Enterprise Data Warehouse Governance Best Practices

Enterprise Data Warehouse Governance Best Practices

Nayem Rahman (Intel Corporation, Hillsboro, OR, USA)
Copyright: © 2016 |Pages: 17
DOI: 10.4018/IJKBO.2016040102
OnDemand PDF Download:
$30.00
List Price: $37.50

Abstract

Maintaining a stable data warehouse becomes quite a challenge if discipline is not applied to code development, code changes, code performance, system resource usage and configuration of integration specification. As the size of the data warehouse increases the value it brings to an organization tends to increase. However these benefits come at a cost of maintaining the applications and running the data warehouse efficiently on a twenty four hours a day and seven days a week basis. Governance is all about bringing discipline and control in the form of guidelines for application developers and IT integration engineers to follow, with a goal that the behavior of a data warehouse application becomes predictable and manageable. In this article the authors have defined and explained a set of data warehouse governance best practices based on their real-world experience and insights drawn from industry and academic papers. Data warehouse governance can also support the development life cycle, maintenance, data architecture, data quality assurance, the Sarbanes-Oxley (SOX) Act requirements and enforce business requirements.
Article Preview

2. Literature Review

Data warehouses have the potential to provide business intelligence solutions for companies looking for competitive advantage (Rahman, 2013a). Fortune 1000 companies make strategic and tactical business decisions using the data warehouse as the central repositories of their enterprise data (Wixom & Watson, 2001). In an enterprise data warehouse new projects land over the years and a lot of enhancement and maintenance activities occur as part of day to day operations. All these activities require new objects installation or changing existing objects in the data warehouse. Given these activities how do we ensure that these day to day activities do not make data warehouse environment unstable, cause data quality issues, and impact analytical activities?

Based on real world observations of data warehousing projects implementation and past research findings (Arnott, 2008; Rahman, 2013a; Aiken et al. 2011; Bellatreche & Kerkad, 2015; Rabuzin, 2014; Zolait, 2012) the authors have determined that certain key areas of data warehouse activities need to be governed in a disciplined way. The authors believe that data warehouse objects development, installations, measurement, data quality monitoring, performance monitoring are critical for data warehouse implementation and maintenance. All these are needed to ensure that an organization can develop superior firm-wide IT capability to successfully manage their IT resource and realize agility (Lu and Ramamurthy, 2011; Mithas et al. 2011; Rahman et al., 2011; Roberts and Grover, 2012; Akhter & Rahman, 2015).

Complete Article List

Search this Journal:
Reset
Open Access Articles: Forthcoming
Volume 8: 4 Issues (2018): 1 Released, 3 Forthcoming
Volume 7: 4 Issues (2017)
Volume 6: 4 Issues (2016)
Volume 5: 4 Issues (2015)
Volume 4: 4 Issues (2014)
Volume 3: 4 Issues (2013)
Volume 2: 4 Issues (2012)
Volume 1: 4 Issues (2011)
View Complete Journal Contents Listing