Mike has more than 25 years’ experience in the industry helping firms design and develop mission-critical applications in eCommerce, insurance, banking, travel/hospitality, manufacturing, healthcare, and scientific research for organizations including NASA, eBay, Bank of America, Liberty Mutual, Nielsen, EMC, and others. He has written thousands of lines of code, managed development teams, and consulted with dozens of technology firms on product, marketing, and R&D strategy. He is a frequent and sought-after speaker at industry, corporate, educational, and technology events for his audience-designed, insightful, and energetic speeches.
We are glad to have Mike presenting at Data Natives 2015!
Tell us a little bit about yourself: what were the pivotal moments in your career?
I’m analyst now, but most of my career has been as a practitioner software engineer and enterprise architecture. Data has run hard through all the projects I have done.
If you could go back, what would you do differently?
Not sure. I try to stay focused on what I can do in the future.
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What is the future of machine learning?
Massive machine learning automation. Come to my keynote to learn more.
What do you feel about the term Big Data?
Big data is just data, but is so large that analyzing can be a challenging unless you are using a distributed architecture. Fortunately, open source Hadoop/Spark and cloud computing put the capability to analyze big data in the hands of millions.
What comes first in big data, IT infrastructure or business acumen?
Hmmm. I’m not against starting either way. You need both eventually. A large enterprises has so many unrealized big data analytics opportunities that building the infrastructure first will not be a waste of time and resources. A smaller organization should have several hypothesis upfront before commiting to time and resources to infrastructure.
What do you recommend for our readers who are thinking about a career in data science?
Invent the future. Start by thinking about new customer experiences and/or business processes. The hard work will be done by the algorithms so you must understand the categories or algorithms and how to apply them. And, please use modern tools to analyze the data. There is always a place for programming solutions such as R and Python, but modern tools can get you 90% there and save a ton of time.
What is your advice for businesses that want to get into the data science arena?
What? Are you saying that businesses aren’t in data science now? I’d say get going now. Software ruled in the 90s and 00s. Data science rules in 10s and 20s.
What should we expect from your Keynote at Data Natives?
I want to do three things in my keynote: 1) Present the business and technology trends that are driving data science. 2) Demystify data science and show the limits and possibilities. 3) Offer some pragmatic advice on how to get started or take it to the next level.
(image credit: Ari Helman, CC2.0)