Data Governance Vs Data Quality – How They Are Important For Business Success

Introduction

In the modern era, businesses use data-driven strategies to provide a more personalized and efficient user experience. But data-driven management strategies include two main terms that most people interchangeably use. These two confusing terms are data governance vs data quality. However, they possess an absolute difference, but many people don’t know it.

Even you may have used data quality management interchangeably with data governance. With this article, we have completely distinguished data governance vs data quality. Each information related to these two terms is covered in detail, and their importance is also covered here. Read to know everything about these confusing concepts! 

What is Data Governance?

Data governance includes the processes, technologies, and people needed for managing and protecting company data. Data governance is establishing methods that can define the clear responsibilities of an organization to integrate and store corporate data. The main part of data governance is to protect the data of the citizens and customers.

Without the adoption of data governance, complicated data integration efforts can affect the accuracy of analytics applications, business intelligence, and enterprise reporting. Implementing data governance can help businesses prevent analytical or operational issues because of inconsistencies.

 

What is Data Quality?

Data Quality is a metric that shows how to fit the data set to serve or for the necessary needs of the business. The quality of the data can affect the decision of the users. Data should be of high quality, and for building such data, it is necessary to follow a strict set of guidelines ensuring accuracy and consistency.

It is so because low-quality data cannot track the affecting variable and possess a high degree of error. For instance, in an organization, there is an entry for Mr. Jack, and only it is mentioned that he lives in London. But the specific location is not mentioned. This makes the organization limit its efforts and inefficient use of the data. An organization needs to use data quality tools for better efficiency.

Completeness, consistency, accuracy, format, timeframe, and integrity are the six dimensions of the data quality standards. However, data quality can be challenging for some organizations.

 

What is the difference between Data Governance vs Data Quality?

Data governance and data quality management are two different terms that serve various business goals. The difference between data governance and data quality includes the following:

Basis	Data Quality	Data Governance
Meaning	It describes the accuracy, consistency, completeness, and other data attributes	It is a collection of processes and practices that standardize data usage within the organization.
Objective	Creating high-quality data which can be considered for making essential decisions	To provide a framework for the collaboration of departments through a common language.
Role	To help the users and organization authorities to make quality decisions.	Ensuring the organization can easily access, use, and protect the data by keeping it in a good form.
Pros	Better Targeting Better decision making Effective Marketing Better Relations with the Customers Competitive-Edge	Greater Efficiency Better decision-making Better business performance Good business reputation Better data qualitya
Challenges	The difficulty of data integration Difficult to judge data quality Faster data changes No approved and unified data quality standards	Lack of Data Leadership Difficulty in understanding business value Lack of data documentation Lack of trust and ownership

How are Data Governance vs Data Quality related?

Data quality and data governance are related in terms of compliance. The data quality dimensions like completeness, timeliness, accuracy, and validity should support the governance standard to ensure compliance. A data governance policy usually provides a framework for protecting an organization’s data. But, to comply with those standards, the organization must design a data quality system to monitor the information.

Data quality acts as a component of data governance to some extent. It is so because good and effective data governance cannot be achieved without having data quality. However, it is true, for another way round. For obtaining and achieving high-data quality, it is essential to have good data governance. These statements especially stand true when an organization has multiple working software and systems and large volumes of data. This is how data quality and data governance are related and go hand-in-hand for the organization’s success.

How do these strategies overlap?

Data governance and data quality strategies overlap, as getting high-quality data without good governance is impossible. Without good data governance, even a data quality tool is inefficient. Their overlap is discussed in the following points:

1. The main role here is played by data governance which organizations use to impact its accuracy, privacy, roles & responsibilities, integration, and management. All these tasks are necessary for improving data quality.
2. A good data governance policy can form good communication and understanding between data creators and users, which ultimately forms high-quality data.
3. Data governance policy and procedures can be used for onboarding new data and improving the quality of existing data. This ultimately forms high-quality data.

Why are they both important for success?

For an organization, it is necessary to have good data governance and data quality to ensure success. As discussed above, both of these strategies go hand-in-hand. One cannot eliminate one strategy for the betterment of the other. Both of these strategies ensure the success of the organization because:

1. Both strategies allow businesses to make better decisions. When data governance and management policies are aligned, it becomes easier for the management to ensure the data quality and protect the same resulting in the right decisions.
2. Good data governance leads to improved data quality, increased essential data access to data scientists, and lower data management costs.
3. Good data governance can balance privacy mandates and data collection practices.
4. The organization will get more accurate analytics and better compliance with data regulations if a good governance policy is formulated.
5. Data quality and data governance can avoid the introduction of data errors and potential misuse of the customer’s private information and other sensitive data. This creates a better reputation for the company.
6. A business can use technologies like Artificial intelligence and automation to their maximum potential with high data quality.
7. Data quality helps businesses maintain good quality data and comply with the necessary laws.

 

The Final Words

Data management practice is highly adopted by businesses today because of compliance, efficiency, and better business decision ability they get from it. Data Governance and data quality management are two inseparable parts of the data management practice. However, it is essential to know their differences and similarities to adopt these practices to their maximum efficiency.

We have covered all the necessary points with this article. Their difference lies in their meaning and objectives. Therefore, to achieve better organizational results, it is essential to understand their work. Collecting data is easy but collecting high-quality and safeguarding it in compliance with data governance policy is more difficult!