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.
Explore data security solutions
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This step also involves considering often-overlooked data, including unused software logs, outdated emails and former customer records.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.
The post How to craft a comprehensive data cleanliness policy appeared first on Security Intelligence.