Data Management Vs. Data Governance

Data Management Vs. Data Governance
February 10, 2021 | Agile Data Governance and Compliance

Data Management Vs. Data Governance

Data management is simply defined as the implementation of tools, processes as well as architectures designed to achieve your organization’s objectives. On the other hand, Data governance can be defined as the management of how data is accessed and handled in a data management strategy, including authentication and access granted to users of the data and compliance procedures.

Simply put, data governance seeks to establish policies and procedures of handling data, while data management seeks to enact these policies and procedures to make meaningful use of that data for onward processing and organizational decision-making.

Let us compare these two further by defining each.

What is data management?

Data management is the development and thereafter the implementation of structures, policies, and procedures to manage the full data lifecycle in an organization. These policies and procedures are critical in an organization to help in analyzing complex and big data. Data in modern times is treated as the most important asset in any organization, and therefore, it needs to be managed as such.

In the 2019 State of Data Management report, data governance ranks in one of the top 5 strategic initiatives undertaken by global organizations in 2019.

What are some of the elements of data management?

Data preparation

Data preparation is the process of cleaning as well as transforming raw data to enable accurate analysis of the said data. In an organization, the rush for reporting and analysis, this critical first step oftentimes gets missed, leading to bad decision making from bad data.

Data pipelines

These are channels used to automatically transmit data from a system to another.

Data governance

Data governance helps to define policies and procedures so as to maintain data security and data compliance.

Data catalogs

Data catalogs help to create and capture a complete picture of the data by helping in the management of metadata as well as making data easier to find and track.

Data warehouses

These consolidate all data sources to provide a clear route to data analysis.

Data extract, transform, load

Simply abbreviated as Data ETL, this refers to the process of transforming data for it to load in an organization’s data warehouse. Data ETLs are mainly automated processes once they are built.

Data security

Data security consists of all processes that are put in place to safeguard your data from unauthorized access or being corrupted.

We have defined Data management, so what is data governance?

Data governance can be defined as the process of managing availability, usability, security and integrity of the data in an organization, Data governance is carried out based on the organization’s internal data standards as well as policies that also control the same organization’s data usage. An effective data governance strategy seeks to ensure that data is consistent and trustworthy, and it doesn’t get corrupted.

A good data governance strategy includes a governance team that works together to develop the standards and policies for governing the organization’s data, as well as implementation and enforcement procedures, primarily carried out by data stewards. Besides the governance team, executives and other representatives from the organization’s business operations also have a role, in addition to the IT and data management teams.

Why does data governance matter?

Without an effective data governance model, inconsistencies in data across an organization do not get resolved. Data errors do not be identified and thereafter, fixed, further affecting business intelligence, analytics accuracy, and decision making. Poor data governance hampers compliance efforts, causing problems for organizations seeking to comply with data privacy and protection regulations, such as the European Union’s GDPR and the California Consumer Privacy Act (CCPA).

An organization’s data governance strategy leads to the development of common data definitions as well as standard data formats to be applied in all business systems for internal use and compliance purposes.

Benefits of Data Management and Data Governance

Low data privacy risks

As regulators continue to tighten the rules governing the use of data, adequate data governance processes and procedures ensure that organizations don’t incur hefty fines or damage to their reputation when they fail in compliance.

Improved insights and decision making

Data management and governance ensure accurate data. Accurate data boosts organization-wide performance. Executives, Business and line managers can more accurately assess the performance of their teams and identify areas that can be improved on.

Simply put, data governance and data management mean better, cleaner, and leaner data, leading to better analytics, which leads to better decision making, which ensures better results.  Better results lead to better market positioning and better reputation.


In both concept and practice, data management and data governance do not refer to the same thing. However, they are both critical to ensure meaningful and valuable use and management of data in an organization. DQLabs platform helps you implement these two seamlessly in your systems.

While the two are different, their goals remain the same; to create a solid, and trustworthy data foundation that empowers teams in an organization for optimal results.