6. Data and Analytics

Data and analytics help organizations describe what happened, understand what is changing, and support better decisions. This chapter introduces data, analytic work, and the quality problems that can make analysis misleading.

Video

What is Data Science? by IBM Technology

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Learning Objectives

By the end of this chapter, you will be able to:

1. Describe entities, records, identifiers, and relationships in an organizational dataset.

2. Distinguish warehouses, lakes, and operational storage by their intended uses.

3. Interpret dashboard measures and distinguish descriptive evidence from predictive claims.

4. Evaluate accuracy, completeness, consistency, timeliness, and data-quality controls.

Concept Map

Data and Analytics

Data

• Databases
• Transactions
• Metadata

Analytics

• Dashboards
• Patterns
• Decisions

Data Quality

• Accuracy
• Completeness
• Validation

Glossary

#	Term	Explanation
1	Data management	The practices for collecting, storing, securing, integrating, and using data effectively.
2	Database	An organized collection of data managed for efficient access and update.
3	Database management system	Software used to define, store, retrieve, secure, and administer data.
4	Relational database	A database organized into tables connected by keys.
5	Table	A database structure made of rows and columns.
6	Record	One row in a table, representing one entity or event.
7	Field	One column or attribute in a table.
8	Primary key	A field or combination of fields that uniquely identifies each record.
9	Foreign key	A field that links one table to a related record in another table.
10	Query	A request for data or analysis from a database.
11	SQL	A standard language for querying and managing relational databases.
12	Data warehouse	A curated store of historical data organized for reporting and analysis.
13	Data lake	A large repository that stores raw or lightly processed data in many formats.
14	Business intelligence	Tools and processes that turn organizational data into reports, dashboards, and insights.
15	Analytics	The use of data, methods, and models to describe, explain, predict, or prescribe action.
16	Dashboard	A visual display of key metrics and information used to monitor performance.
17	Data visualization	Graphical presentation of data to reveal patterns, comparisons, and relationships.
18	Data quality	The degree to which data is accurate, complete, consistent, timely, and useful.
19	Data governance	Roles, policies, and controls for managing data as an organizational asset.
20	Master data	Core shared data about key entities such as customers, products, suppliers, or employees.

Lecture Slides

Chapter 6 Slides

These lecture drafts follow the authored sections and aligned assignments.