Lesson 1

Database designers, regardless of their chosen approach, share the same overarching strategy:

1. Accomplish proven design objectives.
2. Apply systematic design methods, supported by tools, through the database life cycle.

This module introduces those objectives, compares the subject and application approaches to design, illustrates the three-schema architecture, and explains both the design and post-design stages of the database life cycle. We also examine the role of CASE tools in automating and improving these processes.

After completing this module, you will be able to:

1. Describe the overall strategy of database design.
2. Differentiate between the subject approach and the application approach.
3. Define the three-schema architecture: user view, logical schema, and physical schema.
4. Identify the design stages of the database life cycle.
5. Explain the post-design stages of the database life cycle.
6. Discuss the role of CASE tools in database design.

All software, including databases, follows a life cycle of usefulness bounded by cost-effectiveness. At some point, legacy systems become too costly or impractical to maintain. Organizations must then replace or retire them. This life cycle reflects the transition of data from immediate use to archival storage and eventual disposal.

1. Need It: Recognition that a data element has value. For example, tracking a credit card transaction charge-back fee amount helps retailers assess profitability, ROI, and budgeting needs.
2. Plan It: Define business rules, frequency, size, and security requirements. At this stage, questions are asked about how the data will be captured, stored, and validated.
3. Collect It: Implement the plan and begin capturing real data. Initial testing may reveal gaps or the need for additional data elements.
4. Store It: Decide on appropriate storage methods. This might range from lightweight formats (e.g., XML files for real-time capture) to enterprise data warehouses for reporting and aggregation. Reliability and performance are key considerations.
5. Combine It: Integrate the data with other elements for reporting and decision-making. When well-defined, data becomes part of the organization’s broader knowledge base and is reused across departments.
6. Act on It: Data drives decisions, from operational changes (e.g., offering cash discounts) to strategic moves (e.g., launching debit card programs). At this stage, data must have an auditable pedigree to support accountability.
7. Archive It: Once data loses immediate relevance, it is transferred to archives. Archived data supports long-term analysis and disaster recovery while reducing the load on production systems.
8. Remove It: Finally, obsolete data is deleted or becomes inaccessible due to outdated media. This “rest in peace” stage acknowledges that not all data retains value indefinitely—though historians may later extract insight from archived fragments.