The Boston-based TDWI chapter is excited to present Mark Madsen, a thought-after speaker on all things BI, as our headline speaker for the September TDWI Boston meeting. Additionally, our own TDWI Boston chapter president Jens Meyer is presenting.
When: Tuesday, September 15, 2015, 12noon – 4:30pm
Where: Boston Children's Hospital - Waltham
December Conference Room
9 Hope Avenue
“Big Data Isn't BI And Vice Versa” by Mark Madsen, Third Nature
“Deploying & Maintaining Analytical Models” by Jens Meyer, First Marblehead
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Big Data Isn't BI And Vice Versa
Big data is hyped, but isn't hype. There are definite technical, process and business differences in the big data market when compared to BI and data warehousing, but they are often poorly understood or explained. BI isn't big data, and big data isn't BI. By distilling the technical and process realities of big data systems and projects we can separate fact from fiction. This session examines the underlying assumptions and abstractions we use in the BI and DW world, the abstractions that evolved in the big data world, and how they are different. Armed with this knowledge, you will be better able to make design and architecture decisions. The session is sometimes conceptual, sometimes detailed technical explorations of data, processing and technology, but promises to be entertaining regardless of the level.
Yes, it’s about the data normally called “big”, but it’s not Hadoop for the database crowd, despite the prominent role Hadoop plays. The session will be technical, but in a technology preview/overview fashion. I won’t be teaching you to write MapReduce jobs or anything of the sort.
The first part will be an overview of the types, formats and structures of data that aren’t normally in the data warehouse realm. The second part will cover some of the basic technology components, vendors and architecture.
The goal is to provide an overview of the extent of data available and some of the nuances or challenges in processing it, coupled with some examples of tools or vendors that may be a starting point if you are building in a particular area.
Deploying & Maintaining Analytical Models
Everybody talks about predictive models and reads about insightful results from building advanced analytics models on small and big data. But once you build a model, how do you ensure continuing usability of the model? How will your model adapt to a changing environment?
This presentation will illustrate a typical data model life cycle and will touch on the operational, technical, and analytics subject matter expertise required to successfully utilize analytics models. Examples and case studies of multiple model implementations will build best practices and lessons learned.
You Will Learn:
- Utilizing analytics models successfully requires a life cycle approach
- Subject matter expertise (operational, technical, analytical) is a critical component of analytics
- Best practices (and how we learned them the hard way)