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Classifying Data

Data is the one of the most important asset for any type of organization therefore it is extremely important for a business to classify its data. Data classification is defined as the process of organizing data by relevant categories based on formal values according to its sensitivity so that it may be used and protected more efficiently.

Data classification often involves a multitude of tags and labels, defining the type of data, confidentiality, and its integrity. Availability is also sometimes considered in data classification processes. High risk data, typically classified “Confidential”, requires a greater level of protection, while lower risk data, possibly labeled “internal” requires proportionately less protection.


Types of Data Classification:

a) Public – Information that can be open to the general population can be public. It is defined as information with very less legal restrictions on access or usage. Public data can be made available for all the people and workers as common data. Some examples are:

  • Publicly posted press release
  • Publicly available marketing materials
  • Publicly posted job announcements

b) Internal – Information that should be protected because of exclusiveness, ethical, or seclusion considerations and needs to be protected from unauthorized access and modification. Some of the examples are as following:

  • General employment data (e.g., excluded SSN, salary)
  • Business partner information where no more restrictive confidentiality agreement exists.
  • Contracts

c) Confidential – The data that have a very high level of sensitivity is classified as confidential data. Unauthorized access of this data can lead to many legal actions and can impact the organization. Some of the examples are as following:

  • Payment Card Industry (PCI)
  • Sarbanes–Oxley Act (SOX)
  • Privacy

d) Regulatory Data – Information that’s safeguard by law and regulations, and governed by a regulatory body is known as regulatory data. These data sets are in compliance with various organizations like Family Educational Rights and Privacy Act (FERPA) and Health Insurance Portability and Accountability Act (HIPAA). Some of the examples are as following:

  • The data that needs to be protected to prevent loss, theft, unauthorized access, and / or unauthorized disclosure as stated by the regulating body or council
  • The data that is destroyed when no longer needed.


Process to classify data:

Data classification can be a complex and cumbersome process. The following steps are recommended for implementing a successful data classification policy.

  1. It is expensive to classify data properly consider before categorizing the data.
  2. Consider the confidentiality and security of the data to be classified.
  3. The integrity of the data should be considered; as low-quality data cannot be trusted.
  4. High availability data needs resilient storage and networking.
  5. Use an effective metadata strategy to tag the data well.
  6. Get the support of the management and employees who will use the system.
  7. Use data cleansing technology to remove redundant, obsolete or trivial content.
  8. Carry out an information audit to gain an accurate view of the nature of the data.
  9. Carry out classification design based on the data audit results.
  10. Monitor and maintain the data classification system over time, tweaking as necessary.

The data classification process not only helps to ease finding the data but has many other advantages. Data classification helps in extracting meaningful information from large amount of data. Data classification provides a better understanding of the data within the organization’s control and an insight of where data is stored, the ways it can be accessed easily, and the best method to protect it from future security risks. Data classification, in practice, provides an organized information framework that facilitates more adequate data protection measures and promotes employee compliance with security policies.