When implementing big data systems, organizations must establish ongoing production monitoring to ensure system performance, efficiency, and reliability.
Why Option A (Key performance indicators) is Correct:
KPIs (Key Performance Indicators) measure the effectiveness and success of big data systems.
KPIs help track system efficiency, data processing speed, accuracy, and resource utilization during production.
Examples of KPIs in big data systems include data ingestion rate, processing time, query performance, system uptime, and error rates.
Why Other Options Are Incorrect:
Option B (Reports of software customization):
Incorrect because software customization reports document system modifications but do not monitor system performance.
Option C (Change and patch management):
Incorrect because change and patch management deals with software updates and security fixes, not ongoing performance monitoring.
Option D (Master data management):
Incorrect because master data management focuses on data governance and consistency, not real-time system performance.
IIA GTAG – "Auditing Big Data Systems": Recommends using KPIs to measure the effectiveness of big data implementation.
COBIT 2019 – APO08 (Manage Performance and Capacity): Emphasizes KPI tracking for IT and data system performance.
NIST Big Data Framework: Highlights the importance of KPIs for monitoring big data system performance.
IIA References:
