Significance of Data Classification in Effortless Database Management and Ensuring Security


Data classification is a key aspect of structuring enterprise databases, which can be understood to organize data into the most relevant generic categories to use them for various purposes and to protect it efficiently. At the baseline, the proper classification of data will make it easier to identify, locate, and retrieve based on the priorities. Data classification is of high significance when it comes to data security, risk management, and compliance.

Data classification primarily involves tagging different sets of data to make it easier to search and track. Classification will also help eliminate any duplication of data and further reduce the storage, backup, and recovery costs, and speed up the entire process. For those who are new to it, the classification may sound so tiresome and technical topic. Still, once you understood the importance of the same and the fundamental data classification principles, it becomes a norm.

Why is data classification needed?

The concept of approaches to data classification has evolved largely. Data classification is used for many purposes now, mostly to support the initiatives related to data security. However, above data security, data classification could be done for various other reasons, including ease to access, maintain regulatory compliance, and meet various types of personal and business objectives related to data management. In many cases, data classification is also mandated as a regulatory compliance measure as data must be kept searchable and easily retrievable within a set timeframe. For data security, classification is a very useful measure to ensure timely security responses based on retrieval, transmission, and data copying.

What types of data classifications are there?

Data classification consists of various labels and tags, which define data characteristics as confidentiality, complexity, and integrity. When it comes to any data classification project, availability is the No.1 consideration to be made. The sensitivity of the data classified is often considered based on the level of its confidentiality. This also directly correlates to data security and the protective measures to be practiced at each level of classification.

There are three major types of classification based on the latest data standards, which are:

  • Content-based classification: This mode of data classification revolves around inspection and interpretation files to look for sensitive information.
  • Context-based classification – To look at the data locations, applications, and creators, among many other variables. These function as indirect indicators of sensitive data.
  • User-based classification: This classification depends on the end-user selection of each category of data. However, user-based classification is only limited to the use cases where users and their privileges to create, edit, and delete the sensitive data are well known.

Enterprises may plan to classify their data as private, public, or restricted in general. In this categorization, data to the public may be the least sensitive information that may have the lowest security significance. In contrast, restricted data is considered the most sensitive piece of critical information to be protected.

Private, public, restricted data classificationsare mostly the generic starting point when organizations first think of data classification. This is followed by further identification and tagging of data to label it based on relevance, quality, security parameters, and other priority classifications based on the functional needs of data. Foolproof data classifications will have follow-up processes and perfect frameworks to safeguard the data at the particular classification.For classifying your data based on your database needs, you can assist expert providers like

How is data classification done?

For those who are new to it, data classification may felt to be a complex process. If you have chunks of raw data to deal with, classification can be cumbersome. There are many automated tools out there to help you streamline the data classification process. Still, an enterprise should determine the classification criteria and categories based on their objectives and goals. This objective framework must be used to define the use of data, classify data, and to delineate the roles and responsibilities of each of those who access the data.

While doing classification, there should be proper protocols and adequate standards corresponding to the data categories you deal with. When implementing the right way, the classification will provide an operational framework for the employees and third parties involved in data storage, retrieval, and transmission.

Before initiating data classification, procedures and policies related to it must be defined by covering the data security and confidentiality requirements. These guidelines should be simple and clear for all the employees and other data users to understand and comply with. Each category must be defined with related information about the type of data it represents, security considerations for the same, rules for storing, retrieving, transmitting that category of data, and the potential risks of violating the policies and procedures related to it.

Considering its benefits, data classification offers many add-on advantages, making it easier to find and retrieve data. In a modern enterprise database management scenario, data classification is necessary to make sense of huge dumps of every-minute data and analyze it for business intelligence and business decision making. Data classification gives a clear picture of data while controlling an organization and understanding where it is stored, how to access it, and how to safeguard it from any security threats. Once implemented well, data classification will offer a well-organized database framework that will facilitate adequate protective measures and promote user compliance with the data security policies.

As we have seen above, to plan for effective data classification, you need first to understand the nature of data you deal with the most appropriate categories under which it falls. There are various methodologies and approaches used for data classification, which you can adopt the best one based on your enterprise objectives and structure. You need to consider your prospects of data too while doing data classification. If you are doubtful about it, it is worth taking the external support of expert consultants to define the classification categories for your data and prepare a structural framework for your data classification.