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According to the Association of Information and Image Management (AIIM), frequent reorganization and deletion of information is critical to the data lifecycle. Too much unstructured data inevitably leads to security breaches, leads to compliance issues, increases storage costs, and impacts day-to-day operations.

Businesses in all industries are realizing that these issues can be mitigated, if not completely avoided, by maintaining up-to-date and “clean” data sets. This is done through data remediation, which should be at the heart of every organization’s data management strategy.

This article provides an overview of the remediation process, its many benefits and its different stages. Read on to find out how companies are using this procedure to improve their workflow by reducing data overload.

By definition, data remediation corrects errors that accumulate during and after data collection. Security teams are responsible for reorganizing, cleaning, migrating, archiving, and deleting data to ensure optimal storage and eliminate data quality issues.

In other words, the main goal of remediation is to manage unstructured data by reducing redundant, stale, and trivial (ROT) data, commonly referred to as dark and dirty data.

You should 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:

  • Modification of external or internal laws and policies: As you probably know, data privacy rules are constantly changing around the world. Additionally, a company’s senior management may implement new internal policies. In both of these situations, it is necessary to remain cautious and correct your data to ensure legal and regulatory compliance.
  • Change of commercial conditions: Software or hardware changes can affect data within a company. Additionally, you should review new data resulting from mergers and acquisitions. In this case, you need data remediation to check for security threats and protect against possible breaches.
  • Human errors: In the workplace, accidents and mistakes are inevitable. When errors are discovered, you should perform data correction to assess data integrity and security. It helps you understand the scope of the incident and how you can mitigate any resulting data quality issues.

Data remediation offers many benefits to business operations, including:

  • Improve data security and reduce risk: Data is either securely stored or deleted after correction. Additionally, unstructured data is classified and secured, greatly reducing the risk of data loss, breaches, and cyberattacks.
  • Ensure regulatory compliance: Frequent data correction processes can keep a business up-to-date and compliant with the latest changes in international data laws and regulations.
  • Reduced storage costs: Data correction minimizes data size, which subsequently reduces storage costs.
  • Performance improvement: After organizing your data sets, employees spend less time managing and browsing 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 businesses at risk of losing data and the entire business. That said, businesses need an effective remediation process and a comprehensive backup solution to ensure business continuity and security,” says the Senior Product Manager at NAKIVOone of the industry leaders in data protection and recovery.

But what is effective data remediation? Let’s explore this process in more detail.

There are several steps you need to take before starting the patching process:

  • Create a data correction team define responsibilities and roles.
  • Develop data governance policies and be sure to apply them throughout your organization.
  • Identify priority areas that require immediate attention.
  • Allocate the necessary resources and budget based on labor costs.
  • Set expectations and goals to understand the problems you might be facing and how you can overcome them.
  • Monitor progress and develop reports to ensure that the data correction process is serving its purpose.

The remediation procedure may not be straightforward, but you can get the most out of it by following the steps below:

Step 1: Assess your data

First and foremost, you need to gain a comprehensive understanding of the data you have within your organization. It is necessary for remediation as it helps you to identify critical data, its size and storage locations. Additionally, you can learn the amount of unstructured data, allowing you to set a primary goal for cleaning and organizing data.

Step 2: Classify existing information

Now that you know how much data you have, you need to separate it based on usability and importance:

  • Data that can be securely deleted without hampering day-to-day business operations. He understands:
    • Redundant, obsolete and trivial data.
    • Dark data that you haven’t used for a long time.
    • Dirty data that is duplicated, inaccurate, or outdated.
    • Typical data easily accessible and used by many users in daily procedures.
    • Sensitive data that requires high security measures and protection.

Step 3: Implement your data governance policies

The next step is to apply the internal procedures you defined during the preparation phase. Naturally, different types of data require different policies, management strategies, and remediation approaches.

Based on all the information you have gathered so far, you can go ahead and select the most suitable correction technique for each type of data. The most common methods include modifying, deleting, indexing, migrating, and cleaning data.

Step 5: Evaluate the process and generate reports

The final step is to revisit the data correction procedure and evaluate the results. It can be useful to create reports and use them as a basis for future resolutions.

Data remediation has proven to be a very valuable part of data management for all organizations, regardless of industry. Below are some examples of practical use cases.

Employee Data Management

When an employee leaves your organization, you need to ensure that no data is lost or taken. That’s where remediation comes in. It lets you review and remove company data from the employee’s device to ensure privacy and protect sensitive information.

Financial data management

Financial institutions such as banks collect massive amounts of data on a daily basis. Traditional tools fail to prevent data overload and these organizations end up with countless amounts of useless information. Frequent data correction allows banks to organize incoming data and remove redundant sets of information.

Health data management

It goes without saying that clinical data is of the utmost importance since it allows healthcare organizations to improve their services. With the substantial amount of data collected, institutions are left with large amounts of unstructured data. Data remediation allows hospitals and clinics to organize their information to provide better solutions to patients.

A must have for data management

Data remediation is an essential part of data management due to its many benefits. With the right strategy in place, you can organize unstructured data, reduce security risks, meet regulatory compliance, and ultimately reduce operational costs. Businesses across different industries rely on data correction to improve their day-to-day operations and avoid data overload and its harmful consequences.

This article was written by Mariia Lvovych, CEO and Founder of Olmawritings and GetReviewed.

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