‘Big Data’ collaboration: Exploring, recording and sharing enterprise knowledge

Abstract

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 a real-world case study. We motivate the implementation of an enterprise data discovery and knowledge recorder tool called SEEKER (Schema Exploration and Evolving Knowledge Entity Recorder) 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 a digital record of institutional domain knowledge and as 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 (1) avoiding analytic “silos” (i.e., a separate set of data that is not included in an enterprise's data administration), (2) expediting the process of multi-source data integration and (3) intelligently documenting discoveries from collaborating analysts.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 33 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.