In this modern century, nothing is more valuable than data for companies because it paves the way for success. With the help of well-collected data, companies can run marketing campaigns effectively, modify their products/services based on the demand, build a strong relationship with customers, and much more. In a nutshell, companies make better business decisions when they have up-to-date data.
Well, storing data is imperative for companies, but it is paramount for them to make sure that customers’ classified information stays secured. In case of a compromise, they will lose customer loyalty and struggle to safeguard their business integrity against negative reviews trending on social media platforms. Therefore, they should comply with regulations like GDPR. For the very same reason, they should consider data governance and master data management as highly important.
Let’s understand what master data management and data governance are before comparing them both for better understanding:
What is master data management?
Data management means managing the complete data lifecycle by creating and executing the right policies and procedures. This helps with several things like stewardship, accountability, consistency, etc. As a positive outcome, companies enjoy hassle-free data management.
What is data governance?
Data governance means putting certain procedures in place and giving responsibilities to individuals to ensure data quality and security. It allows organizations to use their collected data to make better decisions for their business without worrying about breaches. A governance team makes sure that the data gets used in a controlled way so that no classified information gets compromised due to carelessness.
What’s the difference between master data management and data governance?
Let’s compare master data management and data governance for better understanding:
Master Data Management
Data management is all about creating and using such methods that help with data organization.
Data governance is all about certain policies, rules, and controls that are put into action to govern the data and manage data quality.
The process involves accumulating, organizing, processing, securing, sorting, and maintaining data.
The process involves establishing procedures and theories to prevent data misuse.
It focuses on ensuring more quality and value.
It focuses on keeping the data reliable and safe.
It follows a logistic method to organize the data properly.
It follows a practice to achieve high-quality and ultra-secure data.
It relies on logic and focuses on technology.
It philosophically focuses on overall business strategy.
We hope this comparison table, master data management vs. data governance, has offered you crucial insights.
What are the key components of Master Data Management and Data Governance?
Now, let’s discuss the key components of master data management and data governance to understand how both of them work:
❖ Key components of data governance
- People: Teams across all departments require specific data to carry out their tasks. As we can understand, more people getting access to data means higher chances of misuse and data leaks. Therefore, companies have data governance policies and data stewards, people who make sure that data governance policies get followed closely by all business divisions.
- Standards: Setting standards with respect to data quality is significant for companies. It will help with information regarding how data should be stored and used. It will help in reducing the probabilities of data misuse like duplication.
- Policies: Companies create certain policies regarding who can access data, how long it can be used for, and what type of data can be stored.
❖ Key components of master data management
- Tools: IT teams use a variety of tools for data management, such as data loader, business process automation applications, ETL software, etc.
- Processes: IT teams that have the responsibility of data management usually establish and follow certain processes to ensure data quality, archive or delete data according to business requirements, keep a close eye on data usage metrics, and much more.
- People: Similar to data governance, people play a pivotal role in making data management successful. Therefore, companies should educate their team about data policies so that they can manage classified data painlessly.
Is there a way to ensure that data governance and data management work together seamlessly?
This article is about master data management vs. data governance, but you should understand that these two concepts are meant to work together to let an organization manage and safeguard its data.
When you bring data management and data governance together, you can accomplish your company’s goals revolving around data management and protection. The results of the former depend on processes, while the latter’s on policies.
Data governance lets you meet regulatory requirements by telling you how long customer data should be kept and used. On the other hand, data management requires certain processes to be followed so that data can be archived and deleted properly according to business requirements.
Let’s take the example of data access. A data governance policy dictates that the stored information can be accessed by only those employees that require it to do their job. On the flip side, a data management process asks managers to grant role-based access so that limited employees can use the data in a controlled way.
This is how data governance and data management work together.
It is clear that data-driven actions let you take your business forward and beat the competition. But it is also imperative to understand the difference between data governance and master data management, although their goals are the same: build a solid and trustworthy data foundation so that employees working for an organization can deliver the best results and do their work smartly.
This article was about master data management vs. data governance to help you understand the difference clearly. Right from the definition to the key elements, we have talked about everything you should know. We have also discussed how data governance and data management work together to give you an idea of the kind of relationship they share.