ISTP TU06 - Foundations of Business Intelligence

INFORMATION SYSTEMS: THEORY & PRACTICE TU6: Foundations of Business Intelligence: Databases and Information Management Prof. Dr. Paul Drews Learning Objectives ▪ What are the problems of managing data resources in a traditional file environment? ▪ What are the major capabilities of database manag...

INFORMATION SYSTEMS: THEORY & PRACTICE TU6: Foundations of Business Intelligence: Databases and Information Management Prof. Dr. Paul Drews Learning Objectives ▪ What are the problems of managing data resources in a traditional file environment? ▪ What are the major capabilities of database management systems (DBMS) and why is a relational DBMS so powerful? ▪ What are the principal tools and technologies for accessing information from databases to improve business performance and decision making? ▪ Why are information policy, data administration, and data quality assurance essential for managing the firm’s data resources? 2 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Astro Case 3 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Agenda 1. Managing Data in a Traditional File Environment 2. Database Management Systems 3. Tools for Improving Business Performance and Decision Making 4. Managing the Firm’s Data Resources 4 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Managing Data in a Traditional File Environment File Organization Concepts ▪ Database: Group of related files ▪ File: Group of records of same type ▪ Record: Group of related fields ▪ Field: Group of characters as word(s) or number(s) ▪ Entity: Person, place, thing on which we store information ▪ Attribute: Each characteristic, or quality, describing entity ▪ Example: Attributes DATE or GRADE belong to entity COURSE 5 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS The Data Hierarchy A computer system organizes data in a hierarchy that starts with the bit, which represents either a 0 or a 1. Bits can be grouped to form a byte to represent one character, number, or symbol. Bytes can be grouped to form a field, and related fields can be grouped to form a record. Related records can be collected to form a file, and related files can be organized into a database. FIGURE 6-1 6 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Managing Data in a Traditional File Environment Problems with the Traditional File Environment (files maintained separately by different departments) ▪ Data redundancy: ▪ Presence of duplicate data in multiple files ▪ Data inconsistency: ▪ Same attribute has different values ▪ Program-data dependence: ▪ When changes in program requires changes to data accessed by program ▪ Lack of flexibility ▪ Poor security ▪ Lack of data sharing and availability 7 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Traditional File Processing The use of a traditional approach to file processing encourages each functional area in a corporation to develop specialized applications. Each application requires a unique data file that is likely to be a subset of the master file. These subsets of the master file lead to data redundancy and inconsistency, processing inflexibility, and wasted storage resources. FIGURE 6-2 8 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Agenda 1. Managing Data in a Traditional File Environment 2. Database Management Systems 3. Tools for Improving Business Performance and Decision Making 4. Managing the Firm’s Data Resources 9 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Capabilities of Database Management Systems (DBMSs) Database ▪ Serves many applications by centralizing data and controlling redundant data Source: insights.dice.com/wp-content/uploads/2012/08/shutterstock_94945777.jpg 10 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Capabilities of Database Management Systems (DBMSs) Database management system (DBMS) ▪ Interfaces between applications and physical data files ▪ Separates logical and physical views of data ▪ Solves problems of traditional file environment ▪ Controls redundancy ▪ Eliminates inconsistency ▪ Uncouples programs and data ▪ Enables organization to central manage data and data security 11 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Human Resources Database with Multiple Views FIGURE 6-3 A single human resources database provides many different views of data, depending on the information requirements of the user. Illustrated here are two possible views, one of interest to a benefits specialist and one of interest to a member of the company’s payroll department. 12 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Capabilities of Database Management Systems (DBMSs) ▪ Relational DBMS ▪ Represent data as two-dimensional tables ▪ Each table contains data on entity and attributes ▪ Table: grid of columns and rows ▪ Rows (tuples): Records for different entities ▪ Fields (columns): Represents attribute for entity ▪ Key field: Field used to uniquely identify each record ▪ Primary key: Field in table used for key fields ▪ Foreign key: Primary key used in second table as look-up field to identify records from original table 13 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Relational Database Tables A relational database organizes data in the form of two-dimensional tables. Illustrated here are tables for the entities SUPPLIER and PART showing how they represent each entity and its attributes. Supplier_Number is a primary key for the SUPPLIER table and a foreign key for the PART table. FIGURE 6-4 14 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Capabilities of Database Management Systems (DBMSs) Operations of a Relational DBMS ▪ Three basic operations used to develop useful sets of data ▪ SELECT: Creates subset of data of all records that meet stated criteria ▪ JOIN: Combines relational tables to provide user with more information than available in individual tables ▪ PROJECT: Creates subset of columns in table, creating tables with only the information specified 15 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS The Three Basic Operations of a Relational DBMS FIGURE 6-5 The select, join, and project operations enable data from two different tables to be combined and only selected attributes to be displayed. 16 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Capabilities of Database Management Systems (DBMSs) Capabilities of database management systems ▪ Data definition capability: Specifies structure of database content, used to create tables and define characteristics of fields ▪ Data dictionary: Automated or manual file storing definitions of data elements and their characteristics ▪ Querying and reporting ▪ Data manipulation language: Used to add, change, delete, retrieve data from database ▪ Structured Query Language (SQL) ▪ Many DBMS have report generation capabilities for creating polished reports (Microsoft Access) 17 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Microsoft Access Data Dictionary Features FIGURE 6-6 Microsoft Access has a rudimentary data dictionary capability that displays information about the size, format, and other characteristics of each field in a database. Displayed here is the information maintained in the SUPPLIER table. The small key icon to the left of Supplier_Number indicates that it is a key field. 18 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Example of an SQL Query SELECT PART.Part_Number, PART.Part_Name, SUPPLIER.Supplier_Number, SUPPLIER.Supplier_Name FROM PART, SUPPLIER WHERE PART.Supplier_Number = SUPPLIER.Supplier_Number AND Part_Number = 137 OR Part_Number = 150; FIGURE 6-7 Illustrated here are the SQL statements for a query to select suppliers for parts 137 or 150. They produce a list with the same results as Figure 6-5. 19 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS An Access Query FIGURE 6-8 Illustrated here is how the query in Figure 6-7 would be constructed using Microsoft Access query building tools. It shows the tables, fields, and selection criteria used for the query. 20 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Capabilities of Database Management Systems (DBMSs) ▪ Designing Databases ▪ Conceptual design: abstract model from business perspective ▪ Physical design: how database is arranged on direct-access storage devices ▪ Normalization ▪ Streamlining complex groupings of data to minimize redundant data elements and awkward many- to-many relationships 21 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Capabilities of Database Management Systems (DBMSs) ▪ Referential integrity rules ▪ Used by RDBMS to ensure relationships between tables remain consistent ▪ Entity-relationship diagram ▪ Used by database designers to document the data model ▪ Illustrates relationships between entities ▪ A correct data model is essential for a system serving the business well 22 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS An Unnormalized Relation for Order FIGURE 6-9 An unnormalized relation contains repeating groups. For example, there can be many parts and suppliers for each order. There is only a one-to-one correspondence between Order_Number and Order_Date. 23 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Normalized Tables Created from Order FIGURE 6-10 After normalization, the original relation ORDER has been broken down into four smaller relations. The relation ORDER is left with only two attributes and the relation LINE_ITEM has a combined, or concatenated, key consisting of Order_Number and Part_Number. 24 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS An Entity-Relationship Diagram FIGURE 6-11 This diagram shows the relationships between the entities SUPPLIER, PART, LINE_ITEM, and ORDER that might be used to model the database in Figure 6-10. 25 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Your task ▪ Design a database for running a webshop for selling T-shirts. ▪ Which entities are relevant? ▪ Which attributes are relevant for the entities? ▪ How would you design the tables and columns? 26 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Capabilities of Database Management Systems (DBMSs) Non-relational databases: “No SQL” ▪ More flexible data model ▪ Data sets stored across distributed machines ▪ Easier to scale ▪ Handle large volumes of unstructured and structured data Source: docs.microsoft.com/de-de/azure/documentdb/media/documentdb-nosql-vs-sql/nosql-vs-sql-overview.png 27 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Capabilities of Database Management Systems (DBMSs) Databases in the cloud ▪ Appeal to start-ups, smaller businesses ▪ Amazon Relational Database Service, Microsoft SQL Azure ▪ Private clouds Source: caspio.com/wp-content/uploads/2015/05/caspio- features-illustr_cloud-data_3_2x.png 28 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Agenda 1. Managing Data in a Traditional File Environment 2. Database Management Systems 3. Tools for Improving Business Performance and Decision Making 4. Managing the Firm’s Data Resources 29 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Tools for Improving Business Performance and Decision Making Blockchain ▪ Distributed ledgers in a peer-to-peer distributed database ▪ Maintains a growing list of records and transactions shared by all ▪ Encryption used to identify participants and transactions ▪ Used for financial transactions, supply chain, and medical records ▪ Foundation of Bitcoin, and other crypto currencies Image Source: https://jaxenter.de 30 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS How Blockchain Works FIGURE 6-12 31 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Tools for Improving Business Performance and Decision Making Big data ▪ Massive sets of unstructured/semi-structured data from web traffic, social media, sensors, and so on ▪ Petabytes, exabytes of data ▪ Volumes too great for typical DBMS ▪ Can reveal more patterns, relationships and anomalies ▪ Requires new tools and technologies to manage and analyze 32 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Big Data Source: researchgate.net/profile/Javier_Andreu/publication/280124446/figure/fig15/AS:316675636383747@1452512765301/Six-V%27s-of-big-data-value-volume-velocity-variety-veracity-and-variability-which.png 33 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Tools for Improving Business Performance and Decision Making Business intelligence infrastructure ▪ Today includes an array of tools for obtaining information from separate systems and from big data Contemporary tools ▪ Data warehouses ▪ Data marts ▪ Hadoop ▪ In-memory computing ▪ Analytic platforms 34 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Tools for Improving Business Performance and Decision Making Data warehouse ▪ Stores current and historical data from many core operational transaction systems ▪ Consolidates and standardizes information for use across enterprise, but data cannot be altered ▪ Provides analysis and reporting tools Data marts ▪ Subset of data warehouse ▪ Summarized or focused portion of data for use by specific population of users ▪ Typically focus on single subject or line of business 35 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Contemporary Business Intelligence Infrastructure A contemporary business intelligence infrastructure features capabilities and tools to manage and analyze large quantities and different types of data from multiple sources. Easy-to-use query and reporting tools for casual business users and more sophisticated analytical toolsets for power users are included. FIGURE 6-13 36 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Tools for Improving Business Performance and Decision Making Hadoop ▪ Enables distributed parallel processing of big data across inexpensive computers ▪ Key services ▪ Hadoop Distributed File System (HDFS): data storage ▪ MapReduce: breaks data into clusters for work ▪ Hbase: NoSQL database ▪ Used by Yahoo, NextBio 37 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Tools for Improving Business Performance and Decision Making In-memory computing ▪ Used in big data analysis ▪ Uses computers main memory (RAM) for data storage to avoid delays in retrieving data from disk storage ▪ Can reduce hours/days of processing to seconds ▪ Requires optimized hardware Analytic platforms ▪ High-speed platforms using both relational and non-relational tools optimized for large datasets 38 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Tools for Improving Business Performance and Decision Making Analytical tools: Relationships, patterns, trends ▪ Tools for consolidating, analyzing, and providing access to vast amounts of data to help users make better business decisions ▪ Multidimensional data analysis (OLAP) ▪ Data mining ▪ Text mining ▪ Web mining 39 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Tools for Improving Business Performance and Decision Making Online analytical processing (OLAP) ▪ Supports multidimensional data analysis ▪ Viewing data using multiple dimensions ▪ Each aspect of information (product, pricing, cost, region, time period) is different dimension ▪ Example: How many washers sold in the East in June compared with other regions? ▪ OLAP enables rapid, online answers to ad hoc queries 40 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Multidimensional Data Model The view that is showing is product versus region. If you rotate the cube 90 degrees, the face that will show product versus actual and projected sales. If you rotate the cube 90 degrees again, you will see region versus actual and projected sales. Other views are possible. FIGURE 6-14 41 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Tools for Improving Business Performance and Decision Making Data mining ▪ Finds hidden patterns, relationships in datasets ▪ Example: customer buying patterns ▪ Infers rules to predict future behavior ▪ Types of information obtainable from data mining: ▪ Associations ▪ Sequences ▪ Classification ▪ Clustering Source: copyrightuser.org/wp-content/uploads/2013/06/text_data_mining.jpg ▪ Forecasting 42 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Tools for Improving Business Performance and Decision Making Text mining ▪ Extracts key elements from large unstructured data sets ▪ Stored e-mails ▪ Call center transcripts ▪ Legal cases ▪ Patent descriptions ▪ Service reports, and so on ▪ Sentiment analysis software ▪ Mines e-mails, blogs, social media to detect opinions 43 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Tools for Improving Business Performance and Decision Making Web mining ▪ Discovery and analysis of useful patterns and information from web ▪ Understand customer behavior ▪ Evaluate effectiveness of Web site ▪ Web content mining ▪ Mines content of Web pages ▪ Web structure mining ▪ Analyzes links to and from Web page ▪ Web usage mining ▪ Mines user interaction data recorded by Web server 44 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Tools for Improving Business Performance and Decision Making Databases and the Web ▪ Many companies use the web to make some internal databases available to customers or partners ▪ Typical configuration includes: ▪ Web server ▪ Application server/middleware/CGI scripts ▪ Database server (hosting DBMS) ▪ Advantages of using web for database access: ▪ Ease of use of browser software ▪ Web interface requires few or no changes to database ▪ Inexpensive to add web interface to system 45 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Linking Internal Databases to the Web FIGURE 6-15 Users access an organization’s internal database through the web using their desktop PC browsers or mobile apps. 46 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Agenda 1. Managing Data in a Traditional File Environment 2. Database Management Systems 3. Tools for Improving Business Performance and Decision Making 4. Managing the Firm’s Data Resources 47 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Managing the Firm’s Data Resources Establishing an information policy ▪ Firm’s rules, procedures, roles for sharing, managing, standardizing data ▪ Data administration ▪ Establishes policies and procedures to manage data ▪ Data governance ▪ Deals with policies and processes for managing availability, usability, integrity, and security of data, especially regarding government regulations ▪ Database administration ▪ Creating and maintaining database 48 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Managing the Firm’s Data Resources Ensuring data quality ▪ More than 25 percent of critical data in Fortune 1000 company databases are inaccurate or incomplete ▪ Redundant data ▪ Inconsistent data ▪ Faulty input ▪ Before new database is in place, a firm must: ▪ Identify and correct faulty data ▪ Establish better routines for editing data once database in operation 49 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Managing the Firm’s Data Resources Data quality audit ▪ Structured survey of the accuracy and level of completeness of the data in an information system ▪ Survey samples from data files, or ▪ Survey end users for perceptions of quality Data cleansing ▪ Software to detect and correct data that are incorrect, incomplete, improperly formatted, or redundant ▪ Enforces consistency among different sets of data from separate information systems 50 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS Tasks for this week T6-1. Please actively read chapter 6 - including the case studies. (3-4 hours) T6-2. Introduction to blockchain technology and potential applications (15 minutes) Please view the following videos: https://www.youtube.com/watch?v=SSo_EIwHSd4 https://www.youtube.com/watch?v=G3psxs3gyf8 51 | ISTP | TU 6 | FOUNDATIONS OF BUSINESS INTELLIGENCE | PROF. DR. PAUL DREWS CONTACT PROF. DR. PAUL DREWS Institute of Information Systems Universitätsallee 1 | 21335 Lüneburg Fon 04131.677-1993 | [email protected] www.leuphana.de/institute/iis/personen/paul-drews