Today the Internet leads as the primary information source for all data workers. However, most companies have unstructured data that takes up around 80% of the total amount of data available, making the processing, storing, and retrieving of valuable information challenging. While operating on structured data is comparatively easier, organizations need sophisticated means to work on unstructured data. Therefore, when organizations acquire data load more than ever before, it's only natural for professionals to look out for ways to streamline the process and make data processing hassle-free and convenient.
This is where data classification comes into action.
Data classification helps knowledge professionals to analyze fragmented data and make storing, processing and retrieving it easier with the help of auto-categorization based on file type, content, metadata, etc.
What does Data Classification Mean?
The process of dividing data into appropriate categories to be used and safeguarded more efficiently is known as data categorization. The classification process makes data easier to identify and retrieve at its most basic level. Data classification is critical for risk management, compliance, and data security. It entails categorizing information to make it more searchable and trackable. It also removes multiple data duplications, saving money on storage and backup while speeding up the search process.
Strategically formulated classification policies clarify what categories and criteria the business will use to categorize data and the roles and duties of people within the organization, making it easy to locate and retrieve critical data. In addition, an efficient data classification system addresses security standards that outline proper data management procedures for each category and storage standards that define the data's lifecycle requirements.
What Makes Data Classification a Must-Have for Data-Driven Enterprises?
Some of the most critical (however, not the only) purposes of implementing data classification and its increasing relevance for unstructured data in the current hyper-competitive, data-reliant business environment include the following:
- Allows limited access to personally identifiable information
- Gives complete control over location and access to intellectual property
- Reduces surface area for attacking sensitive data
- Promotes data confidentiality by providing access control over information
- Identifies data governed by regulatory laws like GDPR, HIPAA, CCPA, PCI, and other future regulations
- Applies metadata tags to protected data, allowing additional tracking and controls
- Allows to make sound decisions to protect data from internal misuse or external attacks
- Enables easy and much more efficient access to content based on type, usage, relevance, etc.
- Discovers and eliminates data redundancy
- Moves heavily utilized data to cloud-based infrastructure
- Maximizes the value of data recognizing it as a practically useful asset distinguished by its completeness, provenance, and quality
- Helps increase the validity, reliability, and accuracy of data
What are the Types of Data Classification?
Two main paradigms are to be followed when implementing a data classification process. Of course, others exist; however, most use cases will fall into one of these groups. These two options imply that you can either assign users the duty of classifying the data they generate or use an automated system to do it for them.
It is a classification system based on a manual end-user selection process of each document or data set. You must define sensitivity levels, train your users to identify each level and provide a way to tag and classify every new file they create when you want them to classify their data. The benefit of using user classification is that humans are quite competent at determining whether the material is sensitive, and the classification accuracy can be fairly good with the right tooling and simple rules. However, manually tagging data is time-consuming, and getting people to retrospectively tag past data is a massive issue if you have large amounts of pre-existing data.
It is a classification system based on a file parser with a string analysis system to find data in files. Automated data classification engines use a file parser and a string analysis system to find data in files. The data categorization engine can read the contents of various types of files with the help of a file parser. The data in the files is then matched to search parameters provided by a string analysis engine. Although auto-categorization is far more efficient than user-based classification, the accuracy is dependent on the parser's quality. To help validate findings and reduce false positives, your classification engine should incorporate a few critical characteristics, such as text proximity, negative keywords, match ranges, and validation methods.
How to Effectively Implement Data Classification?
Understanding the meaning, roles played, and significance of data classification is only beneficial when enterprises implement the right data classification system across the organization. For this, enterprises can follow the traditional route by following the standard steps:
- Understand the current setup
- Create a data classification policy
- Prioritize and organize data
Though this method is doable and generates results, it can be challenging to implement across the different types of apps, and interfaces data workers use in the current business environment. A better means to achieve the data classification objectives you set up for your organization is to employ an auto-categorization tool. With the efficient use of artificial intelligence and machine learning, auto-categorization can help you classify massive volumes of data seamlessly.
How Needl.AI Caters To Your Data Classification Needs?
The major challenge for today's enterprises is to successfully navigate complexity with a working classification strategy and a technology that is both versatile and adjustable while being simple to use. The best categorization tool should not be difficult to use; it should conceal complexity. Moreover, it should blend nicely with the way end-users work daily.
Needl.AI, an AI-enabled collaboration platform, envisions managing fragmented information flows removing data silos across the organization. The platform is a personal cloud computing gateway, assisting users in handling information at scale and distil insights in a simpler, quicker manner. By unbundling data across apps, devices, and platforms, Needl.AI helps segregate the data and stitch it into a single repository, offering a unified view of all your data and streamlining how you access, analyze, and share the categorized data with individuals and across teams.
Whether it's flexibility, technological integration and cross-functionality, or application and file type support, Needl.AI offers a collaboration platform for auto-categorization that meets your organization's demands not only now but in the future as well.