It's not just December chill. Many companies are now bracing for a global financial downturn as the frosty winds from recession continue to blow. Many in the tech industry are looking to reduce costs by cutting staff and freezing hiring, but companies without a payroll cushion are looking for ways to extract value from all their assets. It is time to rethink data as an asset. Data may be overlooked when valuing a business or securing collateral for a loan. However, it could actually have more value than any other asset on the balance sheet for some businesses. Hand sifting dataBleepingcomputer.com
Data is becoming a valuable collateral. Big players have already begun to use data to help them weather difficult economic times. For example, American Airlines and United Airlines were able to secure multi-billion dollar loans that were backed by customer loyalty data during the pandemic. Small and medium-sized companies, however, have a more difficult path to data collateralization. This is unfortunate because data collateral offers unique benefits for both the lender and the borrower. The data can be used to raise non-dilutive funds. This allows founders to keep equity and retain control. Data is more secure than traditional venture debt. Even in default, the worst scenario is that the lender keeps a copy of the data assets to sell, while the borrower retains the original, without any interruptions in operation. The terms are much friendlier when you compare that to the seizing of physical assets and hostile creditor takeover.
Lenders also have unique benefits from data assets. Data assets are a non-depleting, progenerative and non-exclusive asset that can easily be monetized to pay off a defaulted loan. Lenders can also investigate data assets to uncover a detailed and current portrait of a borrower before and after the loan term. Data can tell the whole story about a company's health, far better than any other indicator. The intangibles are worth valuing
It is difficult to get a lender and borrower to agree on a data set's value. Although there are established methods for valuing physical assets such as inventory and real estate, data is an intangible asset and represents a new asset class. There are still third-party experts and consulting firms who offer data valuation services. This helps to make loan proceedings easier. Although each consultancy will have its own valuation methodology, there are two common methods of valuing data: the cost basis and Relief from Royalty. Both methods take into account the direct financial value that a business derives from its data, but they differ in how they assign costs for holding those assets. RRM is derived from other intangible assets like trademarks or copyrights. It estimates hypothetical royalty payments for leasing an asset from a third party licensor. Instead, cost basis calculates the cost to produce or purchase the data. This includes factors such as research and development, storage costs and labor.
Valuing intangible assets can be time-consuming and labor-intensive. Even the most wealthy consultants can take weeks to complete their work. Expertise can also be expensive, sometimes in excess of a quarter-million dollars.
Traditional data valuation is not accurate
The lack of market comps is perhaps the biggest problem with traditional data valuation. Although some valuation models attempt to determine willingness-to-pay data prices, the lack of transparency in data brokerage markets obscures market rates as well as potential buyers.
The data market is vast, scattered, and opaque from a 30,000-foot view. According to Transparency Market Research, the global data market will grow to $462 billion in the next decade. It currently includes more than 5,000 companies worldwide. These marketplaces and exchanges don't publish transaction details and many data exchanges remain peer-to-peer. This makes it difficult to understand the market and the demand for various types of data.
A few emerging players in data valuation are using search and AI technologies to penetrate this veil and tap into these markets. Nomad Data, an online platform that allows users to search for third-party data, has already begun to collect and analyze such metadata. Gulp Data, which offers data-backed loans and uses machine learning to analyze thousands of data sets in active markets, is a neolender that performs data valuations within hours instead of weeks or months. They can track the data liquidity markets of various data exchanges that have over 15 billion records. This gives them real-time insight into data market demand. They can also identify specific buyers and see differences between markets worldwide, unlocking the secrets of monetization and providing true market comps.
These technology-backed valuations will be more important as the data market evolves and companies seek to maximize their data assets. Combining these tools with real expertise can lower some of the barriers for SMEs to access the global information market and democratize their data. Understanding the value of your data is key to making the most of it. The path to understanding data's true market value is becoming easier.