Optimized data management and data security are critical within any organization. However, given the sheer amount of data that businesses collect daily, this is easier said than done. When data accuracy, quality, storage and security suffer, it can lead to poor decision making, data breaches and non-compliance issues. This is where data remediation becomes necessary, as it helps businesses clean up, organize, and efficiently move their data to a secure and clean environment.
From a compliance perspective, businesses need to be careful about the type of data they collect. Storing data without consent or legitimate business purposes can raise GDPR compliance issues. Here again, data remediation might be a necessary step, as it can help businesses improve compliance by getting rid of duplicate data or unnecessary and unused data.
All of this makes data remediation a critical tool for sanitizing data management and ensuring data network security within an organization.
What is data remediation?
The process of treating data by cleaning, organizing, and migrating it to a safe and secure environment for optimized usage is called data remediation.
Generally, people understand data remediation as a process involving deleting unnecessary or unused data. However, the actual process of data remediation is very detailed and includes several steps, including replacing, updating, or modifying data along with cleaning it, organizing it, and getting rid of unnecessary data.
5 stages of effective data remediation
Data remediation is a comprehensive process involving five stages that are designed to address the issues of unclean or unnecessary data. Let’s review each of these stages to understand their importance to data privacy and security.
1 – Assessing your data
The first stage involves assessing your data. Only once you have a clear understanding of your data, including its types and values, will you be able to take any corrective action. This assessment will also help you determine the time, effort, or resources you will need to use for successful data remediation.
2 – Organizing and segmenting of data
While sensitive data needs robust protection, other types of non-sensitive data might not necessarily require the same degree of protection and security. Therefore, data can be classified into different categories to determine separate storage and protection requirements. Unfortunately, businesses often make the mistake of storing all their data in one place, even sensitive data, which increases the risk of a data breach or data contamination. Therefore, it becomes necessary to carefully organize data for effective data remediation.
Data accessibility is another aspect of effective data segmentation. Every business has data that is used routinely or in everyday tasks. It makes sense to ensure this data is easily accessible to employees. On the other hand, sensitive or high-value data will need additional security to ensure regulatory compliance and for legal purposes.
Data can also be segmented based on the duration of data retention. GDPR compliance requires businesses to adhere to a retention schedule for storing each data category and time limits for erasure.
These are just two examples of how data can be segmented. Businesses can further segment their data depending on other aspects of the type of data they’re collecting.
3 – Data classification
The next step involves data classification, where both structured and unstructured data will be organized into properly defined categories. The data will be classified based on business needs and the level of sensitivity. Data classifications based on sensitivity can include the following;
Data classification can help a business improve risk management, establish and adhere to regulatory compliance protocols, and prioritize security measures.
4 – Migrating
Organizations often need to consolidate their data and migrate it to a secure and clean storage environment. For this reason, data migration may be necessary for effective and successful data remediation. However, it is important to note that not every data remediation process necessarily involves data migration. Some organizations might follow all the other steps without opting for data migration.
The most common reasons for data migration are the need to move from legacy systems and solutions to highly scalable and clean environments, or to ensure improved data security compliance and improved levels of accessibility.
5 – Data cleansing
Stored data can be incomplete, inaccurate, duplicate, corrupt, or irrelevant. Therefore, data cleansing is a critical aspect of data remediation. First, low-quality data should be identified and then replaced or modified. If left unchecked, dirty data will overwhelm data management and severely impact the quality of data stored. For this reason, data cleansing is vital for any organization and a critical part of data remediation.
Why is data remediation important for data security and privacy?
Businesses today face the challenge of maintaining the quality of their data. Data management challenges include the continuously changing face of data and iterative data models, inaccuracies in data or corrupt data, and new and emerging data protection regulations. Poor data health lowers the business’s operational efficiency and impacts effective decision-making.
Data remediation is an important factor in data security and compliance with privacy policies. Poor data quality stems from the lack of appropriate data sanitization processes. Without the necessary data management and data security protocols, data within an organization is vulnerable to common data health issues such as inaccuracies and corruption. The business also suffers from a build-up of unregulated data and becomes vulnerable to data breaches. Furthermore, poor data management lowers the ability of the business or organization to ensure compliance with data privacy protection laws, which can lead to penalties.
For these reasons, organizations need to ensure that their data is clean, enriched, secure, and compliant with privacy policies. Additionally, sometimes data needs to be moved to a secure and clean environment. Data remediation can address each of these objectives.
Unregulated data can burden the data network of a business or organization over time. Moreover, unregulated data can increase the risk of data breaches. All of these can severely impact every aspect of data management. In addition to the increased risk of data breaches, unregulated or dirty data can increase the risk of non-compliance with data privacy regulations. The risk is especially high for businesses that collect and store large amounts of data.
For all these reasons, organizations cannot ignore data remediation.
After data remediation, businesses can benefit from improved data insight and will be better equipped to build a more accurate and transparent data ecosystem. Clean data that is organized and safely stored can improve data security and ensure that the organization’s data is compliant with privacy regulations and other mandatory legal obligations.