Chapter 6

1. Chapter 6 THE ESSENTIALS OF BUSINESS INTELLIGENCE

2. Origins and Driversof Business Intelligence • Organizations are being compelled to capture, understand, and harness their data to support decision making in order to improve business operations • Business cycle times are now extremely compressed; faster, more informed, and better decision making is therefore a competitive imperative • Managers need the right information at the right time and in the right place • Table shows the business value of BI analytical applications

3. Origins and Driversof Business Intelligence

4. General Process of Intelligence Creation and Use • To be successful in today’s business environment, enterprises must: • Assess their readiness for meeting the challenges posed by these new business realities • Take a holistic approach to BI functionality • Leverage best practices and anticipate hidden costs • Key questions as a framework for BI analysis: • How can enterprises maximize their BI investments? • What BI functionality do enterprises need, and what are they using today? • What are some of the hidden costs associated with BI initiatives?

5. General Process of Intelligence Creation and Use • 全称萨班斯·奥克斯利法案（Sarbanes－Oxley Act），起源于美国安然公司倒闭后引起的美国股市剧烈动荡，投资人纷纷抽逃资金。为防止和保证上市公司财务丑闻不再发生，由美国参议员Sarbanes和美国众议员Oxley联合提出了一项法案，该法案即以他们的名字命名。 • BI implemented by one firm runs a performance management System. • Perform flawless analysis and compilation of thousands of transaction and journal entries. • Balance more access to data with the need to control access to sensitive insider information. • Deliver reports to the SEC in less time. • Get “more eyes on the data” and key performance indicators and build in strict security controls. • Provide live reports that allow people to drill down to the lowest level of transaction detail. • And so on.

6. General Process of Intelligence Creation and Use

7. General Process of Intelligence Creation and Use • Intelligence creation and use and BI governance • BI governance: Project prioritization process • When a BI/DW is in place, the general process of intelligence creation starts by identifying and prioritizing specific BI project. For each potential BI project in portfolio, it is important to use return on investment and total cost ownership (TCO) measures estimate the cost-benefit ratio. • Benefit analysis involves the end-user examinations of decision-making impact. • Who serve as the decision maker involve the Project Prioritization process. • Issues for the BI governance team is to address the following: • Creating categories of projects • Defining criteria for project selection • Determining and setting a framework for managing project risk • Managing and leveraging project interdependencies • Continually monitoring and adjusting the composition of the portfolio

8. Major Characteristics of Business Intelligence • Business intelligence is NOT transaction processing • Online analytical processing (OLAP) An information system that enables the user, while at a PC, to query the system, conduct an analysis, and so on. The result is generated in seconds • Online transaction processing (OLTP) Handle a company’s ongoing business, which is are computer-processing systems in which the computer responds immediately to user requests. Each request is considered to be a transaction, that is, a computer records a discrete event. Most of operational data in enterprise resource planning (ERP) systems, supply chain management or customer relationship management system are stored in what is referred to as OLTP systems.

9. Major Characteristics of Business Intelligence • Business intelligence is NOT transaction processing • OLTP concentrates on processing repetitive transactions in large quantities and conducting simple manipulations • OLAP involves examining many data items complex relationships • OLAP may analyze relationships and look for patterns, trends, and exceptions • OLAP is a direct decision support method .

10. Major Characteristics of Business Intelligence • The information factory view • Terms of data warehouse is associated with the concept of a factory. • Enterprise information factory as a way to describe how companies conduct and organize BI efforts. • A cornerstone component of that factory concept is the DW • An information factory is connected to other internal information system, such as ERP, CRM, and e-commerce, as well as the external information, usually, via internet or extranet. • An information factory is illustrated in figure.

11. Major Characteristics of Business Intelligence

12. Inputs Data sources Acquisition Storage DW Data marts Processing of inputs Analysis Data mining Outputs Data delivery BI applications Major Characteristics of Business Intelligence • An information factory has:

13. Major Characteristics of Business Intelligence • Data warehousing and business intelligence • Data warehouse is a collection of data, designed to support management decision making. DW contains a wide variety of data that present a coherent picture of business conditions a at single point in time. • Create a database infrastructure that is always online and that contains all the information from the OLTP systems, including historical data, but reorganized and structured in such a way that it is fast and efficient for querying, analysis, and decision support • Teradata advanced analytics methodology • Teradata created a methodology for BI that is illustrated in figure. BI applications (upper-left side) are supported by advanced analytics techniques and tools (left side). The right side as a cyclical process that circles the enterprise DW.

14. Major Characteristics of Business Intelligence

15. Toward Competitive Intelligence and Advantage • Strategic imperative because: • Barriers to entry of a new competitor to an industry are being significantly diminished • An organization that has a strong position within its industry could easily face new competitors because the costs and other constraints to becoming a player in the market have decreased • Due to globalization • The ability to delivery goods throughout the world readily available supply chain channel as such FedEx, UPS and DHL, as well as e-commerce.

16. Toward Competitive Intelligence and Advantage • Competitive intelligence (CI) • Competitive intelligence often involves more than the BI initiatives used in most organizations, there are some overlaps. • CI implies tracking what competitors are doing by gathering sources of materials on their recent and in-process activities. • BI initiatives use some outside sources of data are included in the analysis process, but they are often available from third-party vendors.

17. Toward Competitive Intelligence and Advantage • Competitive strategy in an industry • Competitor analysis is a component of industry analysis, which serves as a basis for strategic planning processes • Several generic strategies are commonly based on an industrial analysis for strategic planning. For example, Porter five forces model. • For example, low cost strategy. • BI applications in this context might include scrutinizing quality metrics associated with specific production processes, analyzing raw materials from various suppliers to assess defect rates, tracking costs of goods sold as a percentage of run volume, and so on. • For example, clickstream analysis.

18. Toward Competitive Intelligence and Advantage • Competitive strategy in an industry • Focus on a particular market niche, perhaps through some form of product or service differentiation • BI applications in this context might include: • Making sure customer needs are met and loyalty is built • Tracking and remembering customer preferences in the next customer encounter • For example, a gambler gets a same treatment if they are in any of the geographical locations of the company’s business. The rewards the gambler earned from visiting any locations accrue with the company, creating a loyal and frequently returning customers, which is the targeted, high-value market niche.

19. Toward Competitive Intelligence and Advantage • Sustaining competitive advantage • As with other IT investment, using the BI or DW attempting to achieve a competitive advantage is only one aspect. • Another perspective is the sustainable advantage. • Most strategic analysts agree that low-cost leadership may not yield a sustainable advantage unless the low cost can be sustained • BI projects and DW are becoming increasingly important weapons in sustaining competitive advantage across industries

20. Successful Business Intelligence Implementation • The fundamental reasons for investing in BI must be aligned with the company’s business strategy. BI cannot simply be a technical exercise for the IS department. It must serve as a way to change the manner the company conducts business by improving its business processes and transforming decision-making processes to be more data driven.

21. Successful Business Intelligence Implementation • A framework for planning is a necessary precondition • At the business and organizational levels, it is important to define strategic and operational objectives while considering the available organizational skills to achieve those objectives • Upper managers must build enthusiasm for those initiatives and procedures for the intra-organizational sharing of BI best practices • Plans to prepare the organization for change must be in place • One of the first step in that process is to assess the IS organization, the skill sets of the potential classes of users, and whether the culture is amenable to change.

22. Successful Business Intelligence Implementation • If a company’s strategy is properly aligned with the reasons for a DW and BI initiatives, if the company’s IS organization is or can be made capable of playing its role in such a project, and if the requisite user community is in place and has the proper motivation, it is wise to start BI and establish a BI competency center (BICC) within the company • Demonstrate how BI is clearly linked to strategy and execution of strategy • Serve to encourage interaction between the potential business user communities and the IS organization. • Serve as a repository and disseminator of best BI practices. • Standards of excellence of BI practices. • Organization can learn a great deal of knowledge about variety of the analytical tools • Better understand why the DW platform must be flexible enough. • Help the stakeholders to see how BI can play an important role.

23. Successful Business Intelligence Implementation • Attaining real-time, on-demand BI • The demand for instant, on-demand access to dispersed information has grown as the need to close the gap between the operational data and strategic objectives has become more pressing • New data-generating technologies, such as RFID, is accelerating this growth and the subsequent need for real-time BI • Traditional BI systems use a large volume of static data that have been extracted, cleansed, and loaded into a DW to produce reports and analyses. • Users need business monitoring, performance analysis, and an understanding of why things are happening. • These can alert users, virtually in real-time, about changes in data or the availability of relevant reports, and so on

24. Structure and Components of Business Intelligence

25. Structure and Components of Business Intelligence • Data warehouse • Data flows from operational systems (e.g., CRM, ERP) to a DW, which is a special database or repository of data that has been prepared to support decision-making applications ranging from those for simple reporting and querying to complex optimization • Business analytics • Online analytical processing (OLAP) Software tools that allow users to create on-demand reports and queries and to conduct analysis of data

26. Structure and Components of Business Intelligence • Data mining • Data mining is a class of database information analysis that looks for hidden patterns in a group of data that can be used to predict future behavior • Used to replace or enhance human intelligence by scanning through massive storehouses of data to discover meaningful new correlations, patterns, and trends, by using pattern recognition technologies and advanced statistics

27. Structure and Components of Business Intelligence • Business performance management (BPM) • Based on the balanced scorecard methodology—a framework for defining, implementing, and managing an enterprise’s business strategy by linking objectives with factual measures • Dashboards A visual presentation of critical data for executives to view. It allows executives to see hot spots in seconds and explore the situation

28. Business Intelligence Today and Tomorrow • Recent industry analyst reports show that in the coming years, millions of people will use BI visual tools and analytics every day • Today’s organizations are deriving more value from BI by extending actionable information to many types of employees, maximizing the use of existing data assets • A potential trend involving BI is its possible merger with artificial intelligence (AI) • BI is spreading its wings to cover small, medium, and large companies • BI takes advantage of already developed and installed components of IT technologies, helping companies leverage their current IT investments and use valuable data stored in legacy and transactional systems

29. Business Intelligence Today and Tomorrow • Recent industry analyst reports show that in the coming years, millions of people will use BI visual tools and analytics every day • Today’s organizations are deriving more value from BI by extending actionable information to many types of employees, maximizing the use of existing data assets • A potential trend involving BI is its possible merger with artificial intelligence (AI) • BI is spreading its wings to cover small, medium, and large companies • BI takes advantage of already developed and installed components of IT technologies, helping companies leverage their current IT investments and use valuable data stored in legacy and transactional systems

30. The Business Analytics (BA) Field: An Overview • Business intelligence (BI) The use of analytical methods, either manually or automatically, to derive relationships from data • The essentials of BA • Analytics The science of analysis. • Business analytics (BA) The application of models directly to business data. BA involves using MSS tools, especially models, in assisting decision makers; essentially a form of OLAP decision support BA is a broad category of applications and techniques for gathering, storing, analyzing, and providing access to data to help enterprises users make better business and strategic decisions. • BA is also known as analytic processing. BI tools, BI applications.

31. The Business Analytics (BA) Field: An Overview • An analytic application is a step upward in sophistication form merely providing analytic techniques or tools. It allows for activities such as: • Automating the thinking and, inmost cases, a portion of the decision making of a human being. • Typically using complex quantitative techniques, such as multivariate regression analysis, data mining, artificial intelligence, nonlinear programming. • There are lots of the BA tools, we can divide into three categories.

32. The Business Analytics (BA) Field: An Overview

33. The Business Analytics (BA) Field: An Overview • MicroStrategy’s classification of BA tools: The five styles of BI • Enterprise reporting • Cube analysis • Ad hoc querying and analysis • Statistical analysis and data mining • Report delivery and alerting

34. The Business Analytics (BA) Field: An Overview

35. The Business Analytics (BA) Field: An Overview • SAP’s classification of strategic enterprise management • Three levels of support • Operational. SAP R3 mainly supports the transaction processing on the operational level. • Managerial. Middle management can access all reports, arranged by functional areas. Managers can make queries and drill down. • Strategic. The company offers products under the title SAP SEM (strategy Enterprise Management) which includes BA.

36. The Business Analytics (BA) Field: An Overview • Executive information and support systems • Executive information systems (EIS) Provides rapid access to timely and relevant information aiding in monitoring an organization’s performance • Executive support systems (ESS) Also provides analysis support, communications, office automation, and intelligence support • Capabilities of EIS/ESS, see table in next slide.

37. The Business Analytics (BA) Field: An Overview

38. Online Analytical Processing (OLAP) • Drill-down The investigation of information in detail (e.g., finding not only total sales but also sales by region, by product, or by salesperson). Finding the detailed sources. • Online analytical processing (OLAP) An information system that enables the user, while at a PC, to query the system, conduct an analysis, and so on. The result is generated in seconds

39. Online Analytical Processing (OLAP) • Types of OLAP • Multidimensional OLAP (MOLAP) OLAP implemented via a specialized multidimensional database (or data store) that summarizes transactions into multidimensional views ahead of time • Relational OLAP (ROLAP) The implementation ofan OLAP database on top of an existing relational database • Database OLAP and Web OLAP (DOLAP and WOLAP) • Desktop OLAP

40. Multidimensional conceptual view for formulating queries Transparency to the user Easy accessibility: batch and online access Consistent reporting performance Client/server architecture: the use of distributed resources Generic dimensionality Dynamic sparse matrix handling Multiuser support rather than support for only a single user Unrestricted cross-dimensional operations Intuitive data manipulation Flexible reporting Unlimited dimensions and aggregation level Online Analytical Processing (OLAP) Codd’s12 Rules for OLAP

41. Data Visualization • Data visualization A graphical, animation, or video presentation of data and the results of data analysis • The ability to quickly identify important trends in corporate and market data can provide competitive advantage • Check their magnitude of trends by using predictive models that provide significant business advantages in applications that drive content, transactions, or processes • New directions in data visualization • In the 1990s data visualization has moved into: • Mainstream computing, where it is integrated with decision support tools and applications • Intelligent visualization, which includes data (information) interpretation

42. Data Visualization

43. Data Visualization

44. Data Visualization • New directions in data visualization • Dashboards and scorecards • Will be discussed in later chapter. • Visual analysis • Financial data visualization

45. Geographic Information Systems (GIS) • Geographical information system (GIS) An information system that uses spatial data, such as digitized maps. A GIS is a combination of text, graphics, icons, and symbols on maps • As GIS tools become increasingly sophisticated and affordable, they help more companies and governments understand: • Precisely where their trucks, workers, and resources are located • Where they need to go to service a customer • The best way to get from here to there

46. Geographic Information Systems (GIS) • GIS and decision making • GIS applications are used to improve decision making in the public and private sectors including: • Dispatch of emergency vehicles • Transit management • Facility site selection • Drought risk management • Wildlife management • Local governments use GIS applications for used mapping and other decision-making applications

47. Geographic Information Systems (GIS) • GIS combined with GPS • Global positioning systems (GPS) Wireless devices that use satellites to enable users to detect the position on earth of items (e.g., cars or people) the devices are attached to, with reasonable precision • GIS and the Internet/intranets • Most major GIS software vendors provide Web access that hooks directly to their software • GIS can help the manager of a retail operation determine where to locate retail outlets • Some firms are deploying GIS on the Internet for internal use or for use by their customers (locate the closest store location)

48. Real-Time BI, Automated Decision Support (ADS), and Competitive Intelligence • Real-time BI • The trend toward BI software producing real-time data updates for real-time analysis and real-time decision making is growing rapidly • Part of this push involves getting the right information to operational and tactical personnel so that they can use new BA tools and up-to-the-minute results to make decisions • Automated decision support (ADS) or enterprise decision management (EDM) A rule-based system that provides a solution to a repetitive managerial problem. Also known as enterprise decision management (EDM) • Business rules Automating the decision-making process is usually achieved by encapsulating business user expertise in a set of business rules that are embedded in a rule-driven workflow (or other action-oriented) engine

49. Real-Time BI, Automated Decision Support (ADS), and Competitive Intelligence • Real-time BI • Characteristics and benefits of ADS ADS are most suitable for decisions that must be made frequently and/or rapidly, using information that is available electronically • Concerns about real-time systems • An important issue in real-time computing is that not all data should be updated continuously • when reports are generated in real-time because one person’s results may not match another person’s causing confusion • Real-time data are necessary in many cases for the creation of ADS systems

50. Real-Time BI, Automated Decision Support (ADS), and Competitive Intelligence • Capabilities of ADSs • Rapidly builds rules-based applications and deploys them into almost any operating environment • Injects predictive analytics into rule-based applications • Provides services to legacy systems • Combines business rules, predictive models, and optimization strategies flexibly into state-of-the-art decision-management applications • Accelerates the uptake of learning from decision criteria into strategy design, execution, and refinement