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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:
- 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.
Why is data remediation important?
Data remediation provides numerous advantages to business activities, including:
- Improving data security and reducing risks: Data is either safely stored or removed after remediation. In addition, unstructured data is classified and secured, and it dramatically lowers the threat of data loss, breaches, and cyberattacks.
- Ensuring regulatory compliance: Frequent data remediation processes can keep a company updated and compliant with the latest changes in international data laws and regulations.
- Reducing storage costs: Data remediation minimizes data size, which subsequently lowers storage costs.
- Enhancing performance: After organizing your datasets, employees spend less time managing and browsing through data which streamlines productivity. It also reduces operational costs.
Remember that remediation alone cannot protect your data despite these benefits. “In today’s data-driven world, sophisticated attacks such as ransomware and phishing schemes put companies at risk of losing data and the entire business. That said, companies need an effective remediation process and a comprehensive backup solution to ensure business continuity and security,” says the lead product manager at NAKIVO, one of the industry leaders in data protection and recovery.
But what is effective data remediation? Let’s explore this process in more detail.
How to prepare for data remediation
There are several steps that you should go through before starting the remediation process:
- Create a data remediation team to set responsibilities and roles.
- Develop data governance policies and make sure that you enforce them throughout your organization.
- Identify priority areas that require immediate attention.
- Allocated the needed resources and budget based on labor costs.
- Set expectations and goals to understand the issues you might face and how you can overcome them.
- Monitor progress and develop reports to ensure that the data remediation process fulfills its purpose.
The stages of the data remediation process
The remediation procedure might not be a simple one, but you can make the best out of it by following the stages below:
Step 1: Evaluating your data
First and foremost, you need to gain complete knowledge of the data you have within your organization. It is necessary for remediation since it helps you identify critical data, its size, and storage locations. In addition, you can learn the quantity of unstructured data, which allows you to set a primary goal for cleansing and organizing data.
Step 2: Classifying existing information
Now that you know how much data you have, you should segregate based on usability and importance:
- Data that could be safely deleted without impeding day-to-day business activities. It includes:
- Redundant, obsolete, and trivial data.
- Dark data which you haven’t used in a long time.
- Dirty data that is duplicated, inaccurate or outdated.
- Typical data that is easily accessible and used by many users in daily procedures.
- Sensitive data that requires high-security measures and protection.
Step 3: Implementing your data governance policies
The next stage is to apply the internal procedures that you set in the preparation phase. Naturally, different data types require varying policies, management strategies, and remediation approaches.
Step 4: Choosing the proper data remediation methods
Based on all the information that you have gathered so far, you can go ahead and select the remediation technique that is most suitable for each type of data. The most common methods include data modification, deletion, indexation, migration, and cleansing.
Step 5: Assessing the process and generating reports
The final stage is to look back at the data remediation procedure and evaluate the results. It can be helpful to create reports and use them as a basis for future remediations.
Practical use cases of data remediation
Data remediation has proven to be a highly valuable part of data management for all organizations regardless of their industry. Below you can view some examples of practical use cases.
Employee data management
When an employee leaves your organization, you need to make sure that no data is lost or taken. This is where remediation comes into play. It allows you to examine and delete company data from the employee’s device to guarantee confidentiality and protect sensitive information.
Financial data management
Financial institutions such as banks collect considerable quantities of data daily. Traditional tools fail to prevent data overload and these organizations are left with countless amounts of useless information. Frequent data remediation allows banks to organize incoming data and delete redundant sets of information.
Data management in healthcare
It goes without saying that clinical data is of utmost importance since it allows healthcare organizations to improve their services. With the substantial amount of collected data, institutions are left with vast quantities of unstructured data. Data remediation provides hospitals and clinics with the ability to organize their information to offer better patient solutions.
An essential for data management
Data remediation is an essential part of data management due to its numerous benefits. With a proper strategy in place, you can organize unstructured data, reduce security risks, adhere to regulatory compliance, and ultimately reduce operational costs. Companies in different industries rely on data remediation to enhance their daily activities and avoid data overload and its detrimental consequences.
This article was contributed by Mariia Lvovych, CEO and founder of Olmawritings and GetReviewed.
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