what would the value of lost or corrupted data be? Points to consider would be the financial impact which may include actual loss, opportunity cost, regulatory fines and litigation settlement values. If the company has cybersecurity insurance, there's a coverage limit on the policy which may differ from the actual claim settlement value and the overall cost to the company.
A bigger risk than data loss is the failure to use data to drive value, according to Genpact's Srivastava.
There's no silver bullet
No single equation can accurately assess the value of a company's data. The value of data depends on several factors, including the usability, accessibility and cleanliness of the data. Other considerations are how the data is applied to business problems and what the value of the data would be if it were directly monetized, combined with other data, or used in machine learning to improve outcomes.
Further, business leaders should consider not only what the value of their company's data is today, but the potential value of new services, business models or businesses that could be created by aggregating data, using internal data or, more likely, using a combination of internal and external data. In addition, business leaders should contemplate the risk of data loss, corruption or misuse.
While there's no standard playbook for valuing data, expect data valuation and the inability to value data to have a direct impact on startup, public company, and merger and acquisition target valuations.