Business justification with
business intelligence
Jayanthi Ranjan
Institute of Management Technology, Ghaziabad, India
Abstract
Purpose – The paper intends to find out the business justifications and requirements for
incorporating business intelligence (BI) in organizations because many organizations that already
have systems in place to collect data and gather information, often find themselves in a situation where
they have no tools or roadmaps to put their vast data and information into use for strategic decision
making.
Design/methodology/approach – In this paper BI and the growing potential for implementing BI
is explained. The paper also explains a checklist for implementing BI.
Findings – During the last ten years, the approach to business management in the entire globe has
deeply changed. Firms have understood the importance of enforcing achievement of the goals defined
by their strategy through metrics-driven management. Firms are evolving into new forms based on
knowledge and networks in response to an environment characterized by indistinct organizational
boundaries and fast-paced change. New and complex changes are emerging that will force enterprises
to operate in entirely new methods. Understanding the data and transforming, and shaping them into
networked marketplaces is a key strategy for any organization to achieve competitive advantage. The
business success factor for any enterprise is finding ways to bring the vast amount of data that are
flowing within and across the business processes together and making sense out of them. Business
Intercenine includes extraction, transformation and loading (ETL), data warehousing, database query
and reporting, multidimensional/online analytical processing (OLAP) data analysis, data mining and
visualization.
Originality/value – The paper provides useful information on business justifications and
requirements for incorporating business intelligence in organizations.
Keywords Intelligence, Value analysis, Corporate strategy
Paper type Conceptual paper
1. Introduction
In today’s highly competitive and increasingly uncertain world, the quality and
timeliness of an organization’s “business intelligence” (BI) can mean not only the
difference between profit and loss, but also even the difference between survival and
bankruptcy.
BI is the conscious, methodical transformation of data from any and all data sources
into new forms to provide information that is business-driven and results-oriented. It
will often encompass a mixture of tools, databases, and vendors in order to deliver an
infrastructure that not only will deliver the initial solution, but also will incorporate the
ability to change with the business and current marketplace.
The purpose of investing in BI is to transform from an environment that is reactive
to data to one that is proactive. A major goal of BI is to automate and integrate as many
steps and functions as possible. Another goal is to provide data for analytics that are as
tool-independent as possible (Biere, 2003).
Centralized centers of competency were created to provide a means for end-users to
become productive quickly. The need to set corporate standards for analysis tools was
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Business
justification with
BI
461
VINE: The journal of information and
knowledge management systems
Vol. 38 No. 4, 2008
pp. 461-475
q Emerald Group Publishing Limited
0305-5728
DOI 10.1108/03055720810917714

one of the most significant benefits from these centers. The information warehouse
proved that accessing data in place are not always desirable, but capturing the
metadata about existing information makes perfect sense. Before we transform current
information, we need to know all we can about its current contents and form. We are
entering an era where packaged BI solutions are desired. One driving force behind
these is the need to deliver sophisticated metrics and analyses to top management.
BI decision-support applications facilitate many activities, including:
multidimensional analysis, for example, online analytical processing (OLAP);
click-stream analysis; data mining; forecasting, business analysis; balanced
scorecard preparation; visualization; querying; reporting and charting (including
just-in-time and agent-based alerts); geospatial analysis; knowledge management;
enterprise portal implementation; mining for text, content, and voice; digital dashboard
access; and other cross-functional activities.
Examples of BI decision-support databases include enterprise-wide data
warehouses, data marts (functional and departmental), exploration warehouses
(statistical), data mining databases, web warehouses (for click-stream data),
operational data stores (ODSs), operational marts (oper marts), other cross-functional
decision-support databases.
Enterprises today are constantly trying to outpace the competition while still
keeping up with changing business cycles, security, globalization and regulatory
compliance. As companies struggle to respond more quickly and efficiently to business
change, they are forced to examine the underlying architecture that supports their
business to determine future scalability.
With BI, organizations can integrate powerful capabilities in business event
management, analysis, reporting, score carding, data integration, dashboards, and
more, all within a service-oriented architecture. This requires web services to leverage
and integrate a number of technologies, improving performance and the speed with
which data can be accessed. This allows organizations to maximize their investment in
such infrastructure as application servers, security, RDBMS, operating systems, and
metadata. The result is an open, extensible platform for gleaning and communicating
BI in a distributed and dynamic enterprise.
History tells us that when we build business applications we create the need for BI.
Traditionally, as this capability was not built into earlier infrastructures at the outset,
it is supplied via the data warehouse, some time after events have occurred – not
because that is what the business problem required, but because that was what the
technology allowed. Some organizations are just getting started with BI and want to
plan the way forward, while others are looking at their existing BI environment and
determining how to make it better. A common concern for enterprises as they
undertake a BI strategy initiative is ensuring that tactical information needs are being
met while the organization is moving towards a more strategic approach to BI
(Hostmann, 2007).
Companies face a number of challenges in making business performance
management more strategic and driving its application throughout the enterprise
(Ruddy, 2006). These challenges include defining key performance indicators (KPIs)
that align with business objectives; extending usage of those KPIs throughout a wide
range of levels and functional areas within the organization; and integrating data so
that executives can truly understand the impact of operational activities on financial
outcomes and on the strategic goals and objectives of the organization. Advances in
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