Data Management and Data Governance: What’s the Difference?

You have probably heard about data management and data governance several times in our blogs. You aren’t the only one assuming they mean the same or involve similar functions. Although they are related, there are significant differences between them. Let’s dive into understanding how these two concepts differ and operate.   

What is Data Management?  

Simply put, data management refers to end-to-end data management across the lifecycle with clearly defined policies, processes, and best practices. Effective data management facilitates complex data analysis and enforces rules to standardize data collection, storage, processing, usage, and protection activities across an organization’s IT systems, applications, architecture, and teams. Data management benefits include increased regulatory compliance, enhanced data security, lesser data redundancy, and better quality of data and data insights to power important executive and business decisions.   

Data management processes involve these tasks:  

1. Data Preparation: The first step is always the crucial step. Data preparation can make or break an enterprise’s long-term data-related decisions and practices. It involves dedicated efforts to clean data and ensure data quality to facilitate accurate analysis and reporting. Oversights at this stage can result in good and precise datasets powering essential decisions.   
2. Data Pipeline: This method involves raw data extracted from multiple sources and added to the data lake or warehouse. Data pipelines are necessary for supporting data projects like data analyses, monitoring, machine learning tasks, and visualization. The data pipeline architecture includes data integration, transformation, and storage.   
3. Data Extract, Transform, and Load (ETL): An ETL pipeline is sometimes used synonymously with a data pipeline. It involves extracting, transforming, and loading data into the data storage repository. ETL pipelines are implemented in enterprises using cloud-native tools or applications that allow anytime-to-anytime data access and storage across platforms and systems.   
4. Data Catalogs: Data teams and professionals need seamless access to data, and data catalogs allow it by leveraging metadata and data management tools and providing comprehensive inventory data. Data catalogs simplify data access without IT intervention or concerns around data compliance and governance, enabling faster and better data analysis.   
5. Data Warehouse: Once an enterprise has collected large data volumes that a standard database cannot handle to run analytics, they rely on data warehouse systems. The recent versions of data warehousing systems are hosted on the cloud and have built-in analytics and visualization tools. A typical data warehouse has a central database, ETL pipeline tools, metadata, and data access tools. A data warehouse helps store data from anywhere across the enterprise systems and platforms to be quickly managed and analyzed.   
6. Data Governance: Data governance refers to all policies, internal controls, standards, and best practices to ensure data protection and compliance throughout the data lifecycle. It also includes assigning data ownership, reducing silos, implementing data security practices, and ensuring the safety and quality of data for timely insights and analysis.  
7. Data Architecture: Every enterprise decides on a flowchart of their data from the collection, transformation, storage, distribution, and usage stages. Data architecture represents how the data moves across the data storage systems and serves as a cornerstone to data processing tasks. Data engineers and architects use data architecture design to define and identify data models and structures. The data architectures are purely decided based on the business needs, but their end goal is to enable data integration across platforms, teams, functions, and geographies, eliminating data silos.  
8. Data Security: An essential component of data management is data security, a collective set of practices and processes safeguarding digital data from loss, theft, or unauthorized access. It entails keeping physical storage systems like hardware and devices safe with relevant administrative and access control and implementing data protection strategies for transparency into data processing activities. Data security also involves the protective adoption of tools for data encryption, data masking, data audit, sensitive data redaction, and data compliance.   

What is Data Governance?  

As we already know, data governance is a sub-process of data management involving all activities to keep data safe, compliant, and accessible for ethical processing and analysis. 2023 recorded over 8 billion data breaches, and lessons from around the world underpin robust data governance’s importance. In 2024, 60% of data leaders prioritize data governance to cope with the evolving sophistication of cybersecurity attacks. Most organizations invest in data governance leaders (CDOs, Directors of Enterprise Data, and Data Governance Officers) and teams to draft compelling strategy or data governance models.   

The data governance framework is unique to each enterprise. However, it does include standard components in the process, such as:   

• Data Team and Leaders: Every organization establishes roles and responsibilities to oversee and manage the data governance processes across data ownership, data collection, data storage, data management, and data usage.   

• Data Policies: Data governance teams help define a comprehensive roadmap and policies to identify and classify data sets as personally identifiable information (PII), increase data literacy, data compliance, and consistency, and monitor the impact of the data governance plan.  

• Standard Data Process: It involves all procedures to standardize and regulate communication, protection, and management of data processing activities.   

• Data Quality & Security: Processes and best practices committed to maintaining data quality and reliability help with the large organizational goal of being more data-driven. Data security enables defining and labeling data while managing access, usage, storage, sharing, and transfer risks.   

• Data Compliance: Building a set of rules to ensure that data processing activities adhere to regulatory norms and are free of non-compliance risks.   

• Data Stewardship: It involves monitoring how teams use data resources across the organizations and identifying stewards, which helps ensure that data is approached ethically and safely.   

• Data Transparency: It helps maintain enough transparency into how the organization is using data and the purpose of the data processing activities. It also helps account for the sources and method of data collection.   

Data Management and Data Governance with OpenPages with Watson 

Privacy becomes a leading concern across departments and systems as data volumes increase. Regulation changes as per the comprehensive EU’s GDPR, California Consumer Privacy Act (CCPA), and others keep data privacy stakeholders on their toes. Highly integrated GRC solutions like the OpenPages Data Privacy Management module simplify how data teams can access and view data risks across the value chain. Its holistic view benefits users by helping them understand how, where, when, who, what, and why privacy data is used while meeting compliance requirements.   

Let’s dive into the four specific advantages of the Data Privacy Management module of the GRC platform for complete data governance and privacy:  

• Complete data inventory: With the help of the Watson Knowledge Catalog, users can maintain a single repository of data assets and sources classified as private or sensitive data.   

• End-to-end Assessment: It enables running assessments of each data asset to classify privacy information and run compliance checks.   

• Demonstrate Privacy Compliance: Leverage data privacy questionnaires and reporting to demonstrate compliance with recent privacy norms.   

• Embedded AI, Automation, & Security: OpenPages helps users become risk experts by allowing them to gain a complete view of how sensitive data is used, stored, and accessed.   

Suppose you want to bring your enterprise’s data management and data governance up to speed with iTech GRC’s expertise in OpenPages Data Privacy Management. In that case, we can connect you with the right experts.   

Contact our team today!