Martin Hack co-founded Skytree in 2010 and served as its Chief Executive Officer until 2014 when he took on his current role as Chief Product Officer. He is responsible for all aspects of Skytree’s technology, product strategy and direct engagement with customers.
Martin has 20+ years of experience creating game changing technology products, services and strategies. His experience includes the management of product lines with revenues totaling $1.8B/year. He has launched ground-breaking products, driven world wide strategies and helped set de-facto industry standards. As an expert on Trusted Computing, Virtualization and High Performance environments, he became a sought after advisor to many Fortune 500 companies and government organizations.
Advanced analytics- Skytree’s area of speciality- are in hot demand in the age of big data. More and more industries are moving past traditional retrospective analysis, and towards advanced techniques such as predictive analytics. We spoke to Martin about this recent trend, his extensive experience in the field, and the future of data science & data scientists.
Give us a little background on you and your company.
I’m the Chairman, Chief Product Officer & Co-Founder of Skytree. We started the company about four years ago and we came out of stealth mode in 2012. And my background is primarily in Silicon Valley product companies. I worked for Sun Microsystems for a long time, then did a couple of projects- one of which was GreenBorder, which was acquired by Google. Ultimately I met with Alex Gray, who is the co-founder Skytree, we partnered together and that’s kind of how I ended up here.
What are the major developments you’ve witnessed during your time in advanced analytics space?
I think what we’re seeing is maybe not the revolution, but certainly the evolution of analytics. Things like Business Intelligence have certainly been around for a long time- but the technologies were primarily looking at yesterday’s data. Nowadays, the focus is on looking to the future- finding predictions & discovering patterns in data that haven’t been uncovered before.
Machine learning advanced analytics is essentially starting to unleash whole new capabilities that we just couldn’t do ten, fifteen years ago. Predictive analytics is certainly in accordance with a lot of this. I think people are not talking, even, about prescriptive analytics so much of them are gonna predict what’s gonna happen but tell you what you should do next. So, this whole notion of prescriptive analytics, I think, is gonna take place, or really it’s gonna make a big impact on the number of industries in the future. Where all of this is going, and it’s kind of the holy grail of all of this, is towards automation where, essentially, the whole analytics map cycle is become much more automated from data preparation to data modeling, to apply the algorithms, all of these become much, much more automated and we think, very much, that it’s gonna happen and, as a matter of fact, it’s already happening to a certain degree.
What would you say to companies who have yet to adopt any advanced or predictive analytics, or perhaps haven’t yet been convinced to adopt these methods?
I would say companies shouldn’t just be sold on the fact that it’s cool and everybody else is doing it. They should be doing it because of the potential impact for their individual business- whether it’s preventing loss or increasing revenue in your particular situation. So when you start thinking about your business case, it gives us, essentially, a checklist of things to do: Do you have the data that’s required? Do you know what approach you need to take? We essentially start with the technology, and businesses say “Here’s the machine learning approach that we want to take, and here’s the data analytics and the data science approach that we will take”- and we can take it from there. But the starting point should always be the business case and the business application.
Microsoft’S CEO, Steve Ballmer, recently said that machine learning was the next era of computer science. Do you agree with this?
We can definitely see the benefits of machine learning, because we are right at the heart of it. One of the reasons why we’re excited about the entire field is it’s really exploding in many ways. You can now get a machine learning degree, you can get a PhD in machine learning — that’s really not what’s new. What’s really new is we can now do more with these techniques than ever before. I think that’s what’s changing dramatically and I would say that there are certain industries that are just waking up to these possibilities. I’ve been in the industry fifteen, twenty years and I’ve seen new technologies come along and people are like, “Wow, we can now do things that we just couldn’t do five years ago.” That’s certainly exciting for us, being in the center of all that, but we can certainly see how it’s going to get even bigger- I would definitely agree machine learning is going to have a massive impact in the next few years.
Are there any particular applications of machine learning that you think are particularly intriguing, or have really caught your eye recently?
I think a trend we’re seeing now is what we call asset-intensive markets, or industrial asset- intensive markets. These range from transportation, to oil & gas, to utilities, to manufacturing. Many of these industries have collecting huge amounts of data for quite some time. The Internet of Things in particular is changing this landscape dramatically, in terms of what you can do with this data. In the past, you were just collecting the data, and running analysis on it later. We’re now heading towards predictive maintenance and predictive analysis of what’s going to happen next. This area is so interesting to me because it’s touching multiple use cases and multiple industries and pretty much everybody we talk to in these industries, they’re extremely excited about it. The Internet of Things is really going to open up what we can do with our data, and has implications across the board.
I would say that the combination of machine learning, the Internet of Things and big data is essentially the perfect storm for the next era. These three things combined will have a massive impact on the future.
When you’re looking to expand your machine learning team, what skills are you looking for?
Every university around the world has a different set-up for teaching machine learning. Some places it’s attached to computer science, sometimes it’s attached to statistics, sometimes to math- it’s happening in several different departments. What we’ve found works for us best is looking for those candidates who combine machine learning skills with a strong computer science knowledge. But even these candidates may come from a maths or statistics background originally.
Essentially, we’re looking for the same skills that fuel our platform- a fusion of computer science and algorithms. These two aspects combined seems to be the winning formula for us.
What are you thoughts on the future of data science in general?
I think that data science is a premise that’s here for right now. We definitely see there’s a necessity for this, and now there are universities that are offering data science degrees, and we hear alot about a global shortage of people with the “data science” skillset. I think any business which is even remotely relying on making decisions based on data, will have a Chief Data Officer or Chief Data Scientist. Data scientists have kind of a unique profile because they have to understand the business, but you also have to be pretty good at applying the technology and navigating the data as well. There’s a need for “data science” as a concept, and even if some consider it a fad, we feel at the moment that it’s here to stay.
I think what we’re seeing right now is somewhat of a sea-change in the way people are looking at data. We’ve spoken to many companies- both tech and non-tech- who are really making big data analytics a key theme for the next year. They’ve realised there’s a lot of benefit in doing that, and that big data analytics is crucial to staying competitive. Advanced analytics is no longer a capability that’s merely nice to have; it’s a must-have.
(Image credit: Skytree)
