1. "BI, A Dashboard to Strategic HR Management"
EKEMINI ANTHONY ESSIET
Post Graduate Student (Department Of Computer Applications),
SRM University, Chennai.
canyaclopex@yahoo.com
Abstract - Business intelligence plays an active role achieving an edge over competitors in today’s
economy. Businesses using a business intelligence methodology are able to develop intelligence
based information systems to gain useful business insight and make faster and more reliable business
decisions. With organizations making use of business intelligence in various areas of their businesses
to improve productivity, the need to take advantage of this in Human Resource Management area has
been left uncontrolled. This paper tends to contribute a positive approach to employ the BI trend in
reverse to Human Resource Strategic Development in the following ways; (1) Separating the business
from the Personnel involved (2) Analyzing the employability trend of Top-shot markets (3) Providing
modularity for creating and analyzing the collected data in relation to current business performance.
We develop a strategy to this approach that can be introduced into an existent system. HR already
manages large quantities of employee data; employee profiles, appraisals, compensation, benefits;
thus there is a need to translate this data for future candidate screening, cost containment or
improving productivity.
Keywords
Data Mining, OLAP (Online Analytical Processing), BIDW (Business intelligence Data
Warehousing), Data Warehouse, AI (Artificial Intelligence) and DSS (Decision Support Systems).
I. Introduction
Business intelligence as it is understood today is said to have evolved from the decision support
systems (DSS) that began in the 1960s and developed throughout the mid-1980s. DSS originated in
the computer-aided models created to assist with decision making and planning. From DSS, data
warehouses, Executive Information Systems, OLAP and business intelligence came into focus
beginning in the late 80s.

2. In 1988, an Italian-Dutch-French-English consortium organized an international meeting on
the Multi-way Data Analysis in Rome.[5] The ultimate goal is to reduce the multiple dimensions down
to one or two (by detecting the patterns within the data) that can then be presented to human decision-
makers.
In 1989, Howard Dresner (later a Gartner Group analyst) proposed "business intelligence" as an
umbrella term to describe "concepts and methods to improve business decision making by using fact-
based support systems."[6] It was not until the late 1990s that this usage was widespread. Concerning
the business intelligence we refer to the definition made by Martre report (1994), which states that
business intelligence is a set of coordinated actions of research, treatment and distribution of
information in order to use it by economic actors. It’s required in order to develop and implement a
coherent strategy and tactics necessary to achieve the objectives set by the company in order to
improve its position in its competitive environment.
Moreover, Massé and Thibaut (2001) argue that to adopt business intelligence behavior, it’s
imperative to put yourself in the place of others to see what you represent for them, to predict their
reactions and to understand their appreciation and perceptions. It’s essential to ask ourselves” if I
were the Human Resource Manager, what would I do to better understand my organization’s
demands ". Also, it’s very important to get into the heads of others: “If I were them, what would I
do?” which of course is a necessary strategy of a good enterprise resource planner in order to
conclude by taking strategic steps and adopting best practices in decision making.
II. COMPONENTS OF A BUSINESS INTELLIGENCE SYSTEM
The BI system consists of a number of component systems that are interdependent. For the system to
function effectively the components must work in an integrated and coordinated way. The various BI
components may be broadly classified into the following four sub-systems: Data Management,
Advanced Analytics, Business Performance Management, and Information Delivery.
1. The Data Management sub-system includes components, relating to Data warehouses, Data
marts, and Online Analytical Processing (OLAP). The people who work mainly in this area are
“technologists”, who specialize in Computer Science, Management Information Systems (MIS), or a
related discipline.
This sub-system deals with all aspects of managing the development, implementation and operations
of a data warehouse or data mart including extraction, transformation, cleaning, and loading of data

3. from different sources. The subsystem also includes meta-data management, security management,
backup and recovery, and data distribution.
The data warehouse is the foundation for business intelligence system operations, two of which are
multi dimensional analysis through OLAP and data analytics. The core of any OLAP system is an
OLAP cube (also called a 'multidimensional cube' or a hypercube). An OLAP cube is a data structure
that allows fast and efficient analysis of large volumes of data from multiple dimensional views.
Online analytical processing, as defined by the OLAP Council, is a category of software technology
that enables analysts, managers and executives to gain insight into data through fast, consistent, and
interactive access to a wide variety of possible views of information that have been transformed from
raw data to reflect real dimensionality of the enterprise as understood by the user. OLAP functionality
is characterized by dynamic multi-dimensional analysis of consolidated enterprise data supporting
end user analytical and navigational activities including calculations and modeling applied across
dimensions, through hierarchies and/or across members, trend analysis over sequential time periods,
slicing subsets for on-screen viewing, drill down to deeper levels of consolidation, reach-through to
underlying detail data, and rotation to new dimensional comparisons in the viewing area. OLAP is
implemented in a multiuser environment and offers consistent, quick response, regardless of database
size and complexity. OLAP helps the user synthesize information through comparative, personalized
viewing, as well as through analysis of historical and projected data in various "what-if" data model
scenarios. This is achieved through use of an OLAP server.
The data warehousing and OLAP focus on gaining insight into their historic data stored in their data
warehouses. They use the past data to answer questions, such as - What happened? Why did it
happen? For example, the employees‟ past data can shed light on employee attrition fluctuations and
factors that were responsible for the fluctuations.
While there is value in knowing what happened in the past, The Advanced Analytics subsystem
enables organizations to answer deeper questions on what to do if the trend continues; the best
actions to take if it arises and what happens as a result of these actions.Thus providing a framework
for smart solutions.
III. CURRENT TRENDS - Individual Approach
Business intelligence system: In the course of my research, I came across the fact that more than
46.67 % - about 58% papers discuss about individual approach the theoretic method and software of
business intelligence system. The paper writes the definition, methodology, architecture, case study
and software that are used in business intelligence system. An Enterprise Marketing Campaign

4. Automation (EMCA) system that can provide data for businesses to instantly assemble them for
determining effective and accurate marketing campaign strategy. By generating a mailing list targeted
to a specific group of buyers with reference to their buying habits can reduce marketing cost by just
mailing the promotional items to the specific group of buyers. With the survey results, predictive
conclusions can be drawn to better understanding the market implications as well as employability
strategy developed by competitors. [7] The current situation of business environment and business
intelligence systems (BIS) framework at first, and studies the theoretic and methods about the
business intelligence system based on ontology. Without ruling out on the use of ontology, this paper
proposes an integration framework for business intelligence systems taking into cognizance the sole
aim of establishing a business framework.
Integrated Approach
Integration between BI, Supply Chain: References to other papers prove that 3.33 % papers discuss
about integrated between BI and Supply Chain Management. Supply Chain Business Intelligence
introduces driving forces for its adoption and describes the supply chain BI architecture. The global
supply chain performance measurement system based on the process reference model is described.
The main cutting-edge technologies such as service-oriented architecture (SOA), business activity
monitoring (BAM), web portals, data mining and their role in BI systems are also discussed. Finally,
key BI trends and technologies that will influence future systems are described.
Integration between BI, CRM System 6.67% papers discuss about integration of BI with Customer
Relationship Management System.CRM systems and Business Intelligence provides a holistic
approach to customers which include improvements in customer profiling, simpler detection value for
customers, measuring the success of the company in satisfying its customers, and create a
comprehensive customer relationship management. A conceptual and a technological infrastructure
was proposed and integrated into a Student Relationship Management (SRM) system associated with
Business Intelligence concepts and technologies used to obtain knowledge about the students and to
support the decision making process. [11]E-business intelligence aims to develop a tremendous
spectrum of business opportunities and user's adoption of the business intelligence is very important
and relevant propositions are made.

5. Integration of BI with Data Mining: With 5% papers talking about integration of BI with Data
Mining, a data mining methodology called Business Intelligence Driven Data Mining (BIDDM)
combines knowledge-driven data mining and method-driven data mining, and fills the gap between
business intelligence knowledge and existent various data mining methods in e-Business. Business
intelligence is information about a company's past performance that is used to help predict the
company's future performance. It can reveal emerging trends from which the company might profit.
Data mining allows users to sift through the enormous amount of information available in data
warehouses; it is from this sifting process that business intelligence gems may be found.
Integrated between BI, AI (Artificial Intelligence) Integrated between BI and Artificial Intelligence
papers discuss about3.33% papers discuss about integrated. A business intelligence application of
neural networks in analyzing consumer heterogeneity in the context of eating-out behavior in Taiwan.
The data set for this study has been collected through a survey of 800 Taiwanese consumers. The
results of our data analysis show that the neural network rule extraction algorithm was able to find
distinct consumer segments and predict the consumers within each segment with good accuracy.
[15]On the other hand, firms should handle more accurate business information to support their
business intelligence (BI) system to make better business decisions.
E. Integrated between BI and OLAP 3.33% papers discuss about integrated between BI and OLAP.
The use of business intelligence and OLAP tools in e-learning environments and presents a case study
of how to apply these technologies in the database of an e-learning system. The study shows that
students spend little time with course courseware and prefer to use collaborative activities, such as

6. virtual classroom and forums instead of just viewing the learning material.[17]The importance of
Intelligence Systems as well as the architecture of OLAP decisional interactive support systems.
IV. OBSERVATION AND RECOMMENDATION.
The most popular approach: The most popular approach is single approach Business Intelligence
System with 46.67% of paper discussing on it. There are many software that are used in Business
Intelligence System research like SharePoint Server 2007,Microsoft SQL Server 2005, Microsoft
business intelligence stack and BI products, and finally, describes how to deliver BI solution.
The most popular BI integrated research: The most popular BI integrated research is integrated
between Business Intelligence and CRM System with 6.67% papers. Integrated between BI, Data
Mining 5 % and Integration between BI, AI and, Data Mining 5 % .The topic that is integrated with
BI research that is found in this research is Supply Chain Management, CRM system, Data Mining,
Artificial Intelligence, OLAP. We believe that analyzing the demographics of a workforce could
become an increasingly important HR function in the future. Companies, particularly in the more
traditional markets, face the problem of an ageing workforce and thus, with the advent of the ‘global
employee’, there is often intense competition for the best new talent. Using BI to make sense of this
data will give HR a central role in adapting to the ever-changing market dynamics and better position
your organization to developing new ideas and products. Building on this, BI has become a key
instrument in analyzing the true value of the human capital within a company, as BI helps link people
to a company’s financial performance. HR must analyze the key skill sets and demographics of their
existing workforce, assess whether it is best helping them to meet their central business targets and
then identify whether HR is doing the correct things to help the company progress. We believe that
taken further, BI can be used to predict future personnel trends and used to help draw, stimulate and
retain the best candidates while monitoring other competitor’s strategy.
To summarize, I've listed some of the main potential benefits of adopting BI throughout the HR
department and other existing departments in the organization:
Analyzing Hr Success – To link employee performance to financial performance and identify areas
for change / improvement and compare with industry benchmarks.
Sick Days And Holiday – To get useful information and analyze why and when employees are taking
a leave of absence. Evaluate any trends for example key demographics.

7. Enhanced Compensation – To understand how compensation impacts on performance, to make sure
compensation is level and fair throughout the company and to make sure performance related
compensation is in line with the company’s key strategic objectives.
Workforce Optimization – Using analysis throughout the company, top and bottom performers can
be identified and then moved up or down. This could be used to inform future spending on training
and to identify learning needs of employees.
Target-market Optimization – To better develop strategy on new ideas, ceasing competitor’s
privileges and becoming a top-shot, Business intelligence provides such a platform with fair chances
of loses if any may exist.
Personnel Influence – Helps to better analyze the influence of the presence or absence of dedicated
employees, employee strength and influence in organizational performances, business objectives and
set goals.
We firmly believe that BI is becoming increasingly useful for a wide range of people. HR can use BI
to improve results across the whole organization and this synergy will play an increasingly central
role as companies look for new ways to adapt to rapidly changing market conditions.
V. CONCLUSION
Employees are known for innovation. The most innovative retailers of today are those who are using
business intelligence to gain sustained competitive advantage .The wisdom is gathered by analyzing
huge amount of data, and should reach every corner of the organization. This paper reviews is based
on a literature on business intelligence approaches. Relates articles appearing in the international

8. journal and IEEE conference papers from 2000 to 2013 are gathered and it was found that about
46.67% research is in single approach Business Intelligence System, Integrated between BI and CRM
System is the most popular evaluating criteria with 6.67%. Integrated between BI, AI and Data
Mining 5%.Thus this paper takes advantage of the weaknesses of researchers in developing broad
new ideas from BI. The end objective is to convert this wisdom into effective action. And for this the
entire organization should be able to leverage the business intelligence network by its application
across various departments.
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