Cambridge
Academy
In London

Introduction

High data quality is essential for reliable reporting, sound decisions, and efficient operations. This practical program builds core skills in defining data quality standards, profiling data, designing validation rules, monitoring issues, and driving fixes through simple governance and repeatable routines.

Course Objectives

By the end of this course, participants will be able to:

·        Explain key data quality dimensions and common causes of errors

·        Profile data and identify quality issues quickly

·        Design practical validation rules and checks

·        Set up monitoring dashboards and issue workflows

·        Improve quality through root-cause fixes and prevention controls

Target Audience

This course is designed for:

·        Data management and data governance specialists

·        Data stewards and business owners of data

·        Analysts responsible for reports and dashboards

·        IT/data teams supporting data pipelines and systems

·        Anyone responsible for data accuracy and completeness

Course Outlines

Day 1: Data Quality Foundations

·        What data quality means and why it matters

·        Quality dimensions: accuracy, completeness, timeliness, consistency, uniqueness

·        Common issue sources: manual entry, integrations, definitions, process gaps

·        Data standards and “fit-for-purpose” quality targets

·        Activity: Identify top quality risks for your key datasets 

Day 2: Data Profiling and Issue Discovery

·        Profiling basics: patterns, ranges, nulls, duplicates, outliers

·        Simple methods using spreadsheets/SQL concepts (overview)

·        Prioritizing critical data elements (CDEs)

·        Documenting findings: issue log and evidence

·        Workshop: Build a basic profiling checklist and run-through

Day 3: Validation Rules and Control Design

·        Types of validation: format, range, referential, business rules

·        Designing rule statements (clear “if/then” logic)

·        Prevent vs detect controls (where to place checks)

·        Exceptions handling: tolerances and approvals

·        Activity: Draft validation rules for 20 key fields

Day 4: Monitoring, Dashboards, and Issue Management

·        Defining quality KPIs: defect rate, completeness %, SLA timeliness

·        Thresholds and alerts (RAG rules)

·        Issue workflow: capture, triage, assign, fix, close

·        Roles: data owner, steward, custodian (simple RACI)

·        Case study: Set up a monthly data quality review pack

Day 5: Root Cause, Remediation, and Prevention

·        Root-cause analysis for data issues (5 Whys, process mapping)

·        Remediation plans: quick fixes vs structural fixes

·        Data cleansing vs process improvement (when to use each)

·        Sustaining quality: standards, training, audits, change control