ITM 102 Chapter 6: Business Intelligence & Databases

• ITM 102 Business Information Systems Winter 2025, Mahdi Abouei

Chapter 6: Foundations of Business Intelligence, Databases, and Information Management

Learning Outcomes

• Problems of managing data resources in a traditional file environment
• Major capabilities of database management systems (DBMS)
• Why a relational DBMS is so powerful
• Principal tools and technologies for accessing information from databases to improve business performance and decision-making
• Why data governance and data quality assurance are essential for managing the firm’s data resources

Data Hierarchy

• The data hierarchy consists of bits, bytes, fields, and components of data hierarchy

File Organization Terms and Concepts

• Database: A group of related files
• File: A group of records of the same type
• Record: A group of related fields
• Field: A group of characters as word(s) or number(s)
• Entity: A person, place, or thing on which to store information
• Attribute: Each characteristic, or quality, describing an entity

Problems with the Traditional File Environment

• Data maintained separately by different departments
• Data redundancy
• Data inconsistency
• Program-data dependence
• Lack of flexibility
• Poor security
• Lack of data sharing and availability

Database Management Systems (DBMS)

• Database serves many applications by centralizing data and controlling redundant data
• Database management system (DBMS) interfaces between applications and physical data files
• A DBMS separates logical and physical views of data, solving problems of traditional file environments
• DBMS benefits include controlling redundancy, eliminating inconsistency, uncoupling programs and data, and enabling managing data and data security centrally

Relational DBMS

• Represent data as two-dimensional tables
• Each table contains data on an entity and attributes
• Rows (tuples): records for different entities
• Fields (columns): represents attributes for an entity
• Key field: identifies each record uniquely
• Primary key: field in a table used for key fields
• Foreign key: Primary key used in the second table as a look-up field to identify records from the original table

Operations of a Relational DBMS

• SELECT: creates a subset of data of all records that meet stated criteria
• JOIN: combines relational tables to provide more information
• PROJECT: creates a subset of columns in the table, containing only information specified

Designing Databases

• Conceptual design vs. physical design is part of database design
• Normalization: Streamlining complex groupings of data to minimize redundant data elements and awkward many-to-many relationships is part of database design
• Referential integrity: Rules used by RDBMS to ensure relationships between tables remain consistent during database design
• Entity-relationship diagram: visual representation of entities and their relationships in a database

Non-Relational Databases

• Cloud Databases and Blockchain
• Non-relational databases are often called "No SQL"
• Non-relational databases have a more flexible data model
• Data sets are stored across distributed machines
• Easier to scale
• Can handle large volumes of unstructured and structured data
• Cloud databases appeal to start-ups and smaller businesses
• Amazon Relational Database Service and Microsoft SQL Azure are clous databases
• Private clouds are also cloud databases
• Distributed Databases are stored in multiple physical locations
• Google Spanner is a distributed database
• Blockchain uses distributed ledgers in a peer-to-peer distributed database
• Blockchain maintains a growing list of records and transactions shared by all
• Encryption identifies participants and transactions in blockchain
• Blockchain technology is used for financial transactions, supply chain, and medical records
• The foundation of cryptocurrencies is blockchain

The Challenge of Big Data

• Big data are massive sets of unstructured or semi-structured data from web traffic, social media, sensors, and so on
• Big data is too large for typical DBMS
• Requires new tools and technologies to manage and analyze
• It can reveal more patterns, relationships, and anomalies

Business Intelligence Infrastructure

• An array of tools for obtaining information from separate systems and big data
• Data warehouse
• Data mart
• Hadoop
• In-memory computing
• Analytical platforms

Data Warehouse

• Stores current and historical data from many core operational transaction systems
• Provides analysis and reporting tools

Data Marts

• Subset of data warehouse
• Typically focuses on a single subject or line of business

Hadoop

• Distributed parallel processing of big data across inexpensive computers
• Hadoop Distributed File System: data storage
• MapReduce: breaks data into clusters for work
• Hbase: No SQL database
• Used by Yahoo and NextBio

In-Memory Computing

• Used in big data analysis
• Use computer main memory (RAM) for data storage to avoid delays in retrieving data from disk storage
• Processing can be reduced from hours/days to seconds
• Requires optimized hardware

Analytic Platforms

• High-speed platforms using both relocation and non-relational tools
• Optimized for large datasets

Analytical Tools

• Tools for consolidating, analyzing, and providing access to vast amounts of data to make better business decisions
• Multidimensional data analysis (OLAP)
• Data mining
• Text mining
• Web mining

Online Analytical Processing (OLAP)

• Supports multidimensional data analysis
• Allows viewing data using multiple dimensions
• Each aspect of information (product, pricing, region, time) is a different dimension
• Enables rapid, online answers to ad hoc queries

Data Mining

• Finds hidden patterns and relationships in datasets
• Customer buying patterns are an example
• Associations
• Sequences
• Classification
• Clustering
• Forecasting

Text Mining and Web Mining

• Text mining extracts key elements from large unstructured text data sets
• Sentiment analysis software also helps with text mining
• Web mining involves the discovery and analysis of useful patterns and information from the web
• Types of web mining include web content mining, structure mining, and usage mining

Databases and the Web

• The web can make some internal databases available to customers or partners
• Advantages for using the web for database access include ease of use of browser software
• Having a web interface requires few or no changes to database
• Web interface is inexpensive to add to a database

Data Governance

• Policies and procedures to manage data as an organizational resource
• Establishes rules for sharing, disseminating, acquiring, standardizing, classifying, and inventorying information
• An organization's information policy that specifies that only selected members of a particular department can view certain information

Data Quality Assurance

• Before a new database is in place, the firm must identify and correct faulty data
• Establish and maintain better routines for editing data once database is in operation
• Data quality audit
• Data cleansing

Discussion Questions

• What are the differentiating IT capabilities that are expected from a bank compared to a retailer and why does IT costs so much more for a bank compared to a retailer?
• Where should CT Bank expect to spend more for in its IT using the IT infrastructure framework?
• What is CRM, and why is it important for CT Bank and CT Retail?
• Describe the Triangle reward system and why a customer would provide detailed information in return for rewards
• Provide one example of a digital-enabled banking service from the CTB webpage and compare to a similar banking service at a major Canadian bank