The toolkit of available for data mining and advanced analytics has expanded greatly with the advent of “big data”: instituting great power of choice through NoSQL datastores, Data Warehouses, and Hadoop clusters, and also introducing great confusion and uncertainty as to which advanced analytical tasks are good fits for what technology.
If the classical architecture consisted of operational data stores feeding a data warehouse from which BI and analytical modelling tasks were sourced, the new architectures introduce scalable computing power and data stores at all levels. This forces us to question some assumptions about how data has been managed so far.
This talk will review how analytics are architected among large enterprises, covering real-time analytics, BI reports, ad-hoc queries, and with a special focus on predictive modelling. As most large enterprises have adopted a Hadoop cluster, we will examine how this cluster is used and the type of analytical tasks which are executed on it. Importantly, we will consider all the workload which has not migrated to Hadoop and the reasons for that. In each case, there are strong business reasons leading to a technology choice which must balance flexibility, agility, reactivity, cost and commitment.
We will conclude by categorizing the use cases we have encountered in the field which are best fits for the new technologies and the business value that it generates for enterprise users.
Dr. Edouard Servan-Schreiber is Director for Solution Architecture at 10gen, advising customers on how to make MongoDB make their business simpler, faster, and better.
Previously, Edouard was director for cross-channel analytics at Teradata, leading projects in advanced analytics and predictive modeling with customers in all heavily data-driven industries such as telco, retail, finance, high tech manufacturing.
Edouard’s specialty is to help customers extract business value from their data through the effective use of technology and analytics.
Edouard began practicing artificial intelligence and statistical learning models at Carnegie Mellon University for his bachelor’s degree, before going to UC Berkeley for his PhD in Computer Science.
Duncan has been a data miner since the mid 1990s. He was Director of Advanced Analytics at Teradata until 2010, leaving to become Data Director of Experian UK. He recently rejoined Teradata to lead their European Data Science team.
At Teradata he has been responsible for developing analytical solutions across a number of industries, including warranty and root cause analysis in manufacturing, and social network analysis in telecommunications. These solutions have been developed directly with customers and have been deployed against some of the largest consumer bases in Europe.
In his spare time Duncan has been a city Councillor, chair of a national charity, founded an award winning farmers’ market, and is one of the founding Directors of the Society of Data Miners.
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