Effects of business intelligence techniques on enterprise

EFFECTS OF BUSINESS INTELLIGENCE TECHNIQUES ON ENTERPRISE
1PRODUCTIVITY
2 Abstract
There are certain techniques used for analyzing business process data. One of the
most
prominent groups of these techniques is called Business Intelligence. Business
Intelligence
mainly deals with capturing and assessing various aspects of an enterprise, its customers and
competitors. These techniques are used in gathering, storing, analyzing, and providing access
to intelligent information about enterprise data in order to identify significant trends
or
patterns that assist decision-making process. Therefore enterprises can make more accurate
decisions about tactical and strategic managerial issues; like determining their supply chain or
competing in a specific market. Business Intelligence applications commonly used in
Decision
Support Systems are query and reporting, online analytical processing (OLAP), statistical
analysis, forecasting, and data mining. These applications usually use data gathered from a
data warehouse or a data mart and they also can be a part of an Enterprise Resource Planning
System. In this study, the methodologies used for performing Business Intelligence tasks
in
enterprises, the reasoning mechanisms used by Business Intelligence tools in the
decision
making process and their contribution to improve enterprise productivity will be discussed.
Keywords: Business Intelligence, Decision Support Systems, Enterprise Productivity
Introduction
One of the key assets of an enterprise is information. Huge amounts of raw data is
produced during every operational transaction in an enterprise. Processing raw data
into
valuable information or knowledge provides an enterprise to take more accurate decisions
into
action. This point of view about enterprise data states that strategic knowledge is embedded
in
the collection of a company's data and extracting actionable knowledge will help a company
improve its business (Loshin, 2003:11).
Many organizations have made significant investments on technological platforms that
support business processes and strengthen the efficiency of operational structure during the
last decades and most of them have reached a point where the use of tools to support the
decision making process at the strategic level emerges as more important than ever. Such
needs of organizations have marked the need for a new approach known as Business
Intelligence (BI) that provides access to relevant information through intensive use of
information technology (Petrini and Pozzebon, 2009:178). BI systems have the capability to
maximize the use of information by presenting it in standard formats, integrating and storing
1
2

it in a data warehouse and making it accessible for extraction of useful and hidden
information, thereby increasing the accuracy of business decisions and creating competitive
advantage that can also be called as „„competing on analytics” (Davenport, 2005:9).
In this study, the concept of BI and its importance for enterprises are reviewed firstly.
Later on, methodologies used for performing BI tasks, reasoning mechanisms employed by
BI
tools in the decision making process and their contribution to improve enterprise productivity
are discussed. Finally contribution of using BI tools for productivity is assessed in certain
cases and a survey about BI usage is evaluated among sample ERP users from Turkish
industry.
Concept of Business Intelligence
Business Intelligence (BI) is one of the main techniques for analyzing business process
data and supporting decision making process in enterprises. It might be considered as the
most
recent stage among the development phases of Management Information Systems during the
last decades. Main tasks of Business Intelligence Systems are intelligent exploration,
integration, aggregation and a multidimensional analysis of data originating from various
information resources (Olszak and Ziemba, 2007: 136). In order to perform these
tasks
Business Intelligence systems employ certain products, technologies and methodologies that
are based on a certain information system infrastructure including tools such as data
warehouses and Enterprise Resource Planning (ERP) systems.
Business Intelligence can be defined as the combination of products, technology, and
methods to organize key information that enterprises need to improve profit and performance.
The main role of Business Intelligence in an enterprise is leveraging information assets in
terms of business information and business analyses within the context of key
business
processes that lead to accurate decisions and actions resulting in improved business
performance (Williams and Williams 2006:2).
Business intelligence takes the volume of data collected by an organization and stores,
and turns it into meaningful information that decision makers can use in their day-to-day
activities. It helps to find the useful representation of information and provides one version of
the truth and information in accessible reports and analysis, so that better and timelier
business decisions can be taken in all operational, tactical and strategic levels.
BI systems should include an effective data warehouse and also a reactive component
capable of monitoring the time-critical operational processes to allow tactical and operational
decision-makers to tune their actions according to the company strategy (Matteo and Stefano
and Luris, 2004). The main objective of BI systems is to provide an in-depth analysis of
detailed business data, including database and application technologies, as well as analysis
practices. In order to perform such analyses, these systems should have the capability of
potentially encompassing knowledge management, enterprise resource planning, decision
support systems and data mining (Gangadharan and Swamy, 2004:140). For processing raw
data, BI systems include several tools and software for Extraction, Transformation and
Loading (ETL), data warehousing, database query and reporting, multidimensional/on-line
analytical processing (OLAP) data analysis, data mining and visualization.
BI Methodologies and Tools
Business Intelligence methodologies are commonly used for following business
purposes to improve organizational performance:
progress towards business goals. These activities are a part of the Corporate
Performance Management methodology which includes a set of tools such as Portals,

scorecards or dashboards. A certain combination of these tools might be the metric for
certain business goals. For example, a balanced scorecard that displays portlets for
financial metrics combined with productivity, organizational learning and growth
metrics.
to perform Business Knowledge Discovery. These activities involve tools such as data
mining, statistical analysis techniques that predict or provide certainty measures on
facts, forecasting, predictive analytics, predictive modeling and business process
modeling.
serve the strategic decision making mechanism of a business. These activities involve
tools such as data visualization and OLAP.
sharing and Electronic Data Interchange. Such an infrastructure would allow for real
time distribution of metrics through email, messaging systems and/or interactive
displays.
practices to identify, create, represent, distribute and enable adoption of experiences
that are true business knowledge.
The fundamental functionality of BI tools can be summarized as storing, integrating
and structuring data; querying and reporting information; and extracting knowledge. BI
systems generally offer an integrated set of tools, technologies and software products that are
used to integrate heterogenic data from distributed sources and analyze the integrated data so
that extracted knowledge can commonly be used. BI tasks employ a combination of the
technological structure of the BI systems (Olszak and Ziemba, 2007: 138). The most common
tools used for Business Intelligence tasks are:
OLAP (Online analytical processing):
It can simply be defined as programmatic analysis of data warehouse or data mart data
to yield actionable business intelligence. 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. These
systems process queries required to discover trends and analyze critical factors. Reporting
software generates aggregated views of data to keep the management informed about the
state
of their business. Other BI tools are used to store and analyze data, such as data mining and
data warehouses; decision support systems and forecasting; document warehouses and
document management; knowledge management; mapping, information visualization,
and
dash boarding; management information systems (Ranjan, 2009:61).
Data Mining:
Data mining methodologies have been developed for exploration and analysis of large
quantities of data to discover meaningful patterns and rules by automatic or semi-automatic
means. In Business Intelligence context, Data mining is used for finding and
extracting
meaningful knowledge in corporate data warehouses that can support business decisions. It is
a complementary tool to other data analysis techniques such as statistics, OLAP,
spreadsheets,
and basic data access. Data mining discovers patterns and relationships hidden in data, and it
is considered to be the core stage of Knowledge Discovery in Databases. Knowledge
Discovery in Databases is the whole process of using the database along with any required
selection, preprocessing, sub-sampling, choosing the proper way for data transformation or
representation, applying data mining methods to enumerate patterns from it and evaluating
the

products of data mining to identify the subset of the enumerated patterns that can be regarded
as knowledge (Fayyad and Piatetsky-Shapiro and Smyth, 1996:82).
While exercising data mining software, it is still needed to know the business,
understand the data, or be aware of general statistical methods. Moreover, the knowledge
discovered by data mining should still be verified, therefore it helps business analysts to
generate hypotheses, but it does not validate the hypotheses (Rygielski and Wang and Yen,
2002:485).
Data mining involves various techniques including statistics, neural networks, decision
trees, genetic algorithms, and data visualization to handle huge quantities of data. Data
mining
outputs are generally categorized as association, clustering, classification, and
prediction
(Chien and Chen, 2008:281). Association is the discovery of association rules showing
attribute-value conditions that occur frequently together in a given dataset. Clustering is the
process of dividing a dataset into several clusters in which the intra-class similarity is
maximized while the inter-class similarity is minimized. Classification derives a function or
model that identifies the categorical class of an object based on its attributes. Prediction is a
model that predicts a continuous value or future data trends (Chien and Chen, 2008:281).
Data warehouse:
A data warehouse is simply a single, complete, and consistent store of data obtained
from a variety of sources, optimized for distribution and made available to end users in a
way
they can understand and use it in a business context. It collects and stores integrated sets of
historical data from multiple operational systems and feeds them to one or more data marts.
A data warehouse supports the physical propagation of data by handling the numerous
enterprise records for integration, cleansing, aggregation and query tasks. It can also contain
the operational data which can be defined as an updateable set of integrated data used for
enterprise wide tactical decision-making of a particular subject area. It contains live data, not
snapshots, and retains minimal history. Data sources can be operational databases, historical
data, external data for example, 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 (Ranjan, 2009:62).
Data marts:
Data Marts are designed to facilitate end-user analysis of data. It typically supports a
single, analytic application used by a department or a unit in a business. A data mart as
described by (Inmon, 1999:10) is a collection of subject areas organized for decision support
based on the needs of a given department. It can support a particular business
function,
business process or business unit. The data mart of each department is specific to its own
needs and is optimized for access. The data mart for a certain department is just slightly
similar to the data mart of another department. Perhaps most importantly, (Inmon, 1999:10)
the individual departments own the hardware, software, data and programs that constitute the
data mart. 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.
ETL (Extraction-Transformation-Load):
This concept implies for a set of tools that are used to extract, transform and load data
in BI Systems. These tools are mainly responsible for data transfer from transaction systems
and the Internet to data warehouses. They involve the processes of extracting data from
outside sources, Transforming it to fit operational needs which can include quality levels,
loading it into the end targets such as databases or data warehouses
Role of BI in Enterprise Productivity
In the past decades BI systems were considered as tools that were used exclusively to
support strategic decision-making. Recently organizations have begun to realize that these
systems could be capable of supporting wider business activities. Organizations now use BI
systems for tactical and operational process improvements in certain functions like supply
chain, production and customer service. These new developments have allowed line
managers
to access relevant and timely information such as daily customer and product updates and
make better and instantaneous decisions (Elbashir and Collier and Davern, 2008:149).
In today‟s business cycle, BI is not only a luxury for international
corporations but also a competing advantage for businesses in every size including
Small and Medium Enterprises (SME). SMEs or companies with a weak organizational
culture can realize the necessity with respect to some indicators. Indicators for
determining the necessity for implementing BI within the organization are as follows:
information is used,
Applications of BI techniques in business functions
Most of the business functions depend on each other which means BI Applications
associated to them are also dependent on each other. BI applications are commonly used in
the following business functions apart from their use in strategic decision making:
collaborative filtering, customer satisfaction, customer lifetime value, customer loyalty
production effectiveness
planning
control, distribution analysis
customer attrition, social network analysis (Loshin, 2003:17-24)
In the literature there are many studies about adapting data mining techniques to
certain business cases. Business Process Intelligence tools are based on three main
components which are Process Data Warehouse Loader that extracts data from process logs;
Process Mining Engine that employs data mining techniques creating sophisticated models to
help users identify the causes of behaviors of interest as well as predicting the occurrence of
behaviors in running processes and the Cockpit that is a graphical interface to provide reports
to users (Grigoria and Casati and Castellanos and Dayal and Sayal and Shan, 2004:325).
Another popular application field of data mining is Customer Relationship
Management (CRM). In CRM applications data mining can be used to predict the
profitability
of prospects as they become active customers, their period of being active customers and how
likely they are to leave. In addition, data mining can be used over a period of time to predict
certain changes in details about customer data and lifecycle events so that campaigns or other
CRM tools can be used to keep customers active from desired customer segments. This data
3

mining approach for CRM is called marketing data intelligence and it is based on
two
components that are customer data transformation and customer knowledge discovery
(Rygielski and Wang and Yen, 2002:494).
Another study is about personnel selection for companies that heavily rely on human
capital. This approach includes a data mining framework to extract useful rules from the
relationships between personnel profile data and their work behaviors so that
personnel
selection process becomes more accurate. In addition, the mined results are also planned to
guide the decisions in improving human resource management activities including job
redesign, job rotation, mentoring, and career path development. The data is based on
work experience to predict their work performance and retention (Chien and Chen,
2008:288).
Another study is about improving productivity in manufacturing environments using
data mining. In manufacturing environments, numerous factors effect productivity and
occasionally these factors are dependent on each other. This approach is based on
establishing
a knowledge base in which rules are extracted by adapted association rule mining techniques
and all dependent rules that are extracted before are updated
(http://ieweb.uta.edu/vchen/AIDM/AIDM-Ajoku.pdf).
The final study to mention is about analysis of productivity impacts that information
technology investments do. In fact it can be generalized as the analysis of the impact of
certain remarkable investments to the productivity of an organization. This approach is based
on the comparison of long-term productivity growth that depends on the growth accounting
framework and the discovery of the organizational practices that enhance or hinder
productivity growth using a data mining technique. This data mining technique is called
association rule networking and it is an extension of association rule mining (Poon and Davis
and Choi, 2009:2215).
Important perspectives for establishing an effective BI system
As the investment for BI systems is increasing, it becomes essential to use them
effectively. In order to do that, the business system should be analyzed and identified clearly
and business goals should be stated with respect to sound performance metrics. Factors that
BI Systems are capable of tracing these indicators in strategic, tactical and operational
purposes as long as a sound and complete information system is established in the
organization.
An organization should analyze the environment and business system of itself before
establishing an effective BI System. Moreover it should define the methodologies for every
process in its business system no matter how strategic or operational they are. Finally the
organization should have an adaptation methodology for different decisions which
would
provide flexibility. The important perspectives for establishing an effective BI System are
classified as follows:
management. First of all, strategic drivers of the competitive environment should be
understood. Following, uncertainties about planning, budgeting, controlling,
monitoring, measuring, assessing and performance improving activities in relation to
the strategic goals should be solved. Finally tools and methods to support key business
processes should be identified and technical procedures for identifying, acquiring,
integrating, staging, and delivering the data and information needed by managers

should be established.
tactical and strategic business functions so that the efficiency for these processes can
be measured and monitored.
the processes are subject to change with the structured framework mentioned above
(Williams and Williams 2006:16-21).
The perspectives mentioned above are not only important for implementing a BI
system but also important for achieving an institutionalized organization. Apart from them,
the most frequent technical challenges that organizations face while implementing business
intelligence are as follows:
objectives.
of multiple applications.
networks.
solution.
Swamy, 2004:142).
Examples of Business Intelligence Usage
In this section some examples of business intelligence usage from various firms in the
world will be given and benefits obtained by business intelligence will be shown.
A German company specialist manufacturer of high-tech textiles for the aviation,
automotive, shipbuilding and utilities sectors and operates from seven locations on
four
continents. With employees working in numerous locations and time zones across the globe,
it
was not easy for subsidiaries to communicate and collaborate effectively. Gradually, each site
began to adopt slightly different working practices, making it difficult to access and share
sales and business data across the company. Company senior management wanted to define a
global set of business processes that the whole company would follow, which would help
speed the flow of information throughout the business. A central part of this standardization
program was the introduction of a single data warehouse for the whole company, and the
deployment of business intelligence tools. These tools provide a dashboard with easy access
to reports, helping users understand the current status of the business and identify trends and
employees see the information that is most relevant to them, and are quickly alerted to areas
that need their attention. The sales department at company is benefitting from a range of new
reports, which provide comparisons of current sales figures with historical data. For example,
sales teams can easily view month-on-month or year-on-year percentage changes in revenue,
which helps them analyze performance and respond more effectively (http://www-
01.ibm.com/software/success/cssdb.nsf/CS/STRD-
7PJDK3?OpenDocument&Site=default&cty=en_us ).
A Turkish Bank, needed to increase the volume and effectiveness of its customer
marketing campaigns and implemented a business intelligence tool. With this tool
each
department of the bank can now access enterprise data and perform complex ad hoc queries
rapidly and easily. The company needed to manage immense amounts of information
produced in its daily operations and allow the data to be analyzed. To do this, it needed to
find
an alternative to its current system, one that would achieve a smooth, fast migration path and
deploy into production before new credit card products and services were offered and
advertised. As a result the system delivered 154 percent return on investment
(http://www.sybase.com/files/Success_Stories/Yapi_Kredi_Bank_casestudy_091307.pdf ).
Norway's largest brewer/beverage distributor, sought a better overview of the freight
flow at its production facilities. The company wanted to optimize the flow by gaining better

control of the location and status of containers on trailers at any given time. To do this,
company needed to know whether they were on- or off-site, and the time they were parked at
the facility, unloaded, loaded and driven away. The company implemented a system
for
recording container location and status, based on Radio Frequency Identification
(RFID)
technology. Antennas placed at the main gate and at each of the 40 load gates of the
RFID-tagged container. This system creates new, actionable information about product
location and flow, including truck and container location and status, and loading port status,
enables optimization of container and trailer use through analysis of location and status data
to spot trends and bottlenecks and lays the groundwork for expanded control of the supply
c4hain, through application to other product categories, implementation at other facilities and
more extensive usage within each facility, such as at the product pallet
4

Effects of business intelligence techniques on enterprise

More Related Content

What's hot

Similar to Effects of business intelligence techniques on enterprise

Recently uploaded

Effects of business intelligence techniques on enterprise