How much data is your organization collecting every day? How do you sort it out for it to be valuable to use? These are the questions you should ask if you would like to leverage your data and make it profitable in your business operations.
What is data classification?
Data classification involves organizing data so that it is easy to locate, retrieve and use it. Other than classifying data for ease of use, data classification is also done to comply with data protection laws. These laws stipulate the right security response, subject to the type of data being retrieved, transmitted, or copied. Levity’s data classification tool gives you in depth information on data classification.
In the first place, you have to first understand the different data that are there for you to classify them. Data is broadly categorized into structured or unstructured forms. Structured data is mainly categorized as quantitative data (numbers and values) for example names, dates, credit card numbers, geolocation, stock information, addresses, and more. This type of data is easy to search and analyze and is more or less in its original format.
Unstructured data is qualitative data in the form of text, audio files, images, video files, social media posts, mobile activity, surveillance imagery, satellite imagery, etc. This type of data is difficult to process, as it is not organized in a predefined manner where you can use conventional data methods and models. AI does most of its analysis.
To classify this data, three approaches are commonly used: content-based classification, user-based classification, and context-based classification.
- Content-based classification: This approach organizes files based on the type of content and its level of sensitivity.
- User-based classification: This is a manual approach where a person or team decides how to classify the data. It depends on the user’s personal discretion on what should fall under sensitive data.
- Context-based classification: This approach classifies data based on context. The parameters in creating this classification include the application used to create file or file type, user who created file or/and physical location where data is created.
How do you go about creating a data classifier? Here are 7 best ways to create a data classifier.
Define the compliance objectives of your data classification. These include mitigation of risk associated with unauthorized disclosure and access, complying with industry standards, or upholding data subject rights.
Here is a review of how to approach data classification based on which regulations and standards your organization is subject to:
Personally identifiable information (PII) is information that can identify someone. This data includes but is not limited to, name, personal identification numbers (social security number, passport number, driver’s license number, etc.), personal address, personal telephone numbers, biometric data, etc.
Federal laws protecting PII include:
- Gramm-Leach-Bliley Act — Financial information
- Health Insurance Portability and Accountability Act (HIPAA — Healthcare information
- Family Educational Rights and Privacy Act (FERPA) — Students’ educational records
- Children’s Online Privacy Protection Act (COPPA) — PII of children under 13
● Data Classification for GDPR
The General Data Protection Regulation (GDPR) is a data protection law that guides the collection and processing of personally identifiable information of EU citizens. Under GDPR, there is the Data Protection Impact Assessment (DPIA) that guides how companies should collect data, identify risks that come with processing personal data, how to minimize those risks as early as possible, and be compliant.
To do this, you’d need to do data classification, of which GDPR allows for thorough data discovery, data profiling, data asset cataloging, and taxonomies for data sensitivity.
2. Determine what data you are collecting and classifying
Data classification can be done based on its level of sensitivity. If this data is disclosed, altered, or destroyed without authorization, what would be the impact? Three sensitivity levels used to classify data include:
- Restricted: This is the highest level of security control and any unauthorized disclosure, modification, or destruction of data poses a significant risk to the company.
- Private: This type of data is where unauthorized disclosure, alteration, or destruction can cause a tolerable risk level.
- Public: Unauthorized disclosure, alteration, or destruction of this type of data has little or no risk to the company.
By classifying data with this approach, you’re able to determine your risk management, regulatory compliance mechanism, and legal discovery. It also helps prioritize security measures, and validates budget requests for investing into data security.
3. Establish processes and documentation for managing data
Develop a clear dedicated system and tools for classifying and documenting data.
Having a simple classification process makes it easy for users to determine what labels to use. As time goes on, more definite labels can be put on, defining each section and what kind of information belongs to it.
Documenting and managing your data is an integral part of an effective data governance and security strategy. To find success in your documentation, it is important to emphasize to users to classify data at the point of creation. This is because the likelihood of users allowing the system to classify by default would just be the same as not having a classification system of data to begin with.
4. Identify how data will affect your business goals
What business goals do you want to achieve by collecting and classifying your data? Defining your aim will ensure you’re collecting the right data and meeting your set business objectives. Business objectives for your data classification can include:
- Understanding and learning more about your customers, what is important to them at a deeper level, and focus on this to deliver better experiences and grow your revenues
- Segmenting your customers based on demographics or behaviors, so you can market to each segment more effectively
- Facilitate data-driven decision making
- Improving reporting and forecasting and give much more accurate projection outlook for the business
5. Create documentation explaining how data management will be done
As your business grows, so will your data and its complexity. There will be more steps, more people, and it may not always be possible to arrange your data in the most efficient way. A data documentation process will be the first step to remove these inefficiencies that are bound to arise.
This is a guide that helps employees of all levels quickly understand the company data process. It will help you achieve 5 key things:
- It will help you identify inefficiencies, how to improve or get rid of them
- It will help train new employees on your data process and even experienced employees can reference it whenever they want to make sure they are following the right data process
- It will help you create institutional memory. By documenting your data process, you get to preserve and use your data even after an employee has left.
- It helps mitigate risks to your data and maintains operational consistency
- A detailed data process documentation is key to creating patents and trade secrets.
6. Train employees on data protection
As you create a data documentation process, it is also important to train employees on data protection. Data protection is about storing and securing data accurately and with confidentiality. By training your employees on data protection, you educate them on industry standards on how to protect data from loss, theft, destruction, or modification.
Since data compromises can be by mistake or malicious intent, data protection training will teach employees on how to properly handle data. Provide this training at regular intervals using bite-sized content. Customize the content further to adapt to employees by job role, team, department, or geographic location.
7. Manage unused data in compliance with regulations
Your organization will rarely use all the data that it collects. Unused data is a loss, as it costs your organization high storage and maintenance costs. On top of that, you also run into privacy regulations and compliance issues.
According to the GDPR, the principles of handling personal data are:
- The Storage Limitation Principle: Under this principle, businesses should not keep personal data longer than necessary for its intended purpose. Meaning if organizations keep personal data that it no longer needs, they are non-compliant under the GDPR.
- The Purpose Limitation Principle: Under this principle, businesses can only explicitly use personal data for its original intent. So, for example, if the data collected is for improving product features, they cannot repurpose it for marketing use in the future. This means businesses should only collect data on a need to have basis rather than good to have basis to be GDPR compliant.
- The Accuracy Principle: Under this principle, data has to be accurate and up to date. If an organization has data from 10 years ago and hasn’t touched it since, under the GDPR, they are required to delete or anonymize the data.
To determine privacy compliance requirements for your organization on unused data, go through the General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), California Consumer Privacy Act (CCPA) among other data privacy laws to see if you’re compliant.
Develop further an internal “Data Retention Policy”. This policy should guide you on how long you should keep data, when will you delete the data, will you anonymize data for future use, this will make clear your business data practices to prevent having unused data and costs of storing unused data.
Data is the new oil, as the pundits put it. Classifying your data will ensure you get the most out of this oil. Consider using the above tips to create your data classifier that will help you gain better insight into your business operations.