Data recovery: what it is and why it matters

We’re excited to bring Transform 2022 back in person on July 19 and virtually July 20 – August 3. Join AI and data leaders for insightful conversations and exciting networking opportunities. Learn more about Transform 2022

According to the Association of Information and Image Management (AIIM), regularly 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 day-to-day operations.

Companies across all industries are realizing that these problems can be mitigated or even avoided completely by keeping data sets up-to-date and “clean”. It is done through data recovery, which should be at the heart of any organization’s data management strategy.

This post provides an overview of the recovery process, its many benefits and its various stages. Read on to learn how companies are using this procedure to improve their workflow by reducing data overload.

By definition, data recovery is the correction of 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 recovery is to manage unstructured data by reducing redundant, obsolete, and trivial (ROT) data, commonly known as dark and dirty data.

You should perform regular data recovery to ensure that your organization’s data is continuously updated, protected and compliant. However, there are times when remediation is required to avoid security breaches or legal consequences:

Change in external or internal laws and policies: As you probably know, data privacy rules are constantly changing globally. In addition, a company’s senior management may implement new internal policies. In both situations, it is necessary to stay on the safe side and recover your data to ensure legal and regulatory compliance. In addition, you should research new data arising from mergers and acquisitions. In this case, you need data recovery to check for security vulnerabilities and to protect against potential breaches. Human errors: Accidents and mistakes inevitably happen in the workplace. When errors are discovered, you should perform data recovery to assess data integrity and security. It helps you understand the magnitude of the incident and how to mitigate any data quality issues.

Data recovery provides numerous benefits to business operations, including:

Improve data security and reduce risk: Data is securely stored or deleted after recovery. In addition, unstructured data is classified and secured, dramatically reducing the threat of data loss, breaches and cyber-attacks. Ensuring Regulatory Compliance: Frequent data recovery processes can keep a business up-to-date and comply with the latest changes in international data laws and regulations. Reduce Storage Costs: Data recovery minimizes data size, which in turn lowers storage costs. Improve performance: After organizing your datasets, employees spend less time managing and browsing data, streamlining productivity. It also lowers operating costs.

Remember, recovery 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 company. That said, businesses need an effective recovery process and a comprehensive backup solution to ensure business continuity and security,” said the chief product manager at NAKIVO, one of the industry leaders in data protection and recovery.

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

There are several steps you need to go through before starting the recovery process:

Assemble a data recovery team to establish responsibilities and roles. Develop and enforce data governance policies across your organization. Identify priority areas that require immediate attention. Allocate necessary resources and budget based on labor costs. Set expectations and goals to Understand the issues you may face and how to resolve them. Monitor progress and develop reports to ensure the data recovery process is achieving its goal.

The clean-up procedure may not be easy, but you can make the most of it by following the steps below:

Step 1: Evaluate your data

First and foremost, you must have full knowledge of the data you have within your organization. It is necessary for recovery as it helps you identify critical data, its size and storage locations. In addition, you can learn the amount of unstructured data, which can help you 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 based on usefulness and importance:

Data that can be securely deleted without interfering with day-to-day business operations. It includes: Redundant, obsolete and trivial data. Dark data that you have not used for 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 is high require security measures and protection.

Step 3: Implement your data governance policy

The next step is to apply the internal procedures that you set in the preparation phase. Of course, different data types require different policies, management strategies, and recovery approaches.

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

Step 5: Review the process and generate reports

The last stage is to look back at the data recovery procedure and evaluate the results. It can be useful to create reports and use them as a basis for future remediation actions.

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

Employee data management

When an employee leaves your organization, you must ensure that no data is lost or taken with them. This is where remediation comes into play. Allows you to view and delete company data from the employee’s device to ensure confidentiality and protect sensitive information.

Financial data management

Financial institutions such as banks collect significant 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 recovery allows banks to organize incoming data and remove redundant sets of information.

Data management in healthcare

It goes without saying that clinical data is paramount as it enables healthcare organizations to improve their services. With the substantial amount of data collected, institutions are left with huge amounts of unstructured data. Data recovery enables hospitals and clinics to organize their information to provide better patient solutions.

Essential for data management

Data recovery 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. Businesses across industries rely on data recovery to improve their day-to-day operations and avoid data overload and its ill effects.

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

DataDecision makers

Welcome to the VentureBeat Community!

DataDecisionMakers is where experts, including the technical people who do data work, can share data-related insights and innovation.

If you want to read about the latest ideas and up-to-date information, best practices and the future of data and data technology, join us at DataDecisionMakers.

You might even consider contributing an article yourself!

Read more from DataDecisionMakers

This post Data recovery: what it is and why it matters

was original published at “”

No Comment

Leave a reply

Your email address will not be published. Required fields are marked *