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In the early 1990s,
Howard Dresner, then an ana­lyst at the Gartner Group, coined the term business intelligence due to the growing need for applications designed
to support decision making based on data collected. Nowadays, business leaders
and top management have access to more data than ever before; however data by
itself doesn’t generate insights. Business Intelligence (BI) Tools have become
the go-to resource for helping companies harness the power of big data and
analytics and make smarter, data-driven decisions.

During the various years, there have been various
definitions of BI according to its form, usage and the industry it is applied
to. Many of them are focused only on the software used for business
intelligence and neglect to include the primary goal of business intelligence.
While the term is often heard in relation to software vendors, there’s more to
BI than just software tools.

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Literature
Review

Business intelligence
became a popular term in the business and Information Technology (IT)
communities only in the 1990s. Business intelligence (BI) refers to a managerial
philosophy and a tool used to help organizations manage and refine business
information with the objective of making more effective business decisions
(Ghoshal and Kim, 1986; Gilad and Gilad, 1986). Dresner (1988) defined business
intelligence as the “concepts and methods to improve business decision making
by using fact-based support systems.” The term BI can either be used to refer
to the relevant information and knowledge describing the business environment,
the organization itself, and its situation in relation to its markets,
customers, competitors, and economic issues or to an organized and systematic
process by which organizations acquire, analyze, and disseminate information
from both internal and external information sources significant for their
business activities and for decision making (Lönnqvist and Pirttimäki, 2006).

In European literature, the
term BI is considered a broad umbrella concept for competitive intelligence (CI)
and other intelligence-related terms, such as market intelligence, customer
intelligence, competitor intelligence, strategic intelligence, and technical
intelligence. Indeed the term has been defined front several perspectives
(Casado, 2004), however they all focus on a shared purpose, analyzing data and
information. As Gilad and Gilad (1986) have stated, organizations have collected
information about their competitors since the dawn of capitalism. The real revolution
is in the efforts to institutionalize intelligence activities.

BI presents business
information in a timely and easily consumed way and provides the ability to
reason and understand the meaning behind business information through, for
example, discovery, analysis, and ad-hoc querying (Azoff and Charlesworth,
2004). Today, business intelligence is defined by Evelson and Nicolson (2008)
at the Forrester as “a set of methodologies, processes, architectures, and
technologies that transform raw data into meaningful and useful information
used to enable more effective strategic, tactical, and operational insights and
decision-making.” Business Intelligence today is never a new technology instead
of an integrated solution for companies, within which the business requirement
is definitely the key factor that drives technology innovation (Ranjan, 2009).
Ranjan (2009) stated that the major challenge of a BI application to achieve
real business impact is to identify and creatively address key business issues.

After discussing the many
definitions of BI, the question of why do companies use it naturally arises. The
primary goal is to stay ahead of the competition and make the right decision at
the right time. Those decisions can be made around pretty much any aspect of
running a business, such as figuring out how to increase the effectiveness of
marketing campaigns, deciding whether and when to enter new markets, and improving
products and services to better meet customers’ needs. One of the key aspects
of business intelligence is that it’s designed to put information in the hands
of business users. Organizations are required to make decisions at an
increasingly faster pace, so today’s business intelligence tools help decision
makers access the information they need without having to first go through the
IT department or specifically designated data scientists.

Components
or Tools of BI

BI includes several software
for Extraction, Transformation and Loading (ETL), data warehousing, database
query and reporting, (Berson et.al, 2002; Curt Hall, 1999)
multidimensional/on-line analytical processing (OLAP) data analysis, data
mining and visualization.

 Figure 1: Business Intelligence Diagram

Data
and Data Sources

            Business intelligence all starts with the data. As mentioned
in the introduction, businesses have access to more data than ever. Data
sources can be operational databases, historical data, external data (from
market research companies or from the Internet), or information from the
already existing data warehouse environment. The data sources can be relational
databases or any other data structure that supports the line of business
applications. They also can reside on many different platforms and can contain
structured information, such as tables or spreadsheets, or unstructured
information, such as plaintext files or pictures and other multimedia
information.

Extract,
Transform, Load (ETL)

            A key part of BI is the tools and processes used to
prepare data for analysis. When data is created by different applications, it’s
not likely all in the same format, and data from one application can’t
necessarily be looked at in relation to data from another. In addition, if
business intelligence is relied on to make critical decisions, businesses must
make sure the data they are using is accurate. The process of getting data
ready for analysis is known as Extract, Transform, and Load (ETL). The data is
extracted from internal and external sources, transformed into a common format,
and loaded into a data warehouse. This process also typically includes data
integrity checks to make sure the data being used is accurate and consistent.

Data
Warehouse and Data Marts

The data warehouse is the
significant component of business intelligence. It is subject oriented,
integrated. The ETL process ends with data being loaded into the warehouse,
because when the data is contained within the separate sources, it’s not much
use for intelligence. A data warehouse is a repository containing information
from all the business’s applications and systems, as well as external sources,
so it can be analyzed together. A data mart as described by (Inmon, 1999) is a
collection of subject areas organized for decision support based on the needs
of a given department.

Similar to data warehouses,
data marts contain operational data that helps business experts to strategize
based on analyses of past trends and experiences. The key difference is that
the creation of a data mart is predicated on a specific, predefined need for a
certain grouping and configuration of select data. There can be multiple data
marts inside an enterprise. A data mart can support a particular business
function, business process or business unit.

OLAP
(On-line analytical processing)

It refers to the way in
which business users can slice and dice their way through data using
sophisticated tools that allow for the navigation of dimensions such as time or
hierarchies. Online Analytical Processing or OLAP provides multidimensional,
summarized views of business data and is used for reporting, analysis, modeling
and planning for optimizing the business. OLAP techniques and tools can be used
to work with data warehouses or data marts designed for sophisticated
enterprise intelligence systems.

Advanced
Analytics

It is referred to as data
mining, forecasting or predictive analytics, this takes advantage of
statistical analysis techniques to predict or provide certainty measures on
facts.

Corporate
Performance Management (Portals, Scorecards, and Dashboards)

This general category
usually provides a container for several pieces to plug into so that the
aggregate tells a story.

Real
time BI

It allows for the real time
distribution of metrics through email, messaging systems and / or interactive
displays.

Discussion

Overall, Business Intelligence
provides benefits to companies utilizing it. Initially, BI reduces IT
infrastructure costs by eliminating redundant data extraction processes and
duplicate data housed in independent data marts across the enterprise. For example,
3M justified its multimillion- dollar data warehouse platform based on the
savings from data mart consolidation (Watson, Wixom, and Goodhue, 2004, pp.
202-216). Moreover, it can eliminate a lot of the guesswork within an
organization, enhance communication among departments while coordinating
activities, and enable companies to respond quickly to changes in financial
conditions, customer preferences, and supply chain operations.

Figure 2: Spectrum of BI
benefits. As business users mature to performing analysis and prediction, the
level of benefits become more global in scope and difficult to quantify.

Over time, organizations
evolve to questions like “Why has this happened?” and even “What will happen?”
As business users mature to performing analysis and prediction, the level of
benefits become more global in scope and difficult to quantify (Watson and Wixom,
2007). Information is often regarded as the second most important resource a
company has (a company’s most valuable assets are its people). So when a
company can make decisions based on timely and accurate information, the
company can improve its performance.

However, there are also a few
issues regarding Business Intelligence. Firstly,
Most BI benefits are intangible before the fact. An empirical study for 50
Finnish companies found most companies do not consider cost or time savings as
primary benefit when investing in BI systems (Hannula and Pirttimaki, 2003).
The hope is that a good BI system will lead to a return at some time in the
future.

Secondly, experts view BI
in different ways. Ranjan (2009, pg 62-63) is of the opinion that to data
mining experts BI is set of advanced decision support systems with data mining
techniques and applications of algorithms, while to statisticians BI is viewed
as a forecasting and multidimensional analysis based tool. Data warehousing
experts view BI as supplementary systems and is very new to them. These experts
treat BI as technology platform for decision support application.

Third, very few
organizations have a full-fledged enterprise data warehouse. The main key to
successful BI system is consolidating data from the many different enterprise
operational systems into an enterprise data warehouse.  Berson (2002) emphasizes that in view of emerging
highly dynamic business environment, only the most competitive enterprises will
achieve sustained market success. The organizations will distinguish themselves
by the capability to leverage information about their market place, customers,
and operations to capitalize on the business opportunities.

Conclusions
and Future Study

The business intelligence
(BI) has evolved over the past decade to rely increasingly on real time data. Enterprises
today demand quick results and it is essential that not only is the business
analysis done, but also actions in response to analysis of results and
instantaneously parameters’ changes of business processes. The paper explored
the concepts of BI, its components, benefits and issues of BI. It is important to
examine the impact BI has on each individual company and on the economy as a whole.
The possible future of Business Intelligence lies in cloud computing. Security,
data protection, lack of control, and several other barriers prevent widespread
adoption of the BI; however cloud computing promises significant benefits, which
need to be maturely and reasonably assessed.

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