The Emerging Data Lake IT Strategy: An Evolving Approach for Dealing with Big Data & Changing Environments
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  Thomas Kelly   Thomas J Kelly
Practice Director, Enterprise Information Management
Cognizant Technology Solutions
  Sean Martin   Sean Martin
Founder & CTO
Cambridge Semantics


Tuesday, April 29, 2014
10:15 AM - 11:00 AM

Level:  Business/Strategic

To take maximum advantage of Big Data and changing environments, enterprises are re-thinking their IT architecture for collecting and analyzing data. Traditional data warehouse and ETL approaches require high up-front labor intensive data set up effort. These approaches are not well suited for unstructured data and fast evolving environments where searching, analyzing and integrating the data are iterative processes and are best done by the user. Having IT involved in this iterative process using traditional data warehouse and ETL approaches creates long lead times and user frustration. As unstructured content grows, there is a growing need for a new approach.

Cheap storage and fast processing are allowing IT departments to deploy a Data Lake IT strategy. In this approach, IT first makes sure that all relevant data is captured and stored as data becomes available for further mining and integration. On the front-end, relevant data from varied sources are auto tagged, profiled and stored along with metadata in a Big Data archival system. Semantic middleware is used for managing the metadata, tags and profiles and for easy access and data migration on the backend. More extensive content mining and text processing are done on the back-end, as business needs are identified or when data is needed. Using the Semantic middleware, users are provided increased self-serve analytics capabilities and user-driven automated & semi-automated approaches for extracting, curating and integrating relevant extracted data with data from internal systems

In this session we will discuss:
  • Introduction to the key elements of this strategy
  • The early learnings of who is considering such an approach and for what types of use cases
  • The implication to the existing IT architecture
  • Technology and tools being deployed
  • The value this approach seeks to provide

And the challenges it creates.

Thomas Kelly is a Practice Director in Cognizant's Enterprise Information Management (EIM) Practice and heads its Semantic Technology Center of Excellence. He has 20-plus years of technology consulting experience in leading data warehousing, business intelligence and big data projects, focused primarily on the banking/financial services, life sciences and healthcare industries.

Sean Martin has been on the leading edge of Internet technology innovation since the early nineties. As founder and CTO of Cambridge Semantics, Sean applies his vision on how semantic technologies will continue to impact enterprise data management in the coming years. Prior to founding Cambridge Semantics, he spent fifteen years with IBM Corporation where he was a founder and the technology visionary for the IBM Advanced Internet Technology group. Sean invented and implemented both IBM's first Web application server and content-manager, WOM, along with its distributed Web application hosting environment, the Womplex. Sean led the invention and implementation of IBM's Sash Weblication Manager and played a key role in Sash, including IBM's first ever Web Service SOAP & XML-RPC client stacks and associated developer tooling. At IBM, Sean was honored with many awards including IBM's Outstanding Technical Innovation award, the Chairman's Award.

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