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Understanding Business Intelligence Systems: From Raw Data to Knowledge

This review explores Business Intelligence (BI) systems, utilizing data from Acxiom Corporation's extensive demographic records to predict loan defaults. BI systems integrate various technologies and algorithms to sift through vast amounts of data to uncover patterns, trends, and provide actionable insights. By comparing key systems such as TPS, MIS, DSS, and EIS, we delve into the evolution from data to information and ultimately to knowledge. We will use tools like Excel and Access in practical labs to make predictions and analyze product associations in the realm of BI.

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Understanding Business Intelligence Systems: From Raw Data to Knowledge

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  1. Business Intelligence &Exam 1 Review

  2. Business Intelligence (BI) • INPUT: Acxiom Corporation collects 300 million individual demographic records. • OUTPUT: Who is going to default on a loan. • Q: How do you process this input to output?

  3. Business Intelligence (BI) System • BI Systems use information technology (hardware, software, but even algorithms) to find patterns, relationships, and trends. • Data  (MIS) Information • Information  (DSS) Good Decisions • Information  (BI systems) Knowledge

  4. BI includes • Reporting Tools • Examples: Pivot Charts & Online Analytical Processing • Data Mining Algorithms • Examples:Apriori Algorithm for finding association rules • Knowledge Management Tools • Examples: • IBM’s email knowledge bank

  5. Review • TPS – Transaction Processing Systems • PCS – Process Control Systems • MIS – Management Information Systems • DSS – Decision Support Systems • EIS – Executive Information Systems • ECS – Enterprise Collaboration Systems

  6. Background People/Machines  (TPS) Data People/Machines  (PCS) Data & Routine Decisions Data  (MIS) Info. & Routine Decisions Information  (DSS) Complex Decisions Information  (BI systems) Knowledge Information  (EIS) Strategic Decisions Data & Info (ECS) Data & Info.

  7. The Big Problem • Old problem: Too much data • figured out some solutions • New problem: Too much information • Moving from info. to knowledge is a bigger problem • Where humans are still needed

  8. What we will learning soon • Pivot Chart Lab & Logic • We will learn how to make accurate predictions (knowledge) given raw data about behavior • Excel will be our tool • Market Basket Lab • We will find the strongest product associations (knowledge) given millions of possibilities (raw data) • Access with be our tool • Large companies pay $millions for sophisticate tools to do these things

  9. What we have done (review part) • Intro Lab • Getting to know the procedures • CMCC Lab • Word is just a document maker • Google Docs is more of an Information System • GIS Lab • First DSS example • Data  Information • Tables  Maps  Reports • Excel Lab • More than just an accounting calculator • A tool for automation and information processing • Searching (vlookup) and logic (if statements)

  10. Things to study • Remember 5, 8, and 6 things

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