Knowledge is power. That has always been true. But this age-old phrase takes on a modern meaning in an era when organizations depend on digital data like never before to transform operations, accelerate growth, and drive a competitive advantage.
Competitive organizations rely on data
The world produces 2.5 billion gigabytes of data every single day, which organizations capture, store, and analyze. From that data, they can glean valuable insights to become more efficient, productive, intelligent, and innovative.
The ability to effectively utilize data as a strategic asset determines how competitive a company is…or isn’t.
The reality is that many organizations’ attempts to be more data-driven are often handicapped by data privacy concerns. 451 Research suggests, “It is clear that the inability to consistently execute on data privacy creates a certain organizational hesitation around the leverage of data: hesitation that can directly slow down business progress and competitive viability.”
Challenges of a data-driven world
Today’s data is more plentiful, insightful, and actionable than the knowledge of the past. Unfortunately, it is significantly more complicated, risky, and regulated, too.
Turning mountains of data into actionable insights takes no small amount of effort. That data, especially if it is sensitive or personal in nature, is under a constant barrage of cyberattacks that grows more sophisticated, audacious, and (worst of all) successful with each passing year. And if sensitive or personal data gets mishandled – in any way, at any step – it could result in non-compliance with an increasing number of data protection and privacy regulations enforced at the local, state and international levels.
Anxiety around these issues led a survey of executives to rank cyber incidents as the biggest risk in 2022, ahead of business interruptions and pandemic-related problems. So how do organizations maximize the value of data while minimizing its vulnerability? By sticking to a sensitive data strategy.
Building a modern data strategy
For organizations to put data at the forefront of everything they do, they must first acknowledge that any piece of sensitive, personal, or regulated information can either help them or hurt them—and put the right steps in place to fully protect their data.
Key components of an effective data strategy
A successful data strategy puts measures in place to mitigate risks and facilitate discovery. What those measures entail depend on the organization and its data. However, the data strategy will always look like a detailed set of policies and protocols for how to handle every piece of data, especially sensitive or personal identifying information (PII). Those policies will address three key components:
- Data Security – How to avoid accidental or intentional data breaches and how to ensure that sensitive data receives extra levels of protection.
- Data Privacy – How to keep PII from being improperly exposed so that it meets consumer expectations and regulatory requirements.
- Data Governance – How to keep data integrated, organized and accessible in a way that preserves access, uncovers insights, and serves strategic objectives.
Data classification: the key to a successful data strategy
An effective data strategy determines how to keep data secure, private, and well-governed based on a detailed understanding of the unique requirements of each record. Therefore, before any data strategy can start (or succeed), there needs to be a strong focus on data classification.
After all, an enterprise’s greatest defense is knowing what sensitive data exists, restricting where it’s stored, limiting who has access to it, protecting it, and monitoring its usage. To exert such control, enterprises must first find and classify their sensitive data in order to better control it.
Traditional data classification applies categorizations, or “classes,” to new data as it is created to determine how much protection is required. The class determines how the data gets collected, used, stored, shared, archived, or destroyed.
The challenges of data classification
It’s easy to underestimate just how critical data classification is to any organization trying to capitalize on data.
Data classification matters because if companies don’t know the source, sensitivity, purpose, or regulatory requirements of specific data, it’s impossible to handle it as laws and best practices dictate. Consequently, sensitive data ends up in insecure locations, PII gets revealed, and valuable data goes dark and becomes toxic. Anything and everything can (and usually will) go wrong.
The surest way to derail a data strategy is to treat all data identically. Getting it right depends on handling different data in different ways, so that sensitive and personal data get treated securely, while everything else moves efficiently around the organization.
Why manual classification methods aren’t good enough
Data classification is a major undertaking even with a modest amount of data or a large team involved because data is dynamic and constantly changing.
A 2021 Ponemon Institute study found that a majority (77%) of organizations still classify data manually, in whole or in part. But given the speed and scale of today’s data, relying on employees to classify it manually is no longer tenable. Human errors, classification inconsistencies, and workflow circumvention are inevitable, meaning data will either end up in a growing backlog or with the wrong classification—meaning the organization cannot trust the classifications.
In that way, the prevailing approach to data classification is a drain on resources (time, staff, budget etc.) in the service of something that never did work very well…and will only continue to get worse.
Understanding what’s at stake
Improving data classification to be more automated, accurate, and scalable makes any data strategy more effective and more achievable. As difficult as high-volume data may be to manage carefully, it gets much simpler once that data has been classified correctly.
But the inverse is also true: data that has not been classified correctly makes that information more likely to be a liability than an asset. It’s fair to call data classification the linchpin of modern data strategy and governance.
The foundational role of data classification begs two questions in particular. First, does data classification need improvement? Second, what’s the best way to make those improvements quickly and cost-effectively? Spirion has answers to both questions.
Introducing Spirion’s new context-based data classification
Spirion has extended its best-of-breed automated and purposeful classification to give organizations even more flexibility in how they organize and understand their data through context-rich data classification techniques.
Context-rich classification allows organizations to more precisely define their data based on new out-of-the-box categories including process, purpose, preference, regulatory, and custom to fine-tune the specific data protection needs of individual datasets and enable more effective data privacy, security, and compliance strategies.
These new contextual classifications go well beyond standard sensitivity labels to help organizations better define their data and stay compliant with privacy and security mandates that require documentation of the reason the data is being collected.
Spirion’s granular context-rich classification categories include:
- Sensitivity according to public, internal, confidential, and restricted categories.
- Process to identify how data is collected, stored, and used.
- Purpose to define why data is stored and how it’s expected to be used.
- Preference to highlight how customers want their data used and provide opt-outs for highly sensitive personal data.
- Regulatory to automate compliance for CPRA, PCI, GDPR, HIPAA, and other standards..
- Custom categories to address user-specific needs and maintain a flexible approach toward how they map to and follow specific security frameworks and regulatory standards.
Get your complete guide to context-rich data classification
To help illustrate why context is the most important part of data classification—and the missing piece from most data strategies—we created a new whitepaper, Unify Data Security, Privacy, and Governance with Contextual Data Classification.
It takes a deep dive into data classification, explaining why it matters, revealing what needs improvement, and highlighting how to get classification correct based on business context. Then, it explores several use cases and case studies of organizations using next-generation data classification to improve upon their data governance, security, and privacy initiatives.
Spirion’s white paper is valuable reading for any company starting (or struggling) to get serious about turning their data into a strategic asset.
