Tagged: analytics, big data, competency center, data science, end-to-end solution, enterprise, intermediate, introduction, lessons learned, manufacturing, NoSQL, operational excellence, project steps, Spark
September 25, 2017 at 12:35 pm #718
Assaf Araki, Big Data Analytics Architect at Intel
Big data analytics brings value to enterprises, helping them achieve operational excellence. The big question is how you implement it. How do you combine people with many different job functions (data science, SW development, business acumen, etc.) into one unit to solve a real problem and deliver an end-to-end solution; different business organizations inside the enterprise (product development, manufacturing, sales and marketing, security, etc.) in order to have a cross-enterprise effect; and different technologies (NoSQL databases, Spark, relational databases, data science tools, etc.) in order to create a platform that can serve different needs?
The Advanced Analytics team at Intel has been in charge of delivering dozens of big data analytics projects over the last five years. The projects the team delivered in 2015 brought an ROI of over $200M. Assaf Araki and Itay Yogev share how Intel built a big data analytics competency center, exploring the key elements that help Intel grow its people and capabilities (in an environment that keeps shrinking IT services) and the challenges and lessons learned, drawing on firsthand stories from the project in the domains of validations, manufacturing, and sales. This story is not finished yet. The Advanced Analytics team continues to bring value to the enterprise, growing big data analytics into a $1B business.
- Different roles in the big data analytics competency center, the capabilities they share, and the unique task of each role in each stage of the project
- How projects are executed: at each step (requirement gathering, exploration, development, implementation, etc.) a different function is leading the effort
- The 5/6/10 rule: 5 people work for 6 months and bring a $10M ROI
- Timing: When to enter a new domain in the enterprise?; Is the customer ready for an advanced analytics solution?; How to understand quickly if you are able to solve the problem?
- What technology to develop and what to take from the community, finding the right balance while building reusable engines you can use later in other projects
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