Lecture 5_Foundation of Business Intelligence-Databasea and IS.pdf

Lecture 5 BHMS4472 ICT in Business Foundation of Business Intelligence: Databases and Information Systems ICT (Information, Communication, and Technology) Learning Objectives What are the problems...

Lecture 5 BHMS4472 ICT in Business Foundation of Business Intelligence: Databases and Information Systems ICT (Information, Communication, and Technology) 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 data governance and data quality assurance essential for managing the firm’s data resources? Astro: Leveraging Data for Customer- driven Service Problem – Increasing local and international competition – Data fragmented across multiple systems and files Solutions – Astro Data Lake – Simple Storage Service – Elastic Compute Cloud Illustrates the importance of data management for better decision making and customer analysis 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 Figure 6.1 The Data Hierarchy 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 Traditional File Processing 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 Human Resources Database with Multiple Views 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 Figure 6.4 Relational Database Tables 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 The Three Basic Operations of a Relational DBMS Capabilities of Database Management Systems Data definition Data dictionary Querying and reporting – Data manipulation language ▪ Structured Query Language (SQL) Many DBMS have report generation capabilities for creating polished reports (Microsoft Access) Access Data Dictionary Features Example of an SQL Query An Access Query 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 An Unnormalized Relation for Order Normalized Tables Created from Order An Entity-Relationship Diagram Non-Relational Databases, Cloud Databases and Blockchain (Slide 1 of 3) 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 Non-Relational Databases, Cloud Databases and Blockchain (Slide 2 of 3) Cloud databases – Appeal to start-ups, smaller businesses – Amazon Relational Database Service, Microsoft SQL Azure – Private clouds Distributed databases – Stored in multiple physical locations – Example: Google Spanner Interactive Session: Technology: New Cloud Database Tools Help Vodafone Fiji Make Better Decisions – Case Study Class discussion – Define the problem faced by Vodafone Fiji. What management, organization, and technology factors contributed to the problem? – Evaluate Oracle Autonomous Data Warehouse and Oracle Analytics Cloud as a solution for Vodafone Fiji? – How did the new Oracle tools change decision making at Vodafone Fiji? – Was using cloud services advantageous for Vodafone Fiji? Explain your answer. Non-relational Databases, Cloud Databases, and Blockchain (Slide 3 of 3) 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 How Blockchain Works 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 Business Intelligence Infrastructure (1 of 4) Array of tools for obtaining information from separate systems and from big data – Data warehouse – Data mart – Hadoop – In-memory computing – Analytical platforms Business Intelligence Infrastructure (2 of 4) 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 – Typically focus on single subject or line of business Business Intelligence Infrastructure (3 of 4) 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: No SQL database – Used by Yahoo, NextBio Business Intelligence Infrastructure (4 of 4) 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 Contemporary Business Intelligence Infrastructure 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 Online Analytical Processing (O L A P) 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 to the sales forecast? OL AP enables rapid, online answers to ad hoc queries Multidimensional Data Model 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 Text Mining and Web Mining Text mining – Extracts key elements from large unstructured text 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 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/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 Linking Internal Databases to the Web Data Governance Data governance – Encompasses policies and procedures through which data can be managed as an organizational resource. – Establishes rules for sharing, disseminating, acquiring, standardizing, classifying and inventorying information ▪ Example: Firm information policy that specifies that only selected members of a particular department can view certain information Data Quality Assurance 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 THANK YOU 43