Sanchit Dilip Jain/Understanding Data Governance and How to Achieve It on AWS 🔍

Understanding Data Governance and How to Achieve It on AWS

Introduction

What is Data Governance?
- Data governance is the practice of managing data to ensure its availability, usability, integrity, and security within an organization. According to Gartner, by 2025, 80% of organizations seeking to scale digital businesses will fail because they do not take a modern approach to data and analytics governance.
- Data governance involves a set of processes, roles, policies, standards, and metrics that ensure the effective and efficient use of information to enable an organization to achieve its goals.
Key Components of Data Governance:
- Data Stewardship: Data stewards are individuals responsible for ensuring the quality and fitness of data. They manage and oversee the data lifecycle, from creation to retirement.
- Data Ownership: Data owners are typically senior executives responsible for data policy decisions, including regulatory and compliance aspects. They decide on data access and definitions and resolve escalations.
- IT Involvement: IT roles facilitate the navigation of systems that produce and consume data, providing tools and capabilities for data management.
Why is Data Governance Critical for Digital Business?
- Effective data governance is essential for digital business success. In a survey by MIT CDOIQ, 45% of Chief Data Officers (CDOs) identified data governance as a top priority. Key reasons for this include:
  - Ensuring data availability to the right people and applications.
  - Maintaining data security and compliance.
  - Enhancing data quality to support business initiatives and operations.

Achieving Data Governance on AWS

AWS provides a comprehensive suite of tools and services to help organizations implement data governance effectively. Here’s a step-by-step guide on how to achieve data governance on AWS:
- Understanding the Data
  - Data Profiling: Use tools like AWS Glue for data profiling to systematically examine data, identify issues, and understand data characteristics. This helps in managing ongoing data quality and designing databases.
  - Data Cataloging: AWS Glue also supports building a data catalog to help users find, evaluate, and access data. A good data catalog includes business context and metadata, such as data quality statistics and data lineage.
  - Data Lineage: AWS Glue and other services provide capabilities to trace data origins, transformations, and storage locations, ensuring transparency in data flow and transformations.
- Curating the Data
  - Data Quality Management: Implement data quality management practices using AWS Glue to develop and monitor data quality rules. Focus on addressing specific data issues that impact business initiatives.
  - Data Integration: Use AWS Glue and AWS Lake Formation to integrate data from various sources, ensuring coherence and consistency. This includes transforming and merging data to support unified analysis and business operations.
  - Master Data Management: Manage master data such as customer, product, and supplier information using modern tools that support automated workflows, cross-referencing, and hierarchical data management.
- Protecting the Data
  - Data Security: Implement robust data security measures using AWS Lake Formation, which supports fine-grained access control at column and row levels. AWS Identity and Access Management (IAM) helps define who can access data and under what conditions.
  - Data Compliance: Ensure compliance with regulations using AWS services like Amazon Macie, which scans data for sensitive information and helps manage compliance documentation.
  - Data Lifecycle Management: Manage the data lifecycle using AWS tools to determine data retention periods, optimize storage costs, and automate data movement based on access requirements.

Aligning Data Governance with Business Initiatives

Data governance should always align with business initiatives to ensure its value and effectiveness. Here’s how to align data governance efforts with business initiatives:
- Support Business Initiatives:
  - Identify Business Initiatives: Start by identifying existing business initiatives that require data support. These initiatives should already be funded and prioritized within the organization.
  - Understand Data Needs: Determine the data required to support these initiatives and assess their current condition.
  - Position Data Governance: Ensure that data governance activities are positioned to support these initiatives, addressing data quality, accessibility, and security issues.
- Prioritize Data Governance Work:
  - Work Backwards from Outcomes: Use Amazon’s “working backward” approach by starting with the desired business outcomes and determining the data governance actions needed to achieve those outcomes.
  - Shared Data Priorities: Look for opportunities to share data across multiple business initiatives, ensuring scalability and reusability.

A Good Model for Data Governance

A robust data governance model involves understanding, curating, and protecting data. Here’s a detailed look at what constitutes a good model for data governance:
- Understanding the Data
  - Data Profiling: Systematically examine data to identify issues and understand its characteristics.
  - Data Catalog: Create a data catalog that helps users find and evaluate data, ensuring it is fit for use.
  - Data Lineage: Track the origin, movement, and transformation of data to maintain transparency and trust.
- Curating the Data
  - Data Quality Management: Focus on specific data quality issues that impact business initiatives, using tools to monitor and enforce data quality rules.
  - Data Integration: Ensure coherent integration of data from various sources, supporting unified analysis and business operations.
  - Master Data Management: Reconcile and manage master data such as customer, product, and supplier information to maintain consistency and accuracy.
- Protecting the Data
  - Data Security: Implement fine-grained access controls and robust data security measures to protect sensitive data.
  - Data Compliance: Ensure compliance with regulatory requirements using appropriate tools and practices.
  - Data Lifecycle Management: Optimize data storage and access based on data retention needs and access patterns.

Retail Industry Example: Integrating Data for Customer Experience

To illustrate how data governance can be applied effectively, let’s consider a retail industry example. A retail company needs to integrate data about its customers across various channels, including sales data from the web and retail locations, customer data across channels, web interactions, and other data.
This integration effort was part of a customer experience initiative aimed at providing a seamless and personalized shopping experience. However, the data came from various systems and required significant effort to ensure coherence and integration.
The data governance program was positioned to ensure that the data fit together to serve the needs of the customer experience initiative. Business and IT collaborated to ensure the data could be linked coherently. Because of this integration effort, the data was available for various other initiatives to reuse, avoiding redundant work and enhancing data quality and integrity across the organization.

Conclusion

Data governance is essential for ensuring the availability, usability, integrity, and security of data within an organization. AWS offers a robust set of tools and services to help organizations implement effective data governance practices.
By understanding data, curating it, and protecting it using AWS solutions, businesses can ensure their data is in the right condition to support their initiatives and drive digital transformation.
Aligning data governance efforts with business initiatives and following a comprehensive model for data governance will help organizations achieve their strategic goals and deliver near-term business value.