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Data recovery: What it is and why is it important


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According to the Association for Information and Image Management (AIIM), regularly reorganizing and discarding information is essential to the data lifecycle. Exceed unstructured data inevitably lead to security holecauses compliance issues, increases storage costs, and affects day-to-day operations.

Businesses in all industries recognize that these problems can be minimized or even completely avoided by maintaining “clean” and up-to-date data sets. It is done through data processing, 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 discover how companies are using this process to improve their workflow by reducing data overload.

By definition, data remediation is to correct mistakes that accumulate during and after data collection. Security teams are responsible for reorganizing, cleaning, moving, 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, obsolete, and trivial (ROT) data, commonly known as dark and dirty data.

You must perform regular data repairs to ensure that your organization’s data is continuously updated, protected, and compliant. However, sometimes remediation is required to avoid security breaches or legal consequences:

  • Change external or internal laws and policies: As you probably know, data privacy rules are constantly changing all over the world. In addition, the company’s senior management may implement new internal policies. In both of these cases, it is essential to keep your data safe and repair it to ensure compliance with laws and regulations.
  • Changing business conditions: Software or hardware changes that may affect data within the company. Furthermore, you should check for new data due to 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 an error is discovered, you must perform data remediation to evaluate the integrity and security of the data. It helps you understand the extent of the problem and how you can mitigate any data quality issues.

Data processing brings many advantages to business operations, including:

  • Improve data security and reduce risk: Data is safely stored or deleted after remediation. In addition, unstructured data is classified and secure, and it greatly reduces the risk of data loss, breaches, and cyberattacks.
  • Ensure compliance with regulations: Regular data processing procedures can help a company stay up to date and compliant with the latest changes in international data laws and regulations.
  • Reduce storage costs: Data processing minimizes data size, thus reducing storage costs.
  • Enhance performance: After organizing your data sets, employees spend less time managing and browsing through data, helping to streamline 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 entire businesses. That said, companies need an efficient remediation process and a comprehensive backup solution to ensure business continuity and security,” said lead product manager at NAKIVOone of the leading companies in the field of data protection and recovery.

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

There are several steps that you should take before starting the repair process:

  • Create a data processing group to establish responsibilities and roles.
  • Develop data governance policy and make sure you enforce them throughout your organization.
  • Identify priority areas requires immediate attention.
  • Allocate necessary resources and budget based on labor costs.
  • Set expectations and goals to understand the problems you may face and how you can overcome them.
  • Monitor progress and develop reports to ensure that the data processing fulfills its purpose.

The fix process may not be straightforward, but you can get the best out of it by following the steps below:

Step 1: Evaluate your data

First of all, you need to have complete knowledge of the data you have in your organization. It is necessary to fix because it helps you to identify important data, size and storage location. In addition, you can learn the amount of unstructured data, allowing you to set key goals to clean and organize the data.

Step 2: Categorize existing information

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

  • Data can be securely deleted without interfering with daily business operations. It includes:
    • Redundant, outdated, and trivial data.
    • Dark data that you don’t use for a long time.
    • Duplicate, incorrect or outdated dirty data.
    • Typical data is easily accessed and used by many users in everyday procedures.
    • Sensitive data requires high security and privacy measures.

Step 3: Implement your data governance policies

The next stage is to apply the internal processes that you laid out in the preparation phase. Naturally, different types of data require different policies, management strategies, and remediation approaches.

Based on all the information that you have gathered so far, you can go ahead and choose the most appropriate remediation technique for each data type. The most common methods include modifying, deleting, indexing, moving, and cleaning data.

Step 5: Evaluate the process and generate the report

The final stage is to review the data processing process and evaluate the results. It can be helpful to generate reports and use them as the basis for future remedial measures.

Data processing has proven to be a highly valuable part of data management for all organizations regardless of their industry. Below, you can see some examples of real-world use cases.

Employee data management

When an employee leaves your organization, you need to ensure that no data is lost or taken away. This is where the remedy comes into play. It allows you to inspect and erase company data from employee devices to ensure confidentiality and protect sensitive information.

Financial data management

Financial institutions like banks collect significant amounts of data on a daily basis. Traditional tools fail to prevent data overload, and these organizations are left with loads of useless information. Regular data processing 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 as it enables healthcare organizations to improve their services. With a significant amount of data collected, organizations are left with a large amount of unstructured data. Data processing gives hospitals and clinics the ability to organize their information to deliver better solutions for patients.

A necessity for data management

Data processing is an essential part of data management due to its many benefits. With the right strategy, you can organize unstructured data, reduce security risks, comply with regulations, and ultimately reduce operational costs. Companies in various industries rely on data processing to enhance their daily operations and avoid data overload and its adverse consequences.

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

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