Data remediation: What it is and why it matters

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According to the Association of Information and Image Management (AIIM), frequently reorganizing and discarding information is essential to the data lifecycle. An excess of unstructured data inevitably leads to security vulnerabilities, causes compliance issues, increases storage costs, and impacts daily activities.

Businesses in all industries realize that these issues can be mitigated or even avoided entirely by maintaining up-to-date and “clean” data sets. It is done through data remediation, which should be at the core of every organization’s data management strategy.

This post provides an overview of the remediation process, its many benefits, and its different stages. Read on to find out how companies use 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 primary goal of remediation is to manage unstructured data by reducing redundant, obsolete, and trivial (ROT) data, commonly known as dark and dirty data.

You should perform data remediation regularly to ensure that your organization’s data is continually updated, protected, and compliant. However, there are times when the correction is mandatory to avoid security breaches or legal repercussions:

  • Change in laws and external or internal policiesNote: As you probably know, data privacy rules are constantly changing around the world. In addition, a company’s top management may implement new internal policies. In both situations, it is necessary to stay safe and remediate your data to ensure legal and regulatory compliance.
  • Change in trading conditions: Software or hardware changes can affect data within a company. In addition, you must examine new data resulting from mergers and acquisitions. In this case, you need data remediation to check for security threats and protect yourself from potential breaches.
  • human errors: In the workplace, accidents and mistakes are bound to happen. When errors are discovered, you should perform data repair to assess the integrity and security of the data. It helps you understand the scope of the incident and how you can mitigate any resulting data quality issues.

Data remediation provides numerous benefits for business activities, including:

  • Improve data security and reduce risks: Data is securely stored or deleted after repair. Additionally, unstructured data is classified and protected, dramatically reducing the threat of data loss, breaches, and cyberattacks.
  • Ensure regulatory compliance: Frequent data remediation processes can keep a business current and compliant with the latest changes in international data laws and regulations.
  • Reduced storage costs: Data remediation minimizes data size, which subsequently reduces storage costs.
  • Performance improvement: After organizing their data sets, employees spend less time managing and examining data, speeding up productivity. It also reduces operating costs.

Remember that remediation alone cannot protect your data despite these benefits. “In today’s data-driven world, sophisticated attacks like ransomware and phishing schemes put companies at risk of losing data and the entire business. With that said, businesses need an effective remediation process and comprehensive backup solution to ensure security and business continuity,” says Senior 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.

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

  • Create a data remediation team establish responsibilities and roles.
  • Develop data governance policies and be sure to apply them throughout your organization.
  • Identify priority areas that require immediate attention.
  • Allocated the necessary resources and budget. based on labor costs.
  • Set expectations and goals. to understand the problems you might face and how you can overcome them.
  • monitor progress and develop reports to ensure that the data remediation process is fit for purpose.

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

Step 1: Assess your data

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

Step 2: Classification of existing information

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

  • Data that could be safely deleted without hampering daily business activities. It includes:
    • Redundant, obsolete and trivial data.
    • Dark data you haven’t used in a long time.
    • Dirty data that is duplicated, inaccurate, or out of date.
    • Typical data that is easily accessible and used by many users in daily procedures.
    • Sensitive data that requires high security and protection measures.

Step 3: Implementing your data governance policies

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

Based on all the information you’ve collected so far, you can go ahead and select the most appropriate remediation technique for each type of data. The most common methods include data modification, deletion, indexing, migration, and cleansing.

Step 5: Process Assessment and Report Generation

The final stage is to look back on the data remediation procedure and evaluate the results. It can be useful to create reports and use them as a basis for future corrections.

Data remediation has proven to be an invaluable part of data management for all organizations, regardless of their industry. Below you can see 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. This is where remediation comes into play. It allows you to examine and remove company data from the employee’s device to ensure confidentiality and protect sensitive information.

Financial data management

Financial institutions, such as banks, collect considerable amounts of data on a daily basis. 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 eliminate redundant information sets.

Data management in healthcare

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

An essential element for data management.

Data remediation is an essential part of data management due to its many benefits. With the right strategy, you can organize unstructured data, reduce security risks, meet regulatory compliance, and ultimately reduce operational costs. Companies from different industries rely on data remediation to improve their daily activities and avoid data overload and its damaging consequences.

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


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