Assume you’re the CEO of a 10,000-employee company that generates millions of files and emails daily. Some of that data is sensitive, and if it’s leaked or stolen, you’re in for a major security breach with significant penalties. Moreover, prioritizing risk mitigation or complying with privacy rules might be difficult when you don’t know which information requires military-grade protection. This is where data management comes in.
The terabytes of data require efficient management for data workers to derive meaning and gather valuable insights. And one of its important aspects is data integration, which makes data from various sources easily accessible to users.
This blog discusses the first and most crucial data management and integration steps: Data Categorization.
What is Data Categorization?
Data categorization separates data into groups that can be better used and protected. The classification process makes data easier to identify and access at its most basic level. The process is required for risk management, compliance, and data security. It entails classifying data to make it easier to locate and track. It also removes numerous data duplicates, saving money on storage and backup while speeding up the search.
Imagine receiving a collection of search results automatically sorted into easy-to-read lists depending on where they were located, how often they occurred, what type of result hit they are, what project they are related to, and any other “sortable” information in the file’s metadata.
You will not only be able to include—or just as easily exclude—large sets of results that are relevant or irrelevant to your case if your data management platform has the feature known as auto-categorization.
It is a novel method for dealing with the flood of unstructured, unindexed, and unorganized digital content that threatens to overwhelm knowledge workers in corporations and governments. Auto-categorization software can classify digital data according to predefined taxonomies, extract concepts and entities for taxonomy construction, and tag material.
How does Auto Categorization Work?
Auto categorization engines use a file parser and a string analysis system to find data in files. The data classification engine can read the contents of various types of files using a file parser. The data in the files is then matched to search parameters using a string analysis algorithm.
Auto-categorization is far more efficient than manual categorization, although accuracy is dependent on the parser’s quality. When choosing an automated classification product, accuracy, efficiency, and scalability are critical factors. It can take time to complete an initial categorization scan of a great multi-petabyte environment because efficient data management systems like Needl.Ai keep track of all data creation and modification. As a result, their categorization system is much more reliable.
Some categorization engines demand that each object be indexed. If you’re concerned about storage space, search for a solution that doesn’t require an index or only indexes items that follow a specific policy or pattern.
Organizations may choose either one or a mixture of user-generated or auto-categorization. It’s usually good to equip users with the necessary training and tools to ensure data protection.
Remember, data management doesn’t have to be complicated and time-consuming. Needl.Ai, a cloud-based collaboration platform, has a deep search algorithm and intelligent validation, which allows you to have a unified view and work with data seamlessly. Check out their website today!
