Computer-Aided Software Engineering (CASE) tools are used by system analysts, designers, and developers to capture information about business requirements, create a design for the data structures to fulfill those requirements, and generate front-end and server program code. CASE tools can automate much of the manual, repetitive, and error-prone work needed for system development, and can greatly increase the productivity, accuracy of the design, and robustness of the implementation.

System Development Life-Cycle

CASE Application Development Method (CADM) – 7 phases:
1. Strategy
2. Analysis
3. Design
4. Build
5. Test
6. Implementation
7. Maintenance

1. Strategy

The purpose of the Strategy phase is to formulate a basic description of the overall scope of the project and how the project will proceed. The entire focus is on the business. The goal is to gain a clear understanding of the business area’s goals, objectives, processes, direction, and needs in order to structure and document the vision of project. Results of this phase include strategy ERD, process flows, strategy document, cost-benefit analysis, and workplan.

Strategy Document is also referred as project charter, project definition document, and scope document. Strategy document outlines the scope of a project and defines an agreement about what the project is committed to deliver. It also includes the estimated budget, time frame, resources, controls, and standards within which the project must be completed.

2. Analysis

The analysis phase breaks logically into two parts: information gathering and requirement analysis.

Within the information gathering portion, techniques for getting at user requirements include interviews, questionnaires, and joint application development (JAD) sessions, along with reviews of the current system, the report audit, and user and system documentation.

In requirements analysis, we extract the system requirements form the information gathered and place them in an organized structure. This can be done at both the unit level and, ultimately, at the system level.

3. Design

The Design phase includes two broad subphases:
- Database Design
- Application Design

You should already have a complete ERD from the Analysis phase, but need to do the physical database design. The basic strategy of database design is to map entities to tables, making adjustments fo subsets, denormalization, aggregation, and summary tables.

At the simplest level, application design is the design of software, including the creation of structures that address modules, programs, procedures, navigation, internal controls, and security.

4. Build

In the Build phase, you build the database and application. The Build phase deliverables consist of the following:

- Unit tested application system
- Populated databases
- System documentation
- User documentation
- Help system and online documents

5. Test

In the Test phase, you develop a test plan that should describe not only test to be run but also how test failures or variances will be handled. Tests include unit testing, system testing, user acceptance tests, and training and documentation. Through this phase, the new system is formally tested.

6. Implementation

In this phase, the project team will rollout a system to production environment and provide adequate user support. The rollout/migration process involves transition period where the system is closely monitored and support is transferred from the project team to the system operation group.

7. Maintenance

Maintenance is a process that continues throughout the life of the system. A predefined process must be put into place to handle ongoing problems, modifications, and enhancements.

“I have hands-on skills and experience in database design and data modeling.  In previous jobs I have worked as the technical lead to design large scale database systems in Oracle, Sybase, and Microsoft SQL Server.  I have experience in several database design tools such as Oracle Designer, PowerDesigner, and ERWin.  Using these tools I collected and documented user requirements, analyzed business rules and entities, created entity relationship diagrams, designed logical database based on the ERD, and generated SQL scripts to build the physical database., and configured the database server and setup the database.  I also used database design tools to reverse-engineer existing database when necessary.  Database design tools are very helpful for me to automate many tasks through the database design lifecycle, but in many cases I still need to fine-tune the design manually.  I also have experience in using design tools to create data repository for meta data management.  In one of my jobs I worked as the repository administrator responsible for building and maintaining Oracle data repository in Oracle Designer for our term members to work together in a common data design platform through database design phases.  ”

A CASE tool usually provides functionalities to help us systematically move through the System
Development Lifecycle. Some of major functions include:

Business strategies
User requirements
System prototypes
Data model diagrams
Data flows
Construction
Testing
Quality assurance

These are fundamental questions to test your knowledge of data warehousing. To answer the questions, just briefly explain each phase of the lifecycle:

1. Data acquisition processes—extract, transform, load (ETL)
The processes include sourcing, cleansing, transforming, and aggregating data using parallel technology tools to build industrial-strength ETL processes that accommodate high data volumes from disparate sources. We identify the best sources for data elements, reconstructing data when required and deploying the most appropriate tools to retrieve the data from its primary sources. Through the cleansing process, we enhance data quality by ensuring data accuracy, type, and consistency, as well as eliminating duplicate records.

2. Data repositories
In the phase you need to build a variety of data repositories, including operational data stores, data marts, data warehouses, web warehouses, and data hubs. You start by implementing and properly documenting a physical data model, ensuring data from all functional areas is sufficiently integrated to support cross-functional analyses. Then you perform database tuning, model denormalization, and aggregation as necessary to support information delivery requirements. When scalability requirements call for it, you partition and distribute data into a parallel architecture.

3. Information delivery applications
You implement information delivery applications that allow corporate users to access the data in the warehouse. These include decision support tools, data mining and analytic tools, and applications that optimize supply chain, campaign management, billing, and industry-specific processes. In this phase you also ensure near- and long-term reporting and access requirements are met as well. These may include fixed-frequency static reports; ad-hoc reports; dynamic, multidimensional queries; Internet/intranet application interfaces; and data mining.

4. Data warehouse administration
As the data warehouse grows, administration (or management) of the repository is a crucial step in optimizing results and return on investment. You provide data warehousing administration services such as performance analysis, user analysis, benchmarking, auditing, and tuning to help clients measure the ongoing success of their data strategies.