'Big Data' Collaboration: Exploring, Recording and Sharing Enterprise Knowledge
- ORNL
As data sources and data size proliferate, knowledge discovery from "Big Data" is starting to pose several challenges. In this paper, we address a specific challenge in the practice of enterprise knowledge management while extracting actionable nuggets from diverse data sources of seemingly-related information. In particular, we address the challenge of archiving knowledge gained through collaboration, dissemination and visualization as part of the data analysis, inference and decision-making lifecycle. We motivate the implementation of an enterprise data-discovery and knowledge recorder tool, called SEEKER based on real world case-study. We demonstrate SEEKER capturing schema and data-element relationships, tracking the data elements of value based on the queries and the analytical artifacts that are being created by analysts as they use the data. We show how the tool serves as digital record of institutional domain knowledge and a documentation for the evolution of data elements, queries and schemas over time. As a knowledge management service, a tool like SEEKER saves enterprise resources and time by avoiding analytic silos, expediting the process of multi-source data integration and intelligently documenting discoveries from fellow analysts.
- Research Organization:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- DE-AC05-00OR22725
- OSTI ID:
- 1110914
- Journal Information:
- Information Services & Use, Vol. 33, Issue 3
- Country of Publication:
- United States
- Language:
- English
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