Business Intelligence:

Transcription

1 Business Intelligence: An Information Technology Course on Transforming Data into Information to support Business Users and their Decisions by Geoffrey Richard Anderson A Capstone Project Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science in Information Sciences and Technology Approved by: Supervised by Dianne P. Bills and Jai W. Kang Department of Information Sciences and Technology Golisano College of Computing and Information Sciences Rochester Institute of Technology Rochester, New York March 2011 Dianne P. Bills, Graduate Coordinator, Associate Professor Co-Chair, Department of Information Sciences and Technology Jai W. Kang, Associate Professor Co-Chair, Department of Information Sciences and Technology Steven J. Zilora, Associate Professor Committee Member, Department of Information Sciences and Technology

2 Capstone Release Permission Form Rochester Institute of Technology Golisano College of Computing and Information Sciences Title: Business Intelligence: An Information Technology Course on Transforming Data into Information to support Business Users and their Decisions I, Geoffrey Richard Anderson, hereby grant permission to the Wallace Memorial Library to reproduce my capstone in whole or part. Geoffrey Richard Anderson Date

3 iii Copyright c 2011 by Geoffrey Richard Anderson. This work is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License. To view a copy of this license, visit or send a letter to: Creative Commons 171 Second Street, Suite 300 San Francisco, California, 94105, USA

4 iv Abstract Business Intelligence: An Information Technology Course on Transforming Data into Information to support Business Users and their Decisions Geoffrey Richard Anderson Supervising Professors: Dianne P. Bills and Jai W. Kang This should be a short description of the work and the results. Just the facts...

5 v Contents Abstract Contents Glossary iv v vi 1 Introduction Significance Literature Review Proposed Solution and Deliverables Methodology Anticipated Project Schedule Conclusions References

6 vi Glossary D Decision Support System (DSS) Decision Support System - an interactive, computer-based system intended to provide support to the decision makers engaged in solving various semi- to ill-structured problems involving multiple attributes, objectives and goals [Nemati et al., 2002, p. 144], p. 1. E Explicit Knowledge Explicit Knowledge - knowledge that can be expressed formally using a system of language, symbols, rules, objects, or equations, and can thus be communicated to others; it consists of quantifiable data, written procedures, universal principles, mathematical models, etc. [Nemati et al., 2002, p. 145]., p. 3. K Knowledge Management (KM) Knowledge Management - the practice of adding actionable value to information by capturing tacit knowledge and converting it to explicit knowledge; by filtering, storing, retrieving and disseminating explicit knowledge; and by creating and testing new knowledge [Nemati et al., 2002, p. 145], p. 3. R Rochester Institute of Technology (RIT) Rochester Institute of Technology. T Tacit Knowledge Tacit Knowledge - includes the beliefs, perspectives, and mental models so ingrained in a person s mind that they are

7 taken for granted; it consists of subjective expertise, insights and intuitions that a person develops from having been immersed in an activity or profession for an extended period of time [Nemati et al., 2002, p. 145]. vii

8 1 1 Introduction The Information Sciences and Technology (IST) Department at the Rochester Institute of Technology has various coursework that covers the theory, implementation, and foundational concepts in data warehousing and data mining. The IST department could further benefit by having additional coursework that would extend that foundation to include higher level theories, concepts, and implementation details with respect to the field of business intelligence (BI) and Decision Support Systems. I propose the creation of a new graduate level course focused on defining business intelligence from the perspective of the information technologist in which students will learn how to define the role of BI in a company, implement a BI system with supporting best practices, and understand concepts that are necessary to reshape data into information and later, information into knowledge. The subsequent step, converting knowledge into decisions, would be beyond the scope of this course.

9 2 2 Significance When a company or organization integrates data from disparate systems (e.g. implementing a data warehouse system), it becomes one step closer to discovering meaningful information for decision making [Gangadharan and Swami, 2004, p. 140]. The next crucial and logical step is setting up a BI system because it supports the addition of context to existing data which can create valuable information to aid in future business decisions and predictions. For information technologists, it is important to have experience with the multitude of applications available for BI and more importantly, the concepts surrounding the field and associated technologies. The discipline of business intelligence also has strong relationships to other fields covered in existing coursework, such as data warehousing and information assurance (IA). A BI system will primarily interface with a data warehouse in an effort to convert the collected data into reports and other analytical formats that can provide meaningful information. When it comes to information assurance, all the important concepts, availability, confidentiality, integrity, authentication, and non-repudiation, should be addressed in a chosen BI solution to ensure that the correct information is delivered through a secure medium to the right people at the right time and place [Nemati et al., 2002, Takai, 2007]. Each BI solution will handle the IA concepts differently, but it is important for an information technologist to ensure that each concept is addressed by the chosen BI solution (or by additions to the chosen solution).

10 3 3 Literature Review Business intelligence has become more prevalent over the past few years and as such, more tools and literature surrounding the field have emerged. IBM s International Technical Support Organization drafted a Business Intelligence Certification Guide in January, 2000 [Reinschmidt and Francoise, 2000]. While this document may be a bit dated, the concepts and discussion areas it covers are still very relevant and useful in developing coursework. Likewise, Gangadharan and Swami s 2004 paper on Business intelligence systems: design and implementation strategies does a very good job fleshing out some additional concepts in BI by first looking at the design aspect of a BI solution and then later discussing implementation details for a specific solution [Gangadharan and Swami, 2004]. The document also poses meaningful questions that should be addressed before implementing a BI solution and mentions that business intelligence needs are not only restricted to multinational corporations with huge investments and human resources...[but] small and medium enterprises (SMEs) [also] have intelligence needs and should consider seeking out relevant information [Gangadharan and Swami, 2004, p ]. Nemati et al. s paper, Knowledge warehouse: an architectural integration of knowledge management, decision support, artificial intelligence and data warehousing, discusses the role of a Decision Support System in an organization and how to better leverage a data warehouse implementation to include information not inherently stored in data [Nemati et al., 2002]. While some of the material in this paper is out of scope for this capstone project, there are some concepts that mention the relationship between people and data in an organization (such as the difference between Explicit Knowledge and implicit knowledge), which is valuable for business intelligence. Similarly, Herschel and Jones s paper, Knowledge management and business intelligence: the importance of integration [Herschel and Jones, 2005], further enforces the concepts covered in Nemati et al. s paper with respect to the role a Decision Support System plays in Knowledge Management and business intelligence.

11 4 4 Proposed Solution and Deliverables A foundation for a course focusing on the concrete definition of business intelligence, as it relates to information technologists, is the deliverable for this capstone project. This foundation will include: a syllabus to thoroughly document learning objectives and topics to be covered reference material to be used throughout course content an evaluation of technologies to be employed in achieving the learning outcomes a data warehouse design and implementation for exercises and student projects some exercises to demonstrate practical application of BI theory This foundation is be meant to pave the way for course content that would briefly review concepts from data warehousing, data mining, and information assurance as they relate to BI. Additionally, this foundation covers the theory and concepts of business intelligence and how a BI system would be established atop an existing OLAP system. These fundamental theories and concepts of BI will help future information technologists to understand the process of evaluating a company s BI needs, interpreting data through different contexts in an OLAP system, and converting data into information for end users. Lastly, the requirements for a large, course-long project will be developed in which students will implement a BI system of their choosing to sit atop an existing data warehouse. The course project will include a business case for one of two theoretical companies; one company with a large budget and one smaller company with a tighter budget. Each of these companies will have their freedoms and constraints (as seen in Table 4.1) that students will need to operate under in order to deliver their BI solution. Students will be required to choose one of the theoretical companies with which they can then apply what they have

12 5 learned to determine the best BI solution through exploration of existing tools, methods, and their own understanding of BI concepts. Table 4.1: Course Project Business Choices Big company Small company Large budget and therefore, greater tighter budget which will result in freedom in BI product choices a more limited choice of products More restrictive security practices a lot more security clearances and approvals needed to use different domains of data May be restricted to integrating BI solution with very specific preexisting hardware and software platforms Most, if not all, of the data will be found internally and may need to be cleaned before use (probably open source tools) Less restrictions on security practices. More freedom in access to data across different domains Greater flexibility in re-working existing infrastructure to make integration of BI solution easier Some data will be found internally, while other data may come from outside sources and/or may need to be purchased

13 6 5 Methodology To construct the needed content for the proposed deliverables, information will need to be extracted from existing research and reference material relating to the business intelligence field and then organized into a document to outline key topics, sub-topics, and supporting activities to direct students towards the identified learning objectives. All the material to be created can be built in a text editor using an extensible markup language (such as LaTex) that can be exported to multiple formats (PDF, HTML, etc.).

14 7 6 Anticipated Project Schedule This capstone project will be largely dependent on information discovered from extensive research in which key concepts are identified, defined, and recorded. As such, it will probably take about three to four weeks to complete compiling enough research to begin drafting the initial layout of concepts for the course. Development of supporting lecture material and exercises will easily take double the time of the research since concepts will need to be thoroughly fleshed out and crafted to enhance student learning and understanding of the topics. Along with the development of lecture material and exercises, requirements for a course-long project, which includes one or more business cases, can be drafted and critiqued in order to provide the most complete exploratory learning experience into the tools, applications, and services available for business intelligence.

15 8 7 Conclusions...

16 9 8 References Michael A. Eierman, Fred Niederman, and Carl Adams. Dss theory: A model of constructs and relationships. Decision Support Systems, 14(1):1 26, ISSN doi: DOI: / (94)00012-H. URL com/science/article/b6v8s-3y45v6w-s/2/ b89e36946cc d41. G.R. Gangadharan and S.N. Swami. Business intelligence systems: design and implementation strategies. In Information Technology Interfaces, th International Conference on, pages Vol.1, june doi: /ITI Jody Gillette and Steve Zilora. User dashboard for business key success factors, Execute Course Summary. Richard T. Herschel and Nory E. Jones. Knowledge management and business intelligence: the importance of integration. Journal of Knowledge Management, 9(4):45 55, ISSN doi: DOI: / URL http: // Jaspersoft. Scalable bi for every enterprise how open source bi meets enterprise-class requirements. Technical report, Jaspersoft, B. Liautaud and M. Hammond. e-business intelligence: turning information into knowledge into profit. McGraw-Hill, Inc., New York, NY, USA, ISBN

17 10 Hamid R. Nemati, David M. Steiger, Lakshmi S. Iyer, and Richard T. Herschel. Knowledge warehouse: an architectural integration of knowledge management, decision support, artificial intelligence and data warehousing. Decision Support Systems, 33(2): , ISSN doi: DOI: / S (01) URL com/science/article/b6v8s-44x042p-2/2/ f77ea13e ff9e2d33a32a372d. J. Reinschmidt and A. Francoise. Business intelligence certification guide. IBM International Technical Support Organisation, R. Sabherwal and I. Becerra-Fernandez. Business Intelligence. John Wiley & Sons, ISBN URL google.com/books?id=t-jvpdecm0oc. Teresa M. Takai. Information assurance implementation. Technical Report E, Department of Defense, April URL dtic.mil/whs/directives/corres/pdf/850001p.pdf. Turban, Efraim, Sharda, Ramesh, Delen, and Dursun. Decision Support and Business Intelligence Systems. Prentice Hall Press, Upper Saddle River, NJ, USA, 9th edition, ISBN X,