Ch 6 : Understanding Data Value and Relational Databases

Data Timeliness

Relevance of data based on current situation.

Data Quality

Accuracy and reliability of data for decision-making.

High-Quality Data

Data that significantly improves decision-making outcomes.

Low-Quality Data

Data that leads to poor business decisions and errors.

Data Stewardship

Management of data assets for quality and accessibility.

Data Governance

Overall management of data availability and integrity.

Master Data Management (MDM)

Ensures accuracy and consistency of critical data.

Data Validation

Testing to ensure data correctness and compliance.

Database

Stores data about objects, events, people, and places.

Database Management System (DBMS)

Software for creating and managing databases.

Structured Query Language (SQL)

Language for querying relational databases.

Transactional Data

Data generated from daily business transactions.

Analytical Data

Data used for analysis and decision-making.

Data Inconsistency

Discrepancies in data across different sources.

Data Integrity Issues

Problems affecting the accuracy of data.

Business Advantages of Relational Databases

Improved data management and decision-making capabilities.

Real-Time Data

Data available immediately as events occur.

Data Granularity

Level of detail in data representation.

Data Formats

Different structures for organizing data.

Data Entry Standards

Guidelines for consistent data input.

Customer Relationship Management

Strategies for managing company interactions with customers.

Marketing to Nonexistent Customers

Ineffective targeting due to poor data quality.

Query-by-example (QBE)

Graphical tool for designing database queries.

Entity

Rows in a database table representing objects.

Attribute

Columns in a table representing data characteristics.

Record

Collection of related data elements in a table.

Primary key

Unique identifier for a database table's record.

Foreign key

Field linking to a primary key in another table.

Data redundancy

Unnecessary duplication of data within a database.

Data integrity

Measures the accuracy and consistency of data.

Integrity constraint

Rules ensuring data quality and validity.

Relational integrity constraint

Enforces fundamental data relationships in databases.

Business-critical integrity constraint

Rules vital for organizational success and operations.

Scalability

Ability of a database to handle increased loads.

Performance

Efficiency of data storage and retrieval processes.

Increased data latency

Delay in storing or retrieving data.

Access control

Regulates user permissions to database resources.

Identity management

Controls user identities and access rights.

Spreadsheet limitations

Restricted rows, single-user access, and data visibility.

Increased flexibility

Database adapts quickly to user needs and changes.

Logical views

User-specific perspectives on the same data.

Physical view

Actual data storage layout on devices.

Database security features

Mechanisms to protect data from unauthorized access.

Password protection

Authentication method for securing database access.

Document Formats

Includes letters, memos, faxes, and reports.

Presentation Types

Product, strategy, process, financial, and competitor presentations.

Spreadsheet Types

Sales, marketing, financial, and order spreadsheets.

Database Types

Customer, employee, sales, and supplier databases.

Granularity Levels

Detail, summary, and aggregate reporting levels.

Detail Reports

Reports for each salesperson, product, and part.

Summary Reports

Reports for all sales personnel and products.

Aggregate Reports

Reports across departments and organizations.

Transactional Data

Includes sales receipts and airline tickets.

Analytical Data

Includes sales projections and product statistics.

Data Quality Characteristics

Includes accuracy, completeness, consistency, timeliness, uniqueness.

Accuracy in Data Quality

Checks for incorrect values in data.

Completeness in Data Quality

Ensures no missing values in data.

Consistency in Data Quality

Aggregate data agrees with detailed data.

Timeliness in Data Quality

Data is current for business needs.

Uniqueness in Data Quality

Each transaction represented only once.

Missing Data Example

No first name recorded for a customer.

Incomplete Data Example

Missing street address in customer record.

Inaccurate Data Example

Invalid email format for a customer.

Potential Wrong Data Example

Duplicate phone and fax numbers in records.

Probable Duplicate Data

Similar names and addresses suggest duplicates.

Relational Database Components

Includes customers, orders, products, and distributors.

DBMS Steps

Enter new customer, find order, enter products.

Business Advantages of Databases

Increased flexibility, scalability, integrity, and security.