What Is Data Remediation?

Over 95% of businesses say that managing their data is an ongoing problem and challenge. And this is a challenge that’s only going to increase, given the sheer volume of data generated daily. Adding to this challenge is the fact that approximately 80% of data is unstructured and vulnerable to breach. All this combined creates many ongoing problems for organization’s security teams. In general, the more data an organization has, the more risk there is for error. And errors in data result in workplace inefficiencies, hinder the decision-making process, lead to unnecessary costs, and perhaps most importantly, can put organizations in legal compliance risk. This is why data remediation is essential to helping organizations ensure the quality and security of their data.

What Is Data Remediation?

Simply put, data remediation is about correcting errors and mistakes in data to eliminate data-quality issues. This is done through a process of cleansing, organizing, and migrating data to better meet business needs. The ultimate goal of data remediation is to help your organization decide if it’s going to keep, delete, migrate or archive information.

Benefits of Data Remediation

Although it may seem like a daunting task, the lasting benefits of data remediation to the organization far outweigh the effort. Because of its many benefits, organizations should include ongoing data remediation as part of their business activities. The main benefits include:

• Reduced Costs – Your organization will minimize the risk of financial loss through fines, lawsuits, and reputational damage that come with a data breach. Also, data remediation will reduce your overall volume of data, and therefore data storage costs.
• ‍Reduced Risk – Sensitive data that was once exposed and leaving your organization vulnerable to risk of breaches and leaks will now be stored securely or safely deleted. ‍
• Compliant With Privacy Laws and Regulations –Your organization can rest assured that it’s fully compliant with the ever changing privacy laws and regulations, including GDPR, CCPA, CPPA, PCI DSS, HIPAA and more.‍
• Streamlined Operations and Efficiency – Your team can work more efficiently and effectively by having easy access to reliable data. This will also help your team make faster data-based decisions.   ‍
• Data Minimization – Your organization will safely delete data that has no legal or business justification of storing. You’ll only be left with data that’s legally required or provides value to your organization.  

Data Remediation Key Terms

As you explore the topic of data remediation you should familiarize yourself with the following terminology:

• Data Discovery – The process of searching for patterns in data sets to identify structured or unstructured data within an organizations systems, either manually or automatically.
• ROT – Redundant, obsolete, and trivial data. Meaning this data has surpassed it’s recommended retention period and is no longer useful data for the organization.
• Dark Data – Information that businesses collect and store but do not utilize for other purposes. Storing this data within an organization usually carries more of an expense and risk than it does provide value.
• Dirty Data – This is data that is incomplete, duplicated, inaccurate, and/or outdated, and can damage the integrity and reliability of an organization's data set.
• Data Overload – Occurs when an organization has acquired too much data often including dirty data and dark data. Data overload can make the task of data identification, data classification, and data remediation a difficult and lengthy process.
• Data Cleansing – Taking data that is in its original form, and changing into a standardized format.
• Data Governance – Management of all of the data that is stored within an organization.

When To Consider Data Remediation?

Ideally, data remediation should be an ongoing business process to ensure the quality of an organization’s data and to protect it against risk. Additionally, an organization should consider data remediation in the following situations:

• Business Changes - Data is at risk when any logistical changes are made within the business environment, including both digital and physical changes. Digital changes to software or systems or a physical change in location can leave data in a vulnerable state. Any changes to the organization’s leadership would also warrant a discussion on data remediation.   ‍
• Mergers and Acquisitions - In the event of a merger or acquisition, it’s essential to remediate any new data coming in from other sources. ‍
• Changes To Laws and Regulations - Data privacy and protection laws are in constant flux. An organization must be aware of any changes to the legal environment and remediate its data to ensure compliance. ‍
• Human Error - There will be times when human errors in data management and governance are discovered. When this happens, data remediation is essential to ensure both the quality and security of data. ‍
• Common Barriers To Data Remediation - Although data remediation is an essential business process, some organizations don’t make it a priority. The following are the most common reasons why. ‍
• Lack of Information - An organization may not understand the need for data remediation if it doesn’t understand the quantity, quality, and sensitivity of its data. Most organizations would be surprised to learn that up to 75% of their data is being retained without a clear legal requirement or business need. Engaging in data remediation gives organizations the information they need to fully manage and benefit from their data. ‍
• Fear of Losing Data  - An important step in data remediation is deleting data. This can be an intimidating step for organizations, given how permanent and irrevocable it is. Organizations need to understand that data is only deleted after it has been fully analyzed. Also, holding onto unnecessary data out of fear creates more potential risks to organization.  ‍
• Data Ownership Issues - Within larger organizations, it may be unclear which team has ownership of the data, and therefore responsibility over data remediation. Initiating a conversation with your data security teams can help everyone understand the role they play in ensuring the quality and security of their data.

Preparing For Data Remediation

To help your organization succeed with data remediation, let’s talk about how to prepare your team for this crucial process.

1. CreateData Remediation Teams –First, your organization should create teams with clear roles and responsibilities, so everyone is clear on the data remediation process.
2. EstablishData Governance Policies –Next, your organization will need clear data governance policies. The data governance policy will outline the people and processes to manage and store data.
3. DeterminePriority Areas –Next, your organization should consider what areas they want to focus on and make a priority for data remediation. For example, if a new privacy law was just enacted, that would be an area of priority.
4. AllocateResources – Now your organization needs to consider how to allocate resources for the data remediation. Your organization should estimate the financial costs and human resource needs for the project.
5. Discuss Expected Outcomes – Next your team should get clear on their expectations for the data remediation process.This will also be a time when any data issues or changes can be discussed.
6. ProgressReporting – As the data remediation process takes place, your organization should have progress reporting measures in place to accurately report on the project and overall return on investment(ROI).

The Data Remediation Process

Now that your team is prepared, let’s talk about the data remediation process in more detail.

Step 1 – Discovery

Your first step is to assess the current state of your data. You’ll want to have a full understanding of how much data your organization possesses.

Step 2 – Classification

Now that you understand how much data you have, you’ll want to understand what’s within that data. First you can identify redundant, obsolete, and trivial (ROT) data that can either be deleted, archived, or reclassified. The remaining non-ROT data can be classified according to sensitivity and value.

Step 3 – Apply Data Governance Policies

Now that you have a complete data inventory, you can apply your internal data governance policies. These policies will tell you what data’s outside of policy and therefore needs to be remediated.

Step 4 – Determine Data Remediation Methods

Now your organization will determine which data remediation strategies are appropriate for the data that’s outside of policy. Common data remediation methods include redaction, masking and deidentification of data.  

Step 5 – Implement Strategy

The final step is to implement the data remediation strategy that you’ve developed based on the proceeding steps.

HowData Sentinel Can Help Your Organization

Data remediation is an involved process that can be both costly and time consuming for your organization. But it doesn’t have to be. Data Sentinel can help your organization make the data remediation process both cost and time effective.

With Data Sentinel, you will know that your data is protected as policy infractions are discovered in your holdings, no matter the scale, location, or data type. On premises, cloud or hybrid cloud environments.

Address data security and privacy regulations such as GDPR, CCPA, CPPA, PCI DSS and HIPAA by employing methods to de-identify data, such as encryption, quarantining and data masking.

Data Sentinel is always hunting for exposed data that is noncompliant with data management policies. When found, the system triggers data remediation actions, specific to the rules that you define within the workflow.

By automating the discovery and remediation of exposed noncompliant data, your organization can minimize the risk of financial loss through fines, lawsuits, and reputational damage. This will allow you to achieve accurate, inexpensive, and rapid, data trust.

Data Sentinel Automated Remediation Options

Dynamic Data Masking - Data Sentinel can trigger dynamic data masking actions as sensitive data anomalies are discovered in structured and unstructured data. This is a user-defined rules based function that ensures sensitive data is obfuscated in near real time. Reduce risk immediately and ensure compliance with global data privacy standards.

Data Sentinel can leverage a number of masking techniques, depending on the policies and use cases that need to be addressed, examples are:

• Data encryption
• Nulling out / deletion
• substitution

Data Quarantining - In addition to dynamically masking your noncompliant data, Data Sentinel can identify, and isolate specific records to a data quarantine. This ensures that no record that does not comply with your data management policies is left vulnerable. This allows you to investigate the policy infraction and remedy the record as needed. A data subject matter expert will be notified when this occurs, allowing your organization to maintain compliance with policies and data privacy regulations.

Data Minimization - ROT (redundant, outdated, or trivial) data, or simply data that is duplicated throughout the organization presents significant added risk to the company, not to mention much added cost. Data Sentinel automated the process of ROT data discovery and elimination.

Next Steps

If you’re ready to discuss strategies to ensure both the quality and security of your data, let’s talk. Click here to schedule a free discovery call. On this call, we’ll discuss any challenges your facing managing your data and what your business goals are so we can develop a tailored plan to help you minimize risk, ensure compliance, and maximize business growth.

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