Case Studies
Experience matters.
We have been there and done that on over $200 billion worth of technology. Here are a few case studies from the hundreds of deals we have done, and what each one meant for the deal.
Security
Cybersecurity is one of the most value-impairing risks. Adverse security events can shrink a client base, incur regulatory penalties, invite class-action lawsuits, and result in long-term brand tarnishment.
Cybersecurity company has bad security
SOC 2 certification misleading
Attack vector against famous people
Privacy
Privacy penalties are now big enough, up to 4% of the acquirer’s global revenue, that they can dwarf deal value. We assess both general privacy regulations like the GDPR and CCPA, as well as sectoral regulations like HIPAA, SOX and COPPA.
In the EU but didn’t know it: penalties still apply
What clients say
“One of the biggest benefits of working with Tech DNA is their ability to determine what the real focus should be. They just know how technology affects a business and exactly what to look for. They understand the ‘So What?’ of technology.”
SVP of M&A · Global Fortune 500 acquirer
Open source licensing
Open source software (OSS) license risk falls into two camps: a) target’s improper use of open source software prevents target from passing clean title of their software to the acquirer, or b) target’s distribution of OSS compels target to give away its valuable IP for free. OSS can also present security risks. See the Security case studies.
Franchise model: give it all away, for free
No get-out-of-jail-free card: you can’t contract around OSS licenses
Built for speed
We work at deal speed.
8–21 days
Typical project length from receipt of code to delivered final report. Can be compressed or lengthened as needed.
24 hours
Typical time to start once under contract.
ML / AI
Machine learning and artificial intelligence (ML/AI) risk is not often a risk per se. More accurately, the issue is overinflated valuations based on dubious ML/AI claims. The exception is regulated industries, such as health care and credit scoring, where incorrect algorithms can cause bodily or financial harm, which in turn can result in significant negative reputation, regulatory interest or liability.
We are often asked to do an ML/AI “sniff test”. With so much ML/AI puffery out there, clients often want a gut check of ML/AI claims up front before incurring any additional deal costs. The first two case studies below are sniff tests.
Not really ML, and not really that good
Basic but solid: right tools, right models
Don’t be fooled by lots of PhDs
Commoditization
Commoditization risk is the risk that the value and competitiveness of target’s tech has been eroded by newer, cheaper technologies.
Trying to compete with free
Outsourcing
Outsourcing comes with several risks: lack of control or ownership of source code, contested IP ownership, impaired ability to retain key talent, and general conflicts of interest between what is best for target and what is best for the dev shop.
Turns out they didn’t write or control much of their own code
Can’t pass clean title: dev shop claims the code is theirs
Obsolescence
Obsolescence risk is that target’s code is at the end of its lifecycle and there are significant near-term costs just to avoid falling behind.
Code so old it’s time to start over (at great expense)
Next step
Every one of these started with a scoping call.
Tell us about the target and we will tell you where we would look first, how long it would take, and who would do the work.