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Vijay Recaps his Big Data Panel Discussion at TIBCO NOW

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On Monday I had the pleasure of participating in the Big Data Panel at TIBCO NOW, a leading technology conference focused on exploring disruptive technological forces like Big Data, Cloud, Mobile, and Social.

The panel, Data Disruption: Driving Competition Advantage with Big Data, was moderated by Boris Evelson from Forrester and consisted of Yuri Bukhan from Cloudera, Darshan Rawal from DataStax, Chris Selland from HP Vertica and me.

Here are a few of my key takeaways from the discussion:

  • At its core, Big Data is about transforming data into actionable information. We all want to answer the question, “How do we turn data into something useful when we have so much of it? When the data we’re getting is so diverse and fast-moving?” We all agreed that there is no solution from any one vendor that will solve this problem on its own. The good news is that MongoDB certainly has a part to play - as the operational database for use cases where low-latency, high availability, and massive scale are important.
  • The Single View (sometimes called Customer 360) use case is here to stay. It didn’t matter what type of database we represented - SQL, key-value, or document-based - each panellist cited the tremendous value of applications that can provide a holistic, single view of the customer. This is a massive validation for the use case I have a personal passion for, best summarized here: https://www.mongodb.com/customers/metlife. For their single view application, the Wall, Metlife chose MongoDB in part because the database was ideally suited to handle semi-structured and unstructured information.
  • The phrase “schemaless” continues to confuse IT folks, and for good reason. In MongoDB, you still have a schema - however, it’s easily modified as your application requirements change. Structures and data types aren’t fixed, records can add new information on the fly, and unlike SQL table rows, dissimilar data can be stored together as necessary. We call this a ‘dynamic schema’, which is a far more accurate term than “schemaless”.
  • While we all love the fringe projects with large customers, the real budgets reside with the CMO, CFO, and operations professionals. Vendors with new and innovative technologies need to demonstrate value to those audiences to win strategic projects.
  • Adopting a Big Data mindset does not happen in one fell swoop. Organizations are better off starting with a discrete, well-defined (and yes, smaller) project and getting it working. Once satisfied with the result, they can start expanding the scope.

Overall, it was a fantastic panel with my esteemed colleagues in the space. I look forward to continuing this dialogue.

For more on Big Data and help with defining your Big Data strategy, I recommend checking out this paper, which outlines examples and guidelines.


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