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Monday, October 6, 2014

11:45 a.m. - 12:45 p.m.


Christopher Blotto, Chief Technologist and Managing Partner, KNOWLEDGENT

In today’s analytically-driven world, organizations are dealing with distributed data sources dependent on shared, synchronized information, driving the need for a unified view of key data entities commonly used across the enterprise. Business users are quickly envisioning new and innovative ways to use data for operational reporting and analytics. These users need rapid access to any data in the enterprise and the ability to leverage their tools of choice to analyze this data and derive insights. However, in many cases, users may find the current enterprise data warehouse to be an untrustworthy source, full of inaccurate data. Additionally, there may be frustrating limitations on the tools or types of data that users can leverage to perform advanced analytics. Without the ability to search for data, finding and accessing existing data may be time consuming and cumbersome for the users. The need for a Data-as-a-Service model (not driven by specific analytics projects) is the new paradigm. The key enabler? MDM.

The concept of the Data Lake is becoming more prevalent in data management circles. Organizations need to consider a number of factors, including processes and architecture attributes, when developing a data servicing strategy. On the process side, the conceptual framework should include master data source identification, acquisition, hub management, integration, and access. In tandem with these processes, the MDM architecture should be capable of long-term high performance and responsiveness to continuous change. Essentially, MDM becomes the reservoir in establishing a search index within the Data Lake. In this Industry Innovation Session, topics to be covered include:

  • Using MDM capabilities to enable search and navigation in distributed environments
  • Transitioning existing data management investments into the Big Data world
  • Distinguishing the hype from the reality through real-world use case overviews

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