Write a 2–3 page paper in which you:
Recommend at least three specific tasks that could be performed to improve the quality of data sets using the software development life cycle (SDLC) methodology. Include a thorough description of each activity per each phase.
Recommend the actions that should be performed to optimize record selections and to improve database performance from a quantitative data quality assessment.
Suggest three maintenance plans and three activities that could be performed to improve data quality.
Suggest methods that would be efficient for planning proactive concurrency control methods and lock granularities. Assess how your selected method can be used to minimize the database security risks that may occur within a multiuser environment.
Analyze how the method can be used to plan out the system effectively and ensure that the number of transactions does not produce record-level locking while the database is in operation.
Read the following articles and incorporate them into your paper.
Sample Answer
A well-structured data management system is crucial for an organization's success. The quality of data directly impacts the accuracy of business intelligence, operational efficiency, and security. This paper will outline how the Software Development Life Cycle (SDLC) can be applied to improve data quality, recommend actions for optimizing database performance, suggest maintenance plans, and propose proactive methods for concurrency control and security.
Improving Data Quality with the SDLC Methodology
The SDLC provides a systematic approach to developing and managing information systems, and its phases can be adapted to improve data quality.
Requirements Gathering & Analysis
Task: Define Data Quality Standards.
Activity: During this initial phase, an interdisciplinary team—including data analysts, developers, and business stakeholders—should identify and document specific data quality requirements. This involves defining what "quality" means for the organization. For example, a requirement might be that a customer record must have a valid U.S. postal code. This activity ensures that everyone agrees on the standards from the outset, preventing miscommunication and rework later.
2. Design & Development
Task: Implement Data Validation Rules.
Activity: In the design phase, the data quality standards established earlier are translated into technical specifications. This involves designing the database schema to enforce integrity constraints, such as unique identifiers, foreign keys, and not-null constraints. During development, these rules are coded into the application's logic. For instance, input forms would include real-time validation to ensure a phone number has the correct format before it's ever stored in the database.
3. Testing & Integration
Task: Perform Data Profiling and Audits.
Activity: Before deploying a new system or data set, rigorous testing is essential. This activity involves data profiling, which is the process of examining a data set to collect statistics and information about it. This can reveal anomalies, missing values, or inconsistent formats. For example, an audit might show that a certain percentage of records are missing an email address. This allows the team to correct data quality issues before they impact operations. Integration testing ensures that data flows correctly between different systems without corruption or loss.
Optimizing Record Selection and Database Performance
Optimizing record selection and database performance is a quantitative data quality assessment.
Action 1: Indexing. Use indexing to speed up data retrieval. By creating an index on frequently queried columns (e.g., a customer_id column), the database can quickly locate data without scanning the entire table. This is similar to using an index at the back of a book to find information quickly instead of reading every page.
Action 2: Query Optimization. Analyze and rewrite inefficient queries. Tools like EXPLAIN PLAN in SQL can show how a database executes a query, helping developers identify bottlenecks and rewrite the query for better performance. For instance, using JOINs correctly and avoiding subqueries can significantly improve execution speed.
Action 3: Normalization and Denormalization. Apply proper database normalization to reduce data redundancy, which improves data integrity and efficiency. However, in certain cases, denormalization (intentionally introducing redundancy) can be used to improve performance for read-heavy applications by reducing the need for complex joins.