Foundations of business intelligence databases and information management
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This document discusses key concepts related to databases and business intelligence. It defines common terms like databases, records, fields, and entities. It explains how relational database management systems (RDBMS) represent data in tables and allow querying, manipulation, and reporting of data through SQL. It also discusses data warehousing, analytics tools, data mining, and ensuring high quality data. The goal is to provide organizations with tools and technologies to access information from databases and improve business performance.
Foundations of business intelligence databases and information management
- 1. Foundations of Business Intelligence: Databases and Information Management
- 2. Terminologies Used…. Analytic Entity Attribute Entity-relationship diagram Big data Field Bit File Byte Foreign key Data administration Hadoop Data cleansing In-memory computing Data definition Information policy Data dictionary Key field Data governance Non-relational database management systems Data inconsistency Normalization Data manipulation language Online analytical processing (OLAP) Data mart Primary key Data mining Program-data dependence Data quality audit Record Data redundancy Referential integrity Data warehouse Relational DBMS Database Sentiment analysis Database administration Structured Query Language (SQL) Database management system (DBMS) Text mining Database server Tuple Web mining
- 3. Learning Objectives • 1 What are the problems of managing data resources in a traditional file environment? • 2 What are the major capabilities of database management systems (DBMS), and why is a relational DBMS so powerful? • 3 What are the principal tools and technologies for accessing information from databases to improve business performance and decision making? • 4 Why are information policy, data administration, and data quality assurance essential for managing the firm’s data resources?
- 4. Video Cases • Case 1: Dubuque Uses Cloud Computing and Sensors to Build a Smarter City • Case 2: Brooks Brothers Closes In on Omnichannel Retail • Case 3: Maruti Suzuki Business Intelligence and Enterprise Databases
- 5. File Organization Terms and 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
- 7. Problems with the Traditional File Environment • Files maintained separately by different departments • Data redundancy • Data inconsistency • Program-data dependence • Lack of flexibility • Poor security • Lack of data sharing and availability
- 9. Database Management Systems • Database – Serves many applications by centralizing data and controlling redundant data • 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 centrally manage data and data security
- 10. Human Resources Database with Multiple Views
- 11. 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. 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
- 14. The Three Basic Operations of a Relational DBMS
- 15. Capabilities of Database Management Systems • Data definition capability • Data dictionary • Querying and reporting • Data manipulation language • Structured Query Language (SQL) • Many DBMS have report generation capabilities for creating polished reports (Microsoft Access)
- 16. Access Data Dictionary Features
- 17. Example of an SQL Query
- 18. An Access Query
- 19. Designing Databases • Conceptual design vs. physical design • Normalization • Streamlining complex groupings of data to minimize redundant data elements and awkward many-to-many relationships • Referential integrity • Rules used by RDBMS to ensure relationships between tables remain consistent • Entity-relationship diagram • A correct data model is essential for a system serving the business well
- 20. An Unnormalized Relation for Order
- 21. Normalized Tables Created from Order
- 23. Non-relational Databases and Databases in the Cloud • Non-relational databases: “NoSQL” • More flexible data model • Data sets stored across distributed machines • Easier to scale • Handle large volumes of unstructured and structured data • Databases in the cloud • Appeal to start-ups, smaller businesses • Amazon Relational Database Service, Microsoft SQL Azure • Private clouds
- 24. The Challenge of Big Data • Big data • Massive sets of unstructured/semi-structured data from web traffic, social media, sensors, and so on • Volumes too great for typical DBMS • Petabytes, exabytes of data • Can reveal more patterns, relationships and anomalies • Requires new tools and technologies to manage and analyze
- 25. Business Intelligence Infrastructure (1 of 3) • Array of tools for obtaining information from separate systems and from big data • 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
- 26. Business Intelligence Infrastructure (2 of 3) • Data marts – Subset of data warehouse – Typically focus on single subject or line of business • 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 Yahoo, NextBio
- 27. Business Intelligence Infrastructure (3 of 3) • 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
- 28. Contemporary Business Intelligence Infrastructure
- 29. 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 - Online Analytical Processing) • Data mining • Text mining • Web mining
- 30. 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
- 32. 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 • Forecasting
- 33. Text Mining and Web Mining • Text mining • Extracts key elements from large unstructured data sets • Sentiment analysis software • Web mining • Discovery and analysis of useful patterns and information from web • Web content mining • Web structure mining • Web usage mining
- 34. 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 the 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
- 35. Linking Internal Databases to the Web
- 36. 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
- 37. Ensuring Data Quality • More than 25 percent of critical data in Fortune 1000 company databases are inaccurate or incomplete • Before new database is in place, a firm must: • Identify and correct faulty data • Establish better routines for editing data once database in operation • Data quality audit • Data cleansing