Data, in all its shapes and forms, is one of the most critical assets a business possesses. Not only does it provide organizations with critical information regarding their systems and processes, but it also fuels growth and enables better decision-making on all levels.

However, like any other piece of company equipment, data can degrade over time and become less valuable if organizations aren’t careful. What’s even more dangerous is that neglecting data hygiene can expose organizations to a number of security threats and regulatory compliance issues.

Understanding data cleanliness

Data cleanliness, also called data hygiene, is the process of ensuring all organizational data maintains accuracy and consistency regardless of where it’s stored and how it’s used. To achieve this, organizations need to ensure their data is regularly checked against six core characteristics:

• Accuracy: Free from errors
• Completeness: No missing values or incomplete records
• Consistency: Maintains format across different systems and platforms
• Validity: Follows pre-defined rules or standards
• Uniformity: Uses correct data inputs, measurements and naming conventions across all datasets
• Timeliness: Up-to-date and relevant

To effectively manage each of these components, organizations can use a variety of data management tools and solutions. These automated systems leverage data profiling and cleansing processes to help detect anomalies as they appear and help organizations resolve them.

Why maintaining clean data is so important

Ensuring organizational data remains free from errors and can be a trusted source of critical business information is essential to ensuring both operational efficiency and resiliency. Considering the amount of digital sources most organizations rely on today, there are several ways that businesses can lose sight of how their data is collected, stored and accessed.

“Organizations today are challenged with a number of issues when trying to maintain the integrity of their critical data,”  Evelyn Kim, a program director with IBM Security, says. ” Data is growing exponentially in more formats and locations causing organizations to lose visibility and control over their sensitive data. We see organizations grappling with shadow data (undiscovered or unknown data) that pose significant risks. Generative AI also presents new risks to data — both from a need to have enough of the right data for gen AI use and from ensuring data is not tampered with.”

Security risks associated with unclean data

While the importance of data integrity may seem limited to helping to support smoother business operations, it is actually a core element of ensuring a strong cybersecurity posture. Below are some of the inherent security risks that can occur if good data hygiene is neglected over time:

Cybersecurity threats

With the proliferation of data, data classification, especially of sensitive data, is even more critical to security. Understanding where sensitive data resides is a key step in monitoring data stores and databases to prevent breaches and detect cyberattacks to reduce the impact and damage across critical networks and connected systems.

The effectiveness of modern security tools and technologies also relies on accurate data. Without establishing a reliable baseline for normal business activity, these security solutions lose their ability to identify suspicious user patterns. They can lead to false positives and inadequate threat detection.

Compliance failures

Data cleanliness plays a crucial role in helping organizations meet various regulatory requirements. “Highly regulated industries tend to have significant data governance/security concerns. We typically see financial services, healthcare, manufacturing and utility/energy sectors leaning heavily on data security investments to assist with their compliance efforts,” states Kim.

Without accurate and complete compliance reporting data, organizations open themselves up to significant compliance violations and associated financial penalties. This can also lead to long-term legal repercussions that can damage a business’s reputation and impact customer loyalty.

Maintaining a clean data environment

Data cleansing isn’t something that organizations schedule throughout the year or complete as a one-time project. It requires an ongoing commitment and the ability to integrate data quality practices into every stage of the data lifecycle. From initial data collection and entry to storage, processing and analysis, organizations should follow numerous proactive data maintenance steps, including:

• Establishing clear data governance policies: Businesses should establish clear roles and accountabilities in their organization when it comes to data entry, validation and updating procedures. This also includes following strict compliance guidelines on how to properly handle data in and out of transit.

• Investing in data quality solutions: Organizations should research and implement next-generation tools that provide automated data cleansing activities in real-time while handling deduplication and validation processes systematically. These tools help identify and address data quality issues proactively, freeing up time and resources for internal teams.

• Adopting a security-first culture: Establishing a business culture that prioritizes data security and integrity is essential. This involves initiating training sessions for employees on the importance of following strict data management standards as well as implementing strict access controls, data encryption and monitoring solutions.

Keep your data healthy

Data is what keeps modern organizations running. However, if you’re not careful, the value of this asset will diminish over time and lead to a number of business consequences. By prioritizing data cleanliness, organizations can uncover the true potential of their critical data, allowing them to make better decisions while creating more resilience in their security and compliance initiatives.

The post Why maintaining data cleanliness is essential to cybersecurity appeared first on Security Intelligence.

Practicing good data hygiene is critical for today’s businesses. With everything from operational efficiency to cybersecurity readiness relying on the integrity of stored data, having confidence in your organization’s data cleanliness policy is essential.

But what does this involve, and how can you ensure your data cleanliness policy checks the right boxes? Luckily, there are practical steps you can follow to ensure data accuracy while mitigating the security and compliance risks that come with poor data hygiene.

Understanding the 6 dimensions of data cleanliness

It doesn’t matter where your company data is sourced — without addressing its quality and accuracy, you won’t be able to rely on it. To create the right data cleanliness policy, you’ll need to understand its different dimensions. These include:

• Accuracy: Identifies to what extent data can be trusted and is free from errors. This requires specific validation protocols and compliance with data collection standards.
• Completeness: Signifies whether or not collected data provides clear answers to certain questions. It involves evaluating any missing data attributes and recognizing any apparent gaps.
• Consistency: Checks that data is properly mirrored when stored in multiple databases and represented by a percentage of matched values.
• Validity: Refers to data adherence against predefined rules or formats. It helps eliminate the violation of logical constraints or data type restrictions.
• Uniqueness: Makes sure all data types reference the same units of measure or support formats to remove the possibility of information overlapping or duplication across data sets.
• Timeliness: Represents the degree to which data remains up-to-date. This ensures data is accessible when it’s required so it can be used properly.

Once you have a grasp on these six core elements, you’re ready to move forward with crafting your data cleanliness policy.

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Step 1: Define policy scope and objectives

The first step to take when creating a data cleanliness policy is to define all appropriate business objectives. Any specific data sets or systems and the intended use of the information within them should be clearly outlined.

This step also involves considering often-overlooked data, including unused software logs, outdated emails and former customer records. If this information is forgotten about, it can lead to security issues down the road when they are left in unsecured locations.

Step 2: Classify data assets

With your policy scope defined, you’ll need to take inventory of all relevant data sources. Data assets can include various databases spread across multi-cloud environments, locally stored spreadsheets or any other areas where data is stored.

Classifying all data assets is another way to minimize forgotten data from compiling and creating high-value targets for cyber criminals. During this process, you’ll also want to categorize data based on its relative sensitivity or regulatory requirements. This will make it easier to implement the right access controls and data retention policies.

Step 3: Establish data quality standards

The data quality standards you develop for your policy should be measurable and easy to understand. To achieve this, you’ll need to lay out specific criteria for each data type, including the acceptable formats data should be in and any validation rules you have in place.

With your metrics in place, you’ll be able to regularly monitor their performance over time. Many times, regulatory requirements will stipulate that data needs to meet certain accuracy and completeness benchmarks. Having these trackable metrics in place provides the transparency needed to ensure these regulations are continuously being met.

Step 4: Assign roles and responsibilities

Establishing clear accountabilities is essential when managing organizational data. Your data cleanliness policy should define the various roles in your organization, including specifying who can access data and what levels of permission they have.

Controlling the amount of individuals who can access, modify or delete data is one of the most important elements of ensuring data integrity over the long term. It helps you to mitigate the danger of insider threats as well as establish clear lines of accountability if and when anomalies are located in data sets.

It is also common to make use of a data governance team that can help to implement and enforce various policy initiatives. These teams can reduce the likelihood of data inconsistency and help support various data security protocols in place.

Step 5: Implement data cleansing procedures

In the event that data issues are discovered, your policy should also cover necessary data correction procedures. This can include standardization, normalization or deduplication of data stored across systems.

Another supporting element of this process is having clear data retention and disposal policies in place. This helps to reinforce best practices when it comes to data lifecycle management. It also minimizes a digital attack surface, making it less likely that sensitive information is left in a vulnerable storage state, and helps to minimize damages in the event of a successful cyberattack.

Maintain healthier organizational data

Being able to rely on the accuracy and consistency of your company data is critical. Not only does data integrity play an important factor in improving the value of your technology investments, but it also helps to strengthen your cybersecurity posture.

By following the steps above, you’ll be able to draft a data cleanliness policy that allows you to maintain healthier organizational data while extracting its full value.

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