Beyond Excel: Structuring Data Outside of Spreadsheets

Explore a robust data management framework beyond spreadsheets, covering governance, quality, architecture, security, integration, and storage to enable a data-driven culture.

Master data management principles and move beyond excel by embracing data governance, data quality, data architecture, and data security. Build a data driven culture that makes data a strategic asset.

Explore data modeling basics, including entities, attributes, and relationships, and learn how one-to-one, one-to-many, and many-to-many connections translate into tables, primary keys, and foreign keys to build robust databases.

Explore how data modeling translates into database design by defining entities, attributes, and relationships. See entities become tables, attributes become columns, and relationships become foreign keys.

Bridge data modeling concepts with practical relational databases, learning tables, columns, rows, primary and foreign keys, and SQL queries like select, where, and order by.

Explore how relational databases translate entities, attributes, and relationships into tables with rows and columns, and master SQL basics like select, where, and order by plus primary and foreign keys.

Explore data governance frameworks that provide structure, guidance, and oversight for managing data responsibly and strategically within an organization.

Explore how a data governance framework acts as the constitution for your data world, defining roles, policies, processes, data standards, and security measures for compliant, valuable data.

Explore how DAMA-DMBOK and COBIT frame data governance, outlining data quality, security, architecture, metadata, and lifecycle practices to help organizations manage data as a strategic asset.

Explore key roles in a data governance program, including the data governance council, data owners, data stewards, and data users, and how policies, procedures, and responsibilities drive data quality.

Data stewardship translates governance into practical action by defining data definitions and business rules, monitoring quality, enforcing cleansing, and training users to ensure compliant, reliable data.

Explore data governance frameworks and the roles of data owners, stewards, and users to implement policies, ensure data quality, compliance, and security, and drive proactive data management.

Explore data quality management as a pillar of data governance frameworks, ensuring collected, stored, and used data is accurate, complete, consistent, timely, valid, unique, and fit for intended use.

Understand how data quality drives analysis, reporting, and decisions. Learn data quality management practices, including assessment and rules, cleansing, monitoring, and improvement, to ensure reliable, actionable insights.

Understand the six dimensions of data quality—completeness, accuracy, consistency, timeliness, validity, and uniqueness—and learn how completeness impacts analysis, reporting, and decision making.

Improve data accuracy by ensuring records reflect the real world and are free from errors and typos, with validation, cleansing, and audits to prevent flawed analysis.

Learn how data consistency ensures uniform representation across systems, preventing duplicates and errors in reporting and analysis by aligning data standards and integration processes.

Ensure data timeliness by keeping information up to date and available when needed, supporting real-time decisions and preventing stockouts, overstocking, and regulatory penalties.

Learn how validity ensures data conforms to defined rules, formats, and ranges, with examples like specific date formats and non negative prices, and apply validation across databases and applications.

Ensure data uniqueness by eliminating duplicate records, preserving data integrity for accurate reporting and CRM insights through deduplication and data cleansing techniques.

Measure and track data quality using common metrics such as error rate, completeness, consistency, and timeliness. Demonstrate the impact of data quality initiatives through these metrics.

Profile data to understand its characteristics, structure, and content, identifying data types, lengths, distributions, value ranges, null values, patterns, and relationships, using data profiling tools to ensure quality and reliability.

Improve data quality by cleansing and standardizing data, deduplicating records, imputing missing values, and correcting errors to support accurate analysis, reporting, and decision making.

Explore the real world impact of poor data quality through a case study of voter registration inaccuracies in the 2016 US election, including duplicates, outdated records, and data matching issues.

Learn how data quality management ensures reliable data by measuring dimensions: completeness, accuracy, consistency, timeliness, validity, and uniqueness. Discover data profiling and cleansing techniques and note the real world impact.

Explore data architecture fundamentals and data quality management, learning how data is organized, stored, managed and accessed within an organization's information technology infrastructure to create a cohesive, efficient data ecosystem.

Define data architecture as the blueprint for your data landscape, detailing data models, storage, integration, access, and security to enable business intelligence, data-driven decisions, and scalable performance.

Explore how poor data architecture creates data silos, fragmentation, and inconsistent definitions. Identify architecture components, patterns, and technologies to improve data access, analysis, security, and compliance, while reducing costs.

Explore common data architectures—data warehouse, data lake, data mesh—and their roles in enabling reporting, BI, and data mining, with ETL transforming sources into centralized, historical, subject oriented analytics repositories.

Store raw, native-format data in a data lake, capturing structured, semi-structured, and unstructured data; leverage flexibility, agility, and cost effectiveness while managing data discovery, quality, and governance.

Adopt a data mesh to decentralize data management, treating data as a product owned by each domain with self-service access and federated governance for scalability and agility.

Unify data from disparate sources through data integration and automated data pipelines, extracting, transforming, and loading data into data warehouses or data lakes to enable accurate reporting and decision making.

Explore data warehousing and dimensional modeling with a focus on the star schema, including fact and dimension tables, and the extraction, transformation, and loading processes for analytics-ready data.

Identify how healthcare data silos across EHRs, billing, labs, PACs, pharmacy, and research databases hinder data integration and a holistic patient view.

Explore the need for a data architecture that unifies patient information across healthcare data sources through data warehousing, data lakes, APIs, and master data management for reporting, analysis, and research.

Explore data architecture as the blueprint for organizing and managing data, including data warehouse, data lake, and data mesh, with data integration, pipelines, ETL, and dimensional modeling for analytics.

Master data governance, quality, architecture, security, and integration to treat data as a strategic asset; model data with entities, attributes, and relationships in relational databases and write basic SQL queries.