The fund focuses on the collision of healthcare and technologyRead more...
Workday Ventures, which launched this month, is focused on machine learning and data science
If you've been following this column, which we have been running for a few months now, you know that we have been looking at numerous early stage funds.
While many firms are looking for a financial return on their investment, not all VC funds are in it for the same reasons.
There are also funds, set up by established companies, who will invest in startups for an entirely different reason: to keep up with whats new and, down the road, be able to work with those companies once they are established.
Ajao co-founded Identified, Inc. and served as its Chief Executive Officer. He also co-founded Tuenti.
He earned a J.D. Law and Masters Economics and Finance degree from ICADE in Madrid and an M.B.A. and certificate in machine learning from Stanford.
VatorNews: What do you like to invest in? What are your categories of interest?
Adeyemi Ajao: Workday Ventures is focused on investing and partnering with startups that are leveraging data science and machine learning to address enterprise challenges. If we step back and look at it more generally, we fundamentally believe that 10 years from now every enterprise company will be a machine learning company or will not exist. This is what our clients are demanding, and we truly back that sentiment.
The start of Workday Ventures can be traced back to Workday’s acquisition of Identified, a company I co-founded, 18 months ago. Since the acquisition, Workday has been developing products focused on machine learning, and we announced those products last November. With that announcement, we started having several behind the scenes discussions with machine learning startups who wanted to learn more about what we were doing in the space. We wanted to build closer relationships with some of those companies, which lead us to Workday Ventures.
We are focused on helping these startups go to market, because we understand the techniques to help them sell to Fortune 500 companies. This is a natural fit because Workday knows how to get products to market. With the companies we invest in we want them to learn about how Workday went from zero to almost 1,000 clients and how they can replicate that on their own.
For us, this is truly all about learning: first, it’s purely about what algorithms are used, and how they are implemented. All four investments we have made have had extremely good engineering teams, and we've had a lot of engineering related conversations.
The other part is about applying these algorithms in different ways, for a specific business purpose.
VN: What would you say are the top investments you have been a part of? What stood out about those investments in particular?
AA: We have invested in four companies so far: Jobr, Metanautix, ThinAir, and Unbabel.
Unbabel makes translation available to everyone through machine learning. This is something we are very aware of, since our clients are multinational companies, and we have to translate every one of our products for them.
Their entrepreneurs had a clear passion about that, and the founders are PhDs in machine learning. They are very passionate and they were a very natural fit.
Jobr is a job hunting app. One of Workday’s products is recruiting so this is an area of interest to us. Jobr provides mobile job recommendations to users, which is the future of how people will look for their next job. This is something we want to support and learn about.
Jobr’s founder is very passionate about this space. He's an investment manager and a really smart person who cares about product.
ThinAir is a cloud security company with a machine learning engine. They will use an algorithm to learn which behaviors are ok, and which are a security threat. For example, if someone is accessing a file at 3 am, 10,000 miles away from where it’s usually accessed, it will see this as a security threat and automatically ask that person for authorization.
This is another founder that is passionate. Tony, the CEO, wrote fraud detection for MasterCard, and is now applying the same principles for ThinAir.
Metanautix solves broad and painful problems, having to do with data access for many different systems, which have to get together to do analysis. This is something our clients experience every day when they have to match their Workday data with their CRM or database data. The Metanautix founder had been an engineer at Google, in charge of data pipeline problems.
The common theme here is that all of these companies are solving enterprise problems, which are problems we care about, and are all lead by very technical teams and very passionate entrepreneurs.
VN: What do you look for in companies that you put money in? What are the most important qualities?
AA: We are all about enterprise problems that can be solved with machine learning. And for us to make an investment, it has to be a personal passion for the entrepreneurs.
We also have to make sure that Workday can help that particular company, and that they are at a stage where we can add value.
Basically what I mean by helping and adding value is that, with most companies, that value comes in two buckets. One is pure engineering value. We have a lot of engineering conversations, and learning goes both ways. We might use their algorithm in ways that they are not, and we can say, 'Here's how we are implementing it for a company that has 300,000 workers.'
We also want to help them go to market. We have a lot of executives who were here when Workday had three clients, and know how the platform and sales evolved to make it what it is today. We are able to give these companies coaching on how to get there too.
VN: Tell me a bit about your background. Where did you go to school? What led you to the venture capital world?
AA: I am originally from Spain. I started my first company during my last year in college, called Tuenti, which was the Facebook for Spain. It was acquired by Telefonica for $100 million. Right after that I came here to Silicon Valley. The rationale was, let’s see if I can make it in the place where things are happening. I went to Stanford business school, and in my second year I started Identified.
Identified was LinkedIn on top of Facebook. We would aggregate and use machine learning for hiring out of Facebook profiles. There are a billion Facebook profiles, and based on the characteristics of their workers, we would tell companies if they should hire this person.
Workday truly believes that more data will lead to better decisions in the enterprise. What are the techniques of analyzing that data? In the process of building products at Workday, and being in touch with the startup ecosystem, investing in them was a natural fit. Now that I'm here this is a great avenue to help companies.
VN: What do you think are the big differences between being an entrepreneur and an investor?
AA: Entrepreneurship and venture are extremely different. I don’t think they have a lot in common, mostly because when you are a running company you are single mindedly focused on problem your company, and what problems you are having and all the stuff that entrepreneurs go through.
As an investor you can take a long term approach, as opposed to being an entrepreneur, where you are always executing.
What I like about being an investor it is that, one, you are able to have your head in different problems at the same time. And, second, I like being able to help entrepreneurs benefit from the vast amount of resources that Workday has.
VN: What is the size of your current fund?
AA: We are not structured like a traditional fund, as in we don't have a particular amount we invest. When we started doing this four months ago, VC experts on our board felt this was the right way to go.
It makes sense to do it this way, not to structure it, because what happens is it becomes kind of a trap. With a traditional fund, you have to invest $20 million by the end of the year, which means $5 million per quarter, and your average has to be $2 million. Even if you don't have something in that range, that’s how they think.
The size, stage, and total capital can vary a lot. It has to be a strategic fit for us to invest.
VN: Is there a typical percent that you want of a round? For instance, do you need to get 20% or 30% of a round?
AA: There is no set percentage that we aim for and we don’t seek control or board seats. We are not doing this for financial returns. We are doing this to learn from companies, and to help them learn.
VN: Where is the firm currently in the investing cycle of its current fund?
AA: We have been investing for four months.
VN: What series do you typically invest in? Are they typically Seed or Post Seed or Series A?
AA: The four that we have done are early, Series A, but that doesn't mean we won’t do later stage. We are looking at some later stage, and it’s important that we are able to help them as well.
VN: In a typical year how many startups do you invest in?
AA: Our target is 12 companies by the end of the year
VN: Is there anything else you think I should know about you or the firm?
AA: Due to the lack of formal structure we have been able to get back to companies within a couple of days, and execute investments in a couple of weeks. We have agility because of the lack of financial constraint.
This is extremely fun work. I get to work with the smartest people and learn from them. I'm glad I came over from Spain to do this.
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Workday Ventures works with promising, early stage companies to fuel the next generation of enterprise applications. In addition to receiving funding to accelerate growth, portfolio companies will have the opportunity to gain strategic guidance from Workday's executives, board members, engineers, customers, and partners. Workday Ventures will focus on companies that apply data science and machine learning principles in the areas of analytics, applications, security, and platform technologies.