K
Kathleen Martin
Guest
According to the Association of Information and Image Management (AIIM), frequently reorganizing and discarding information is essential for the data life cycle. An excess of unstructured data inevitably leads to security vulnerabilities, causes compliance issues, increases storage costs, and impacts day-to-day activities.
Businesses in all industries realize that these problems can be mitigated or even completely avoided by keeping up-to-date and “clean” datasets. It is done through data remediation, which should be at the core of the data management strategy of every organization.
This post provides an overview of the remediation process, its numerous benefits, and its different stages. Read on to discover how companies use this procedure to improve their workflow by reducing data overload.
What is data remediation?
By definition, data remediation is correcting the mistakes that accumulate during and after data collection. Security teams are responsible for reorganizing, cleansing, migrating, archiving, and deleting data to ensure optimal storage and eliminate data quality issues.
In other words, the primary goal of remediation is to manage unstructured data by reducing redundant, obsolete, and trivial (ROT) data, commonly known as dark and dirty data.
When is data remediation required?
You must perform data remediation regularly to ensure that your organization’s data is continuously updated, protected, and compliant. However, there are times when remediation is mandatory to avoid security breaches or legal repercussions:
Businesses in all industries realize that these problems can be mitigated or even completely avoided by keeping up-to-date and “clean” datasets. It is done through data remediation, which should be at the core of the data management strategy of every organization.
This post provides an overview of the remediation process, its numerous benefits, and its different stages. Read on to discover how companies use this procedure to improve their workflow by reducing data overload.
What is data remediation?
By definition, data remediation is correcting the mistakes that accumulate during and after data collection. Security teams are responsible for reorganizing, cleansing, migrating, archiving, and deleting data to ensure optimal storage and eliminate data quality issues.
In other words, the primary goal of remediation is to manage unstructured data by reducing redundant, obsolete, and trivial (ROT) data, commonly known as dark and dirty data.
When is data remediation required?
You must perform data remediation regularly to ensure that your organization’s data is continuously updated, protected, and compliant. However, there are times when remediation is mandatory to avoid security breaches or legal repercussions:
- Change in external or internal laws and policies: As you probably know, data privacy rules are constantly changing worldwide. Additionally, a company’s higher management can implement new internal policies. In both of these situations, it is necessary to stay on the safe side and remediate your data to ensure legal and regulatory compliance.
- Change in business conditions: Software or hardware changes can affect the data within a company. Moreover, you should examine new data resulting from mergers and acquisitions. In this case, you need data remediation to check for security threats and protect from possible breaches.
- Human mistakes: In the workplace, accidents and mistakes are bound to happen. When errors are discovered, you must perform data remediation to assess data integrity and security. It helps you understand the extent of the incident and how you can mitigate any resulting data quality issue.
