Data can be one of the most valuable assets for any organization. With the rise of big data, companies can take advantage of vast stores of enterprise data to gain insights and make better decisions. However, as we create and store more data, consumer privacy concerns are growing. Companies need to comply with increasing numbers of regulations, such as GDPR. Data breaches occur with greater frequency, and they can be extremely damaging to an organization’s reputation as well as expensive to clean up. The risks of poor data policies are severe, even fatal for organizations. It’s essential to create data governance and data management practices to make sure that data is handled properly. Let’s look at data governance vs data management.
What is Data Governance?
Data governance is the set of rules that determine a company’s overall strategy for the appropriate use, handling, and storage of data. A data governance framework helps companies establish polices for:
- Who can access and use data
- Determining processes around data storage and how long data is retained
- Making sure data is stored securely
- Mitigating the business risks associated with storing sensitive data
An effective data governance strategy is essential to maintain regulatory compliance, minimize risks, improve data security, and create accountability for data throughout the organization. Data governance policies can also be used to create and standards for data quality.
Data governance includes several key components that all must work together to be successful:
- People Nearly every employee relies on data in one way or another. Therefore, people play an important role in data governance policies. Data Steward is an increasingly common role in companies. A Data Steward is the person responsible for enforcing data governance policies within business units or divisions.
- Standards Organizations must set standards that help ensure data quality, aid in monitoring data stores for data accuracy and completeness, and help minimize data issues such as duplicate records.
- Policies Policies can cover issues such as who can access and leverage data, how long data is retained, and where certain types of data can be stored.
What is Data Management?
Data management is essentially the execution of the data governance strategy. It’s the implementation of the standards and policies of the data governance framework and can include tasks such as:
- Creating the role-based access rules that determine who can access what data
- Implementing database rules to dispose of data according to the data governance policy
- Establishing and maintaining the data security measures needed to comply with company policy around information security
- Taking the appropriate measures to minimize risk associated with storing sensitive data
- Creating a system for master data management, which is a single view of data across the enterprise.
Using a data management strategy helps ensure that data is handled according to the overall company policies for its entire lifecycle, from when it is created until it is retired.
An organization’s data management practices can include several components:
- Tools The IT teams responsible for data management may use a variety of tools to manage and prepare data, including a data loader or ETL software, data governance tools, or business process automation applications.
- Processes IT teams that are responsible for data management need to establish and follow the business processes to support data quality, to archive or delete data as needed, or to generate metrics on data usage.
- People As with data governance, people are one of the biggest factors in the success of a data management practice. Making sure people are educated about data policies is one of the keys to data management.
What are the Key Differences Between Data Governance and Data Management?
Now let’s consider data governance vs data management. Data governance is a broad set of policies, implemented across the organization. The concept of data management is narrower, focused on executing the specific processes that support the data governance policy.
In other words, when it comes to data governance vs data management, data management is the execution and data governance is the guidance that informs the execution.
How Data Governance and Data Management Work Together
Data management and data governance work together to support a company’s goals on maintaining and protecting data, the former focused on processes and the latter on policy.
For example, a data governance policy may specify that customer data must be retained for seven years to meet regulatory requirements. Data management processes can then be put in place to perform the necessary data archiving and deletion within the data storage systems.
Another example is data access. There may be a data governance policy that dictates that personally identifiable information (PII) can only be accessed by employees who need it to perform their jobs. A data management process can therefore be created to grant role-based access to the employees who meet that criteria.
The topic of this blog may be data governance vs data management, but in fact the two concepts work together to support an organization’s overall management and safekeeping of its data.
Want to Learn More About Data Governance and Data Management?
Data management vs data governance are both hot topics these days. That’s because the consequences of poor data policies can be severe. It’s important to determine and prioritize the financial benefits of implementing robust data governance and data management strategies.
Proper data integration and transformation can help facilitate best practices for data governance and data management. Harness the power of the cloud with Matillion solutions that are purpose-built to work with leading cloud data warehouse environments – Amazon Redshift, Google BigQuery, Microsoft Azure Synapse, and Snowflake. From extracting and loading your data to performing powerful data transformations, Matillion cloud ETL solutions offer simplicity, speed, scale, and savings compared to legacy ETL tools.
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