In this special guest feature, Ron Bodkin, President and Founder of Think Big, a Teradata Company, discusses how the hyper-accelerating big data industry can yield a bewildering number of technology options and how to say “no” in the face of such compelling temptation. Ron founded Think Big to help companies realize measurable value from Big Data. Previously, Ron was VP Engineering at Quantcast where he led the data science and engineer teams that pioneered the use of Hadoop and NoSQL for batch and real-time decision making. Prior to that, Ron was Founder of New Aspects, which provided enterprise consulting for Aspect-oriented programming. Ron was also Co-Founder and CTO of B2B applications provider C-Bridge, which he led to a staff of 900 service consultants and a successful IPO. Ron graduated with honors from McGill University with a B.S. in Math and Computer Science. Ron also earned his Master’s Degree in Computer Science from MIT, leaving his PhD program after presenting the idea for C-bridge and placing in the finals of the 50k Entrepreneurship Award.
The universe of big data has come very far, very fast. The Hadoop galaxy continues to display ferocious, explosive growth and newer technologies like Spark are also on the ascent. At the same time, a steadily increasing number of solutions and alternative delivery models for big data such as cloud offerings are expanding the number of organizations that can put big data techniques to work.
This wealth of choices means that your organization has nearly endless permutations to consider as it carves its unique path to big data. It is an environment full of opportunity, but one in which a significant, potentially debilitating challenge lurks just beneath the surface: with so many choices, you can find yourself trying too many things without honing in on the few things that could support delivery of meaningful results.
Once you filter through hype and enthusiasm, it should be obvious that not every technology is right for every data challenge—there is no one-size fits all approach. IT staff is not, nor can they be, expert in every one of these technologies. And many of these technologies and techniques are not friendly to each other, defying attempts to create a cohesive framework for leveraging your data.
As Steve Jobs is reported to have asked Jony Ive, “How many times did you say no today?”
I believe the lesson here is that if you can’t confidently say “no”—or at a minimum “not yet”—to many of the ideas and technologies that appear daily, you also limit your ability to give a strong “yes” in response to the ones that will ultimately matter. This begs the question of just how to say “no” in the face of such compelling temptation.
First, realize you might be starting in the wrong place. Avoid technology discussions first-out and instead look deep inside the heart of your business. Start with which questions you want to ask about your business and then take a realistic look at which answers you would be prepared to turn into operational action in the near term. Think about intelligence you’d like to automate in areas such as recommendations, personalization, or predictive maintenance.
Next, be realistic about your own analytic and big data maturity. There are overlapping curves of progress at work. Your organization is on its own learning curve in terms of its use of big data, and it is okay to chart your own course. In essence, you don’t have to mimic companies profiled in the trade press because what works for them might not work for you.
Simultaneously, the technologies and techniques available are on their own rapidly developing curves. What started as a mere handful of open source projects a couple of years ago has morphed into dozens today, with more appearing on the near horizon. You don’t want to find yourself too far down a particular road too soon only to find yourself stuck because you don’t have the flexibility to answer key questions that require different techniques than the ones you chose.
A smarter approach to big data begins with clear business needs, followed by an architecture built to serve those requirements. Only then do individual technologies come into the story. If a technology isn’t providing a unique capability in your architecture that fits a business need, you can be confident in saying no. Knowing the kinds of questions that you want to answer is the ultimate map to which big data architecture will lead to results and the lynchpin to your ability to say “yes,” “no,” or “not yet” to a particular technology.
Your goal is to build a flexible data and analytics architecture that is responsive to evolving needs over time. Say no until you see clear business value for your organization that requires a given technology. Saying no may not be popular, but it is essential to focusing on the core mission of your business and charting a path to success.
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Sage advice. Podium also sees huge value in saying “no,” or at least “not yet” when deciding which open source projects to integrate in our enterprise data lake management platform. We agree with Ron that the right starting point should be the business need. For us, in fact it boils down to this core question, “Which open source projects offer compelling advantages that matter to users and are adequately mature for enterprise deployment?” Even more we think platforms, like Podium, can allow organizations to say yes to new technology sooner by addressing concerns around maturity and giving users access to new open stack projects at the right time with lower risk.