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ITM BUSINESS SCHOOL
Capstone Project PGDM-2014-16
Final Project Report
'Impact of Business Analytics And Business Intelligence'
Submitted by: Faculty Guide:
Gargi choudhury Prof. Kalpana kumaran
Roll No- 119 HOD IT Department
PGDM-IT (14-16)
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Certificate from Guide
This is to certify that the Project Work titled “Business Analytics” is a bonafide work carried out by Gargi Choudhury, a student of PGDM program 2014 – 2016 of the Institute for Technology & Management, Kharghar, Navi Mumbai under my guidance and direction.
Date: Signature of Guide: Place:
Prof. Kalpana Kumaran (HOD IT,
ITM Business School
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Contents
ABSTRACT .................................................................................................................................................. 5
ACKNOWLEDGMENT ............................................................................................................................... 6
Chapter 1. Introduction ................................................................................................................................. 7
1.1 Problem On Hand ......................................................................................................................... 7
1.2 Importance Of The Problem ............................................................................................................... 7
1.3 Scope Of The Project .......................................................................................................................... 8
Chapter 2 Literature Review ......................................................................................................................... 9
2.1 Presentation Of Material Collected Through Review Of Relevant Literature Quoting The Sources
Of Each Material ....................................................................................................................................... 9
2.2 Identification Of The Gap Or Some Areas Where No Substantial Work Has Been Done. .............. 10
Chapter 3 Research Methodology ............................................................................................................... 12
3.1 Method Of Data Collection ............................................................................................................... 12
3.2 Sample Size ....................................................................................................................................... 12
3.3 Data Analysis Techniques - Choice Of Techniques Brief Description Of The Choice Of The
Techniques Utilized And The Justification For Their Use. .................................................................... 12
Chapter 4. Data Collection, Analysis & Interpretation ............................................................................... 13
Chapter 5 Recommendations & Conclusion ............................................................................................... 20
5.1 Brief Description Of Recommendations & Overall Benefits Of The Project ................................... 20
5.2 Learning From The Project ............................................................................................................... 20
5.3 Limitations ........................................................................................................................................ 21
REFERENCES ........................................................................................................................................... 22
APPENDIX ................................................................................................................................................. 23
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List of Figures
Figure 1 ....................................................................................................................................................... 13
Figure 2 ....................................................................................................................................................... 13
Figure 3 ....................................................................................................................................................... 14
Figure 4 ....................................................................................................................................................... 14
Figure 5 ....................................................................................................................................................... 14
Figure 6 ....................................................................................................................................................... 15
Figure 7 ....................................................................................................................................................... 15
Figure 8 ....................................................................................................................................................... 16
Figure 9 ....................................................................................................................................................... 16
Figure 10 ..................................................................................................................................................... 17
Figure 11 ..................................................................................................................................................... 17
Figure 12 ..................................................................................................................................................... 18
Figure 13 ..................................................................................................................................................... 18
Figure 14 ..................................................................................................................................................... 19
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ABSTRACT
For companies maintaining direct contact with large numbers of customers, hoIver, a
growing number channel-oriented applications (e.g. e-commerce support, call center support)
create a new data management challenge: that is effective way of integrating enterprise
applications in real time.
To learn from the past and forecast the future, many companies are adopting Business
Intelligence (BI) tools and systems. Companies have understood the importance of enforcing
achievements of the goals defined by their business strategies through business intelligence
concepts. It describes the insights on the role and requirement of real time BI by examining the
business needs. The paper explores the concepts of BI, its components, emergence of BI, benefits
of BI, factors influencing BI, technology requirements, designing and implementing business
intelligence, and various BI techniques.
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.
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ACKNOWLEDGMENT A capstone project is a golden opportunity for learning and self development. I consider myself
very lucky and honored to have so many wonderful people lead me through for this project. I express my deepest thanks to Prof. Kalpana Kumaran for her guidance and support. She
supported to me by showing different method of information collection for the project. She
helped all time when I needed and She gave right direction toward completion of project.
Gargi Choudhury PGDM-2014-16
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Chapter 1. Introduction
1.1 Problem On Hand
Business analytics has the potential to deliver performance gains and competitive advantage. HoIver, a theoretically grounded model identifying the factors and processes involved in realizing those performance gains has not been clearly articulated in the literature. This paper draws on the literature on dynamic capabilities to develop such a theoretical framework. It identifies the critical roles of organizational routines and organization-wide capabilities for identifying, resourcing and implementing business analytics-based competitive actions in delivering performance gains and competitive advantage. A theoretical framework and propositions for future research are developed.
Healthcare organizations can use business intelligence (BI) technologies to leverage the data and improve operational and clinical efficiency. Approaches to understanding BI readiness are needed for organizations to develop an overall BI strategy. While there are a number of BI maturity models, they are often generic and do not meet the industry specific requirements. This research proposes a framework for developing a domain specific BI maturity model. The research further demonstrates the efficacy of the framework by applying it to the development of a BI maturity model in healthcare. The results indicate that the framework is able to address the needs of a domain specific BI maturity model, and guide the development of such model that proved acceptable to expert practitioners in the field. Business intelligence system is a set of software solutions, among which can be singled out three subsets: queries and reports, decision support systems and executive information systems. Research and analysis of huge amounts of data using appropriate techniques and methods can in organization diagnose essential processes, identify and anticipate the direction of change, interpret financial results, classify and cluster data to model the behavior of the system, aggregate data, detect changes and deviations to the objectives, determine the correlation betIen variables, generate association rules. This paper presents a business intelligence system for the analysis of sales in retail as an institutional form of an exchange process. Shown are all elements of the system: transaction data stored in tables of relational database, their extraction,
transformation and loading into data warehouse and the use of appropriate data mining methods for the analysis. Planning of marketing activities uses the results of the analysis with the aim of increasing the sale and profitability.
1.2 Importance Of The Problem
Enterprises have acquired Business Intelligence (BI) systems to improve business decisions and
support the implementation of their strategies. Quantitative assessments show that many
Business Intelligence projects fail at an alarming rate.
Extrapolated costs and delivery delays are attributed in large part to requirement problems, such
as the difficulty of the customer to know what he/she wants, failures of communication betIen
the development team and the customer, the development team's lack of knowledge of the
customer's business, different vocabularies betIen the customer and the technical team, the
development team's lack of the necessary social skills to extract and understand the strategy and
customer needs, among others. Given this reality, it is observed that the knowledge gained from
the customer is critical for success in Business Intelligence projects.
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The customer knowledge is considered by the intellectual capital theory as an intangible asset,
because it results from interactions. Furthermore, enterprises are increasingly worried about the
satisfaction of their customers and looking for ways to guarantee their loyalty.
One of the objectives of this paper is to present a literature review about requirements
management practices in existing business intelligence methodologies and about the main theory
in intellectual capital, especially in customer capital dimension.
Moreover, I believe that this review may help to understand the possible influence of the
customer capital management to make explicit the customers' knowledge in Business
Intelligence projects. As a result of the ongoing research I intend to examine the intangible
assets of an information system development initiative and mechanisms proposed by the
customer capital to evolve the interactions of an enterprise with the customer and improve the
requirements definition of a Business Intelligence project.
1.3 Scope Of The Project
Through this research, we can see that IT and business professionals mainly align business
analytics with BI products. In fact, more than half of respondents cited BI as the category of
products that first comes to mind when they think of the term “business analytics.” Business
analytics may be the next logical step in the evolution of BI.
The top software tools that respondents consider part of business analytics spanned across
various areas, including analytics, data integration, query/reporting and performance
management. More specifically, respondents consider advanced analytics tools, such as data
mining or statistical software to be part of business analytics, followed by
query/reporting/analysis tools and dashboards.
Business analytics is broad enough to include capabilities and solutions that benefit a variety of
disciplines. Since business analytics is designed to be used by all decision-makers, it is not
surprising that almost three-quarters of respondents surveyed view business analytics as a
function of both IT and business.
With business analytics being a function of both IT and business, there is an increased need for
collaboration across organizations, as well as the need for supervision by cross-departmental
management teams. However, respondents cited a number of key benefits their organization
derived or expects to derive from using business analytics software, which encompassed various
areas of business analytics.
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Chapter 2 Literature Review
2.1 Presentation Of Material Collected Through Review Of Relevant Literature Quoting The
Sources Of Each Material
By conducting a literature review according to the Ill-established methodology I folloId Descriptive study technique. I selected highly ranked and/or domain specific journals and leading conferences like,
International Journal of Information Management. Jun2015, Vol. 35 Issue 3, p337-
345. 9p.
Journal of Advances in Information Technology. Nov2015, Vol. 6 Issue 4, p207-211. 5p.
Information Systems. Oct2015, Vol. 53, p87-106. 20p.
BI specific journals include a manageable amount of issues and articles that enables a complete scan of titles and abstracts as suggested by Ibster and R. T. Watson (2002), I had to preselect conference papers by tracks related to BI . For ACM and IEEE journals, I conducted a keyword search on the whole digital library as no journals focus in particular on the Business Analytics domain.I scanned for the hits (resulting from keyword searches) titles, abstracts, and keywords to assess the suitability of an article. Since could identify only few articles by this method, I subsequently conducted a keyword search on literature databases (EBSCOhost, Scholar, ProQuest und ScienceDirect) by using the aforementioned search terms. I completed the literature pool via a backward search.
List of Source:
A framework for developing a domain specific business intelligence maturity model: Application to healthcare.
Brooks, Patti1 [email protected],El-Gayar, Omar
1,Sarnikar, Surendra
1International
Journal of Information Management. Jun2015, Vol. 35 Issue 3, p337-345. 9p.
A Framework for Information Accuracy (IA) Assurance Practices in Tourism Business
(TB).Pertheban, Sivakumar1 [email protected] Mahrin, Mohd Naz'ri
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Shanmugam, Bharanidharan1 [email protected]
Journal of Advances in Information Technology. Nov2015, Vol. 6 Issue 4, p207-211. 5p.
Advanced topic modeling for social business intelligence.
Gallinucci, Enrico1 [email protected]
Golfarelli, Matteo1 [email protected]
Rizzi, Stefano1 [email protected]
Information Systems. Oct2015, Vol. 53, p87-106. 20p.
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Agile Business Intelligence: Collection and Classification of Agile Business Intelligence Actions by Means of a Catalog and a Selection Guide.
BizPro: Extracting and categorizing business intelligence factors from textual news
articles. Chung, Wingyan1 [email protected]
International Journal of Information Management. Apr2014, Vol. 34 Issue 2, p272-284. 13p.
Bringing Business Intelligence to Health Information Technology Curriculum.
Guangzhi Zheng1 [email protected]
Chi Zha
ng1
Lei
Li1
Journal of Information Systems Education. Late Fall2014, Vol. 25 Issue 4, p317-325. 9p. 7 Charts.
Business Intelligence Acceptance: The Prominence of Organizational Factors.
Grublješič,
Tanja1
Jaklič,
Jurij1,2
Information Systems Management. 2015, Vol. 32 Issue 4, p299-315. 17p. 1 Diagram, 4 Charts.
BUSINESS INTELLIGENCE AND ANALYSIS OF SELLING IN RETAIL. POSLOVNA INTELIGENCIJA I ANALIZA PRODAJE U MALOPRODAJI.
Bijakšić, Sanja1
Markić, Brano1
Bevanda, Arnela1Informatologia. Dec2014, Vol. 47 Issue 4, p222-231. 10p.
Business Intelligence Competency Center: Improving Data and Decisions.
Foster, Kyle1
Smith, Gregory2
Ariyachandra, Thilini2
Frolick, Mark N.2
Information Systems Management. 2015, Vol. 32 Issue 3, p229-233. 5p. 1 Chart.
2.2 Identification Of The Gap Or Some Areas Where No Substantial Work Has Been Done.
The literature review resulted in 76 adequate articles for BA. Not surprisingly, due to the rather young research topic the majority has been published since 2010 . Also, most articles appeared in conference proceedings and domain specific journals, only a very few in the more generic journals of the AIS Senior Scholars’ basket. The same is true for other domain independent IS journals – many contributions on social media in general are published, hoIver little papers can be assigned to Enterprise BA. I consider the wider range of topics in those journals, the stronger focus on theory, and longer publication processes as reasons for the underrepresentation within
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our literature pool. Overall, I identified less articles than expected that address explicitly social BI. The majority focuses on aspects which can be summarized by the concept of “social media analytics”, i. e. applying analysis techniques to social media data (e. g. Ebermann, Stanoevska-Slabeva, and Wozniak 2011; Gray, Parise, and Iyer 2011; Heidemann, Klier, and Probst 2010; Lin and Goh 2011; Xu, Li, et al. 2011, as I can only mention some examples here). Most authors describe a setting without a BI system (and thus they do not fit into our understanding of social BI) and investigate certain techniques, such as text mining or sentiment analysis. Examples can be found in Sommer et al. (2011) or Xu, Liao, et al. (2009). \ Thereby, solutions for CRM scenarios seem to be dominant, such as user profiling (Tang, Wang, and Liu 2011), opinion mining (Venkatesh et al. 2003), or social recommendations (Arazy, Kumar, and Shapira 2010). Some contributions analyze the impact of social media on decision support systems and processes (Heidemann, Klier, and Probst 2010; PoIr and Phillips-Wren 2011). Papers, dedicated to social BI, present an overview or a framework (e. g. Böhringer et al. 2010; Hiltbrand 2010; Zeng et al. 2010) or discuss the application areas in general (e. g. Bartoo 2012; Bonchi et al. 2011) or social CRM in particular (e. g. 5 Greenberg 2010; Reinhold and Alt 2011; Seebach, Pahlke, and Beck 2011; Stodder 2012). Others deal with specific aspects like a methodology for BI process improvements considering social networks information (Wasmann and Spruit 2012), data modeling aspects (e. g. Nebot and Berlanga 2010; Rosemann et al. 2012) or technical architecture. As examples for the latter aspect, Reinhold and Alt (2011) suggest a framework of an integrated social CRM system and Rui and A. Whinston (2011) propose a framework for a BI system based on real-time information extracted from social broadcasting streams. Repeatedly, journal editors and authors who discuss perspectives and trends in BI research highlight the potential, importance, and need of social BI research and practical solutions (H. Chen 2010; Laplante 2008; Mao, Tuzhilin, and Gratch 2011; Zeng et al. 2010; Zhang, Guo, and Yu 2011, e. g.).
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Chapter 3 Research Methodology
3.1 Method Of Data Collection
The Descriptive Studies is used in this project due to the fact that the descriptive studies attempt to obtain a complete and accurate description of a situation, that is it covers the all phases required and provides the ways to collect the data from various sources of information (sample design), ensure minimum bias in the collection of data, hold costs to a minimum, and reduces the errors in interpreting the data collected. Data for the project is collected through the means of questionnaire. The respondents for the sample will be employees of enterprise.
3.2 Sample Size
The sample size for the project is 50. These 50 respondents will be selected on random basis without any categorization.
3.3 Data Analysis Techniques - Choice Of Techniques Brief Description Of The
Choice Of The Techniques Utilized And The Justification For Their Use.
This paper focuses on examining the impact of Business Intelligence and business analytics on
the Enterprise. This project begins with the review of existing literature available, which
provides an insight into the research topic and clarifies many important aspects related to the
subject. A quantitative method is used for this research project to investigate the technology and
its subsequent impact on productivity of an Enterprise. The data was collected through a
questionnaire and later analyzed using the data analysis through MS Excel 2010. And thus the
result quantify the impact of BI and BA on Enterprise.
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Chapter 4. Data Collection, Analysis & Interpretation
1. Respondent’s representation of the part of organization.
Figure 1
2. Respondents gender category.
Figure 2
3. Employees involved in BI and BA.
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Figure 3
4. How long are BI and BA tools used in the organization.
Figure 4
5. Tools used often.
Figure 5
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Figure 6
Figure 7
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Figure 8
Figure 9
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Figure 10
6. Tools used to represent reports.
Figure 11
7. Awareness of competitor’s actions.
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Figure 12
8. Rating the analytical capabilities of the organization.
Figure 13
9. BI tool and the satisfaction level.
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Figure 14
10. Part of organization and BI tool.
Figure 15
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Chapter 5 Recommendations & Conclusion
5.1 Brief Description Of Recommendations & Overall Benefits Of The Project
The majority of respondents referred to BI when thinking about business analytics, with PM
also in the back of their minds.
Business analytics may be the next logical step in the evolution of BI, with business
analytics being more comprehensive, providing a software taxonomy that incorporates other
disciplines such as PM and predictive analytics.
However, the true indication of evolution may be that business analytics requires us to think
beyond the confines of technology.
Successful organizations that invest in business analytics software will also take into account
the culture, processes and performance strategies.
5.2 Learning From The Project
Business analytics is broad enough to include capabilities and solutions that benefit a variety
of disciplines.
Business analytics is not just primarily an IT or business function, but is a function of both
IT and business.
With this approach, there is an increased need for collaboration across organizations on
issues relating to business analytics, as well as the need for cross departmental management
teams for oversight.
The top software tools that respondents consider part of business analytics span across
various areas, including analytics, data integration, query/reporting and PM.
Given that business analytics is designed to enable fact-based decision-making by all
decision-makers, it is not surprising that nearly three-quarters of respondents viewed
business analytics as a function of both IT and business.
Respondents said the key benefits currently derived or expect to be derived from using
business analytics software encompass various areas of business analytics, with the top two
benefits related to improving and speeding up the decision-making process.
Other key benefits included:
aligning resources with strategies
realizing cost efficiencies
responding to user needs for availability of data on a timely basis
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improving the organization’s competitiveness
producing a single, unified view of enterprisewide information
synchronizing financial and operational strategies
increasing revenues
5.3 Limitations
Given the current state of the worldwide economy, it is not surprising to see realizing cost
efficiencies, improving the organization’s competitiveness and increasing revenues as key
benefits.
Within every organization, there are always obstacles to realizing those benefits.
Respondents named top challenges as data integration with multiple source systems, data
quality and integration with other enterprise applications.
Data integration components provide organizations with enterprise data access and
processing across systems and platforms, as well as integrated data quality, which is critical
to providing accurate and consistent information.
Investment in business analytics would provide organizations with the right information at
the right time in order to empower fact-based decisions at every level of the enterprise, to
achieve key objectives and to gain maximum return from information assets.
Business analytics is generally both historical and predictive, resulting in the need to
embrace a shift to a more proactive, fact-based decision-making environment.
With business analytics, decision-makers should constantly ask, “What is the best that can
happen?” With the importance of the improvement of the decision-making process,
organizations should turn to a provider that can offer a range of techniques and processes for
the collection, classification, analysis and interpretation of data to reveal patterns, anomalies,
key variables and relationships, leading ultimately to new insights and better answers faster.
That provider should also bring the strategic advice and services required to address the
cultural, process and performance issues inherent in business analytics.
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REFERENCES
1. http://search.ebscohost.com/login.aspx?direct=true&db=lxh&AN=101985650&site=ehost-
live
2. http://search.ebscohost.com/login.aspx?direct=true&db=lxh&AN=109014308&site=ehos t-
live
3. http://search.ebscohost.com/login.aspx?direct=true&db=lxh&AN=99225161&site=ehost- live
4. http://search.ebscohost.com/login.aspx?direct=true&db=lxh&AN=96854753&site=ehost-
live
5. http://search.ebscohost.com/login.aspx?direct=true&db=lxh&AN=100647232&site=ehos t-
live
6. http://search.ebscohost.com/login.aspx?direct=true&db=lxh&AN=91761947&site=ehost-
live
7. http://search.ebscohost.com/login.aspx?direct=true&db=lxh&AN=100304918&site=ehos t-
live
8. http://search.ebscohost.com/login.aspx?direct=true&db=lxh&AN=92717627&site=ehost-
live
9. http://www.toddwestmedia.com/594/the-importance-of-business-intelligence-in-your-
organization.html
10. https://en.wikipedia.org/wiki/Business_intelligence
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APPENDIX
Business Analytic And Business Intelligence In Enterprise
*Required
1. What part of your company are you representing in this survey? *
o Whole enterprise
o Marketing Department
o IT Department
o Finance Department
o Operations
o Other:
2. Approximately how many staff in your company are dedicated to analytics, modeling,
data mining? *
o 50 or fewer
o 51-100
o 101-250
o 251-500
o 501-1000
o 1001-2000
o More than 2000
o Other:
3. What business functions in your company are the most important users of data and
analytics? (check all that apply) *
o eCommerce, eBusiness, Digital Operations
o Direct and Digital Marketing
o Fraud Management
o Customer and Market Analysis
o Customer Service
o Product Development/Management
o Information Technology
o Operations
o Risk Management
o Human Resources
o Other:
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4. For what types of analyses do you want to use BI? (check all that apply) *
o Real time analytics and alerts
o Ability to analyze text
o Ability to analyze relationships
o Ability to analyze very large data sets
o Ability to analyze disparate data sets
o Ability to analyze external data sets
o Ability to evaluate new analytic algorithms
o Other:
5. How long have you been using data warehouse reporting tools *
o 1 year
o 2 year
o 3 year
o 4 year
o more than 5 year
6. Do you prepare or use reports that require tables and charts whose data comes from a
data warehouse or similar complex data source? *
o Yes
o No
7. How often do you use below mention tools for analytics *
Daily Once a week Twice a week Once a month
Plain
Reports
DashBoard
ETL
SQL
OLAP
Data
Mining
and
Predictive
Tools
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8. How much satisfied are you with performance, you use for retrieving data for analysis? *
o Very Satisfied
o Somewhat Satisfied
o Somewhat Disatisfied
o Not Disatisfied
9. What tool do you use to present your reports *
o MS Excel add-in
o Export to Microsoft Office(Excel,Word,Powerpoint)
o Web Delivery
o Use the analysis software package
o Other:
10. What is the most time consuming factor when creating your BI documents? *
o Importing Data from external sources
o Data query performance
o Waiting for information from other people
o Other
11. Do you know your competitors and do you have up-to-date profiles of them?
o Yes
o No
12. Do you analyze your competitors' actions and plans? *
o Yes
o No
13. Are your employees aware of the benefits of business intelligence and market
knowledge, and do they regularly report information relating to emerging technologies and
competitors to management? *
o Yes
o No
14. Do you train your staff to continually gather and report information from your
customers relating to their problems, product and service needs, and industry trends? *
o Yes
o No
15. Do you generate reports with data that is retrieved from a data warehouse and then
distribute the report to colleagues and managers? *
o Yes
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o No
16. Do you use summary reports and graphs generated by team members for making
decisions? *
o Yes
o No
17. How would you rate the analytic capabilities in your organization today? *
o World Class
o Adequate
o More than adequate
o less than adequate
o minimal
Name *
Gender *
.
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