Why I Left My Bank Executive Job to Join BigML
“If you want to hear my honest opinion, what you’re about to do looks like a desperate move to me.” This was the reaction of a good friend and a mentor of mine, when he heard that I am about to accept BigML’s offer to join them as a V.P. of Business Development. Mind you, he has spent most of his career working for large international companies at the C-level. To be fair to him, his first conclusion was based on a five minute phone introduction on BigML and my future role, while I was on the TGV train to Paris on my way to PAPIs.io and he was trying to catch a plane in London himself. He continued: “Mobile payments are finally about to hit the mass market and there’s a huge buzz about all the FinTech topics you’ve been working on for years and instead of cashing out you want to join a small startup in what was the name again? Predictive analytics?”
I should have gone differently about explaining my motivation in exchanging a comfortable life and a well-paid FinTech job. While I am proud to have headed a great team at one of the most innovative Swiss banks that afforded me the opportunity to frequently exchange experiences with other FinTech professionals as a speaker and as a board director of Mobey Forum, I’m leaving it behind for three things:
- The opportunity to actively participate in this unique moment in time, when predictive analytics technologies are starting to changing the world as we know it
- A company that is perfectly positioned to ride this big wave by offering Machine Learning as a Service (MLaaS) to enable not just one but a multitude new business use cases
- A team that is very passionate about making machine learning simple, beautiful and easily applicable into predictive apps and services for all interested parties out there including developers, students, analysts and business experts.
Let me further explain.
Predictive Analytics in Business Context
Coincidentally, my predictive analytics story began around when BigML was founded in 2011. At that time, I had initiated the card linked offers program for my bank in Switzerland. It was in essence pretty similar to what some startups in the U.S. had been offering, but with a twist. Instead of using only simple descriptive data analytics focusing on a selection of customers that have shown a certain behavior in the last X months, we focused our energy to develop a service based on predictive models that could tell us what the customer is likely to do next. This predictive capability was based on a huge amount of data our customers had given us permission to mine. This quickly became our unique selling proposition in the market and turned me and my colleagues into believers.
So what is so exciting about making predictions about unknown events? It boils down to the ability to learn from large datasets rather than following preset business rules, which is a huge improvement in the business world. The majority of the existing business rules within our companies are a part of the heritage of the pre-digital world that have been transferred to the digital age without questioning their present purpose. Meanwhile, we have access to technologies in the area of modeling, machine learning and data mining that enable us to build automated, self-improving processes to better adapt our companies to the present environment. In this context, being able to predict unknown outcomes will make the difference between acting and reacting, between smart and (to state it politely) not-so-smart and between winners and losers of tomorrow’s competitive landscape. Those who don’t count cards will ultimately leave the poker table penniless.
And how about those who specialize on counting cards? Kevin Kelly from Wired magazine wrote: “In fact, the business plans of the next 10,000 startups are easy to forecast: Take X and add AI.” If we take the FinTech space as an example of how many opportunities lie in front of new players who will benefit from the incapability of banks to unlock the full potential of digitization just imagine what kind of potential lies across different industries when it comes to artificial intelligence and predictive apps. Some claim smart apps will have an even bigger impact on our businesses and private lives then the introduction of mobile computing had. As in the case of my good friend from the beginning of this post, being a senior manager and running a multi-million euro business operation doesn’t automatically put you into a position to understand the potential of this technology and the forces at work, which are bound to redefine how we run our day-to-day businesses.
Granted, the idea of machine learning in the cloud is no longer brand new. Last week Computer World UK published the list of “7 cloud tools to harness artificial intelligence for your business”. And the participant list is impressive: Google, Microsoft, Amazon, Alibaba (the announcement was made last week – “huānyíng” guys), IBM and two other startups. One of them is BigML. But back in 2011, only two of those seven companies were at the start line: Google and BigML.
BigML’s early start to introduce large scale machine learning to the masses has turned into a healthy mix of subscription customers as well as corporate engagements including hybrid installations and professional services in a variety of industry segments and predictive use cases. Increasing demand in BigML’s expertise in this field has also resulted in high profile strategic joint ventures such as the partnership with Telefónica Open Future_ to build an automated platform for assess early stage technology startup investment opportunities. There has never been a better time to be in machine learning and it will likely remain a central area of business innovation for the next 5 years as per Gartner’s newly published hype cycle curve. We have got work to do!
What struck me right away when I met the team in Barcelona during PAPIs.io last year was the fact that many of the people working for the company have followed our energetic CEO for the second or a third time into a new venture. This is indispensable proof of trust and leadership qualities. Like in the lyrics of that Bob Marley’s song: “You can fool some people sometimes, but you can’t fool all the people all the time.” Take my first conversation with JAO, BigML’s CTO. He wasted no time coming straight to the point: “Don’t consider starting here by lining up bunch of integration projects as a success. I want our developers to continue further developing and improving our predictive platform rather than running a bunch of short-term snowflake projects leaving it to stagnate.” Bang! This was music to my ears. A company ran by the product guys that aim to first and foremost make a great product. So no more writing about it for me, let’s start!