Define data quality as how well a data set meets a user's needs to support accurate, timely, and duplicate-free data for data-driven decisions and business outcomes.

The lecture outlines five pillars: people, data profiling, defining data quality, data reporting, and data repair, and explains how these drive information quality, return on investment, and business performance.

Master six global data quality dimensions: accuracy, completeness, validity, uniqueness, timeliness, and consistency, and learn how to measure data quality across real datasets.

Data timeliness means accessing data when needed, based on the task, without delays. In emergencies, access should be seconds or minutes via a central system.

Explore the completeness data quality dimension, distinguishing critical from optional fields, and learn how missing data may pass completeness while critical data like allergies or birth date must be present.

Explore creating data quality rules for key data elements like date of birth, translate rules into code, test, and implement validation checks across systems.

Parse data by separating complex entries into separate fields. Convert data formats to gain more control, such as splitting full names into first and last names.

Convert data to a common format and transform nonconforming records to a single standard across the database. Automated tools streamline data standardization, reducing manual edits.

Identity resolution creates a single customer view from multiple data sources into a single source of truth, enabling personalized experiences, better governance, and data-driven decisions across sales, marketing, and service.

Explore data linkage, the process of identifying, matching, and merging duplicate records across datasets to support data cleansing and master data management.

Data cleansing resolves corrupt, inaccurate, incomplete, or irrelevant data by transforming and standardizing data. It builds on parsing, identity resolution, and record linkage to improve data quality.

Enhance data value by appending information from internal and third-party datasets, integrating parsing, standardization, and record linkage to support smarter sales and marketing decisions.

Demonstrate data quality techniques: profiling, parsing, standardization, identity resolution, data linkage, cleansing, enrichment, and monitoring, by loading a supplier dataset, performing step-by-step improvements, and measuring outcomes.

Explore why data quality matters for AI and how massive data sets magnify learning and risk from small errors, like incorrect formats or swapped labels.

Develop data quality skills for an AI world with broader profiling. Learn metadata lineage, governance, and AI basics to link data to model behavior and business impact.

Explore AI-driven data quality improvement on a realistic customer dataset of 1,000 records, fixing missing values, inconsistent categories, and incompatible fields across marketing systems, CRMs, and e-commerce platforms.

Identify and highlight duplicate emails using AI-powered deduplication, review full-row and customer ID duplicates, and save the latest cleaned Excel file.

Perform outlier and anomaly detection on numeric columns using AI tools, applying industry best practices; verify data quality with business logic to catch logical inconsistencies beyond statistical outliers.

Create a data dictionary for a dataset with ChatGPT, export in Excel, include fields, data types, descriptions, rules, constraints, values, and notes, to share with stakeholders.

Join a hands-on data quality audit to detect missing values, duplicates, price outliers, validate relationships, build a data dictionary and audit dashboard, and draft the stakeholder report with AI.

Explore how Copilot in Excel boosts data quality by identifying duplicates and applying AI-driven insights, using the same dataset to compare Copilot with ChatGPT across best practices and tips.

Use Copilot in Excel to detect duplicates by email, phone, and customer ID, create a multi-sheet file, and note that missing values and name matches may not be real duplicates.

Use Copilot to standardize formats across the dataset, fix capitalization, remove extra spaces, and normalize country names, phone numbers, and date formats, then download the cleaned data with ISO codes.

Spot outliers in numeric data using Copilot by applying IQR and z-score methods, listing suspicious values with explanations and rerunning analyses as data changes.

Use Copilot in Excel to generate a clear, leadership-ready summary of data quality issues by severity and category, then review it with human expertise.

Explore how data quality roles, with direct and indirect impacts on data quality management, vary by industry, company size, and budget allocated to data quality.

The data quality manager leads data quality processes, defines critical data and targets, sets escalation thresholds, oversees root-cause analysis, redemption plans, and dashboards, and guides the data quality analysts.

Lead data quality management as a data quality analyst by monitoring quality, performing statistical tests, resolving problems, and collaborating with data quality manager and database developers to prioritize enterprise-wide improvements.

Meet the data custodian, the technical owner of the data environment who maintains the setup for data storage and ETL. They ensure data quality, integrity, and safety throughout ETL processes.