Mis jaiswal-chapter-08

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Mis jaiswal-chapter-08

  1. 1. Intelligent Information Systems
  2. 2. Evolutionary Step Business Question Enabling Technologies Product Providers Characteristics Data Collection (1960s) "What was my total revenue in the last five years?" Computers, tapes , disks IBM, CDC Retrospective, static data delivery Data Access (1980s) "What were unit sales in New England last March?" Relational databases (RDBMS), Struct ured Query Language (SQL), ODBC Oracle, Sybas Retrospective, e, Informix, I dynamic data BM, Microsoft delivery at record level Data Warehousing & Decision Support (1990s) "What were unit sales in New England last March? Drill down to Boston." On-line analytic processing (OLAP), multidimensional databases, data warehouses Pilot, Comshare, Arbor, Cognos, Microstrategy Retrospective, dynamic data delivery at multiple levels Data Mining (Emerging Today) "What’s likely to happen to Boston unit sales next month? Why?" Advanced algorithms, multiprocessor computers, massive databases Pilot, Lockheed, IBM, SGI, numerous start-ups (nascent Prospective, proactive information delivery
  3. 3. • Standard database operations present results to the user as they existed in databases • A report showing the breakdown of sales by product line and region is straightforward for the user to understand because they intuitively know that this kind of information already exists in the database
  4. 4.   Business Intelligence (BI) tools such as query and reporting are used to answer questions by the user These questions deal primarily with the analysis of historical results and trends - what were our sales in the past month in a certain region? - what were our most profitable products? - which of our suppliers were most reliable? - which customers generated the most revenue?
  5. 5. • Extracts information from a database that the user did not know existed • Relationships between variables and customer behaviour that are non-intuitive is the vital information that data mining extracts • Since the user does not know beforehand what the data mining process has discovered, it is a much bigger leap to take the output of the system and translate it into a solution for a business problem
  6. 6. Datamining tools provide answers to questions related to the detection of previously undetected patterns and are undirected in nature such as: and cost- - Who are our best suppliers or most profitable customers? - Should we extend credit to a particular customer? - Which customers are likely to become profitable, when to what extent? - How do we optimally allocate resources to ensure profitability and growth targets? - What are the root causes of quality issues and can we effectively minimize them? - What factors or combinations of factors are directly impacting marketing campaigns?
  7. 7. • Intelligence is the aptitude to learn, comprehend, or to counter new or trying situations • It is the skillful use of reason and the capacity to apply knowledge to influence one's environment or to think conceptually • Business intelligence is a set of notions, methods, and practices, which improves business decisions. It uses information from multiple sources and applies experience and assumptions that helps in understanding accurately the intricacies of business dynamics.
  8. 8. • Business Intelligence (coined by Gartner in the late 1980s) is “a usercentered process that includes accessing and exploring information, analyzing this information, and developing insights and understanding, which leads to improved and informed decision making.”
  9. 9. • BI is the means by which organizations interpret the sea of organizational data to derive insights that are critical to competing in the new economy • BI aids in: - a deeper understanding of customer and partner relationships - indicating key performance indicators - a consistent view of the organization from the executive level to the front line By translating these insights into action companies can: • - increase profits - respond more quickly to changing market demands - improve accountability by giving every employee an accurate view of the organization
  10. 10.  The track - analyze - model decide – monitor loop is referred to as the closed loop model for business intelligence
  11. 11. • Track extracting, transforming, loading (ETL), and integrating data into a data warehouse as well as monitoring data in a real-time or near real-time environment • Transaction capturing systems or operational systems capture data which is later transformed, integrated, and loaded into a data warehouse
  12. 12. • Analyze (analyzing data using BI tools) - query and reporting, multi-dimensional analysis, and data mining - Simple analysis methods like regression, co-relation , factor analysis etc. are available in MS-EXCEL , ORACLE , SPSS, etc., . - Data mining tools are available with software packages like SPSS, SAS, Intelligent Miner, and Data Mind
  13. 13. • Model - formulating models for forecasting, optimization, and scenario planning - utilizing advanced analytics tools •A model (a rule or a hypotheses) is made based on the patterns discovered by data mining tools
  14. 14. •Decide - arriving at a decision based on analysis and preexisting or newly developed models - decision support systems use the models developed as a result of data-mining and business intelligence modeling processes for decision making
  15. 15. •Act - a business manager uses the business analysis results to take an action (e.g., launching a new marketing campaign based on the analysis of previous campaign results, customer behavior, new promotional plan or inventory levels) - approving or denying a request for credit based on past financial activity - re-negotiating sourcing contracts based on supplier delivery trends, product quality, and warranty activity trends, adjusting the type of data being tracked for analysis, etc., .
  16. 16. • Identify buying behavior from customers • Find associations among customer demographic characteristics • Predict responses to mailing campaigns • Market basket analysis
  17. 17. • Detect patterns of fraudulent credit card use • Identify loyal customers • Predict customers likely to change their credit card affiliation • Determine credit cards spending by customer groups • Find hidden correlations between different financial indicators • Identify stock trading rules from historical data
  18. 18. • Claims analysis • Predict which customers will buy new policies • Identify behaviour patterns of risky customers • Identify fraudulent behaviour
  19. 19. • Determine the distribution schedules among outlets • Analyze loading patterns
  20. 20. • Successful BI architecture has four parts - information architecture The information architecture defines what business application systems you need to access, report, and analyze information to enable business decision making. - data architecture The data architecture defines the data, source systems and framework for transforming data into useful information. - technical architecture The technical architecture defines the technology of the products and infrastructure. - product architecture The product architecture includes the actual products used
  21. 21.  Phase I Data Preparation: - Data Integration - Data Selection and Pre-analysis - Data Integration refers to the process of merging data which typically resides in an operational environment having multiple files or databases  Phase II Data Mining processor: - accesses a Data Warehouse that uses a relational database such as DB2 for AIX/6000 - access is done through a standard SQL interface using a middleware product which allows mining of data from multiple sources  Phase III Presentation of facts and follow up:
  22. 22. • a class of computer software built around mathematical models and algorithms (procedures) which, by converting data into information and intelligence, help a manager make better decisions for his organization • DSS are interactive computer based systems and subsystems intended to help decision makers use communication technologies , data , documents , knowledge and/ or models to successfully complete decision process tasks • DSS can be divided into five basic tasks: - communications-driven DSS - data-driven DSS - knowledge-driven DSS - document-driven DSS - model-driven DSS

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