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October 30, 2025

Data Quality & Concurrency

Author By Academic Wizard Categories Blog

Data Quality & Concurrency

  • 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.

Data Quality & Concurrency

Read the following articles and incorporate them into your paper. You are encouraged to review additional articles as well:

  • 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,

Comprehensive answer

Below I provide practical, actionable recommendations that map to SDLC phases, database tuning and record-selection optimizations, maintenance plans, concurrency-control methods (with lock granularity guidance), and analyses of how these choices reduce security risks and avoid excessive record-level locking in multiuser environments.

1) SDLC-based tasks to improve data quality (three tasks per SDLC phase)

SDLC phases: Requirements → Design → Implementation (Development) → Testing → Deployment → Maintenance.

A. Requirements (Task 1: Data quality rules specification)

  • Activity: Define explicit data quality (DQ) requirements up front: required fields, value ranges, referential integrity, uniqueness constraints, format/regex rules, completeness thresholds, acceptable error rates, and SLAs for data timeliness.

  • Why: Clear DQ rules enable validation logic to be designed rather than patched later. (ISO/IEC 25012)

B. Design (Task 2: Data model normalization & stewardship design)

  • Activity: Create a canonical data model normalized to eliminate redundancy where appropriate; identify master data sources and appoint data stewards for each domain; design lineage and metadata capture.

  • Why: Good schema design prevents many quality issues (inconsistency, update anomalies) and assigns ownership for remediation.

C. Implementation (Task 3: Embedded validation & ETL quality controls)

  • Activity: Implement validations at the point of capture (UI constraints, client-side + server-side checks), and design ETL pipelines with staged cleansing (profiling → standardization → enrichment → deduplication) and transactional rollback on quality violations. Use a data quality engine (e.g., OpenRefine, Talend, or commercial DQ tools).

  • Why: Fixing bad data at entry and during ETL reduces downstream errors and rework.

D. Testing (Task 4: Data quality testing & synthetic scenario tests)

  • Activity: Create test suites for data quality: unit tests for validation functions, integration tests for ETL, regression tests for schema evolution, and fuzz and boundary tests. Include data-driven tests that exercise large volumes to detect performance-related corruption.

  • Why: Testing verifies that rules work at scale and that changes do not regress quality.

E. Deployment (Task 5: Monitoring instrumentation & rollout controls)

  • Activity: Deploy with feature flags or phased rollouts, and activate DQ monitoring dashboards (data completeness, error rates, latency). Configure automated alerts for DQ metric thresholds.

  • Why: Early detection in production prevents large-scale contamination.

F. Maintenance (Task 6: Continuous profiling & feedback loop)

  • Activity: Schedule automated profiling (weekly/monthly) to monitor drift, and maintain a feedback loop from consumers to producers with issue-tracking for data defects. Maintain a data catalogue and lineage to speed fixes.

  • Why: Data quality is ongoing — monitoring and governance preserve quality over time.

(These tasks align with academic guidance on lifecycle-based data quality management and software engineering best practices — see S. B. et al., 2019; ISO/IEC 25012.)

2) Actions to optimize record selections and improve DB performance from quantitative DQ assessment

After running a quantitative DQ assessment (metrics like completeness, uniqueness, accuracy, consistency, timeliness), take the following actions:

  1. Indexing strategy based on access patterns

    • Create composite and filtered indexes for frequently-used query predicates revealed by profiling. Use statistics to tune which fields are selective and deserve indexes to optimize record selection.

  2. Partitioning & archiving

    • Use range or hash partitioning on high-volume tables (date-based partitions for time-series) and implement a data retention/archival policy for stale records, reducing the working set and improving query performance.

  3. Materialized views and pre-aggregation

    • For complex analytic queries on large sets, create materialized views or summary tables updated incrementally to avoid scanning base tables repeatedly.

  4. Denormalization where justified

    • Where repeated joins are expensive yet data volatility is low, denormalize selective columns to improve read-heavy workloads, while ensuring processes to maintain consistency.

  5. Statistics & query plan maintenance

    • Regularly update optimizer statistics and capture query plans to identify and fix slow queries (rewrites, hints, or indexes).

  6. Use data quality filters in queries

    • Add predicates to exclude records flagged by DQ assessment (e.g., null keys or flagged invalid records) or route them to special handling pipelines.

Result: These steps reduce I/O, decrease full-table scans, and enable faster, cleaner record selection — measured quantitatively via reduced average query latency, lower CPU I/O, and improved transaction throughput.

3) Maintenance plans (three) and associated activities (three) to improve data quality

Maintenance plans

  1. Scheduled Data Profiling & Remediation Plan (weekly/monthly)

    • Activities: Run automated profiling jobs; generate DQ scorecards; automatically quarantine records failing thresholds; trigger workflow tickets for stewards.

  2. ETL/Streaming Pipeline Health & Reconciliation Plan

    • Activities: Implement end-to-end checksums and row counts between source and target; anomaly detection for throughput spikes; automatic rollback/replay procedures for pipeline failures.

  3. Schema & Change-Control Governance Plan

    • Activities: Use migration tooling (versioned migrations), require DQ regression tests for schema changes, and create backward-compatible migration policies.

Other activities to improve quality: periodic deduplication runs, master data reconciliation, and automated enrichment (geocoding, reference data lookups).

4) Methods for proactive concurrency control and lock granularity; security assessment

Recommended concurrency methods

  1. Multi-Version Concurrency Control (MVCC) — preferred

    • How it works: Readers access snapshot versions; writers create new versions, minimizing read locks. Implemented in PostgreSQL, Oracle, MySQL/InnoDB.

    • Lock granularity: Row-level for writes, no read locks for most reads.

    • Security benefits: Reduces need for escalated locks that expose record timing or allow lock-based inference attacks; audit trails can track version changes. Snapshot isolation reduces contention and keeps transactions short.

  2. Optimistic Concurrency Control (OCC)

    • How it works: Allow transactions to proceed without locks, validate on commit (version checks); abort on conflict.

    • Lock granularity: Minimal locking, mainly at commit.

    • Security benefits: Short-lived or no locks reduce attack surface for lock-based denial; less exposure to lock-table exhaustion attacks.

  3. Two-Phase Locking (2PL) with careful granularity (row-level preferred over page/table)

    • How it works: Acquire locks as needed, hold until commit. Use fine-grained row locks; escalate only when necessary.

    • Lock granularity: Start with row-level; monitor for frequent escalations and tune thresholds.

    • Security benefits: Ensures serializability; with role-based access and audit, unauthorized lock acquisitions are detectable.