Automated Data Classification - 3 Methods to reduce your efforts significantly

Our digital landscape is rapidly evolving, which opens the door for lots of opportunities, but also its challenges. The effective management and protection of data have become a challenge for many organizations. That is a driver for Data classification to emerge as a pivotal practice. But it comes with some drawbacks. In this blog post we let you know how to counter those and give insights into the power that comes with automating Data Classification.

At its core, data classification is the process of grouping data into classes based on shared characteristics, such as their level of confidentiality, relevance to business operations, and content appropriate labels. Thanks to Data Classification, organizations can ensure compliance with regulatory requirements, implement targeted security measures, and increase operational efficiency.

• Regulatory compliance:
  In our interconnected world, data privacy regulations have become increasingly strict. Failure to comply with these regulations can lead to severe legal consequences, tremendous fines, and reputation damage.
• Implement risk aware protective measures:
  Understanding the risk associated with (different classes) of data is crucial for effective risk management. Data classification enables organizations to proactively implement policies that mitigate the risks, reducing the overall vulnerability to cyber threats.
• Increase efficiency of operations:
  Data classification contributes to operational efficiency, by refining the documentation of the available data. With data properly classified, it is easier to efficiently prioritize data management tasks.

The drawbacks of Data Classification

The immense volume of data generated and processed by companies today, combined with the speed at which it is produced, makes manual classification a time-intensive task. It involves locating data across different systems, databases, and repositories, which requires collaboration between various departments and teams to come to a common understanding of the data. Classifying this diverse landscape manually is time-consuming, requiring significant manual effort and human resources. These challenges highlight the importance of considering automation solutions to improve the efficiency of the classification process.

Automating Data Classification

Due to the amount of effort required to classify data by hand, it can be valuable to investigate automated solutions. By extracting and gathering information from the context, a classification level can be proposed. We have identified three different methods to automate data classification with varying sophistication.

1. Lineage-based propagation of concepts/labels:

Lineage-based propagation leverages existing information by considering the technical lineage of columns and previously validated labels. The premise is simple: if columns transfer physical data to each other, it is probable that they share the same conceptual content and thus warrant the same label. A validated label can be propagated down the lineage, as far as no major transformations take place on the data. Major transformations are disregarded here, as they possibly change the context and meaning of the data. A pre-requisite of this technique is thus a lineage with minor transformations and manually validated labels. As all transformations between columns need to be parsed, the propagation can be computationally expensive (here too we run into the curse of dimensionality). However, no access to the physical data is required to use this method.

2. Rule-based classification of concepts:

This method assumes that concepts can be identified by their standard format or set of values. If a pattern or set of values is detected in a column, a link is proposed with the (business) concept. Technically, 3 different approaches can be taken: pattern matching, keyword detection or fingerprint matching, depending on the concept to detect. Pattern matching is useful when there is a set format that every value of a concept has. (Example: International Bank Account Number). Where pattern tests look for a pattern in the data, keyword and fingerprint tests look for values from a reference list. In case of fingerprint matching, you need an exact match between a value in the column and in the reference list. For keyword detection, keywords from the reference list must occur somewhere in your values to return a match. To avoid running these rules on thousands of records, the physical data is profiled (aggregated) and the rules compare against the most common values of a column. Unfortunately, not all concepts have a standard format or set of values, and some concepts have the same format or values. The identification of the pattern and keyword list can be a time-consuming process, just like requesting access to the physical data.

3. Machine learning and Data Classification

As opposed to letting a human reason about detection rules, a machine learning algorithm can also learn how to classify data. With enough examples of classified data, represented by their features and a label, the algorithm can learn how to classify unseen data. The algorithm analyses the input features for hidden patterns and derives the most useful rules to accurately classify the data. Thereby, it is a flexible method that can be deployed in a wide variety of settings. Nevertheless, the skills this set-up requires are often not present in a data management setting. In addition, it requires a high-quality set of examples that are (correctly) classified, meaning manual effort will still be required.

The Validation of a classification proposal

Although useful, these automation methods do not guarantee that their predictions are correct. Validation of a classification proposal is therefore a critical step to ensure accuracy and reliability. It can be debated whether this validation responsibility falls on the shoulders of data responsibles/owners, or can be shown to everyone with a disclaimer. Feedback from owners and users can contribute to the refinement of the automated classification methods and improves future proposals.

Conclusion

Data continues to be a driving force in business, that's why data classification remains an indispensable practice for safeguarding information and maintaining a competitive edge.

Data classification is essential for a robust data management strategy, providing a structured approach to protect the abundance of information that organizations possess. While manual classification is time-consuming, the automation approach offers a powerful solution, combining consistency, efficiency and accuracy. Note that these methods come with their own challenges around skill set, performance and validation of their results. As organizations continue to navigate the data-driven landscape, automated data classification can be a powerful ally, enabling organizations to unlock more value from their data while protecting against potential risks.

Do you want to take your data classification to the next level and see where automation is possible? Reach out to one of our Datashift colleagues!