Data remediation: What it is and why it matters

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According to the Association of Information and Image Management (AIIM), the life cycle of information requires frequent rearrangement and deletion of information. Excess of unstructured data inevitably leads to security vulnerabilities, causes compliance issues, increases storage costs and affects day-to-day activities.

Businesses across all industries understand that these problems can be minimized or completely avoided by keeping up-to-date and “clean” datasets. It is done through data remediation, which should be a major part of every organization’s data management strategy.

This post gives an overview of the healing process, its numerous benefits and its various stages. Read on to find out how companies use this process 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, clearing, transferring, archiving, and deleting data to ensure optimal storage and to eliminate data quality problems.

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

You must perform regular data remediation to ensure that your organization’s data is constantly updated, secure, and relevant. However, there are times when measures are mandatory to avoid security breaches or legal consequences:

  • Changes in external or internal laws and policies: As you probably know, data privacy rules are constantly changing around the world. In addition, the company’s top management may implement new internal policies. In both cases, there is a need to stay on the safe side and improve your data to ensure legal and regulatory compliance.
  • Changes in business conditions: Software or hardware changes may affect company data. In addition, you should check for new data as a result of mergers and acquisitions. In this case, you need a data solution to investigate security risks and prevent potential breaches.
  • Human error: Accidents and mistakes are bound to happen in the workplace. When errors are detected, you must perform data remediation to evaluate the integrity and security of the data. It helps you understand the extent of the event and how you can minimize any resulting data quality problems.

Data remediation offers a number of benefits to business activities, including:

  • Improving data security and reducing risks: Data is stored securely or removed after the remedy. In addition, unstructured data is categorized and protected, and it dramatically reduces the risk of data loss, infringement, and cyber attacks.
  • Ensuring regulatory compliance: Frequent data remediation procedures can keep the company updated and consistent with the latest changes in international data laws and regulations.
  • Reduce storage costs: Data correction reduces data size, which in turn reduces storage costs.
  • Enhancing performance: After organizing your datasets, employees spend less time managing and browsing data which streamlines productivity. It also reduces operational costs.

Remember that despite these benefits, solutions alone cannot protect your data. “In today’s data-driven world, sophisticated attacks like ransomware and phishing schemes put companies at risk of losing data and business as a whole. “Companies need effective solution processes and a comprehensive backup solution to ensure business continuity and security,” said NAKIVO’s Chief Product Manager, one of the industry leaders in data protection and recovery.

But what is an effective data remedy? Let us explore this process in more detail.

You must go through several steps before starting the healing process:

  • Build a data improvement team To determine responsibilities and roles.
  • Develop data governance policies And make sure you apply it to your entire organization.
  • Identify priority areas Which needs immediate attention.
  • Allocation of required resources and budget Based on labor costs.
  • Set expectations and goals To understand the problems you can face and how you can overcome them.
  • Monitor progress And develops reports to ensure that the data remediation process serves its purpose.

The prevention process may not be easy, but you can get the most out of it through the following steps:

Step 1: Evaluate your data

First and foremost, you need to have a thorough knowledge of the data you have in your organization. It is essential for improvement as it helps you identify important data, its size and storage locations. Additionally, you can learn the amount of unstructured data that allows you to set a primary goal for clearing and organizing data.

Step 2: Classify existing information

Now that you know how much data you have, you should differentiate between usability and importance:

  • Data that can be safely deleted without interrupting day-to-day business activities. It also includes:
    • Unnecessary, obsolete and trivial data.
    • Dark data that you haven’t used in a long time.
    • Dirty data that is duplicate, inaccurate or out of date.
    • General data that is easily accessible and used by many users in daily processes.
    • Sensitive data that requires high-security measures and security.

Step 3: Implement your data governance policies

The next step is to apply the internal processes that you set in the preparation phase. Naturally, different data types require different policies, management strategies and solution approaches.

Based on all the information you’ve gathered so far, you can go ahead and choose the technique that works best for each type of data. The most common methods include data modification, deletion, indexing, migration and cleaning.

Step 5: Evaluate the process and generated reports

The final step is to look back at the data remediation process and evaluate the results. It can be helpful to create reports and use them as a basis for future solutions.

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.

Employee data management

When an employee leaves your organization, you need to make sure that no data is lost or retrieved. This is where the remedy comes into play. It allows you to check and delete company data from an employee’s device to guarantee privacy and protect sensitive information.

Financial data management

Financial institutions, such as banks, collect significant amounts of data every day. Traditional tools fail to prevent data overload and these organizations have a lot of useless information left. Frequent data remediation allows banks to organize incoming data and delete unnecessary sets of information.

Data management in healthcare

It goes without saying that clinical data is extremely important as it allows healthcare organizations to improve their services. With a significant amount of data collected, organizations have a huge amount of unstructured data left. Data remediation gives hospitals and clinics the ability to organize their information to provide better patient solutions.

Essential for data management

Due to its many advantages, data prevention is an essential part of data management. With the right strategy, you can organize unorganized data, reduce security risks, comply with regulatory compliance, and ultimately reduce operational costs. Companies in various industries rely on data remediation to enhance their daily activities and avoid data overload and its detrimental consequences.

This article was contributed by Maria Lvovich, CEO and founder of Olmawritings and GetReviewed.

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