Business Intelligence Analytics And Data Science A Managerial Perspective 4/e

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Business Intelligence Analytics And Data Science A Managerial Perspective 4/e – Business Intelligence, Analytics, and Data Science: A Management Perspective Fourth Edition Chapter 3 Descriptive Analysis II: Business Intelligence and Data Warehousing If this PowerPoint presentation contains math equations, make sure your computer has the following installed: 1) MathType Plugin 2) Math Player (free versions available) 3) NVDA Reader (free versions available) The slides in this presentation contain hyperlinks. JAWS users should be able to get a list of links with INSERT+F7 Copyright © 2018, 2014, 2011 Pearson Education, Inc. All rights reserved. All rights reserved

3.1 Understand the basic definitions and concepts of data warehousing 3.2 Understand the architecture of data warehousing 3.3 Explain the processes used in data warehousing development and management 3.4 Explain the processes of data warehousing 3.5 Explain the role of data warehouses in decision support Do slide 2 the textbook list number is LO and the description.

Business Intelligence Analytics And Data Science A Managerial Perspective 4/e

3.6 Define the data integration and extraction, transformation and load (E T L) process 3.7 Understand the essentials of Business Performance Management (B P M) 3.8 Learn the Balanced Scorecard and Six Sigma as a performance measurement system Slide 3 of the LO Numbers and List Handbook with statements.

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4 Opening Vignette Tackling Tax Fraud with Business Intelligence and Data Warehousing Why is it important for the IRS and US state governments to use data warehousing and business intelligence (BI) tools in government revenue management? What challenges did the state of Maryland face regarding tax fraud? Which solution did they choose? Do you agree with their approach? Why? What were their results? Has the investment in BI and data warehousing paid off? What other issues and challenges do you think federal and state governments have that could benefit from BI and data warehousing?

B I was used for everything related to using data to support management decisions, now it is part of Business Analytics B I = Descriptive Analysis.

A physical repository where relational data is specially organized to provide Internet-wide, sanitized data in a standardized format. A relational database? (So ​​what’s the difference?) “A data warehouse is a collection of integrated, subject-oriented databases designed to support DSS tasks, where each unit of data is volatile and associated with a moment in time.”

9 Data Mart A departmental small-scale “DW” that stores only limited/relevant data Dependent Data Mart A subset created directly from the Data Warehouse Independent Data Mart A small data warehouse for a strategic business unit or department is created

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A type of database often used as a staging area for a data warehouse. Describes its acquisition and use

11 Application Case 3.1 A Better Data Plan: Leveraging a Better Established T E L C O to Stay Ahead in a Competitive Industry Questions for Discussion What are the key challenges? How can data warehousing and data analytics help T E L C O’s meet their challenges? Why do you think T E L C O’s are suited to take full advantage of data analytics?

Data acquisition software (back-end) Data warehouse that contains data and software Client software (front-end) that allows users to query and analyze data from the warehouse Two-tier architecture, three In a-tier architecture, the first two layers are shared. A … sometimes there is only one level?

Considerations when deciding which architecture to use: Which database management system (DBMS) should be used? Is parallel processing and/or partitioning used? Are data migration tools used to load the data warehouse? What tools will be used to collect and analyze the data?

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Dependency of information between organizational units Upper management information needs Urgency of data warehousing needs Nature of end-user tasks Limited resources Strategic view of data warehousing Prior to implementation Compatibility with existing systems Compatibility with existing systems Perceived competence of internal IT staff Technical issues Societal/political factors

E T L = Extract Transform Load Data Integration Integration involves three key processes: data access, data federation, and committing changes. Enterprise Application Integration (E A I) A technology that provides a vehicle for pushing data from source systems to a data warehouse Enterprise Information Integration (EI I) An evolving tool space that promises real-time integration of data from disparate sources, such as relative or multiple . Databases, web services, etc.

Issues affecting the purchase of an ETL tool Data transformation tools are expensive Data transformation tools can have a long learning curve Important criteria when selecting an ETL tool Ability to read and write to an unlimited number of data sources/architecture Automatic capture of metadata and delivery Open standards compatibility history User-friendly interface for developer and functional user

Discussion Questions What is B I G S? What were the challenges, proposed solutions and results achieved with B I G S?

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Approaches to data warehouse development Inmon model: EDW approach (top-down) Kimball model: data mart approach (bottom-up) Which model is best? Table 3.3 provides a comparative analysis between EDW and Data Mart, another alternative hosted data warehouse.

Table 3.3 Contrast between D M and ED W Development Approaches Effort D M Approach ED W Approach Scope One Subject Area Multiple Subjects Development Time Months Years Development Cost $10,000 to $100,000+ $1,000,000+ Development Complexity Minimum to Share Common (within the business area) General ( throughout the enterprise) Resources Few operational and remote systems Many operational and remote systems Size megabytes to gigabytes to petabytes Close time horizon Current and historical data Historical data Historical data changes

Table 3.3 Database workgroups or standard database servers Enterprise database servers Users Number of concurrent users 10 s 100 s to 1000 s Types of users Business area analysts and managers Enterprise analysts and senior executives Business Spotlight activities within a business area Enhance cross-functional optimization and decision making

28 Application Case 3.3 Using Teradata Analytics for S A P Solutions Accelerating Big Data Delivery Discussion Questions What were the challenges faced by a large Dutch retailer? What was the proposed multi-vendor solution? What were the implementation challenges? What lessons have been learned?

Business Intelligence: A Managerial Approach: Turban, Efraim, Sharda, Ramesh, Delen, Dursun, King, David, Aronson, Janine E.: 9780136100669: Books

Benefits: Requires minimal investment in infrastructure Frees up capacity on internal systems Frees up cash flow Makes powerful solutions affordable Enables solutions that drive growth Higher quality hardware and software The hardware provides a fast connection … more in the book

Dimensional Modeling A query-based system that supports access to large numbers of queries Star Schema The most common and simplest form of dimensional modeling consists of a fact table surrounded by multiple dimensions Dimension Tables Linked Snowflakes Schema An extension of Star Schema where the diagram is in the form of a snowflake.

31 Multidimensionality Ability to organize, present and analyze data in multiple dimensions, such as sales by region, by product, by vendor and by time (four dimensions) Multidimensional presentation dimensions: products, vendors, market segments, business units, geographic locations, distribution channels, countries or industry statistics: money, sales volume, headcount, inventory profitability, actual vs. forecast Time: daily, weekly, monthly, quarterly Monthly or yearly

O L T P (Online Transaction Processing) Capture and store data from E R P, CRM, P O S, … Main focus is on performing routine tasks O L A P (Online Analytical Processing) Convert data into information for decision support Data Cubes, Drill Down/Roll Up, Slice and Dice, … Request ad hoc reports Create statistics and other analyzes Create multimedia-based applications … More in the book

Business Intelligence Vs Data Analytics: 7 Critical Differences

Standard O L T P O L A P Objectives To support decision making in day-to-day operations and provide answers to business and management questions Data source Transactional database (a general purpose data store primarily focused on efficiency and stability) Data warehouse or dM (a peculiar data repository) primary focused on accuracy and completeness) reporting routine, periodic, narrowly focused reports ad hoc, multiple, broadly focused reports and queries resource intensive general relational databases multiprocessor, large capacity, dedicated database execution speed (business transfer rate) high speed and registration reports slow (large capacity, complex, large-scale searches)

Dice – A segment on more than two dimensions Drill Down/Up – Navigate between data levels from the most summary (top) to the most detailed (bottom) Rollup – All data relationships for one or more dimensions Aggregate pivot – Used to define the dimensions of a report or ad hoc query page view

Starting with the wrong sponsorship chain Raising expectations that you cannot meet Engaging in politically stupid behavior Simply cluttering the data warehouse with information

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