Oracle Analytics: Business Intelligence and Analytic Applications - The CIO's Guide to Oracle Products and Solutions - page 46

2. Infrastructure standardization and consolidation : Companies tend
not to know how much they spend on business intelligence and
data warehousing. The reason for this is that these efforts are usu-
ally undertaken in silos, where each business domain creates its own
solution. Standardizing and consolidating business intelligence and
warehousing is easier said than done because it involves more than
just technology. It also involves political and organizational issues.
3. Offshore sourcing : Sending business intelligence and warehousing
work offshore requires some careful planning, since it requires more
business knowledge and customization than other types of projects.
In spite of significant cost savings, some companies are finding some
disadvantages to this trend, including quality problems and commu-
nications issues. Most recently, onshore companies are developing
capabilities that broaden the issues surrounding the onsite vs. offsite
vs. offshore issue.
4. Strategic approach to information : Very few companies have
embraced the “data as asset” philosophy, although there will always
be a small group of people within an organization who recognize
the strategic value of information. Although companies might not
be implementing business intelligence and data warehousing on an
enterprise-wide basis, they are being incorporated in—and have
become a critical component of—other strategic systems.
5. Regulatory compliance as a driver for business intelligence and data
warehousing : Sarbanes-Oxley, environmental, and data-privacy leg-
islative and regulatory directives have fundamentally shifted man-
agement's view on the need for high-quality data and the business
intelligence systems to analyze this data. Regulatory compliance,
therefore, has become a major impetus driving business intelligence
efforts.
6. Elevating the enterprise data-integration discussion : Many organiza-
tions have already made the decision how to promote effective data
integration. There are a variety of choices: ETL, which is an acronym
for extract, transform, and load; and EAI (enterprise application
integration), which involves the use of software and architectural
principles to integrate two or more enterprise computer applications.
EAI is related to middleware technologies, such as message-oriented
middleware (MOM), and data representation technologies, such
as XML (Extensible Markup Language). Newer EAI technologies