1. Data Resource Management & Business Intelligence
ACM 312 MIS Week 7

2. Organizing Data in a Traditional File Environment
File organization concepts
Database: Group of related files (logically integrated)
File: Group of records of same type
Record: Group of related fields
Field: Group of characters as word(s) or number
Describes an entity (person, place, thing on which we store information)
Attribute: Each characteristic, or quality, describing entity
E.g., Attributes Date or Grade belong to entity COURSE

3. Logical Data Elements 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
Logical Data Elements

4. Database Structures Common database structures… Hierarchical Network
Relational
Object-oriented
Multi-dimensional

5. Relational Structure Most widely used structure
Data elements are stored in tables
Row represents a record; column is a field
Can relate data in one file with data in another, if both files share a common data element

6. Relational Operations
Structured Query Language (SQL) standard language for relational database
Select
Create a subset of records that meet a stated criterion
Example: employees earning more than $30,000
Join
Combine two or more tables temporarily
Looks like one big table
Project
Create a subset of columns in a table

7. Multidimensional Model

8. Evaluation of Database Structures
Hierarchical
Works for structured, routine transactions
Can’t handle many-to-many relationships
Network
More flexible than hierarchical
Unable to handle ad hoc requests
Relational
Easily responds to ad hoc requests
Easier to work with and maintain
Not as efficient/quick as hierarchical or network

9. Object-Oriented DBMS (OODBMS)
Stores data and procedures as objects
Objects can be graphics, multimedia, Java applets
Relatively slow compared with relational DBMS for processing large numbers of transactions
Hybrid object-relational DBMS: Provide capabilities of both OODBMS and relational DBMS
Databases in the cloud
Typically less functionality than
on-premises DBs
Amazon Web Services, Microsoft
SQL Azure

10. 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
Data manipulation language: Used to add, change, delete, retrieve data from database
Structured Query Language (SQL)
Microsoft Access user tools for generation SQL
Many DBMS have report generation capabilities for creating polished reports (Crystal Reports)
Database Administrator (DBA) designing, normalization, etc.

11. DBA is responsible for…
Designing Databases
Conceptual (logical) design: Abstract model from business perspective
Physical design: How database is arranged on direct-access storage devices
Design process identifies
Relationships among data elements, redundant database elements
Most efficient way to group data elements to meet business requirements, needs of application programs
Normalization
Streamlining complex groupings of data to minimize redundant data elements and awkward many-to-many relationships

12. Database Development

13. Database Planning Entity-relationship diagram
Used by database designers to document the data model
Illustrates relationships between entities
Distributing databases: Storing database in more than one place
Partitioned: Separate locations store different parts of database
Replicated: Central database duplicated in entirety at different locations

14. Logical and Physical Database Views

15. Types of Databases

16. Using Databases to Improve Business Performance and
Decision Making
Very large databases and systems require special capabilities, tools
To analyze large quantities of data
To access data from multiple systems
Three key techniques
Data warehousing
Data mining
Tools for accessing internal databases through the Web

17. Data Warehouse and Data Marts

18. Data Warehouse Components
Static data extracted from other databases
Central source of data that has been cleaned, transformed, and cataloged
Filtering out unwanted data
Correcting incorrect data
Aggregating data into new data subsets
Data is used for data mining, analytical processing, analysis, research, decision support
May be divided into data marts
Subsets of data that focus on specific aspects of a company (department or business process)

19. Components of Web-Based System
Hypermedia Databases
Hyperlinked pages of multimedia
Text
Graphics
Photographs
Video clips
Audio segments
Interrelated hypermedia page elements Rather than interrelated data records

20. 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
E.g., how many washers sold in the East in June compared with other regions?
OLAP enables rapid, online answers to ad hoc queries
MULTIDIMENSIONAL DATA MODEL

21. Aspects of Data Mining More discovery driven than OLAP
Finds hidden patterns, relationships in large databases and infers rules to predict future behavior
E.g., Finding patterns in customer data for one-to-one marketing campaigns or to identify profitable customers.
Types of information obtainable from data mining
Associations: Occurrences linked to single event
Sequences: Events linked over time
Classification: Recognizes patterns that describe group to which item belongs
Clustering: Finds groupings within data
Forecasting: Uses series of existing values to forecast what other values will be

22. Why Data Warehousing & Data Mining?
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 query, analysis, and reporting tool
Data mining is the process of analyzing data warehouses to reveal hidden patterns and trends
Market-basket analysis to identify new product bundles
Find root cause of quality or manufacturing problems
Prevent customer attrition
Acquire new customers
Cross-sell to existing customers
Profile customers with more accuracy

23. What is Business Intelligence then?
Tools for consolidating, analyzing, and providing access to vast amounts of data to help users make better business decisions
Principle tools include:
Software for database query and reporting
Online analytical processing (OLAP)
Data mining
Business Intelligence techniques related to data mining are; predictive analysis and text mining
Web mining

24. Business Intelligence cont.
Predictive analysis
Uses data mining techniques, historical data, and assumptions about future conditions to predict outcomes of events
E.g., Probability a customer will respond to an offer
Text mining
Extracts key elements from large unstructured data sets (e.g., stored s)
Web mining
Discovery and analysis of useful patterns and information from WWW
E.g., to understand customer behavior, evaluate effectiveness of Web site, etc.
Web content mining
Knowledge extracted from content of Web pages
Web structure mining
E.g., links to and from Web page
Web usage mining
User interaction data recorded by Web server

25. BI in Practice
Travelocity has launched a new project to help it mine almost 600,000 unstructured comments (to extract meaning) to identify facts, opinions, requests, trends, and trouble spots which combined with structured data can identify trends so that it can better monitor and respond to customer service issues
Applebee’s
Uses back-of-house data to analyze food preparation
Combined with front-of-house data
Determine time spent with customer
What is selling / what to order / what to promote

26. Managing Data Resources
Establishing an information policy
Firm’s rules, procedures, roles for sharing, managing, standardizing data
Data administration:
Firm function responsible for specific policies and procedures to manage data
Data governance:
Policies and processes for managing availability, usability, integrity, and security of enterprise data, especially as it relates to government regulations
Database administration:
Defining, organizing, implementing, maintaining database; performed by database design and management group

27. Ensuring data quality Data quality audit: Data cleansing
More than 25% of critical data in Fortune company databases are inaccurate or incomplete
Most data quality problems stem from faulty input
Before new database in place, need to:
Identify and correct faulty data
Establish better routines for editing data once database in operation
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

28. Managerial activity
Uses data management, data warehousing, and other IS technologies
Companies should have good information about what goes on in the data center in terms of systems and how they interact with each other and interface with the business.
Manages data for business stakeholders. IT integration and adoption issues can make or brake merger and acquisition activities.
Experts and IT managers agree that companies will feel the full impact of the merger and acquisition frenzy directly in their data centers.
It is important to document the knowledge from those people and figure out how to make the processes work with only a handful of employees.

29. Database Management System
DBMS is a software package that is used to…
Create new databases and database applications
Maintain the quality of the data in an organization’s databases
Use the databases of an organization to provide the information needed by end users

30. Application Development
DBMS tools
4GL programming language
Built-in software development tools
Data manipulation language (DML) statements
Eliminate conventional programming
Applications
Data entry screens
Forms
Reports
Web pages