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BigID Next vs Google Cloud Data Loss Prevention comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Jan 22, 2026

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Categories and Ranking

BigID Next
Ranking in Data Loss Prevention (DLP)
7th
Average Rating
8.2
Reviews Sentiment
7.0
Number of Reviews
15
Ranking in other categories
Data Governance (6th), Data Privacy Management Software (1st), Data Security Posture Management (DSPM) (4th), AI Data Analysis (2nd)
Google Cloud Data Loss Prev...
Ranking in Data Loss Prevention (DLP)
27th
Average Rating
9.0
Reviews Sentiment
5.2
Number of Reviews
2
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of February 2026, in the Data Loss Prevention (DLP) category, the mindshare of BigID Next is 3.3%, down from 4.5% compared to the previous year. The mindshare of Google Cloud Data Loss Prevention is 1.0%, up from 0.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Loss Prevention (DLP) Market Share Distribution
ProductMarket Share (%)
BigID Next3.3%
Google Cloud Data Loss Prevention1.0%
Other95.7%
Data Loss Prevention (DLP)
 

Featured Reviews

Aniruddha Nath - PeerSpot reviewer
Senior Security Consultant at a consultancy with 10,001+ employees
Data discovery has transformed compliance workflows and automation now speeds up requests and remediation
The best feature that BigID offers is data discovery and classification, which is the most powerful engine. It allows connecting to many different data sources, ranging from cloud to on-premises to structured to unstructured data. If there is no connector available, you can build your own classifiers as well. Regarding the custom classifier option, you can build custom classifiers using regular expressions, and I have done that if you know how to create regular expressions. Custom connectors are something you create to connect to a database where the connector is not available. BigID has positively impacted my organization as it's a very powerful tool, especially with the increasing regulatory compliances for different countries such as GDPR, CCPA, and India's recent DPDPA act. Having these tools in place greatly helps organizations avoid any penal charges for not being compliant with the regulatory compliances. For example, regarding compliance or reduced risks for my clients, the DSAR process I was talking about allows organizations to respond quickly to user data deletion requests under GDPR law, which traditionally has a 30-day or 60-day timeline. In larger organizations, when the number of requests is high, it becomes tedious. However, using DSAR automation with BigID, it's almost instantaneous; instead of 30 days, you can respond in just one day to what users have requested.
RaviUpadhyay - PeerSpot reviewer
Security Consultant at HCLSoftware
Automated scans have classified sensitive data and protect privacy in complex environments
When someone asks where sensitive data is located and to classify it, they would start working manually and would take four or five months to detect only that sensitive data. Google Cloud Data Loss Prevention helps significantly. They have a predefined template, and whatever category comes under sensitive data has been researched with almost 99% accuracy and included in the template. A person needs to attach the template and scan their data. Once initiated, it will take time, such as 24 hours or 48 hours, depending on the data size. Once the template is applied and the system scans it, a tagging report of the sensitive data locations is returned. A report is available, and what previously would have taken a month can now be achieved by Google Cloud Data Loss Prevention services in detecting sensitive PII data within 48 hours maximum, even if the data size is larger. Once the report is available, the person would know where the sensitive data is located. Dealing with sensitive data is the next question. If data is not to be shared, it has to be hidden, masked, or changed. All kinds of things can be applied to dealing with that data. Google Cloud Data Loss Prevention has another feature available called de-identification services, which deals with sensitive data in applications or production environments.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"BigID offers different scan types for data discovery. The most powerful one is the full scan, which scans both data and metadata. However, the metadata scan is faster in comparison."
"BigID has one of the best technical support teams."
"My experience with BigID has been very positive and memorable, as BigID is simple, straightforward, and reliable when it comes to data discovery and data scanning exercises."
"It provides a unified view across different databases and supports a wide range of data source types, including cloud and on-premises systems."
"The data classification offered by the tool can help companies improve their security strategy"
"The most valuable feature of BigID is its large number of classifiers, which allow us to scan for specific data such as SSN numbers."
"BigID integrates well with our other products."
"The features that I have found most valuable are the user experience, the credentialing, and that BigID is user friendly. Additionally, you can deploy to several other Microsoft platforms and you can use it for other things, like a bigger element or a report."
"It is user- friendly and has a easy integration."
"These tasks can be achieved by Google Cloud Data Loss Prevention services in a programmatic way, very quickly and very efficiently."
 

Cons

"BigID is making some forays into the GRC space, and that's a natural progression. I'd like to see that improve so that data governance is better, data risk is identified, and the ability to control and mitigate it."
"There are some shortcomings when it comes to Calvirus authentication, which is not yet supported by BigID."
"There are some shortcomings when it comes to Calvirus authentication, which is not yet supported by BigID."
"One concern I have with BigID is regarding certain scans, like the multi-scan. The issue is that we can stop and retrieve these scans, but once they start, they go through an enumeration process."
"In terms of what could be improved, when you're looking in a BigID file, you cannot really get the whole file. You have to export it to download it to another platform that allows you to completely view it, or run a program. That was one of the things that was really a disappointing point for me. Not to be able to view everything. There's a lot more data, but you can't get it all at once."
"I want them to focus on data mapping, assessment, automation workflow, and privacy incident management. The privacy tools have not been widely used, and they have not invested much in privacy code privacy tools."
"BigID needs improvement in terms of automation."
"I want them to focus on data mapping, assessment, automation workflow, and privacy incident management."
"Improvement are made as per client requirement."
"Google Cloud Data Loss Prevention has a system, but it is not very mature."
 

Pricing and Cost Advice

"The solution is not licensed per user but rather based on capacity. For instance, organizations with large amounts of data, such as 50 GB or more, are the ones that typically qualify for BigID."
"The pricing depends. If you have thousands of data sources to connect and manage, and you struggled with an MDM package in the past, you'll find BigID valuable and even cheap. But if you're a small business, it's probably not the right tool for you."
"The product is expensive, but so are all competitor tools"
"I think that BigID's pricing is very reasonable."
"The solution is expensive."
Information not available
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Top Industries

By visitors reading reviews
Financial Services Firm
22%
Insurance Company
9%
Computer Software Company
7%
Manufacturing Company
6%
Comms Service Provider
12%
University
11%
Financial Services Firm
9%
Media Company
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business5
Large Enterprise11
No data available
 

Questions from the Community

What do you like most about BigID?
I like BigID's in-depth discovery and scanning capabilities, especially for unstructured data. This feature is a standout compared to competitors. The tool's data classification capabilities are i...
What needs improvement with BigID?
One improvement I would suggest is addressing the intermittent failures of BigID scans, as there are times when some errors occur. I think the BigID team is aware of this and works on resolving iss...
What is your primary use case for BigID?
BigID's main use case is connecting to various data sources to perform the data discovery process, classify the data within those systems, and identify sensitive information across various structur...
What needs improvement with Google Cloud Data Loss Prevention?
When someone asks where sensitive data is located and to classify it, they would start working manually and would take four or five months to detect only that sensitive data. Google Cloud Data Loss...
What advice do you have for others considering Google Cloud Data Loss Prevention?
Further strategies can be defined based on specific use cases. For example, in an R&D company where doctors are dealing with sensitive data and need to share it, if two doctors are sitting in d...
What is your experience regarding pricing and costs for Google Cloud Data Loss Prevention?
Pricing is based on data size. Google offers different pricing models, and they provide good discounts when someone commits to a long-term engagement with their Google services.
 

Overview

 

Sample Customers

Home Depot, Grant Thornton LLP, Cimpress, Fidelity Investments
Information Not Available
Find out what your peers are saying about BigID Next vs. Google Cloud Data Loss Prevention and other solutions. Updated: February 2026.
881,733 professionals have used our research since 2012.