When it comes to managing and utilizing data effectively, many questions arise, leaving users and stakeholders puzzled. How is the data stored? Is it accurate and reliable? Can we trust it? Which data is best suited for our specific needs?
Finding answers to these questions requires a thoughtful approach, and two key areas come into play: data management and data governance. While these terms are often used interchangeably, they represent distinct programs. In this article, we aim to clarify the differences between data management and data governance.
Data Management: An IT Practice
Let’s begin by understanding the foundational element: data management. Without robust data management practices in place, navigating the data landscape becomes challenging. Data management can be best described as an IT program that focuses on organizing and controlling data resources to ensure accessibility, reliability, and timeliness for users.
From an administrative standpoint, IT teams responsible for data management rely on a comprehensive set of practices, theories, processes, and systems to collect, validate, store, organize, protect, process, and maintain data.
Proper data handling is crucial, as mishandled data can become corrupted or unusable, rendering it entirely worthless. It’s important to note that data management covers the entire lifecycle of a data asset, from its initial creation to eventual deletion.
Data Governance: A Business Strategy
While data management deals with the logistics of data, data governance tackles the strategic aspects. Data governance encompasses a broader and more holistic approach, as it involves business programs and requires a consensus-driven approach within the organization.
The goal of data governance is to provide actionable insights on how a business can identify and prioritize the financial benefits of data while mitigating the risks associated with poor data quality. It involves determining which data can be used in various scenarios.
To accomplish this, data governance addresses crucial questions: What constitutes acceptable data? Where is it collected and used? How accurate should it be? What rules should it adhere to? Who should be involved in different aspects of data management? Importantly, data governance goes beyond IT and involves stakeholders from across the enterprise.
To ensure the security and reliability of data, governance requires active participation from stakeholders representing various business sectors. Without a coordinated approach, each business unit developing its own data strategy would lead to chaos and an incomplete picture of the data landscape, rendering it less useful.
Conclusion
In conclusion, data management and data governance are distinct concepts and practices, yet both are vital to ensure the effective and meaningful utilization of data within a business. By establishing strong data management practices and implementing robust data governance strategies, organizations can unlock the full potential of their data assets, driving informed decision-making and fostering a data-driven culture throughout the enterprise.
Note: The article includes a video that presents an alternative approach to explain the differences between data governance and data management.
