Introducing the Data Lab

Ever wonder how Edison managed to invent the light bulb, phonograph, motion picture camera, storage battery, copy machine and whole bunch of other things? Yea, me either. Until one of my friends, who’s a bit of a historian, told me all about it. Then I had to research it. Turns out, the real genius of Edison was in creating a new kind of research laboratory, with a process around invention that brought his ideas to commercial success. He called Menlo Park (New Jersey) his Invention Factory.

What does that have to do with big data? Let’s start with this: while electricity was the hot new resource in Edison’s day, today that resource is data. Edison, at Menlo Park, disrupted a whole lot of industries in his day. Think about the big disruptors today – the Ubers, Airbnb, Alibaba, etc. They’ve figured out how to use massive amounts of data, generated from people, from processes and from things. Furthermore, they’ve figured out how to commercialize data using technologies like Apache Hadoop. Think of dynamic pricing by peak time of day, or personalized, localized advertising, made possible because of data. And it’s not just high tech, it’s happening in every industry, from Agriculture to Zoology (ok, not sure that’s an industry. But I needed a Z).

The lesson is: leading companies are disrupting their industries by being first to unlock the commercial value in their data. And for the most part, they’re starting by gearing up data labs for agile experimentation – basically, today’s Menlo Park. Research organizations like CERN, the world’s biggest physics lab, use Oracle Big Data Discovery to facilitate data exploration and leverage the power of Hadoop, to transform and analyze data. Ultimately, when enterprise customers combine Oracle Big Data Discovery with the Oracle Big Data Appliance (BDA) with Apache Hadoop it allows the data lab to make incredible breakthroughs.

A data lab empowers you to get at the commercial value in your data, by providing a complete sandbox for easy access to raw materials – data – to drive agile experimentation and innovation. A best practice data lab should feed operational environments so you can commercialize your ideas quickly. Your data lab should enable you to explore the data you need to quickly make decisions that can transform your business and by leveraging best of breed tools you can reduce your time to value.

When it comes down to brass tacks, what exactly do you build and to whom do you give it?  Let’s summarize best practices from a customer’s perspective. First, pick a problem to solve, a question to answer, or project to see through. Customers need to determine what the negative consequences of not starting a big data project would be.  What is the best case scenario of a successful project and how to get there? Second, customers need to determine what is required for success. Many of our customers embrace the concept of a data-lab-in-a-box – by combining the Oracle Big Data Appliance, for lower TCO and simplicity, with Big Data Discovery they can manage and visualize data for the problem they are trying to solve.  This allows them to not only see the data but also gather different streams of data while maintaining data governance with tools like Cloudera Navigator, part of Oracle BDA — finally operationalizing the insights. The best R model in the world won’t do anyone any good if it stays on your laptop. It’s not just about innovation, but innovation and integration.

Edison knew this. His solid process to commercialize inventions was the hallmark of his success. In fact, he vowed to push out a minor invention every six weeks and a major one every six months. And he did just that at Menlo Park. What would your business look like if you did the same, commercializing your data inventions, straight out of your own Menlo Park data lab?

Learn how to get started from this joint Cloudera and Oracle webcast and from other customer success stories at



Lucie focuses on Oracle’s big data analytics and discovery solutions. She has over 20 years experience in engineering, product management, marketing, project management, and sales, and has worked with analytics of all sorts, in particular predictive analytics and forecasting, as well as performance management applications, business analytics and big data analytics. She holds a BS in Electrical Engineering and an MBA, from McGill University, Montreal, Canada, and can be found most nights at the dance studio, prepping for her next ballroom competition.

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