The internal information of IBM

  • Authors:

  • J. A. Vayghan;S. M. Garfinkle;C. Walenta;D. C. Healy;Z. Valentin

  • Affiliations:

  • IBM Enterprise Business Information Center of Excellence, Minneapolis, MN;IBM Enterprise Business Information Center of Excellence, Hopewell Junction, NY;IBM Corporate Headquarters, Enterprise On Demand Group, Hopewell Junction, NY;IBM Corporate Headquarters, Enterprise On Demand Group, Somers, NY;IBM Corporate Headquarters, Enterprise On Demand Group, White Plains, NY

  • Venue:
  • IBM Systems Journal
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

Abstract

The ability to utilize data as an enterprise asset is central to every enterprise transformation initiative. This ability is critical for reusing data consistently throughout the enterprise and deriving actionable knowledge from it. Accurate and high-quality data must consistently propagate meaning and value throughout the enterprise and comply with the policies and processes of the enterprise. For a variety of reasons, large enterprises manage data at a local level (e.g., for each department and location), resulting in information "silos" where data is redundantly stored, managed, and processed, each with its own policies and processes, leading to inconsistency. IBM has begun a transformation process to establish a program for the management of its critical data, beginning with the creation of an enterprise data strategy that is aligned with IBM business strategy. In this paper, we describe the progress, to date, of the IBM transformation process. We focus on the activities of the IBM Enterprise Business Information Center of Excellence (EBI CoE), which is responsible for establishing, implementing, and deploying the enterprise data architecture program. The EBI CoE uses leading-edge information management technology and services from IBM and its partners to address enterprise data challenges. We present lessons learned and best practices derived from this ongoing internal transformation process that can be useful for enterprises facing similar data challenges as they transform their operations and business models.