What is a good model for data governance? | Amazon Web Services

Summary

In this masterclass, Kevin Lewis delves into data governance with AWS, emphasizing the holistic approach required for effective data management. He outlines key practices including data profiling, cataloging, lineage, and quality management, crucial for aligning data with business initiatives. Lewis stresses the importance of collaboration between IT and business, the role of data stewards, and the need for a strategic roadmap to prioritize and scale data governance efforts.

Takeaways

• 📚 Data governance is not just about data cataloging or access rights; it's a holistic approach to managing data effectively for business initiatives.
• 🔍 Data profiling is crucial for systematically examining data to identify issues that could hinder the success of business initiatives.
• 🗂️ A robust data catalog is essential for making data easily accessible and well-documented for end users and application developers.
• 🌐 Data lineage is important for understanding the origins and transformations of data, which is crucial for data transparency and trust.
• 🛠️ Data quality management involves addressing specific data issues that could impede targeted business initiatives, often requiring a partnership between IT and business.
• 🔗 Data integration is necessary for combining data from various sources coherently, which is not just a technical process but also involves field-by-field alignment.
• 🎯 Master data management focuses on entities like customers, suppliers, and products, ensuring that data about the same entity is consistent across systems.
• 🛡️ Protecting data involves implementing basic security measures, access controls, and compliance with regulations to safeguard data privacy and integrity.
• 🔄 Data lifecycle management considers the cost-effective storage of data over time, balancing the need for access with the desire to optimize storage costs.
• 📈 The success of data governance lies in its ability to support specific business initiatives and improve overall data management capabilities incrementally.

Q & A

What is the main focus of the masterclass on Data Governance with AWS?

The main focus is on the data governance capabilities and data management aspects that are crucial for preparing data to be successful with business initiatives.

Why is it a mistake to equate data governance with just a data catalog or access rights?

Equating data governance with just a data catalog or access rights is a mistake because it overlooks the holistic approach required for effective data management. Data governance includes understanding, protecting, and curating data, which involves more than just cataloging or access control.

What are the three broad buckets that encompass data governance capabilities?

The three broad buckets of data governance capabilities are understanding the data, protecting the data, and curating the data.

What is data profiling and why is it important?

Data profiling is the systematic examination of data through statistics and other elements to identify any issues that may hinder the success of business initiatives. It's important for understanding the data and ensuring it's in the right condition to support business initiatives.

How does data cataloging fit into a data governance program?

A data catalog is an important part of a data governance program as it helps make data easily accessible and well-documented for end users and application developers, facilitating the use of data for projects.

What is data lineage and why is it significant in data governance?

Data lineage refers to understanding the history and origins of data, including which data sources it came from and how it has been transformed. It's significant for tracing data's journey and ensuring its reliability and trustworthiness.

Why is the partnership between IT and the business crucial in data governance?

The partnership between IT and the business is crucial because it combines technical expertise with business knowledge, helping to identify and address data quality issues, prioritize initiatives, and ensure that data supports business goals effectively.

What role does data quality management play in curating data?

Data quality management plays a critical role in curating data by identifying and addressing data quality issues that could impede business initiatives. It involves prioritizing issues, establishing data quality rules, and setting up proactive monitoring and reporting.

How does data integration differ from master data management?

Data integration involves combining data from various sources to create a coherent whole, while master data management takes on special responsibilities for certain entities like customers, suppliers, and products, ensuring that the master data is in the necessary condition for integration and is managed effectively across systems.

What are the key aspects of protecting data in a data governance program?

Protecting data in a data governance program involves implementing basic security measures, establishing access controls, ensuring compliance with regulations, and managing the data lifecycle to store data in the most cost-effective way over time.

Why is it important to prioritize data management practices based on targeted business initiatives?

Prioritizing data management practices based on targeted business initiatives ensures that resources are focused on the most critical areas first, leading to more effective data management and better support for specific business goals. It also helps in building momentum and capability over time.