In today’s data-driven economy, companies are racing to build innovative data products that offer insights, drive decisions, and create value. However, the success of these data products doesn’t depend solely on cutting-edge technologies or complex algorithms. A critical, and often underappreciated or unknown factor is data governance. Understanding the relationship between data governance and data products is essential for building scalable, reliable, and trustworthy data solutions.
Let’s talk definitions.
What are data products?
A data product is any tool, service, or application that uses data to deliver insights or perform tasks. Whether a recommendation engine, an AI-driven customer service chatbot, or a predictive maintenance system for manufacturing, data products leverage large volumes of data to provide value to end users. Data products are at the core of many modern businesses, offering personalized experiences, streamlining operations, and enhancing decision-making. However, they are only as good as the data they are built on. This is where data governance comes into play.
What is data governance?
Data governance is the framework that ensures data is consistent, accurate, secure, and available throughout its lifecycle. It involves defining data policies, implementing data management processes, and assigning responsibilities to ensure data is used properly across an organization. Key aspects of data governance include data quality, metadata management, data security, and regulatory compliance. While data governance may seem like an operational overhead to many, it is a critical foundation for building data products that deliver reliable insights and long-term value.
So, what is the relationship?
- Data Quality of any data product determines the accuracy and quality of the data it processes. A machine learning algorithm, for example, can only produce accurate predictions if it’s trained on high-quality data. Poor data quality leads to incorrect insights, potentially damaging user trust or resulting in costly business decisions.
- Critical Data Elements – Every data domain (Customer, Product, Supplier, Finance, Marketing…) has a core set of conceptual and logical data elements used to define and manage data. Documenting it in a data catalog provides clarity on what that attribute means. In addition, teams must build workflows around them to manage them continuously.
- Compliance and Regulatory Requirements – Whether it’s GDPR, CCPA, PII, PCI, or HIPAA, these regulations mandate how data is collected, stored, and used. Data governance frameworks help businesses ensure their data products comply with these regulations. By setting up governance processes around data access, anonymization, and auditing, organizations can avoid legal risks and maintain trust with their users.
- Security and Privacy – Data products often process sensitive and personal information. Whether customer preferences, financial records, or health data, ensuring the security and privacy of this information is critical. Any breach of trust or data leak can result in significant reputational and financial damage. Data governance focuses on access control, encryption, and system monitoring, ensuring that sensitive data is handled securely throughout its lifecycle.
- Data Lineage and Transparency – Where did the data come from? Where is it going? Who is using it? Why they are using it? Data governance tracks and documents this lineage, providing transparency and accountability for data products. When discrepancies or anomalies arise, teams can quickly trace back through the data pipeline to identify and resolve the issue.
- Communication and Collaboration Across Teams – Building data products is a cross-functional effort involving data scientists, engineers, analysts, and business teams. Effective data governance enables collaboration by setting clear guidelines and roles related to data access, usage, and responsibility. By providing a shared framework, data governance fosters collaboration and alignment between teams for faster and more efficient data product development.
The future of data governance and data products
As data products become more integral to business strategy, the importance of strong data governance will only grow. We are entering an era where data governance and data products are no longer two separate functions but deeply intertwined.
Emerging concepts like data mesh emphasize the idea of decentralized data ownership, where each team or domain becomes a “data product owner.” In this model, governance doesn’t exist as a centralized authority but becomes embedded into the development of data products themselves. This approach creates a more scalable and agile environment for organizations to build and deploy data solutions.
Moreover, the rise of AI-driven data governance tools is transforming how companies manage their data. These tools can automatically detect data quality issues, monitor compliance, and even suggest improvements to data pipelines, reducing the manual overhead typically associated with governance.
Data products are only as good as the data they are built on, and data governance provides the foundation for that data. Ensuring quality, compliance, security, and consistency isn’t just a best practice – it’s essential for the success of any data product. As Informatica eloquently put it, “Everyone is ready for AI, except your data”. For data to ready, it must rely on data governance.
Arvind Murali, Chief Data Officer