Data Governance & Data Quality for Oracle

The foundation that makes Oracle — and Oracle AI — actually work. Data strategy, ownership frameworks, quality measurement, and how to build the data foundation that makes Oracle reporting and AI capabilities deliver in practice. 6 hrs · 2 sessions · Intermediate. Overview Dirty data is the number one cause of Oracle implementation underperformance. Not configuration errors, not user adoption failures, not consulting mistakes — data. Duplicate suppliers that create payment fraud risk. Customer records with inconsistent names that break the AR ageing report. Employee records with missing fields that corrupt the payroll. Item master records with no standard cost that make inventory valuation meaningless. These problems were all present before the Oracle go-live; the Oracle implementation simply made them visible and consequential at scale. This course covers the data governance and data quality disciplines that Oracle programmes require — from the governance framework that assigns ownership and accountability, through the quality assessment that establishes the current state, to the master data management principles that prevent quality degradation after go-live. The final session covers what Oracle's native AI and analytics capabilities specifically require from a data quality perspective: the thresholds, the completeness requirements, and the 12-month data improvement roadmap that makes AI activation realistic rather than aspirational. Learning outcomes • Build a data governance framework with clear ownership at domain and field level • Conduct a data quality assessment across completeness, accuracy, consistency, and timeliness • Prioritise data remediation work correctly relative to the Oracle implementation timeline • Establish the master data management principles that prevent quality degradation after go-live • Understand what Oracle's native AI and analytics capabilities specifically require from data quality • Build the 12-month data improvement roadmap that makes Oracle AI activation realistic Prerequisites • No Oracle technical knowledge required • Basic familiarity with data concepts (records, fields, master data) is assumed • Most valuable during an Oracle programme or in the 12–24 months after go-live Modules 1. Data governance framework, ownership & the quality assessment (3 hours) Why data kills Oracle programmes: the five failure patterns most commonly observed in Oracle implementations across Africa and the Middle East — duplicate master data, missing mandatory fields, inconsistent naming conventions, stale reference data, and data that is technically correct but operationally meaningless. The data governance framework: the policy layer (standards and rules), the ownership layer (who is accountable for what), the stewardship layer (who maintains quality day to day), the quality standards layer (what good looks like for each data domain), and the tooling layer (what you need to manage quality at scale). Data ownership mapping: the difference between domain-level ownership ("finance owns supplier data") and field-level ownership ("the procurement manager owns supplier payment terms"). Why the difference matters. The data quality assessment: how to measure your current state across the four dimensions of completeness, accuracy, consistency, and timeliness — and how to prioritise what needs to be fixed before the Oracle go-live versus what can be cleaned post-go-live. The data quality plan: sequencing remediation work correctly relative to the implementation timeline. Topics: Data failure patterns; Governance framework layers; Policy and standards; Data ownership models; Field-level accountability; Data stewardship; Quality assessment; Completeness assessment; Accuracy measurement; Prioritisation framework 2. Master data management, Oracle AI readiness & post-go-live governance (3 hours) Master data management in an Oracle context: what MDM means operationally, when a dedicated MDM tool is required and when Oracle's native capabilities are sufficient, the golden record concept and how to implement it practically, and the deduplication and merge rules that prevent master data proliferation after go-live. Data governance after go-live: the data stewardship model (who reviews data quality, how often, with what authority), the data quality dashboard (what to measure and how to visualise it), and the data issue escalation path (how a field-level quality problem gets from a data entry clerk to the data owner and back to resolution). Oracle AI readiness: what Oracle's embedded AI capabilities — invoice matching agents, anomaly detection, generative summaries, payroll exception identification — specifically require from a data quality perspective. The actual quality thresholds (not marketing language — operational thresholds based on Simpl'IT implementation experience) for each AI capability to function reliably. Building the 12-month data improvement roadmap that sequences data quality improvements to unlock AI value progressively. Topics: MDM principles; Golden record concept; Deduplication rules; Post-go-live stewardship; Data quality dashboard; Issue escalation; Oracle AI prerequisites; Invoice matching data requirements; Anomaly detection thresholds; AI activation roadmap Delivery Recommended for a mixed audience of data owners, IT leads, finance directors, and business process owners. The shared understanding of data ownership — who is accountable for what, at field level — is the most valuable output and requires joint participation. Most effective during the design or build phase of an Oracle programme, before data migration begins. Certification Certificate of completion Price USD 950 · per participant per person · indicative · group pricing available HRDC eligibility Up to 75% refundable Mauritius-registered employers may claim this training via the HRDC levy. We provide full documentation to support your claim. Languages English Provider reference SIMPLIT-015-DATA_GOVERNANCE

• Data owners and data stewards responsible for Oracle data quality • IT directors responsible for Oracle data architecture and quality • Finance directors whose reporting depends on Oracle data accuracy • Transformation directors building the data foundation for an Oracle programme • Analytics and reporting leads who need Oracle data to be trustworthy • Anyone responsible for activating Oracle AI capabilities that depend on data quality