When you think about data quality, the overall state of data fitness in your company, you will find it influences and changes with organization-wide data activities and practices. Data management and data governance contexts work together, helping you to make sense of, deal with, and spell out all these different factors.

Whether you take data governance or a data management perspective determines how you plan and implement data quality activities. For example, you may want better data quality to improve your relationships with your customers.

Approaching this problem from a data management viewpoint considers data a valuable resource that needs controlled planning and behaviors. By implementing good data management, your business leverages data successfully, including ensuring good data quality for survival and growth.

Data governance takes a slightly different approach in advancing data quality. Data governance provides services to coordinate people, technologies, and processes supporting the data quality required to keep your customers.

So, you want to be sure to switch from both data management and data governance contexts to get the best data activities and planning of both worlds. Understanding each concept will help you plan and cover the most variables affecting your business’ desired data quality threshold. Read further to learn:

  • What Is Data Management?
  • What Is Data Governance?
  • What Are the Differences Between Data Management and Data Governance?
  • How Do Data Management and Data Governance Work Together to Improve Data Quality?
  • Final Words


Data Management describes all the principles, practices, programs, and processes a company must have to operationalize its valuable data, from its creation to deletion. Data management paints a broad stroke, covering everything a person or system does with data, involving data governance and data quality, too.

You will find, under the data management umbrella, any structure, and service responsible for data creation, transformation, usage, storage, maintenance, and deletion, including:

  • Data Quality
  • Data Governance
  • Data Integration
  • Data Architecture
  • Master Data Management
  • Data Warehousing
  • Data Storage
  • Big Data
  • Business Intelligence/Analytics
  • Metadata Management
  • Data Security

The entire concept means you need to be aware of and, control, and coordinate all the components that make up data management together. So, when you perform data management to get better data quality, as a side effect, the other aspects become better too.

If you’re interested in learning more about Master Data Management then make sure you have a read of our comprehensive MDM guide here.


Data governance is a sub-discipline of data management. The Data Governance Institute defines data governance as a practical and actionable framework that helps various data stakeholders across any organization identify and meet their information needs. Applying data governance to something means implementing data management services, such as documentation, negotiation, or training, to support the business and its data quality.

Data governance services use control mechanisms to guide people and systems with their data activities, minimize risks, and maximize rewards. Through various data governance structures, data security and accessibility become more transparent and explicit.

Then, the business becomes more compliant with data privacy laws and uses its data to innovate. Also, data governance standardizes data quality requirements, reducing the costs to reclean data due to confusion among data stakeholders and mismatching information among systems.


Data management and data governance solve slightly different problems.


If you want to know how to connect an organization’s data to its business strategy through using and handling data across its lifecycle, you need a data management perspective. Upon wearing a data management hat, you need to plan and implement everything data-related, encompassing what data systems you have and require to what kind of data storage equipment you need to use, and so forth.

Good Data management reveals a larger data vision and data strategy, aligning with your entire business plan and activities. That picture informs all the data management elements listed in the “What is Data Management” section above.


When taking a data governance perspective, you address the problem about combinations of people, processes, and technologies required to support the data strategy and data management visions. For example, when managing data systems, you will need a document map of your systems throughout the organization and agreement, through stakeholder meetings, about what kind of technologies to keep or add, among other services.

Also, you need server automation to assign data access to groups of people or machines. You must have employee training on how to secure work systems at home or on the road.


Looking at data quality from a data management viewpoint, you see how data quality needs to support its data vision and strategy and how each data management component interfaces with data quality. For example, the company’s data vision may state that a high customer service level will come through high data quality.

As one step towards achieving this data management objective, you may prioritize deduplicating your data and integrating customer data entries from seven different databases and spreadsheets for a single view. You list the systems needed to work with your data cleansing application and contain unique data values for each customer profile.

In wearing the data governance hat, you identify the people you need to consult about integrating customer information into one view. You arrange a meeting with your colleagues and come up with a business rule set to apply when automatically and manually cleaning the data. These data governance rules support more consistent, accurate, and reliable data improving your data quality.

The diagram below shows the relationship between data management, data governance, and data quality contexts.

ven diagram of data management, data governance and data quality


If you want to improve data quality, you need to step back and look at data management and data governance contexts. Data management recognizes covers various components that have relationships to each other.

Data governance covers the continuous services needed to get good data quality to support the business and data management goals. You need data management and data governance to inform good data quality and validate standard customer data.

Written by Michelle Knight

Michelle Knight has a background in software testing, a Master's in Library and Information Science from Simmons College, and an Association for Information Science and Technology (ASIST) award. At WinPure, she works as our Product Marketing Specialist and has a knack for explaining complicated data management topics to business people.

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