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6 Top Data Classification Trends

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Data classification—organizing data by relevant categories—should be a key part of an enterprise’s larger data management strategy. Tagging data can make it more searchable, and therefore more useful. It can also eliminate duplicate data, which reduces storage needs and expenses and improves the speed of queries and analytics. Misclassified data provides inaccurate results and can lead to security incidents when it is mistakenly made public because it was labeled incorrectly.

Historically, organizations were often lax about data classification, creating problems that compounded quickly and led to data sprawl, lost productivity, and security concerns. But as data becomes increasingly essential for business—and accumulates in massive volumes—organizations have begun to consider data classification a pillar of their data management efforts. Here are the six top data classification trends for 2023.

1. AI is Driving Data Classification Efforts

Artificial intelligence (AI) had a banner year in 2023, and data science—like most industries—has begun to reap the benefits. Legacy data classification systems required challenging implementations and lacked the ability to perform context-based classification, but new solutions use AI to incorporate content awareness and context analysis into classifying and sorting data.

AI powered automation in data classification can help companies analyze and label unstructured data at unprecedented scales, and with minimal human intervention. This allows organizations to classify more data more quickly. It also lets them circumvent the industry-wide qualified staffing shortage.

AI also provides data leaders with actionable visibility into how data is used, shared, and acted on by different users, making it easy to flag suspicious data.

2. More Data Regulations are Being Implemented and Enforced

As more and more data breaches come to light, especially in critical infrastructure, governments have begun to tighten their grip around tech companies that violate data management and localization principles. New data privacy laws abandon the harm-based approach—preventing and punishing violations of consumer data—in favor of a rights-based approach that gives individuals control of how their data is managed, used, and processed.

The European Union is currently undertaking its largest cross-border investigation under the General Data Protection Regulation (GDPR) and taking action against member states that allow data attacks to thrive. While the U.S. has historically had a more lenient approach toward how organizations collect and classify data, that might be changing—after passage of the watershed California Consumer Privacy Act (CCPA), other states including Colorado, Utah, and Virginia have pursued similar legislation.

Additional policies like the National Cybersecurity Strategy, Gramm-Leach-Bliley Act (GLBA), and Family Educational Rights and Privacy Act (FERPA) will create multiple federal regulators in the U.S. to oversee implementation of data governance policies, and assist with classification, usage, and archival of data in the entire data lifecycle management.

3. Better Technologies are Making Data Classification More Effective

Technology is fueling a new wave of data democratization, providing simpler access controls, more secure delivery, and greater decentralization. At the forefront is the integration of data fabric—which stitches together metadata to aid data classification—and data mesh, which can reduce information silos and aid in governance by putting the onus on teams that produce data.

The combination of technologies helps companies process data from multiple sources, producing faster insights and creating a frictionless web for all stakeholders to engage with processed data. It also helps build an autonomous, company-wide data classification and coverage interface that provides self-service access to fragmented datasets.

Enterprises can reduce operational expenses by up to 400 percent by classifying data without having to move it and creating a data abstraction layer. Enterprises can also manage their security postures with improved data access and intelligent query escalation, allowing them to build a top-down data service.

4. Zero-Trust Data Privacy Vaults are Being Used for Sensitive Data

Data classification plans must also secure confidential and restricted data by de-identifying critical datasets and exposing only the information needed to complete a task. As tech firms face greater compliance demands from regulators, privacy vaults are increasingly drawing attention as an interesting solution. A zero-trust vault eases personally identifiable information (PII) compliance concerns by providing a controlled environment to protect sensitive data.

Most privacy vaults use polymorphic encryption, two-factor authentication, and regular data audits to detect vulnerabilities and keep customer data attack-proof. They also allow governments and businesses to work together on privacy by design in big tech by redacting confidential datasets, tokenizing sensitive information, and restricting the flow of personal data in large language models (LLM) like ChatGPT.

Privacy vaults are especially popular in the pharmaceutical field, where proprietary research has to be protected across the drug lifecycle.

5. Unstructured Data is Powering Business Intelligence

Unstructured data—emails, text messages, and multimedia, for example—poses particular challenges for data classification. It is like the anti-matter of the universe in that it is difficult to detect and mostly impossible to analyze, but it accounts for a significant portion of the data enterprises collect and use.

The growing focus on unstructured data is driven by the time crunch that businesses face in a fiercely competitive market. They have to feed data pipelines faster, move only the data they need—and that has already been classified—and eliminate manual efforts to find classified datasets.

Finding ways to process and classify unstructured data can provide improved storage capacity, a data-driven way to measure consumer experience, and a better understanding of user sentiment.

Read our Comprehensive Guide to Data Pipeline Design.

6. Companies are Assessing Risks to Prevent Shadow Access

Shadow access—unintended, uninvited, and unnoticed access to datasets—is an increasingly exploited risk facing businesses with large volumes of poorly classified data. That risk is only expected to grow as more data gets stored and shared in the cloud.

About 80 percent of all data breaches occur because of existing credentials—employees intentionally or inadvertently share confidential information or access unauthorized applications and cloud services. With blurred lines between personal and professional domains and the growing complexity of cloud identity, shadow access has become an even thornier issue.

Because you can’t protect what you don’t know, new tools to assess risk for shadow access are garnering attention from data leaders. They allow them to identify data types that are vulnerable to security risks and take necessary steps to mitigate those risks.

Bottom Line: Enterprise Data Classification is Evolving

As enterprises race toward the creation of data-safe environments, their data classification policies will increasingly become one of the differentiating factors. At the moment, the field of data classification is in flux, driven by the advent of generative AI, a greater demand for customer experience, and growing pains of data sprawl. But organizations that tap into these innovations to shore up their data classification efforts and their larger data management strategies will ride the wave to a more successful, more secure, and more actionable data future.

Read The Future of Data Management to see other trends in how enterprises work with and keep tabs on mission critical information.

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