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Preparing 21st Century Business Intelligence Students Through 21st Century Approaches

The Problem. . . . . . Mountains of Data. Business People. . . GAP. . . . How do I increase sales????. How do I make my product better???. . How do I retain customers?. The Problem. . . . . . Mountains of Data. Business People. . . GAP. . . . How do I increase sales????. How do I make my product be

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Preparing 21st Century Business Intelligence Students Through 21st Century Approaches

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    1. Preparing 21st Century Business Intelligence Students Through 21st Century Approaches Bellevue College

    2. The Problem

    3. The Problem

    4. What is BI? The process by which an organization manages large amounts of data, extracting pertinent information, and turning that information into knowledge upon which actions can be taken.

    5. What is BI? Business intelligence (BI) is a broad category of application programs and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions.

    6. BI Involves PEOPLE and Technology Involves using a rational approach to management Involves a continuous cycle of measurement, adjustment & remeasurement

    7. The BI Cycle

    8. Reasons for BI BI enables organizations to make well informed business decisions and gain competitive advantage. BI enables organizations to use information to quickly and constantly respond to changes.

    9. To Note: Although we call it Business Intelligence, the concepts and techniques are applicable to almost any organization including those in health care, biotech, education, government …

    10. Why This Curriculum? Discovered new, growing field The world is a database Target students both from Business and Technology Upgrade skills Jobs won’t be outsourced example: business analyst, data analyst, data warehouse support specialist, data architect, reporting analyst These jobs won’t be outsourced business analyst, data analyst, dw / data mart support specialist, reporting analyst, decision support analyst, relational database developer, programmer analyst Jobs: Data Analyst – 40-50K with BA 2 years experienceThese jobs won’t be outsourced business analyst, data analyst, dw / data mart support specialist, reporting analyst, decision support analyst, relational database developer, programmer analyst Jobs: Data Analyst – 40-50K with BA 2 years experience

    11. Why this Curriculum? Organizations have lots of data Data is not in a form that is useful to decision-makers Not easy to review Not informative nor insightful

    12. Jobs, Jobs, Jobs

    13. BI Curriculum Analytics Reporting Analytic Tools Performance Management Dashboards Score Cards Data Visualization Moving away from spreadsheets to come with the single version of the “truth” Dimensional Modeling: foundation for understanding the data and how they all fit together; used to build the data warehouses Data Warehouse: includes data marts and are created based on the models created above. Gather all the data together so that it can be shared by all and stored in a consistent manner. OLAP: create cubes used for reporting the information. Instead of 2 dimensions, can include regions, sales, products, and time. Used for ad hoc reports and to provide decision support interfaces. Data Visualization: presenting the data in an insightful, easy to read format, along with KPIs and other indicators to further the analysis. Data Mining: A discovery process, improve goals by using predictions, forecasting trends, based on the stored data. Our list of courses cover these topics: Dimensional modeling Data warehousing I and II Multi-dimensional analysis I and II Data visualization Data mining Mostly theory with some hands on applicationsMoving away from spreadsheets to come with the single version of the “truth” Dimensional Modeling: foundation for understanding the data and how they all fit together; used to build the data warehouses Data Warehouse: includes data marts and are created based on the models created above. Gather all the data together so that it can be shared by all and stored in a consistent manner. OLAP: create cubes used for reporting the information. Instead of 2 dimensions, can include regions, sales, products, and time. Used for ad hoc reports and to provide decision support interfaces. Data Visualization: presenting the data in an insightful, easy to read format, along with KPIs and other indicators to further the analysis. Data Mining: A discovery process, improve goals by using predictions, forecasting trends, based on the stored data. Our list of courses cover these topics: Dimensional modeling Data warehousing I and II Multi-dimensional analysis I and II Data visualization Data mining Mostly theory with some hands on applications

    14. BI Curriculum Data Warehouses Multi-Dimensional Databases (cubes) Data Mining Dimensional Modeling

    15. Analytics and Data Visualization All about analyzing the data and presenting information in a meaningful format

    16. Analytics Reporting Applications Limited user interaction Fulfill a significant portion of an organization’s information needs SQL Server Reporting Services Analytic Applications Allow users to visualize and explore data following their train of thought Extensive interactivity Tableau

    18. Analytics – Performance Management Key Performance Indicators Dashboards Score Cards

    19. Dashboard Example

    20. Bridging the Gap Need software to provide access to the data, allow flexible manipulation, and provide meaningful presentation Need data storage structures to facilitate fast analysis of huge volumes of data

    21. Data Storage Structures Multi-Dimensional Databases Cubes

    22. Multi-Dimensional Databases Measures Any quantitative expression Some are designated as Key Performance Indicators (KPI) Appropriate to the business process. Dimensions How we describe the measures: Product/Customer/Region/Time These are the “By’s “What were our Customer Sales by Product Line by Region by Quarter for the past two years?”.

    23. Logical Structure Here is an Imports cube, which contains two measures, Packages and Last, and three related dimensions, Route, Source, and Time. Last is the last ship date The smaller alphanumeric values around the cube are the members of the dimensions. Example members are ground, Africa, and 1st quarter. The values within the cube represent the two measures, Packages and Last. The Packages measure represents the number of imported packages, and it aggregates by the Sum function. The Last measure represents the date of receipt, and it aggregates by the Max function. The Route dimension represents the means by which the imports reach their destination. The Source dimension represents the locations where the imports are produced. The Time dimension represents the quarters and halves of a single year. Business users of a cube can determine its measures' values for each member of every dimension. This is possible because the members aggregate measure values. For example, the measure values shown in the preceding illustration aggregate within a standard calendar hierarchy in the Time dimension as follows. In addition to aggregating within a single dimension, measures aggregate for all combinations of members from different dimensions. This allows business users to evaluate measures by members in multiple dimensions simultaneously. For example, if an business user wants to analyze quarterly imports that arrived by air from the Eastern Hemisphere and Western Hemisphere, the business user can issue the appropriate query on the cube to retrieve the following dataset.Here is an Imports cube, which contains two measures, Packages and Last, and three related dimensions, Route, Source, and Time. Last is the last ship date The smaller alphanumeric values around the cube are the members of the dimensions. Example members are ground, Africa, and 1st quarter. The values within the cube represent the two measures, Packages and Last. The Packages measure represents the number of imported packages, and it aggregates by the Sum function. The Last measure represents the date of receipt, and it aggregates by the Max function. The Route dimension represents the means by which the imports reach their destination. The Source dimension represents the locations where the imports are produced. The Time dimension represents the quarters and halves of a single year. Business users of a cube can determine its measures' values for each member of every dimension. This is possible because the members aggregate measure values. For example, the measure values shown in the preceding illustration aggregate within a standard calendar hierarchy in the Time dimension as follows. In addition to aggregating within a single dimension, measures aggregate for all combinations of members from different dimensions. This allows business users to evaluate measures by members in multiple dimensions simultaneously. For example, if an business user wants to analyze quarterly imports that arrived by air from the Eastern Hemisphere and Western Hemisphere, the business user can issue the appropriate query on the cube to retrieve the following dataset.

    24. Multi-Dimensional Databases (Cubes) Mention compressed storageMention compressed storage

    25. Multi-Dimensional Databases

    26. Software Applications

    27. Summary Students learn to: Create multi-dimensional databases Create professional quality reports Use analytics to provide in-depth data analysis

    28. Data Warehouse Topics Decision Support Systems history Requirements Gathering Where data located, owners, definition, how often updated Data Analysis Determine table structures (Facts & Dimensions) What makes good data Textbook: The Data Warehouse ETL Toolkit Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data, Kimball, Caserta, Wiley Technology Publishing, ISBN: 0764567578 Topics: Decision Support Systems: history, give examples of how they work without data warehouse Requirements Gathering: who wants to see what, when, how often, from where. Is the data available, must it be calculated? Data Analysis: see the big picture, various types of source data: flat file, relational db, different hardware, different software, owners of data, data definitions ETL Processes & Deliverables Cleaning & Conforming: what does Good data look like? Dimensional schemas Dimension Tables: characteristics of data Fact Tables: measurements required for reports We do not go into implementationTextbook: The Data Warehouse ETL Toolkit Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data, Kimball, Caserta, Wiley Technology Publishing, ISBN: 0764567578 Topics: Decision Support Systems: history, give examples of how they work without data warehouse Requirements Gathering: who wants to see what, when, how often, from where. Is the data available, must it be calculated? Data Analysis: see the big picture, various types of source data: flat file, relational db, different hardware, different software, owners of data, data definitions ETL Processes & Deliverables Cleaning & Conforming: what does Good data look like? Dimensional schemas Dimension Tables: characteristics of data Fact Tables: measurements required for reports We do not go into implementation

    29. Data Warehouse ETL Processes & Deliverables Cleaning & Conforming Valid, missing Address, gender Staging areas Schemas Dimension Tables Fact Tables Use tools to extract data from the various data sources (Excel files, flat files, Access, Oracle, DB2, SQL Server databases, etc) Determine from requirements what is the “one version of the truth” about the data Use scripting tools to transform the data and move to a staging area or data mart or datawarehouseUse tools to extract data from the various data sources (Excel files, flat files, Access, Oracle, DB2, SQL Server databases, etc) Determine from requirements what is the “one version of the truth” about the data Use scripting tools to transform the data and move to a staging area or data mart or datawarehouse

    30. Key Concepts Design and plan the data extraction Data source type => extract/load => Target database Analyze the source data Column name, data type, delimiter, transformation Target Database Determine logical data mapping

    31. Logical Data Mapping

    32. Slowly Changing Dimensions Updating and inserting records into a data warehouse dimension tables Type 1 New changes will overwrite existing data Type 2 New changes create new records instead of updating existing ones. Will change date to indicate current or expired record. Type 3 Limited historical data; add columns for “old” value, update “new” data

    33. Data Mining The process of identifying patterns in data Goes beyond simple querying of the database Goes beyond multi-dimensional database queries as well

    34. Data Mining Data Mining works for problems like: Develop a general profile for credit card customers … Differentiate individuals who are poor credit risks … Determine what characteristics differentiate male & female investors.

    35. Data Mining vs. Data Query Use data query if you already almost know what you are looking for. Use data mining to find regularities in data that are not obvious.

    36. Data Mining Applications Fraud detection Targeted Marketing Risk Management Business Analysis

    37. Origins of Data Mining Mathematics Statistics Numerical Analysis Artificial Intelligence/Machine Learning Computer Science Data Storage and Manipulation

    38. How does Data Mining work? Uses induction-based learning: The process of forming general concept definitions by observing specific examples of concepts to be learned.

    39. How does Data Mining work?

    40. How does Data Mining work?

    41. Data Mining Process

    42. Overview of Mining Strategies

    43. Skills Written communication Problem Solving Analytical Troubleshooting Software Microsoft SQL Server Management Studio SQL Server BI Development Studio SQL Server Reporting Services Tableau

    44. E-Modularity Delivery online Available across the country Available across foreign lands Each class has modules How to get started Introduction to the topic Intermediate Advanced Each with hands on applied assessments

    45. Delivery Methods Use of Camtasia for Software demonstrations PowerPoint lectures Elluminate Camtasia Relay You tube

    46. Marketing Internal Faculty Career center Workforce, Workfirst Community Relations office (press release) External Agency Web pages, brochures, posters, radio ad Continuing Education: Chamber of Commerce contacts, memberships to business organizations External: grant from the state for high-demand areas - jobsExternal: grant from the state for high-demand areas - jobs

    47. Higher Level Designed for those students already in the field: database analyst, developer, business analyst How do we prepare students? Certificates as a stepping ladder

    48. Certificates Database Analyst (6 classes) SA & D, programming, reporting, spreadsheets, db theory 2 quarters full-time/ 3 quarters part-time Database Report Developer (3 additional classes) Programming, SQL, group processes Web site: www.bellevuecollege.edu/prog Relational DB Analyst: Certificate of Accomplishment Relational DB Developer: Certificate of AchievementRelational DB Analyst: Certificate of Accomplishment Relational DB Developer: Certificate of Achievement

    49. Certificates Business Intelligence Analyst (5 classes) Analytics, Multi-dimensional analysis, data warehousing, data mining, statistics, general business 2 quarters full-time/ 3 quarters part-time Business Intelligence Developer (4 additional classes) Dimensional modeling, data visualization, multi-dimensional analysis II, data warehousing II (more programming with SQL Server) Web site: www.bellevuecollege.edu/prog/bi BI Analyst: Certificate of Accomplishment BI Developer: Certificate of AchievementBI Analyst: Certificate of Accomplishment BI Developer: Certificate of Achievement

    50. Student Placement Outside Businesses have found our program Hiring Agencies/Recruiters Microsoft Consulting firms Coldwater Creek Start-ups Advisory Board volunteers Small Businesses Curriculum To work with the students who are in the program.To work with the students who are in the program.

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