M&A and Technology: Mistakes and Biases to Avoid in the Valuation of Technological, Deep-Tech, Data, or R&D Assets: Crypto, IP, Patents, etc.(I)

In my experience working in M&A operations from a tech perspective, there are three important errors that a big corporation should be able to handle correctly in order to get a successful deal in the mid and long-term. In this article I am going to comment on the first one: not evaluating or valuing technologies and intangible assets (software, datasets, patents, ML models, crypto-assets, prototypes, etc.) objectively.

The global M&A market has continued a growing trend for years, hitting $6 Trillion at the end of 2021 (source: KPMG). According to this KPMG survey (1), eight in 10 (86%) CEOs say inorganic means will be their main source of growth in the next three years, including M&A, joint ventures and strategic alliances.

Technology, financial services, industrials, and energy sectors account for the majority of deals during the last five years. And this trend will increase for the next decade, with M&A processes more complex every day due to the impact of exponential technologies and needs of valuations very different from those that existed two decades ago.?

First of all, the company should be able to manage the C-level expectations and biases, both conscious and unconscious, about technology and what they can expect from it, especially if we are talking about deep technology or very innovative applications of technology that is currently emerging or beginning to be used by new business models, experimental services or very innovative applications.?

We talk a lot about biases in data science and machine learning, but human biases are really more dangerous, especially in a Mergers and Acquisitions process where emotional factors and feelings are frequently disabled by the M&A teams in financial aspects but are many times undervalued in technical aspects. Especially when we talk about the valuation of intangible, but strategic assets such as datasets, machine learning models, IP - patents and even papers - or any kind of crypto assets the acquired or merged company has, including tokens or participation by nodes in blockchain consortiums or experimental testnets.?

FOMO (fear of missing out), hype, and excessive distrust, can be your worst enemies as a tech advisor during an M&A deal. One of the main skills a technologist should have is complex problem solving, starting with the main problem in a world full of emerging technologies: how to separate the signal from the noise.

In essence, an M&A process with a strong technological, scientific or innovation component consists of detecting the signal among the noise and determining how strong and valuable the signal is.

Find the signal. Separate it from the noise. Measure the noise. Measure the signal. Determine how the signal could be increased or the noise decreased. That will be key to making decisions in a completely objective way.?

Many times, C-level has a set of preconceived ideas about a technology or a tech business application many times with no data behind. If you don’t have data, you only have an opinion. I have many different biases, in favor or opposing topics like open-innovation strategies, proprietary or open-source blockchains, (depending many times on previous experiences about issues such open source or collaboration culture), tokens or protocols, communities, and even programming languages. E.g.: Should you invest in a fintech that is creating their business on a permissionless blockchain like Ethereum? Should we acquire a company that has a lot of apparently valuable datasets if I don’t understand how they use them in their neural networks? Is it a better idea to buy their data or their models? Or both? Or neither? In this case, it’s important to have experts in the M&A team, but especially to find a way to test and get data about the core technology or asset you are analyzing.?

For example, if you have an open-source technology underpinning the company or killer products, you should get metrics about adoption, figures about how active the community is, about the leadership and reputation of main actors or contributed involved, social media interactions and sentiment analysis, value of contributions for the company itself (time value and money value) or metrics like defect resolution velocity density, or existing papers about the technology. Other important info could be how this technology impacts talent acquisition/retention, level of dependency of the business model from this technology, or simply, how difficult and expensive it is to get talent with enough expertise: size or quality of the community can be a useful indicator.?

Let’s talk about data. Setting a value for an existing dataset is another important challenge in an M&A process and it should be executed with more attention if data is a key part of the deal. In this case, you may analyze intrinsic and extrinsic data value. Extrinsic data value is useful to determine the value of data in terms of exploitation and consumption. Intrinsic value is more complex but could be especially useful in buy-sell data strategies or in any sector like cybersecurity, industrial supply chain or retail. Additionally you can organize the data valuation according to three different strategies: enhance current business, enter adjacent businesses or develop new businesses. It’s important not only the value of the datasets but also, how they can complement and enrich existing datasets in the “buyer” company.?

In any case, you can find proven systems, methodologies and processes to value almost any type of digital or technological asset, such as datasets or ML algorithms. Even in patents valuation - in my opinion, one of the most difficult assets to evaluate in M&A. Do not invent the wheel or get carried away by intuition. You need a proven and effective method of assessment, not feelings. No matter how advanced and complex it may be.

Reserve your intuition for innovation and moonshot thinking sessions, not for M&A processes, at least in the early stages. With one exception: during the analysis it is important to be able to see opportunities and risks not taken into account by the company being analyzed, the “seller”. Find them and connect the dots (especially dots between both companies), but always do so supported by data and objective analyzes that support the guesswork. This will be a useful supplement to the objective assessment, but cannot replace it.

My final advice: get real data and metrics about what you want to buy, a lot of data, present, and future. Well done data projections are data as well. We all have feelings about technologies and innovative systems, especially if you are a tech leader or a C-Level engineer, but feelings can be a dangerous item in a big M&A operation.