It’s been two years since the GDPR went into effect. Yet, 79% of businesses still struggle to comply with its provisions. Meanwhile, 62% of customers think it’s impossible to go through a day without companies collecting their data. Not surprisingly, Gartner surveys show that companies are more focused on data privacy than competitive agility, digital transformation, and talent recruitment. In other words, we’re in the midst of a data privacy revolution.
As businesses grapple with the seemingly never-ending avalanche of privacy regulations, multiple compliance solutions have hit the marketplace to ease the burden of compliance. But, what makes each of them different? Why should you choose solution A over solution B? Once you figure out the scale and scope of the solution you want, trying to identify the architectural nuances that define them can be a challenge.
In a nutshell, it’s the architecture that makes the difference — not the features. The privacy compliance solutions market is still young. New features are being added as of this writing. How your solution approaches data is what really matters. So today, we’re going to talk about Integris versus BigID — two popular data privacy solutions with unique architectures.
We’d be the first to admit it: BigID is a major competitor. Both Integris and BigID solve compliance issues with DSARs, and we’ve both built platforms to comply with data regulations at scale. However, BigID and Integris are designed to approach data in completely different ways. Data privacy regulation is the core of BigID’s business model and the focal point of its entire architecture. At Integris, we’ve built a solution that ensures not just data privacy but also data governance and protection.
Below, we bypass the flashy bullet-point lists and usual marketing buzzwords to reveal the plain truth about Integris versus BigID.
Integris vs. BigID: Identity vs. Elemental Data
BigID uses identity intelligence to map data across all enterprise sources and correlates that data with identities. So, instead of traditional classification-based discovery, which was the primary data discovery solution on the market pre-GDPR, BigID sniffs out the strings between data sets by using identity algorithms. One of BigID’s trademarks is that it moves past personally identifiable information (PII) and into personal information (PI) — allowing for broader data recognition in light of data privacy.
This is important. The GDPR, LGPD, and CCPA all recognize “personal information” as data pertaining to identifiers that can be connected to identified (or identifiable) natural persons. So, where classic data mapping solutions fail, BigID shines. It can identify IP addresses, pronouns, and inferred attributes connected to individuals, giving you tighter granular controls over your data architecture.
Although powerful in scope, this approach comes with challenges. Namely, what happens when you push sensitive data into a data lake that can’t be correlated to a particular identity?
At Integris, we don’t use identity-centric correlations. Instead, we approach data from the elemental level. Inferring data relationships via metadata doesn’t provide a holistic data privacy solution. Sensitive data can’t always be tied to an identity. As businesses continue to increase the surface area of their data landscapes by blending data lakes, simply correlating data with identities doesn’t ensure efficient data governance.
Data lakes digest customer information at alarming rates. And, since all of this data is highly unstructured, it isn’t always intrinsically linked to an identity. Furthermore, that data can’t always be forcibly linked to an identity via metadata and machine learning.
Instead of emphasizing identities, Integris focuses on uncovering all sensitive data at the outset. Our machine learning, NLP, and automation capabilities find sensitive data based on its actual qualities — not it’s relation to an identity. So, even if inferred attributes get buried within a data lake, Integris can unearth it.
Our solution can find over 250 types of sensitive data using intelligent mapping and discovery settings. We don’t approach data as PI or PII. Instead, we think of data as either sensitive or not, even if we can’t directly tie it to an established identity. We still map data to identities (when feasible), but we don’t use identity-centric mapping to uncover the data itself.
In a nutshell, both BigID and Integris are capable of handling DSAR requests. However, we approach data mapping, cataloging, and automation from different perspectives. BigID takes an identity-centric approach by leveraging machine learning to discover identity-sensitive information. On the other hand, Integris approaches data from an elemental level via machine learning data discovery and data connectors to discover sensitive data holistically.
- BigID uses identity-centric mapping to discover sensitive data by tying it to an identity.
- Integris uses elemental-level data mapping to discover sensitive information regardless of its intrinsic tie to an identity.
Integris Versus BigID: Data Privacy Compliance Solution vs. Data Privacy Ecosystem Solution
BigID is centered around compliance regulations. From automated decision-making to DSAR requests, BigID is focused on alleviating the pain points around data privacy. And, that’s what its system was built to do. Recently, BigID added risk assessment scoring features. But again, the primary driver of its system is identity — so these risk features are fueled by identity-centric data discovery.
At Integris, we’re driven by data. It may be easiest to think of us as a data discovery, protection, and governance tool that facilitates data privacy compliance due to our regulatory-agnostic framework. We help comply with data privacy regulations by using connector-based configurations to garner insights into data from any source — in rest or in motion.
By gaining deeper insights into your sensitive data at the elemental level, Integris gives you the tools to improve data security. You can easily identify misclassified or mislabeled sensitive data. And, there’s no need to replicate PI to a master warehouse. Essentially, you get seamless enforcement of your data standards and encryption policies. This promotes data integrity without impacting the productivity and flow of your data lakes. With Integris, you have the ability to scale and achieve a privacy defensibility status or legally defensible security much more effectively than BigID.
Of course, Integris also has the means to fulfill DSAR requests rapidly, map data to regulatory-agnostic data privacy policies, and monitor sensitive data for retention or deletion. But, overall, we dive deeper into data privacy and protection. In a nutshell, Integris creates a data privacy ecosystem — not just a data privacy compliance framework.
- BigID focuses on data privacy compliance via identity-centric mapping.
- Integris focuses on data privacy compliance, data security, and data governance by identifying sensitive data
Integris for Strong Data Security, Regardless of the Identities Tied to Sensitive Data
To be clear, BigID is an excellent solution. An identity-centric data privacy framework may be exactly what you’re looking for. However, Integris’s data element-centric design can be the light at the end of your compliance tunnel. It depends on your needs. But, before you choose a solution, it’s vital that you understand the core architectural differences between the two. BigID is focused on identity, while Integris emphasizes elemental-level data. That small difference creates two wholly unique solutions that will have drastically different results on your overall data framework.
Does Integris sound like the type of comprehensive data privacy, compliance, governance, and security solution your business is looking for? If so, contact us and let’s talk about how we can help you leverage our regulatory-agnostic framework to your advantage.