Preventing Another ‘Theranos Event’ by Analyzing Workforce Data (and Possibly Stopping the Next Madoff)
Back in 2015/2016, Elizabeth Holmes’ house of cards — worth $10 billion at the time — came crumbling down when investigative journalists from The Wall Street Journal did what they do best — investigate and expose. It was then revealed that the Theranos droplet-based blood testing technology didn’t work; that the company has been covering up its failures; and — most importantly — how the health and lives of those patients were put in jeopardy.
Today, this story is almost old news, though the traces of Elizabeth’s illegitimate empire still linger on in the form of her trials and other shady medical tests that are still being advertisable despite not being approved by the FDA.
Lessons to be learned from this event come in rivers, not in drops (sorry for the bad pun). Especially in terms of business ethics and the regulatory landscape of biomedical engineering. But I want to focus on one lesson in particular — how this infamous scenario could’ve been prevented.
It’s All About Data and Patterns
Creating a “successful” startup from scratch is mostly fun, nerve-wracking, challenging and an exciting process (trust me, I should know).
“All” you need is:
- A groundbreaking service/product that fulfills a consumer need in an untapped market
- Sufficient money from investors
- The right people for the job…
… and you’re good to go.
This, however, is easier said than done because at least one of these 3 components tends to be fairly elusive.
For Elizabeth, it was the plausibility of her product.
But even without sufficient scientific data on Theranos’ disruptive patent, as her “strongest cards” were held close to the vest (for obvious reasons), this disaster could have been averted by simply analyzing the workforce structure within her corporation.
Osterus is a platform that enables businesses to — among other things — scan, study and compare workforce data from specific companies.
Whether it is to gain insight into what the competitors are doing or to examine businesses for investment or Merger and Acquisition purposes, or simply to determine if a certain company checks out with their ambitious claims — data analytics software like Osterus can help you track down patterns and use the insights we provide to make smart, data-driven business decisions.
So, how would this work for detecting a fraudulent organization?
Taking a Granular Look at Employees, Teams and Their Qualifications
The secrecy about Theranos’ very product was a huge obstacle, but the one thing they couldn’t hide entirely was the structure of their corporation’s workforce.
Our big-data-based tool could have been used to dive deep into Theranos’ employee framework and recognize potential inconsistencies in terms of:
- Not having enough scientists with top-tier education
- Not having the logical amount of employees with specific qualifications
- Not having the right workforce structure for the product they are claiming they have
- Not having logical employee-team-department allocation for a cutting-edge MedTech company
- Not having workforce patterns similar to leading competitors
Note: Take a look at one of our previous articles where we examine Moderna and BioNTech and what the data signals from employees can tell you about a company.
If there is one thing we learned at Osterus, especially when analyzing powerful and disruptive companies and their competitors, it is that they have a lot of similar workforce patterns and you usually see a few small spikes where businesses have different trends and approaches.
In our Tesla Vs Porsche comparison, we observed such spikes in Tesla in regard to having a higher average of employees with a STEM background. We also saw it with Revolut, who had more engineers & physicists coming from eastern Europe, and the list goes on.
When you use a workforce data analytics platform, you are able to see exactly how a business is organizing its teams, departments, job positions, regular employees, low/mid/high-level managers, as well as hierarchies in order to get tangible insight into their business structure and even their workflows.
We can also provide information about the universities from which a specific organization is hiring their employees, as well as previous companies they worked for in the past and for how long.
Not only does this valuable data enable you to detect suspicious organizations like Theranos, but also lets you analyze companies such as Nikola Motors (video about this questionable company available below), Outcome Health, and other similar potentially illegitimate businesses.
This allows you to figure out if a startup you are looking to invest in or acquire is worth your time and money, while it also lets you understand what the competition is doing right in terms of their hiring strategy, workforce patterns, business intelligence, etc.
Much like it is the case with Theranos, the Osterus platform could’ve been used to analyze the Nikola Motors company as well and come to the conclusion that something awfully fishy is going on under their hood.
Bare-Bones: Legitimacy and Transparency
There are bad apples everywhere, and the modern business landscape is no exception. The Theranos event is not the first nor the last one of its kind. There will always be individuals like Elizabeth Holmes who will stop at nothing to achieve their goals.
Even if it means not being honest to investors and jeopardizing the lives of innocent people.
One of our goals at Osterus is to provide the right tools and features for businesses and investors to examine the legitimacy of the companies they are interested in and use our data, insights and reports to find the best apples out there.
Those that can truly change society for the better.
Try Osterus and Get Valuable Business Insights
Osterus is currently being used by our clients to analyze M&A opportunities, examine the competitive landscape within their target markets, improve and speed up recruiting, increase diversity scores, and identify valuable Business Intelligence insights that help them move forward.
For example, here’s the overview of the comparison between Tesla (Germany) and Porsche (Germany) workforce data that we performed using our software:
Some interesting numbers there, particularly in the number of analyzed CVs. And the following is a chart showing employee education degrees:
Here we can see that, despite a considerable difference in the number of employees, the two companies show quite similar data for staff education levels.
The next graph displays universities that employees attended:
This type of data can be used to detect patterns in the workforce and hiring trends between the two companies.
If you’re interested in using Osterus for your company, feel free to schedule a demo and we’ll explain how this can become your reality. We have a saying here at Osterus, “Let the data speak!”