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BA and data visualization.pdf

  1. Chapter 3 Business Analytics and Data Visualization
  2. Learning Objectives  Describe business analytics (BA) and its importance to organizations  List and briefly describe the major BA methods and tools  Describe how online analytical processing (OLAP), data visualization, and multidimensionality can improve decision making  Describe advanced analysis methods
  3. Learning Objectives  Describe geographical information systems (GIS) and their support to decision making  Describe real-time BA  Describe how business intelligence (BI) supports competitive intelligence  Describe automated decision support (ADS) systems and their benefits
  4. Learning Objectives  Explain how the Web relates to BA  Describe Web intelligence and Web analytics and their importance to organizations  Describe implementation issues related to BA and success factors for BA
  5.  Business intelligence (BI) The use of analytical methods, either manually or automatically, to derive relationships from data The Business Analytics (BA) Field: An Overview  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
  6. Perbandingan Business Intelligence Business Analytics Definition Menganalisa data masa lalu dan saat ini untuk mendorong kebutuhan bisnis saat ini Menganalisa data masa lalu untuk mendorong perencanaan bisnis dimasa depan Usage Untuk menjalankan operasi bisnis saat ini Untuk mengubah operasi bisnis dan meningkatkan produktivitas Ease of operation Untuk operasi bisnis saat ini Unutk operasi bisnis dimasa depan Tools – SAP Business Objects, – QlinkSense – TIBCO, dll – Word processing, – TIBCO, dll Applications Diaplikasikan untuk perusahaan skala besar yang menjalankan operasi bisnis saat ini Diaplikasikan untuk pertumbuhan dan produktivitas perusahaan dimasa depan Field Hampir berada dibawah Business Analytics Berisi data warehouse, information management, dll Perbedaan BI dan BA
  7.  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 The Business Analytics (BA) Field: An Overview
  8. The Business Analytics (BA) Field: An Overview
  9.  MicroStrategy’s classification of BA tools: The five styles of BI 1. Enterprise reporting 2. Cube analysis 3. Ad hoc querying and analysis 4. Statistical analysis and data mining 5. Report delivery and alerting The Business Analytics (BA) Field: An Overview
  10. The Business Analytics (BA) Field: An Overview
  11.  SAP’s classification of strategic enterprise management  Three levels of support 1. Operational 2. Managerial 3. Strategic The Business Analytics (BA) Field: An Overview
  12.  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 The Business Analytics (BA) Field: An Overview
  13.  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 The Business Analytics (BA) Field: An Overview
  14. Drill-down
  15.  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 Analytical Processing (OLAP)
  17.  OLAP versus OLTP  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 Online Analytical Processing (OLAP)
  18.  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 Online Analytical Processing (OLAP)
  19.  Types of OLAP  Relational OLAP (ROLAP) The implementation of an OLAP database on top of an existing relational database  Database OLAP and Web OLAP (DOLAP and WOLAP)  Desktop OLAP Online Analytical Processing (OLAP)
  20. Online Analytical Processing (OLAP) 1. Multidimensional conceptual view for formulating queries 2. Transparency to the user 3. Easy accessibility: batch and online access 4. Consistent reporting performance 5. Client/server architecture: the use of distributed resources 6. Generic dimensionality 7. Dynamic sparse matrix handling 8. Multiuser support rather than support for only a single user 9. Unrestricted cross- dimensional operations 10. Intuitive data manipulation 11. Flexible reporting 12. Unlimited dimensions and aggregation level Codd’s 12 Rules for OLAP
  21. Online Analytical Processing (OLAP)  Four types of processing that are performed by analysts in an organization: 1. Categorical analysis 2. Exegetical analysis 3. Contemplative analysis 4. Formulaic analysis
  22. Reports and Queries  Reports  Routine reports  Ad hoc (or on-demand) reports  Multilingual support  Scorecards and dashboards  Report delivery and alerting • Report distribution through any touchpoint • Self-subscription as well as administrator-based distribution • Delivery on-demand, on-schedule, or on-event • Automatic content personalization
  23. Reports and Queries  Ad hoc query A query that cannot be determined prior to the moment the query is issued  Structured Query Language (SQL) A data definition and management language for relational databases. SQL front ends most relational DBMS
  24. Multidimensionality  Multidimensionality The ability to organize, present, and analyze data by several dimensions, such as sales by region, by product, by salesperson, and by time (four dimensions)  Multidimensional presentation  Dimensions  Measures  Time
  25. Multidimensionality  Multidimensional database A database in which the data are organized specifically to support easy and quick multidimensional analysis  Data cube A two-dimensional, three-dimensional, or higher-dimensional object in which each dimension of the data represents a measure of interest
  26. Multidimensionality  Cube A subset of highly interrelated data that is organized to allow users to combine any attributes in a cube (e.g., stores, products, customers, suppliers) with any metrics in the cube (e.g., sales, profit, units, age) to create various two-dimensional views, or slices, that can be displayed on a computer screen
  27. Multidimensionality
  28. Multidimensionality  Multidimensional tools and vendors  Tools with multidimensional capabilities often work in conjunction with database query systems and other OLAP tools
  29. Multidimensionality
  30. Multidimensionality  Limitations of dimensionality  The multidimensional database can take up significantly more computer storage room than a summarized relational database  Multidimensional products cost significantly more than standard relational products  Database loading consumes significant system resources and time, depending on data volume and the number of dimensions  Interfaces and maintenance are more complex in multidimensional databases than in relational databases
  31. Advanced Business Analytics  Data mining and predictive analysis  Data mining  Predictive analysis Use of tools that help determine the probable future outcome for an event or the likelihood of a situation occurring. These tools also identify relationships and patterns
  32. 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
  33. Data Visualization  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
  34. Data Visualization
  35. Data Visualization
  36. Data Visualization  New directions in data visualization  Dashboards and scorecards  Visual analysis  Financial data visualization
  37. 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
  38. Geographic Information Systems (GIS)  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
  39. 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
  40. 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
  41. Geographic Information Systems (GIS)  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)
  42. Real-Time BI, Automated Decision Support, 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
  43. Real-Time BI, Automated Decision Support, and Competitive Intelligence  Real-time BI  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
  44. Real-Time BI, Automated Decision Support, and Competitive Intelligence  Real-time BI  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)
  45. Real-Time BI, Automated Decision Support, and Competitive Intelligence  Real-time BI  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
  46. Real-Time BI, Automated Decision Support, 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
  47. Real-Time BI, Automated Decision Support, 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
  48. Real-Time BI, Automated Decision Support, and Competitive Intelligence  ADS applications  Product or service configuration  Yield (price) optimization  Routing or segmentation decisions  Corporate and regulatory compliance  Fraud detection  Dynamic forecasting  Operational control
  49. Real-Time BI, Automated Decision Support, and Competitive Intelligence  Implementing ADS—software companies provide these components to ADS:  Rule engines  Mathematical and statistical algorithms  Industry-specific packages  Enterprise systems  Workflow applications
  50. Real-Time BI, Automated Decision Support, and Competitive Intelligence  Competitive intelligence  Many companies continuously monitor the activities of their competitors to acquire competitive intelligence  Such information gathering drives business performance by increasing market knowledge, improving knowledge management, and raising the quality of strategic planning
  51. BA and the Web: Web Intelligence and Web Analytics  Using the Web in BA  Web analytics The application of business analytics activities to Web-based processes, including e-commerce
  52. BA and the Web: Web Intelligence and Web Analytics  Clickstream analysis The analysis of data that occur in the Web environment.  Clickstream data Data that provide a trail of the user’s activities and show the user’s browsing patterns (e.g., which sites are visited, which pages, how long)
  53. BA and the Web: Web Intelligence and Web Analytics
  54. Usage, Benefits, and Success of BA  Usage of BA  Almost all managers and executives can use some BA systems, but some find the tools too complicated to use or they are not trained properly.  Most businesses want a greater percentage of the enterprise to leverage analytics; most of the challenges related to technology adoption involve culture, people, and processes
  55. Usage, Benefits, and Success of BA  Success and usability of BA  Performance management systems (PMS) are BI tools that provide scorecards and other relevant information that decision makers use to determine their level of success in reaching their goals
  56. Usage, Benefits, and Success of BA  Why BI/BA projects fail 1. Failure to recognize BI projects as cross- organizational business initiatives and to understand that, as such, they differ from typical standalone solutions 2. Unengaged or weak business sponsors 3. Unavailable or unwilling business representatives from the functional areas
  57. Usage, Benefits, and Success of BA  Why BI/BA projects fail 4. Lack of skilled (or available) staff, or suboptimal staff utilization 5. No software release concept (i.e., no iterative development method) 6. No work breakdown structure (i.e., no methodology)
  58. Usage, Benefits, and Success of BA  Why BI/BA projects fail 7. No business analysis or standardization activities 8. No appreciation of the negative impact of “dirty data” on business profitability 9. No understanding of the necessity for and the use of metadata 10. Too much reliance on disparate methods and tools
  59. Usage, Benefits, and Success of BA  System development and the need for integration  Developing an effective BI decision support application can be fairly complex  Integration, whether of applications, data sources, or even development environment, is a major CSF for BI