David Douglas, CrinLogic
The Big Data headlines are unrelenting; with each passing day seemingly bringing new discoveries, products, partnerships, venture funds, you name it into the mix. If anything, it is all a bit confusing. Listening to all this you might come to the conclusion that Big Data will solve most of your problems, place your company miles ahead of your competition, drive your Net Promoter Scores through the roof, and fall just short of solving world hunger (ok…maybe not that far).
And one can’t blame you if you think all one needs to do is install the Hadoop ecosystem of projects, conjure up some possible business use cases, throw some commodity hardware into the mix, attend some training, purchase some Big Data analytics software and VOILA, you have arrived and can enjoy the fruits of your Big Data efforts.
With tongue firmly planted in cheek, the reality is vastly different. This talk is partially a reality check on Big Data implementation strategies - starting with Big Data is easy, becoming proficient is hard, fully integrating into a broader enterprise data strategy is very hard – and partially an information sharing session on what we’re learning as we engage with customers in various industries on Big Data. Among other things we will explore: building the business case; software and hardware requirements analysis; selection process and implementation approaches; what tends to work well, not so well, and what to avoid; and how big data is likely to affect enterprise data architecture.
David Douglas is a member of Hadoop-DC User Group and is a co-founder of CrinLogic, a Big Data consultancy based in the greater DC area. He has devoted his 17 years of professional experience to helping clients maximize the value of their strategic IT initiatives. Prior to co-founding CrinLogic, David started two other companies. The first was an angel-backed Sales Force Automation software company he sold in 2002 and the second is a consulting services company that focuses on Agile and Lean software adoption and large-scale program implementation services. He helped start the Data Warehousing practice at American Management Systems and was one of the first consultants to join IBM’s Business Intelligence practice.
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