The Difference Between Data Governance and Data Management

Every day, business is becoming more dependent on data. From reports stating that the world’s data volume will grow by 40% per year to those that predict data analytics market growth of 13.54% CAGR, it’s safe to say that data is and will continue to be central to business. And while data and business become ever more interdependent, it’s understandable that policies that outline how data should be managed and secured will develop. This is where data governance and data management come into play. 

What Are Data Governance and Data Management? 

Data governance and data management are two very different aspects of today’s data-dependent business. Independent of size or industry, both ‌initiatives are critical for your organization; understanding these two concepts, how they are different, and how they are similar, is essential.

Data Governance 

Data governance is the practice of controlling and managing your data with internal policies, standards, and technology. It provides guardrails for how data is used and accessed within an organization – what users have access to what data, under what circumstances, and how. 

A well-executed data governance strategy ensures that data is trustworthy and used only for its intended purposes. As data analytics for better decision-making explodes in popularity and new data privacy regulations take effect worldwide, data governance has an increasingly important role in today’s organizations. For a deeper look at data governance, take a look at our piece on The Principles of Data Governance: Concepts, Frameworks, and Best Practices. 

Data governance initiatives typically focus on:

Data quality. If data quality is poor, business analytics suffer, resulting in poor decision-making. 
Data availability is the concept of having access to data. This may sound straightforward, but with challenges around data siloing and data sprawl, organizations can quickly lose track of critical data, limiting their ability to thrive in today’s data-driven business environment.
Risk management is categorized as people risk, process risk, and data risk, risk management is the study of identifying and mitigating aspects of a given business that induce risk. As organizations become more sophisticated and depend on more third-party tools and a global marketplace, risk management is imperative as it allows a business to grow, while minimizing exposure. 
Data security and compliance. Some implementations of data security and compliance in data governance are role-based access control, zero-trust policy, following data compliance regulations such as HIPAA, GDPR, CCPA, or implementing a perimeter security initiative.

Data Management

Once you’ve understood data governance, data management is easy – data management is simply the implementation of data governance. Without data management, data governance is an ethereal concept. However, with data management—and the support of data management engineers—data governance takes shape through the implementation of architectures, metadata management, and user policies throughout the entire data lifecycle. 

Data preparation data sources typically follow various schema and structure. Data preparation typically sources the data on a landing zone as a way to enable the subsequent stages of the data pipeline.
Data pipelines are a series of steps that move data from a source system to a target system. Fortunately, with the help of intuitive drag-and-drop data pipelines, building data pipelines are easy and straightforward. 
Data extract, transform, load (ETL) is a stage of the data pipeline where data is cleaned and transformed into a uniform data schema and then loaded into a data warehouse. ETL has been the common approach for data transformation until the rise of ELT.
Data extract, load, transform (ELT) is like ETL, however in this data management strategy data is first loaded into a data warehouse before transformation. This strategy is common in cloud-based data warehouses as these cloud-native platforms can easily handle the heavy transformation processes.
Data warehouses are historical databases designed for business intelligence initiatives such as data analysis. Data warehouses come in many iterations, such as a cloud data warehouse, Enterprise Data Warehouse (EDW), Operational Data Store (ODS), Data Mart, and others. 
Data lake uses a centralized repository of raw storage designed to store data of various schema such as unstructured data, structured data, and semi-structured data. Another major benefit of the data lake is the underlying storage. 

Compared to data warehouses that store data in files and folders (think operational databases) a data lake can use inexpensive object storage as the underlying storage infrastructure. 

How Data Governance Differs From Data Management

With the interdependence between data governance and data management, it’s easy to get the two areas confused. To keep things simple, we can think of data governance as the high-level strategy and policies, while data management is the technical implementation of that strategy.

To this point, we’ll often see the role of building and maintaining a data governance strategy map sit with executive leadership. This makes sense since the executive team is tasked with developing high-level initiatives that steer the direction of the organization. 

Comparatively, data management is often implemented at the ground level. We’ll typically find IT staff and engineers implementing perimeter security, role-based access control, zero trust policy architecture, data pipelines, operational databases, data warehouses, and an array of other tools to mirror the high-level initiative set forth in the data governance strategy.

Data Governance
Data Management
Rules, policies, and oversight that outline how data is governed. It sets authority over how an organization manages and controls data.
Implementing data governance rules with role-based access tools, data pipelines, and data security architecture.
C-suite leadership sets the data governance rules and policies.
IT/engineering implements the physical architecture, software, and policies to mirror the data governance initiative.
Focuses on business value as a guiding principle.
Focuses on technology to meet business value needs.

Data Governance, Management, and StreamSets

StreamSets has an open metadata system that feeds many data governance tools ensuring that data pipelines are not a black hole for data governance. Data is generated during pipeline runs and incorporated into global visibility of data movement.
StreamSets connects with many popular LDAP and policy and access controls that help you control who has access to what data and makes sure that sensitive data can be anonymized even to the data engineers working on the pipelines
StreamSets provides scalable smart data pipelines that are critical to executing a comprehensive data management practice.

Ready to start your smart data pipeline journey? Contact us today.

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