September Startup of the Month: Algorithms.io
DataWeek September 2013 Startup of the Month: Algorithms.io
DataWeek is excited to interview Andy Bartley, the CEO and Founder of this month’s Data Startup of the Month – Algorithms.io!
The Algorithms.io team lead by Andy Bartley and Garland Kan took top honors in the 2013 Data 2.0 Summit 2013 startup pitch competition. Their pitch was selected from a field of companies including Karmadata, Virtrue, Subledger, and VertaScale. Judges incuded Ethel Chen from Norwest Venture Partners, David Chen from Lightspeed Venture Partners, and Brian Ascher from Venrock.
I asked Andy a few questions about Algorithms.io:
Geoff: What is your vision for Algorithms.io?
Andy: Algorithms.io makes machine learning available to the Fortune 5,000,000. The company has a unique algorithm packaging system that abstracts machine learning algorithms into reusable building blocks that are used to create custom predictive SaaS applications.
What we’re very focused on right now is predictive analytics for streaming data. The streaming data being generated by technologies in the “Internet of Things” market is a natural fit for solutions that provide machine learning and other algorithms as a service. We’re working with several customers in this space now who are using our platform as their complete cloud analytics backend. We manage all of the data ingestion and storage, and combine that infrastructure with our catalog of algorithms. The end result is that our customers are able to repeatedly bring new applications to market faster and at lower cost than their competitors.
Geoff: Is Algorithms.io a “marketplace for algorithms” or do you plan on producing / curating most of the algorithms internally?
Andy: Right now we are performing the curation internally. When you get past the marketing hype around Big Data, Machine Learning, Predictive Analytics, etc. what you’ll find is most companies still aren’t sure exactly how these technologies can benefit their business. We talk with Fortune 500 companies every week who have few if any data scientists in house, and aren’t using any intelligent algorithms. Our main focus right now is working with those companies to help them understand the use cases and how they integrate with the business model.
Longer term, we think there is an opportunity for an algorithm marketplace. This isn’t a new topic, one of our advisors Ajay Ohri, also the author of Springer’s book on R, wrote about this idea back in 2011 (http://readwrite.com/2011/06/01/an-app-store-for-algorithms#awesm=~ohfvTpPiq6Jmt5). We’ve discussed this topic with folks at some of the potential players like Google who could be interested in this type of marketplace. Two of the primary gating factors for an algorithm marketplace are data quality and use cases. Data quality is still a fundamental challenge, and the really compelling business use cases today can be tackled with a relatively limited set of algorithms. As companies get more sophisticated data infrastructure in the next 2 – 3 years, the bar will begin to rise and an opportunity could emerge for commerce around algorithms. We’re doing a number of things on the technology and IP fronts to position us to play in this space when it emerges.
Geoff: Do you think most established companies will eventually need data science solutions (like Algorithms.io) or an in-house datascientist? How relevant is data science for most companies?
Andy: To answer your second question, data science solutions are becoming industry best practices. The classic use case to demonstrate this is personalization and recommendations for e-commerce. These technologies have become a de facto standard in this space. If you’re not providing customers a more personalized experience then you’re falling behind the competition. As our world becomes increasingly connected, all established companies are becoming data companies and will need to find ways to compete accordingly. So my answer to your first question is yes. Some of these established companies will only need data science solutions and some will need dedicated data scientists. There are a number of factors that determine which approach is right, and this often changes with time. We’ve built out platform to add value in both scenarios. We can deliver complete predictive solutions for companies without a data scientist, or our platform can be used by data scientists to be more effective by reducing the time it takes to deploy their models into production.
Geoff: Do you eventually see your cloud stack “platform”, your algorithms, or your flexible developer infrastructure as your key value to users?
Andy: Our long-term value is in the platform we’ve built. The platform encapsulates and enables the algorithms that we and our partners develop. At the end of the day our customers need solutions to problems, not just algorithms. If you can’t help them with this translation then you’re fighting a lot of headwinds in your sales process and ultimately leaving value on the table.
That said, developers drive many of the decisions we make with regards to the platform. Our entire platform is REST based. We are constantly taking feedback from developers and building those into platform features that make it the easier to use. Great developer products turn difficult technology into a handful of lines of code. We’re well on our way to doing that and expect to have some exciting announcements for this market next year.