Preparing Data for Analytics
TDWI Speaker: Philip Russom, TDWI Research Director
Date: Tuesday, April 1, 2014
Time: 9:00am PT, 12:00pm ET
There’s a fair amount of confusion about how best to collect, integrate, and preprocess data for the purposes of advanced analytics. Many business intelligence and data warehouse professionals think it’s the same as the traditional ETL practices they have applied to their report-oriented data warehouses for years. And some database administrators think it’s just a matter of dumping large volumes of data into a highly scalable repository. Somewhere between these two are emerging best practices for preparing data for advanced analytics. That’s because the transformational processing of ETL alters source data in ways that can expunge the data nuggets that successful analyses depend on. At the other extreme, merely copying data won’t put you in a position to get the most value from the integrated data.
You will learn:
- Business requirements for analytics with big data, and how to meet these
- Why reporting, database administration, and analytics are different, and therefore have unique data management requirements that must be respected (likewise, various analytic methods have various data requirements)
- Emerging best practices in managing data for analytics, how they differ from other established practices, and how your skills can still apply
- New technologies, tools, and data platforms, with a focus on Hadoop and NoSQL, plus the data integration technologies and practices that are appropriate to each
- A glimpse into the future: how big data and big data analytics are changing data architectures
Philip Russom, Ph.D.