Description
Business Intelligence and Its Use for Human Resource Management
The Journal of Human Resource and Adult Learning Vol. 6, Num. 2, December 2010 21
Business Intelligence and Its Use for Human
Resource Management
Bhushan Kapoor, Professor and Chair
Mihaylo Faculty Fellow
Department of Information Systems & Decision Sciences
California State University, Fullerton, USA
ABSTRACT
Business intelligence plays a crucial role to achieve competitive edge over competitors in the
challenging economy we are in. 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. While many organizations are starting to use business intelligence in many
areas of their businesses and make substantial gains, they have not taken advantage of this in Human
Resource Management area. In this paper we examine leading BI vendors to look into the business
intelligence and data analytics features incorporated in their Human Resource Management modules.
Keywords: Business Intelligence, Data Analytics, Data Warehouse, Data Mart, Data Mining, Business
Performance Management, Key Performance Indicators, Dashboards.
INTRODUCTION
Business Intelligence (BI) refers to the ability to use information to gain a competitive edge over
competitors. It is rated as the most wanted technology by businesses across the world. Even in current
times of economic downturn, when IT budgets are being cut, BI is still among the top of executive?s
priorities (Gartner, 2009).
Business Intelligence is a broad field that combines people skills, technologies, applications, and
business processes to make better strategic and tactical business decisions. The technologies and
applications include data management methods for planning, collecting, storing, and structuring data into
data warehouses and data marts as well as analytical tasks for querying, reporting, visualizing, generating
online active reports, and running advanced analytical techniques for clustering, classification,
segmentation, and prediction. Data warehouse focuses on enterprise wide data, and data mart is restricted
to a single process or a department, such as Human Resources (HR) department.
Modern businesses face several challenges, and the challenges almost always bring some
opportunities along side. The goal of BI is to help businesses face many of the challenges and seize
opportunities that exist in the market place. Due to advances in technologies (e.g., improved data storage
capabilities, wide use of the Internet and Intranets) and regulatory changes (e.g., the Sarbanes Oxley act
of 2002, which mandates advance and accurate reporting capabilities in corporate setting), businesses are
collecting and storing data at an alarming rate. Companies collect large volumes of data on their
employees, such as salary information, performance reviews, and education level. As a result, most
organizations face an information overload. By 2020, the amount of data generated each year is projected
to reach 35 zettabytes (1 zettabyte = 1 billion terabytes, 1 terabyte = 1000 gigabytes) (International data
The Journal of Human Resource and Adult Learning Vol. 6, Num. 2, December 2010
22
Corporation, May 2010). And yet this same data, through proper management and analysis, will afford
companies the opportunity to optimize HR and other business operations. Organizations are competing
on Business Intelligence not just because they can-business today is awash in data and data crunchers-but
also because they should (Davenport, 2006). For example, data on education, skills and past performance
of their employees will help companies identify the critical talent within the organization and ensure HR
retains it. The data management and analytics components of Business Intelligence systems are designed
to be able to collect data from various sources, convert the raw data into useful actionable information or
knowledge. Employee data is generally housed in separate HR systems based on vertical HR functions,
such as benefits, payroll and compensation, leave, training and surveys and/or horizontally across
functional areas. Companies need to identify all internal and external data sources and then consolidate
the data into a HR data mart. Some examples of external data sources are US Bureau of Labor Statistics,
Employment and Earnings, Collective agreements, Industry benchmarks, and Labor regulations. There
are numerous Extract, Transform, and Load (ETL) tools on the market to extract data from both internal
and external sources and combine them into a data mart or a data warehouse (IBM White Paper, March
2009).
Another challenge contemporary businesses are facing today is that the business environment is
constantly evolving into a more complex system. And with global competition and the flat and connected
new world (Friedman, 2004), decision-making in organizations has become increasingly intricate and
convoluted. The availability of relatively cheap labor and growing consumers in developing countries,
and aging population in the US, has put pressure on the organizations to go global for business
opportunities. This creates challenges for global organizations? HR departments to manage workforce
diverse in cultures, time zones, expertise, benefits, and compensations. Given that total workforce
compensation represents 60% to 70% of the general expenses, businesses are under pressures to respond
quickly to the dynamic conditions of the business environment. The response often involves redesigning
organizational structures, redefining value propositions, and streamlining processes. Business
Intelligence and analytics can aid in making informed decisions based on knowledge extracted from the
data and options at hand. Organizations that have successfully implemented BI are able to make
decisions quickly and with more accuracy. They have better and faster access to the key activities and
processes that the organizations and its functional departments must pursue to meets its goals and
objectives.
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
The Journal of Human Resource and Adult Learning Vol. 6, Num. 2, December 2010 23
loading of data 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, such as - What if the trend continues? What are the
best actions to take? What will happen as a result of these actions?
2. The Advanced Analytics sub-system includes analytic functions based on statistics, data mining,
forecasting, predictive modeling, predictive analytics, and optimization. The people who work mainly
in this area are “super users”, who specialize in Mathematics, Statistics, Management Science or a
related discipline.
Large BI vendors have incorporated comprehensive statistics packages within their BI software
system. For example, IBM has incorporated the SPSS with their BI Cognos system, and SAS Inc. has
developed their BI software with a core consisting of their celebrated statistical packaged software.
Microsoft has developed XLSTAT, an add-on to Excel for Statistics and multivariate data analysis.
Along with the inclusion of statistical capabilities, BI vendors have also incorporated Data mining
capabilities in their software. Data mining is an extension of statistical techniques such as, classical
and artificial intelligence. Statistical techniques are generally applied to relatively small size random
sample data specifically collected to validate a hypothesis, and the techniques conform to a set of
assumptions about the population. These statistical techniques are called „verification driven
techniques?. Data mining includes techniques - called „discovery driven techniques? - that can attempt
to discover information by using appropriate algorithms automatically. These techniques can be
regarded as discovering information by exploration (Kim, 2002; tan, Steinbach, Kumar, 2006;
Shmueli, Patel, Bruce, 2010).
The Journal of Human Resource and Adult Learning Vol. 6, Num. 2, December 2010
24
3. The Business Performance Management sub-system consists of processes for strategic goals and
objectives, performance measurement and mentoring, analyzing performance and making decisions to
improve business performance.
Strategic Goals and Objectives – A strategic goal is a broad statement of what the management
wants to achieve, and an objective is usually time bound specific course of action that contributes to
the achievement of the strategic goal. There may be several objectives pertaining to a goal. Here is an
example of a goal and its two objectives as planned by Human Resources department of the state of
California (http://www.dpa.ca.gov/hr-mod/ accomplishments-and-goals/mission-statement-goals-and-
objectives.htm):
Goal: Improve and instill high performance in the workplace.
The primary purpose of employee learning and performance management is to improve both
employee and organizational performance. Within state service, staff development is too often
considered an expense rather than an investment. This goal will establish performance management as
a basic objective for improving service to internal and external customers including the citizens of
California.
Objective #1: Ensure supervisors/managers acquire, implement, and apply principles necessary to
foster high performance in the workplace.
Currently, employee appraisals are done irregularly, despite the requirements for annual
appraisals and individual development plans. As we convert to a competency-based HR system,
performance will be used to assess current competencies, identify training needs, and better align
resources to achieve organizational effectiveness. It is essential supervisors/managers continually
provide meaningful feedback.
Properly trained, informed and accountable management is the key to establish and foster high
performance. To achieve this, performance management training and training assessment methods
must be established to evaluate the effectiveness of the training received. Continuing education and
resources (e.g. communication forums, webcasts) must be made available to keep supervisors/
managers informed and apprised of industry best practices. Automated tools must also be created and
made available to support improved methods for ascertaining the achievement of performance goals
and objectives. These will further provide opportunities to more effectively measure management
compliance. A compliant, consistent, and available 80-hour supervisor/manager training program will
be deployed by winter 2009.
Objective #2: Ensure supervisors/managers conduct meaningful timely performance appraisals.
This objective will equip supervisors/managers with the skills necessary to provide constructive
feedback to staff. Supervisors/managers will be held accountable in their personal performance
appraisals for assessing their staff. Initial learning management tools will be available by summer
2009. Identification of performance management tool(s) will be accomplished by fall 2009.
Organizations make operational and financial plans to achieve organization?s strategic goals and
objectives, and then initiate projects to implement the plans.
(1) Performance Measurement and Monitoring – During the entire period of the project, the
outcomes are measured and trends are monitored in real time. The measurements are done
according to certain key performance indicators (KPIs). The KPIs need to be designed for each
functional area and for each of the levels in the organization starting from the highest, moving
towards the lowest.
The Journal of Human Resource and Adult Learning Vol. 6, Num. 2, December 2010 25
Performance indicators are leading indicators that gauge the outcome by examining incremental
progress of the project. Performance indicators with respect to human resources may include
employee retention, job satisfaction, compensation and rewards, employee training, accident
levels, employee absenteeism, and employee performance.
(2) Analyzing Performance – KPIs are compared to the strategic goals and objectives. The results
are utilized to monitor, further analyze and act to improve performance. The Advanced Analytics
subsystem enables organizations to make informed decisions to align their goals and objectives,
as well as programs and budgets to the performance indicators.
(3) Decision Making and Performance Feedback – The organizations are able to adjust their goals
and objectives, modify programs, and re-allocate resources and funds. Performance measures in
essence provide a feedback loop in the process of business performance management.
4. The Information Delivery sub-system gives the business users the ability to access reports and
continuously monitor organizational performance at enterprise and lower levels. According to his or
her role as a technocrat, super user, middle manager, executive manager, or operational user, he or she
will be given role-based rights to access relevant reports in summary and/or detailed formats. End
users are also able to monitor the key activities such as trends, metrics, and KPIs in easy-to-
understand designs, such as configurable information portals, scorecards and dashboards. Depending
on an individual's role and responsibility, he or she is presented with the trends, metrics, and KPIs at
appropriate aggregate levels with security to block non-privileged items.
BUSINESS INTELLIGENCE ADOPTION IN INDUSTRY AND HUMAN RESOURCES
BI is gaining rapidly in popularity, faster than most anticipated. In a report done in April 2010, it
was found that the BI platforms, analytic applications and performance management (PM) software
revenue surpassed $9.3 billion in 2009, a 4.2 percent increase from the 2008 revenue of $8.9 billion
(Gartner, April 2010).
Table 1: Worldwide BI, Analytics and Performance Management Revenue Estimates for 2009 by
Sub-segment (Millions of U.S. Dollars)
Sub-segment
Revenue
Estimate
Market Share
%
BI Platform 5,982.4 64.2
CPM Suites 1,937.1 20.8
Analytic Applications & Performance Management 1,402.4 15.0
Total 9,321.9 100.0
Source: Gartner (April 2010)
BI platforms were found to have a $5.98 billion (64.2%) market share of total worldwide BI
software revenue. Corporate performance management (CPM) suites were found to have a $1.94 billion
(20.8%) market share, and analytic applications and performance management software were found to
have a $1.40 billion (15%) market share. Details are given in Table 1.
The BI software market significantly outperformed the overall enterprise software market in 2009.
Seeing tremendous opportunities in Business Intelligence, there has been a large number of acquisitions
and mergers of BI companies by software giant vendors reported in the past few years. Major software
vendors have made business intelligence software their focal product to develop or acquire, and sell or
The Journal of Human Resource and Adult Learning Vol. 6, Num. 2, December 2010
26
service. The top four vendors (SAP, SAS Institute, Oracle, and IBM) makeup most of the BI market with
64% market share in 2009 – SAP (23.4%), SAS Institute (14.4%), Oracle (14.4%), and IBM (11.1%)
(Gartner, April 2010). The following table gives the major BI vendors, their recent BI acquisitions, and
price paid.
Table 2: Major Software Acquisitions in the recent past and Price Paid
Vendor Select BI Acquisitions Price Paid
SAP Sybase (2010)
Business Objects (2007)
OutlookSoft (2007)
Pilot Software (2007)
$5.8B
$6.8B
$200M
NA
SAS Institute Teragram (2008) NA
Oracle Sun Microsystems (2009)
Hyperion (2007)
Siebel Systems (2006)
Sigma Dynamics (2006)
PeopleSoft (2005)
7.4B
3.3B
5.85B
10.3B
IBM SPSS (2009)
Cognos (2007)
Telelogic AB (2007)
1.2B
5.0B
845M
Source: Gartner (April 2010), and vendors? web sites and press releases.
SAP paid 6.8 billion to acquire Business Objects in 2007, which was number one in business
intelligence at that time. The same year, Oracle bought Hyperion for 3.3 billion and IBM bought Cognos
for 5 billion. Business Objects, Hyperion and Cognos were leaders in business intelligence before they
were bought over by the software giants. IBM and Oracle acquired the most BI software and paid a hefty
price for that. Each software giant has benefitted tremendously because of acquiring these BI focused
companies. The software giant vendors now offer several BI products and are leaders in the area. Each
one of them also offers HR modules along with business intelligence and data analytics capabilities. The
following table gives HR module names, and names of the embedded BI and data analytics products.
Table 3: HR Modules and BI & Data Analytics Software for Giant Vendors
Vendor HR Module BI & Data Analytics
SAP SAP ERP Human Capital
Management
Workforce Analytics
SAS Institute SAS Human Capital
Intelligence
Human capital Predictive Analytics and
Retention Modeling
Oracle Oracle Human Capital
Management
Oracle Human Resources Analytics
IBM IBM Cognos – Human
Resources
Business Intelligence and Human Resource
Performance Management
Source: Vendors? web sites.
BUSINESS INTELLIGENCE AND DATA ANALYTICS FEATURES IN HUMAN RESOURCES
As stated in the previous section, the software joints offer many BI products, including HR
embedded with business intelligence and data analytics capabilities. The vendors claim the following
features and functions in their software:
The Journal of Human Resource and Adult Learning Vol. 6, Num. 2, December 2010 27
SAP ERP Workforce Analytics
This product includes features and functions that support these business activities
(http://www.sap.com/solutions/business-suite/erp/featuresfunctions/workforceanalysis/index.epx):
?
Workforce Planning
Understand current workforce trends – and plan future needs – by using workforce demographic data.
Use predefined reports to analyze headcount development, turnover rates, and workforce composition.
Link the results of this analysis directly into headcount planning, budgeting, and key talent processes,
such as recruiting and learning.
?
Workforce Cost Planning and Simulation
Support HR professionals in all workforce cost-planning tasks, and empower HR executives to
develop effective strategies. Provide access to a broad range of workforce-related data to support
accurate planning, facilitate simulated planning scenarios, and enable continuous monitoring of actual
performance relative to plan.
?
Workforce Benchmarking
Measure standard workforce processes. Compare the measurements with external benchmarks and
internal operating thresholds.
?
Workforce Process Analytics and Measurement
Measure and analyze typical core HR processes, such as payroll, employee administration, time
management, and benefits. Analyze organizational structures, relationships, and attributes of jobs and
positions.
?
Talent Management Analytics and Measurement
Analyze employee skills and qualifications. Evaluate the efficiency of your recruiting processes.
Measure the effectiveness of your learning programs. Assess how well your succession programs
prepare your employees to assume key positions – and ensure continuity of operations. Monitor the
progress of aligning employee goals with corporate goals. Analyze the cost-effectiveness of
employee compensation programs
?
Strategic Alignment
Ensure that all business activities are in line with the strategic goals of your organization. Help
Employee teams work toward common objectives, regardless of location. Use a balanced scorecard
framework, with predefined workforce scorecards that can be integrated into department and
individual management-by-objective (MBO) documents to align employee goals with corporate
strategy.
SAS Human Capital Predictive Analytics and Retention Modeling
This product includes the following features (http://www.sas.com/solutions/hci/hcretention):
? Predicted Turnover Percentage
Ranking by high, medium and low risk of voluntary termination
? Causes of Voluntary Termination
Showing each cause identified by the model in an individual report.
? Organization Exposure
Looking at the high-risk group only and showing a hierarchical view starting at the top and drilling
down through all organizational levels.
? High Risk by Job Category
Identifying those in the high-risk group by EEO job category
The Journal of Human Resource and Adult Learning Vol. 6, Num. 2, December 2010
28
? Top 50 Employees at High Risk
Pinpointing the 50 employees most likely to voluntarily leave
? Top Performer
Identify high-risk top performers and define their reasons for leaving.
Oracle Human Resources Analytics
This product includes the following features (http://www.oracle.com/us/solutions/ent-performance-
bi/hr-analytics-066536.html):
? Workforce Insight
Monitor workforce demographics in line with your recruitment and retention objectives. Analyze
efficiency of the entire recruitment process lifecycle, understand and prevent the drivers of employee
turnover.
? Targeted Workforce Development
Gain insight into the movement of top and bottom performers in the organization to engage and
develop internal talent. Gain insight into learning demand by analyzing course enrollments by job,
delivery methods, and organizations.
? Improved Compensation
Understand how compensation impacts performance, ensure compensation is equitable and consistent
across roles, and align variable compensation with your organization's objectives and goals.
? Leave and Absence
Get a comprehensive view into employees' current, planned, and historical absence events; monitor
absence trends as a predictor for employee engagement.
? Better Understanding of HR Performance
Assess HR's overall performance and employee productivity using industry benchmarks such as
revenue per employee, contribution per headcount, and return on human capital.
? US Statutory Compliance
Monitor US EEO, AAP, and Vets100 compliance reporting.
?
Workforce Planning
Monitor workforce demographics in line with your recruitment and retention objectives. Analyze
efficiency of the entire recruitment process lifecycle, understand and prevent the drivers of employee
turnover.
?
Workforce Cost Planning and Simulation
Support HR professionals in all workforce cost-planning tasks, and empower HR executives to
develop effective strategies. Provide access to a broad range of workforce-related data to support
accurate planning, facilitate simulated planning scenarios, and enable continuous monitoring of actual
performance relative to plan.
?
Workforce Benchmarking
Measure standard workforce processes. Compare the measurements with external benchmarks and
internal operating thresholds.
?
Workforce Process Analytics and Measurement
Measure and analyze typical core HR processes, such as payroll, employee administration, time
management, and benefits. Analyze organizational structures, relationships, and attributes of jobs and
positions.
The Journal of Human Resource and Adult Learning Vol. 6, Num. 2, December 2010 29
?
Talent Management Analytics and Measurement
Analyze employee skills and qualifications. Evaluate the efficiency of your recruiting processes.
Measure the effectiveness of your learning programs. Assess how well your succession programs
prepare your employees to assume key positions – and ensure continuity of operations. Monitor the
progress of aligning employee goals with corporate goals. Analyze the cost-effectiveness of
employee compensation programs
IBM Cognos Business Intelligence and Human Resource Performance Management
This product includes the following features pertaining to the following five HR core areas
(http://www-01.ibm.com/software/data/cognos/solutions/human-resources/index.html):
? Organization and Staffing
What job functions, positions, roles and capabilities are required to drive the business performance?
? Compensation
How should we reward our employees to retain and motivate them for full performance?
? Talent and Succession
What are the talent and succession gaps we must address to ensure sustained performance?
? Training and Development
What training and development do we need to maximize employee performance? Is there a clear
payback?
? Benefits
How do we manage costs and incentives for better performance?
SUMMARY AND CONCLUSION
Business Intelligence is helping businesses become more competitive. Because of technological
progress and regulatory changes, businesses are collecting and storing data at an alarming rate. Because
the business environment is constantly changing, decision making in organizations has become
increasingly intricate. Business Intelligence is helping organizations make faster and more reliable
information based business decisions.
Business Intelligence is seen as consisting of four major inter-dependent sub-systems – Data
Management, Advanced Analytics, Business Performance, and Information Delivery sub-system. The
Data Management subsystem handles data storage into databases, data warehouses, and data marts. With
On-line Analytical Processing (OLAP) built into the system, it is possible to do multi-dimensional
analysis on the data. The Advanced Analytics processing system includes analytic methods based on
statistics, data mining, forecasting, predictive modeling, predictive analytics, and optimization. The
Business Performance system consists of processes for performance measurement and mentoring and
making decisions to improve business performance. Key Performance Indicators (KPIs) take a prominent
role in this sub system. KPIs are defined to measure progress toward organizational goals. The
Information Delivery subsystem provides information to users in real time and in a format they want it.
End users are able to monitor the key activities in easy-to-understand formats, such as configurable
information portals, scorecards and dashboards.
Having seen Business Intelligence as the core enterprise strategy, the giant software vendors have
spent billions on acquiring other business intelligence focused companies. The giant vendors have now
business intelligence solutions for organizations of most types and sizes. While many organizations have
The Journal of Human Resource and Adult Learning Vol. 6, Num. 2, December 2010
30
purchased their BI software and are starting to use in many areas of their businesses and make substantial
gains, but they have not taken advantage of this in Human Resource Management area. Executives view
Human Resources more of a cost center, and less of a strategic asset within their organizations. In the last
section of the paper we have examined the leading vendors to look into the business intelligence and data
analytics features incorporated in their Human Resource Management software. By taking advantage of
the rich business intelligence features in these and other similar products, Human Resources can position
itself as essential value-adding department of the organization.
REFERENCES
Davenport, Thomas H. (2006 January). Competing on Analytics, Harvard Business Review, Prod. #: R0601H-PDF-ENG.
Friedman T. L. (2004). The World is Flat New York: Farrar, Straus and Giroux.
Gartner (2009 February). “Business Intelligence ranked Top Technology Priority by CIOs for Fourth Year in a Row”.
Gartner (2010 April). “Market Share: Business Intelligence, Analytics and Performance management Software Worldwide, 2009”.
International Business Machines (IBM) (2009 March). “How Smart HR departments win with Business Intelligence?”
International Data Corporations (IDC) (2010 May). “The Digital Universe Decade – Are you Ready?”
International Data Corporations (IDC) (2010 September). “Worldwide Business Analytics Software 2010-2014 Forecast and 2009
Vendor Share”.
Kim, D. A. (2002 March). Information Visualization and Visual Data Mining. IEEE Transactions on Visualization and Computer
Graphics, Vol. 8, Issue 1.
Shmueli, Galit, Nitin R. Patel, and Peter C. Bruce (2010). Data Mining for Business Intelligence, Wiley Publication.
Tan, Pang-Ning, Michael Steinbach, and Vipin Kumar (2006). Introduction to Data Mining, Pearson Publication.
doc_929408740.pdf
Business Intelligence and Its Use for Human Resource Management
The Journal of Human Resource and Adult Learning Vol. 6, Num. 2, December 2010 21
Business Intelligence and Its Use for Human
Resource Management
Bhushan Kapoor, Professor and Chair
Mihaylo Faculty Fellow
Department of Information Systems & Decision Sciences
California State University, Fullerton, USA
ABSTRACT
Business intelligence plays a crucial role to achieve competitive edge over competitors in the
challenging economy we are in. 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. While many organizations are starting to use business intelligence in many
areas of their businesses and make substantial gains, they have not taken advantage of this in Human
Resource Management area. In this paper we examine leading BI vendors to look into the business
intelligence and data analytics features incorporated in their Human Resource Management modules.
Keywords: Business Intelligence, Data Analytics, Data Warehouse, Data Mart, Data Mining, Business
Performance Management, Key Performance Indicators, Dashboards.
INTRODUCTION
Business Intelligence (BI) refers to the ability to use information to gain a competitive edge over
competitors. It is rated as the most wanted technology by businesses across the world. Even in current
times of economic downturn, when IT budgets are being cut, BI is still among the top of executive?s
priorities (Gartner, 2009).
Business Intelligence is a broad field that combines people skills, technologies, applications, and
business processes to make better strategic and tactical business decisions. The technologies and
applications include data management methods for planning, collecting, storing, and structuring data into
data warehouses and data marts as well as analytical tasks for querying, reporting, visualizing, generating
online active reports, and running advanced analytical techniques for clustering, classification,
segmentation, and prediction. Data warehouse focuses on enterprise wide data, and data mart is restricted
to a single process or a department, such as Human Resources (HR) department.
Modern businesses face several challenges, and the challenges almost always bring some
opportunities along side. The goal of BI is to help businesses face many of the challenges and seize
opportunities that exist in the market place. Due to advances in technologies (e.g., improved data storage
capabilities, wide use of the Internet and Intranets) and regulatory changes (e.g., the Sarbanes Oxley act
of 2002, which mandates advance and accurate reporting capabilities in corporate setting), businesses are
collecting and storing data at an alarming rate. Companies collect large volumes of data on their
employees, such as salary information, performance reviews, and education level. As a result, most
organizations face an information overload. By 2020, the amount of data generated each year is projected
to reach 35 zettabytes (1 zettabyte = 1 billion terabytes, 1 terabyte = 1000 gigabytes) (International data
The Journal of Human Resource and Adult Learning Vol. 6, Num. 2, December 2010
22
Corporation, May 2010). And yet this same data, through proper management and analysis, will afford
companies the opportunity to optimize HR and other business operations. Organizations are competing
on Business Intelligence not just because they can-business today is awash in data and data crunchers-but
also because they should (Davenport, 2006). For example, data on education, skills and past performance
of their employees will help companies identify the critical talent within the organization and ensure HR
retains it. The data management and analytics components of Business Intelligence systems are designed
to be able to collect data from various sources, convert the raw data into useful actionable information or
knowledge. Employee data is generally housed in separate HR systems based on vertical HR functions,
such as benefits, payroll and compensation, leave, training and surveys and/or horizontally across
functional areas. Companies need to identify all internal and external data sources and then consolidate
the data into a HR data mart. Some examples of external data sources are US Bureau of Labor Statistics,
Employment and Earnings, Collective agreements, Industry benchmarks, and Labor regulations. There
are numerous Extract, Transform, and Load (ETL) tools on the market to extract data from both internal
and external sources and combine them into a data mart or a data warehouse (IBM White Paper, March
2009).
Another challenge contemporary businesses are facing today is that the business environment is
constantly evolving into a more complex system. And with global competition and the flat and connected
new world (Friedman, 2004), decision-making in organizations has become increasingly intricate and
convoluted. The availability of relatively cheap labor and growing consumers in developing countries,
and aging population in the US, has put pressure on the organizations to go global for business
opportunities. This creates challenges for global organizations? HR departments to manage workforce
diverse in cultures, time zones, expertise, benefits, and compensations. Given that total workforce
compensation represents 60% to 70% of the general expenses, businesses are under pressures to respond
quickly to the dynamic conditions of the business environment. The response often involves redesigning
organizational structures, redefining value propositions, and streamlining processes. Business
Intelligence and analytics can aid in making informed decisions based on knowledge extracted from the
data and options at hand. Organizations that have successfully implemented BI are able to make
decisions quickly and with more accuracy. They have better and faster access to the key activities and
processes that the organizations and its functional departments must pursue to meets its goals and
objectives.
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
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loading of data 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, such as - What if the trend continues? What are the
best actions to take? What will happen as a result of these actions?
2. The Advanced Analytics sub-system includes analytic functions based on statistics, data mining,
forecasting, predictive modeling, predictive analytics, and optimization. The people who work mainly
in this area are “super users”, who specialize in Mathematics, Statistics, Management Science or a
related discipline.
Large BI vendors have incorporated comprehensive statistics packages within their BI software
system. For example, IBM has incorporated the SPSS with their BI Cognos system, and SAS Inc. has
developed their BI software with a core consisting of their celebrated statistical packaged software.
Microsoft has developed XLSTAT, an add-on to Excel for Statistics and multivariate data analysis.
Along with the inclusion of statistical capabilities, BI vendors have also incorporated Data mining
capabilities in their software. Data mining is an extension of statistical techniques such as, classical
and artificial intelligence. Statistical techniques are generally applied to relatively small size random
sample data specifically collected to validate a hypothesis, and the techniques conform to a set of
assumptions about the population. These statistical techniques are called „verification driven
techniques?. Data mining includes techniques - called „discovery driven techniques? - that can attempt
to discover information by using appropriate algorithms automatically. These techniques can be
regarded as discovering information by exploration (Kim, 2002; tan, Steinbach, Kumar, 2006;
Shmueli, Patel, Bruce, 2010).
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24
3. The Business Performance Management sub-system consists of processes for strategic goals and
objectives, performance measurement and mentoring, analyzing performance and making decisions to
improve business performance.
Strategic Goals and Objectives – A strategic goal is a broad statement of what the management
wants to achieve, and an objective is usually time bound specific course of action that contributes to
the achievement of the strategic goal. There may be several objectives pertaining to a goal. Here is an
example of a goal and its two objectives as planned by Human Resources department of the state of
California (http://www.dpa.ca.gov/hr-mod/ accomplishments-and-goals/mission-statement-goals-and-
objectives.htm):
Goal: Improve and instill high performance in the workplace.
The primary purpose of employee learning and performance management is to improve both
employee and organizational performance. Within state service, staff development is too often
considered an expense rather than an investment. This goal will establish performance management as
a basic objective for improving service to internal and external customers including the citizens of
California.
Objective #1: Ensure supervisors/managers acquire, implement, and apply principles necessary to
foster high performance in the workplace.
Currently, employee appraisals are done irregularly, despite the requirements for annual
appraisals and individual development plans. As we convert to a competency-based HR system,
performance will be used to assess current competencies, identify training needs, and better align
resources to achieve organizational effectiveness. It is essential supervisors/managers continually
provide meaningful feedback.
Properly trained, informed and accountable management is the key to establish and foster high
performance. To achieve this, performance management training and training assessment methods
must be established to evaluate the effectiveness of the training received. Continuing education and
resources (e.g. communication forums, webcasts) must be made available to keep supervisors/
managers informed and apprised of industry best practices. Automated tools must also be created and
made available to support improved methods for ascertaining the achievement of performance goals
and objectives. These will further provide opportunities to more effectively measure management
compliance. A compliant, consistent, and available 80-hour supervisor/manager training program will
be deployed by winter 2009.
Objective #2: Ensure supervisors/managers conduct meaningful timely performance appraisals.
This objective will equip supervisors/managers with the skills necessary to provide constructive
feedback to staff. Supervisors/managers will be held accountable in their personal performance
appraisals for assessing their staff. Initial learning management tools will be available by summer
2009. Identification of performance management tool(s) will be accomplished by fall 2009.
Organizations make operational and financial plans to achieve organization?s strategic goals and
objectives, and then initiate projects to implement the plans.
(1) Performance Measurement and Monitoring – During the entire period of the project, the
outcomes are measured and trends are monitored in real time. The measurements are done
according to certain key performance indicators (KPIs). The KPIs need to be designed for each
functional area and for each of the levels in the organization starting from the highest, moving
towards the lowest.
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Performance indicators are leading indicators that gauge the outcome by examining incremental
progress of the project. Performance indicators with respect to human resources may include
employee retention, job satisfaction, compensation and rewards, employee training, accident
levels, employee absenteeism, and employee performance.
(2) Analyzing Performance – KPIs are compared to the strategic goals and objectives. The results
are utilized to monitor, further analyze and act to improve performance. The Advanced Analytics
subsystem enables organizations to make informed decisions to align their goals and objectives,
as well as programs and budgets to the performance indicators.
(3) Decision Making and Performance Feedback – The organizations are able to adjust their goals
and objectives, modify programs, and re-allocate resources and funds. Performance measures in
essence provide a feedback loop in the process of business performance management.
4. The Information Delivery sub-system gives the business users the ability to access reports and
continuously monitor organizational performance at enterprise and lower levels. According to his or
her role as a technocrat, super user, middle manager, executive manager, or operational user, he or she
will be given role-based rights to access relevant reports in summary and/or detailed formats. End
users are also able to monitor the key activities such as trends, metrics, and KPIs in easy-to-
understand designs, such as configurable information portals, scorecards and dashboards. Depending
on an individual's role and responsibility, he or she is presented with the trends, metrics, and KPIs at
appropriate aggregate levels with security to block non-privileged items.
BUSINESS INTELLIGENCE ADOPTION IN INDUSTRY AND HUMAN RESOURCES
BI is gaining rapidly in popularity, faster than most anticipated. In a report done in April 2010, it
was found that the BI platforms, analytic applications and performance management (PM) software
revenue surpassed $9.3 billion in 2009, a 4.2 percent increase from the 2008 revenue of $8.9 billion
(Gartner, April 2010).
Table 1: Worldwide BI, Analytics and Performance Management Revenue Estimates for 2009 by
Sub-segment (Millions of U.S. Dollars)
Sub-segment
Revenue
Estimate
Market Share
%
BI Platform 5,982.4 64.2
CPM Suites 1,937.1 20.8
Analytic Applications & Performance Management 1,402.4 15.0
Total 9,321.9 100.0
Source: Gartner (April 2010)
BI platforms were found to have a $5.98 billion (64.2%) market share of total worldwide BI
software revenue. Corporate performance management (CPM) suites were found to have a $1.94 billion
(20.8%) market share, and analytic applications and performance management software were found to
have a $1.40 billion (15%) market share. Details are given in Table 1.
The BI software market significantly outperformed the overall enterprise software market in 2009.
Seeing tremendous opportunities in Business Intelligence, there has been a large number of acquisitions
and mergers of BI companies by software giant vendors reported in the past few years. Major software
vendors have made business intelligence software their focal product to develop or acquire, and sell or
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26
service. The top four vendors (SAP, SAS Institute, Oracle, and IBM) makeup most of the BI market with
64% market share in 2009 – SAP (23.4%), SAS Institute (14.4%), Oracle (14.4%), and IBM (11.1%)
(Gartner, April 2010). The following table gives the major BI vendors, their recent BI acquisitions, and
price paid.
Table 2: Major Software Acquisitions in the recent past and Price Paid
Vendor Select BI Acquisitions Price Paid
SAP Sybase (2010)
Business Objects (2007)
OutlookSoft (2007)
Pilot Software (2007)
$5.8B
$6.8B
$200M
NA
SAS Institute Teragram (2008) NA
Oracle Sun Microsystems (2009)
Hyperion (2007)
Siebel Systems (2006)
Sigma Dynamics (2006)
PeopleSoft (2005)
7.4B
3.3B
5.85B
10.3B
IBM SPSS (2009)
Cognos (2007)
Telelogic AB (2007)
1.2B
5.0B
845M
Source: Gartner (April 2010), and vendors? web sites and press releases.
SAP paid 6.8 billion to acquire Business Objects in 2007, which was number one in business
intelligence at that time. The same year, Oracle bought Hyperion for 3.3 billion and IBM bought Cognos
for 5 billion. Business Objects, Hyperion and Cognos were leaders in business intelligence before they
were bought over by the software giants. IBM and Oracle acquired the most BI software and paid a hefty
price for that. Each software giant has benefitted tremendously because of acquiring these BI focused
companies. The software giant vendors now offer several BI products and are leaders in the area. Each
one of them also offers HR modules along with business intelligence and data analytics capabilities. The
following table gives HR module names, and names of the embedded BI and data analytics products.
Table 3: HR Modules and BI & Data Analytics Software for Giant Vendors
Vendor HR Module BI & Data Analytics
SAP SAP ERP Human Capital
Management
Workforce Analytics
SAS Institute SAS Human Capital
Intelligence
Human capital Predictive Analytics and
Retention Modeling
Oracle Oracle Human Capital
Management
Oracle Human Resources Analytics
IBM IBM Cognos – Human
Resources
Business Intelligence and Human Resource
Performance Management
Source: Vendors? web sites.
BUSINESS INTELLIGENCE AND DATA ANALYTICS FEATURES IN HUMAN RESOURCES
As stated in the previous section, the software joints offer many BI products, including HR
embedded with business intelligence and data analytics capabilities. The vendors claim the following
features and functions in their software:
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SAP ERP Workforce Analytics
This product includes features and functions that support these business activities
(http://www.sap.com/solutions/business-suite/erp/featuresfunctions/workforceanalysis/index.epx):
?
Workforce Planning
Understand current workforce trends – and plan future needs – by using workforce demographic data.
Use predefined reports to analyze headcount development, turnover rates, and workforce composition.
Link the results of this analysis directly into headcount planning, budgeting, and key talent processes,
such as recruiting and learning.
?
Workforce Cost Planning and Simulation
Support HR professionals in all workforce cost-planning tasks, and empower HR executives to
develop effective strategies. Provide access to a broad range of workforce-related data to support
accurate planning, facilitate simulated planning scenarios, and enable continuous monitoring of actual
performance relative to plan.
?
Workforce Benchmarking
Measure standard workforce processes. Compare the measurements with external benchmarks and
internal operating thresholds.
?
Workforce Process Analytics and Measurement
Measure and analyze typical core HR processes, such as payroll, employee administration, time
management, and benefits. Analyze organizational structures, relationships, and attributes of jobs and
positions.
?
Talent Management Analytics and Measurement
Analyze employee skills and qualifications. Evaluate the efficiency of your recruiting processes.
Measure the effectiveness of your learning programs. Assess how well your succession programs
prepare your employees to assume key positions – and ensure continuity of operations. Monitor the
progress of aligning employee goals with corporate goals. Analyze the cost-effectiveness of
employee compensation programs
?
Strategic Alignment
Ensure that all business activities are in line with the strategic goals of your organization. Help
Employee teams work toward common objectives, regardless of location. Use a balanced scorecard
framework, with predefined workforce scorecards that can be integrated into department and
individual management-by-objective (MBO) documents to align employee goals with corporate
strategy.
SAS Human Capital Predictive Analytics and Retention Modeling
This product includes the following features (http://www.sas.com/solutions/hci/hcretention):
? Predicted Turnover Percentage
Ranking by high, medium and low risk of voluntary termination
? Causes of Voluntary Termination
Showing each cause identified by the model in an individual report.
? Organization Exposure
Looking at the high-risk group only and showing a hierarchical view starting at the top and drilling
down through all organizational levels.
? High Risk by Job Category
Identifying those in the high-risk group by EEO job category
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28
? Top 50 Employees at High Risk
Pinpointing the 50 employees most likely to voluntarily leave
? Top Performer
Identify high-risk top performers and define their reasons for leaving.
Oracle Human Resources Analytics
This product includes the following features (http://www.oracle.com/us/solutions/ent-performance-
bi/hr-analytics-066536.html):
? Workforce Insight
Monitor workforce demographics in line with your recruitment and retention objectives. Analyze
efficiency of the entire recruitment process lifecycle, understand and prevent the drivers of employee
turnover.
? Targeted Workforce Development
Gain insight into the movement of top and bottom performers in the organization to engage and
develop internal talent. Gain insight into learning demand by analyzing course enrollments by job,
delivery methods, and organizations.
? Improved Compensation
Understand how compensation impacts performance, ensure compensation is equitable and consistent
across roles, and align variable compensation with your organization's objectives and goals.
? Leave and Absence
Get a comprehensive view into employees' current, planned, and historical absence events; monitor
absence trends as a predictor for employee engagement.
? Better Understanding of HR Performance
Assess HR's overall performance and employee productivity using industry benchmarks such as
revenue per employee, contribution per headcount, and return on human capital.
? US Statutory Compliance
Monitor US EEO, AAP, and Vets100 compliance reporting.
?
Workforce Planning
Monitor workforce demographics in line with your recruitment and retention objectives. Analyze
efficiency of the entire recruitment process lifecycle, understand and prevent the drivers of employee
turnover.
?
Workforce Cost Planning and Simulation
Support HR professionals in all workforce cost-planning tasks, and empower HR executives to
develop effective strategies. Provide access to a broad range of workforce-related data to support
accurate planning, facilitate simulated planning scenarios, and enable continuous monitoring of actual
performance relative to plan.
?
Workforce Benchmarking
Measure standard workforce processes. Compare the measurements with external benchmarks and
internal operating thresholds.
?
Workforce Process Analytics and Measurement
Measure and analyze typical core HR processes, such as payroll, employee administration, time
management, and benefits. Analyze organizational structures, relationships, and attributes of jobs and
positions.
The Journal of Human Resource and Adult Learning Vol. 6, Num. 2, December 2010 29
?
Talent Management Analytics and Measurement
Analyze employee skills and qualifications. Evaluate the efficiency of your recruiting processes.
Measure the effectiveness of your learning programs. Assess how well your succession programs
prepare your employees to assume key positions – and ensure continuity of operations. Monitor the
progress of aligning employee goals with corporate goals. Analyze the cost-effectiveness of
employee compensation programs
IBM Cognos Business Intelligence and Human Resource Performance Management
This product includes the following features pertaining to the following five HR core areas
(http://www-01.ibm.com/software/data/cognos/solutions/human-resources/index.html):
? Organization and Staffing
What job functions, positions, roles and capabilities are required to drive the business performance?
? Compensation
How should we reward our employees to retain and motivate them for full performance?
? Talent and Succession
What are the talent and succession gaps we must address to ensure sustained performance?
? Training and Development
What training and development do we need to maximize employee performance? Is there a clear
payback?
? Benefits
How do we manage costs and incentives for better performance?
SUMMARY AND CONCLUSION
Business Intelligence is helping businesses become more competitive. Because of technological
progress and regulatory changes, businesses are collecting and storing data at an alarming rate. Because
the business environment is constantly changing, decision making in organizations has become
increasingly intricate. Business Intelligence is helping organizations make faster and more reliable
information based business decisions.
Business Intelligence is seen as consisting of four major inter-dependent sub-systems – Data
Management, Advanced Analytics, Business Performance, and Information Delivery sub-system. The
Data Management subsystem handles data storage into databases, data warehouses, and data marts. With
On-line Analytical Processing (OLAP) built into the system, it is possible to do multi-dimensional
analysis on the data. The Advanced Analytics processing system includes analytic methods based on
statistics, data mining, forecasting, predictive modeling, predictive analytics, and optimization. The
Business Performance system consists of processes for performance measurement and mentoring and
making decisions to improve business performance. Key Performance Indicators (KPIs) take a prominent
role in this sub system. KPIs are defined to measure progress toward organizational goals. The
Information Delivery subsystem provides information to users in real time and in a format they want it.
End users are able to monitor the key activities in easy-to-understand formats, such as configurable
information portals, scorecards and dashboards.
Having seen Business Intelligence as the core enterprise strategy, the giant software vendors have
spent billions on acquiring other business intelligence focused companies. The giant vendors have now
business intelligence solutions for organizations of most types and sizes. While many organizations have
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30
purchased their BI software and are starting to use in many areas of their businesses and make substantial
gains, but they have not taken advantage of this in Human Resource Management area. Executives view
Human Resources more of a cost center, and less of a strategic asset within their organizations. In the last
section of the paper we have examined the leading vendors to look into the business intelligence and data
analytics features incorporated in their Human Resource Management software. By taking advantage of
the rich business intelligence features in these and other similar products, Human Resources can position
itself as essential value-adding department of the organization.
REFERENCES
Davenport, Thomas H. (2006 January). Competing on Analytics, Harvard Business Review, Prod. #: R0601H-PDF-ENG.
Friedman T. L. (2004). The World is Flat New York: Farrar, Straus and Giroux.
Gartner (2009 February). “Business Intelligence ranked Top Technology Priority by CIOs for Fourth Year in a Row”.
Gartner (2010 April). “Market Share: Business Intelligence, Analytics and Performance management Software Worldwide, 2009”.
International Business Machines (IBM) (2009 March). “How Smart HR departments win with Business Intelligence?”
International Data Corporations (IDC) (2010 May). “The Digital Universe Decade – Are you Ready?”
International Data Corporations (IDC) (2010 September). “Worldwide Business Analytics Software 2010-2014 Forecast and 2009
Vendor Share”.
Kim, D. A. (2002 March). Information Visualization and Visual Data Mining. IEEE Transactions on Visualization and Computer
Graphics, Vol. 8, Issue 1.
Shmueli, Galit, Nitin R. Patel, and Peter C. Bruce (2010). Data Mining for Business Intelligence, Wiley Publication.
Tan, Pang-Ning, Michael Steinbach, and Vipin Kumar (2006). Introduction to Data Mining, Pearson Publication.
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