Share Email Print
cover

Proceedings Paper

A qualitative readiness-requirements assessment model for enterprise big-data infrastructure investment
Author(s): Mohammed M. Olama; Allen W. McNair; Sreenivas R. Sukumar; James J. Nutaro
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

In the last three decades, there has been an exponential growth in the area of information technology providing the information processing needs of data-driven businesses in government, science, and private industry in the form of capturing, staging, integrating, conveying, analyzing, and transferring data that will help knowledge workers and decision makers make sound business decisions. Data integration across enterprise warehouses is one of the most challenging steps in the big data analytics strategy. Several levels of data integration have been identified across enterprise warehouses: data accessibility, common data platform, and consolidated data model. Each level of integration has its own set of complexities that requires a certain amount of time, budget, and resources to implement. Such levels of integration are designed to address the technical challenges inherent in consolidating the disparate data sources. In this paper, we present a methodology based on industry best practices to measure the readiness of an organization and its data sets against the different levels of data integration. We introduce a new Integration Level Model (ILM) tool, which is used for quantifying an organization and data system’s readiness to share data at a certain level of data integration. It is based largely on the established and accepted framework provided in the Data Management Association (DAMADMBOK). It comprises several key data management functions and supporting activities, together with several environmental elements that describe and apply to each function. The proposed model scores the maturity of a system’s data governance processes and provides a pragmatic methodology for evaluating integration risks. The higher the computed scores, the better managed the source data system and the greater the likelihood that the data system can be brought in at a higher level of integration.

Paper Details

Date Published: 22 May 2014
PDF: 8 pages
Proc. SPIE 9122, Next-Generation Analyst II, 91220E (22 May 2014); doi: 10.1117/12.2050605
Show Author Affiliations
Mohammed M. Olama, Oak Ridge National Lab. (United States)
Allen W. McNair, Oak Ridge National Lab. (United States)
Sreenivas R. Sukumar, Oak Ridge National Lab. (United States)
James J. Nutaro, Oak Ridge National Lab. (United States)


Published in SPIE Proceedings Vol. 9122:
Next-Generation Analyst II
Barbara D. Broome; David L. Hall; James Llinas, Editor(s)

© SPIE. Terms of Use
Back to Top