Interview: Ben Werther, CEO, Platfora on Insightful Analytics for Big Data
We discuss the challenges in implementing end-to-end solutions for Big Data, Platfora use cases, Big Data trends, advice and more.
Ben Werther launched Platfora to transform the way businesses use big data analytics. Under Ben’s leadership, Platfora is now one of the hottest companies in Silicon Valley and a leader in the big data analytics space. Before founding Platfora, Ben was vice president of products for DataStax, where he shaped the company’s enterprise and Hadoop strategy, and was also head of product at big data analytics company Greenplum.
Ben has a B.S. in Computer Science from Monash University and an M.S. in Computer Science from Stanford.
First part of interview
Here is second and last part of my interview with him:
Anmol Rajpurohit: Q6. What are the major challenges of implementing a full stack i.e. end-to-end solution starting with raw data and delivering business insights?
Ben Werther: The biggest challenge we’ve noticed over the past two years is the decision to centralize data in Hadoop. This isn’t easy at all for an organization, as it requires a fundamental shift in architecture and IT operations. That said, it is surprisingly easy to convince Hadoop adopters to put a full-stack native analytics platform on top of their new platform. They realize pretty quickly that building a data lake is not the end goal, it’s supercharged analytics that they are really after.
AR: Q7. Based on client feedback so far, what are your favorite use cases for Platfora?
BW: Our customers are doing amazing things in the areas of security, IoT and marketing just to name a few. Every month we are blown away by a new use case that’s made possible through our big data analytics platform. As one example, Vivint now collects and analyzes IoT data in Hadoop from almost a million homes. Their team originally lacked any reasonable way to analyze IoT information, but now it is easily done in Platfora.
Today, even non-technical business people are leveraging big data analytics for practical improvements such as cutting down on false alarms in home security and gaining a deeper understanding as to how their customers are interacting with their products.
AR: Q8. Where do you see Big Data headed in the next 2-3 years?
BW: Today the vast majority of companies are data rich but insight poor. They are acquiring more and more data about customers, product experiences, behavior and interactions, and beyond. But simply visualizing and making sense of those patterns and behaviors (given the volume and variety involved) is itself ‘rocket science’ and requires state-of-the-art capabilities.
In 2-3 years those abilities will be a baseline requirement for any globally competitive company, and business users will be able to test hypothesis and actively evolve their business processes in a data-driven way at the speed of thought and without exotic IT efforts.The bleeding edge of the next wave of capabilities -- incorporating newer AI and deep learning techniques -- will also be starting to show its potential.
AR: Q9. What is the best advice you have received in your career?
BW: I love the Peter Drucker quote that “culture eats strategy for breakfast”. As the founder and CEO of a fast-growing company, the best way I have leverage today is to make sure we’re hiring incredible people -- holding the bar high on both ability and cultural fit -- and ensuring that everyone in the company knows what it means to work at Platfora and that we’re all ultimately focused on a common set of goals. I’m incredibly passionate about the market opportunity, product and technology, and how we make customers successful, but ensuring we have the right people and culture trumps any amount of smart executive strategizing.
AR: Q10. What soft skills do you think are the most important for practitioners in the field of Data Science?
BW: There are too many data scientists that have a toolbox of statistical and machine learning techniques but don’t understand enough about their business to really know where or how to apply them. The great data scientists and analysts I know are curious and dogged about finding datasets that may be valuable, pulling them together to find meaning across them, and are proactive about engaging with their line-of-business colleagues to both share understanding of what is possible and learn/validate the problems that will have most leverage and impact for the business.
AR: Q11. On a personal note, are there any good books that you’re reading lately, and would like to recommend?
BW: For fiction, I’m a big fan of Charles Stross. His ‘Laundry Files’ books (The Atrocity Archives, The Jennifer Morgue, The Fuller Memorandum, The Apocalypse Codex, The Rhesus Chart) are secret-agent bureaucratic dark comedies inspired by Lovecraftian horror with a modern mathematics/science twist.
For non-fiction, I just read Nick Bostrom’s “Superintelligence” which is a deep academic perspective on the state of AI and risks/dangers of an explosion in machine intelligence. A really intriguing perspective given the recent discussion on the topic by Elon Musk and others.
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