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BigID Next vs Cube 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 AI Data Analysis
7th
Average Rating
8.2
Reviews Sentiment
7.0
Number of Reviews
15
Ranking in other categories
Data Loss Prevention (DLP) (13th), Data Governance (8th), Data Privacy Management Software (1st), Data Security Posture Management (DSPM) (5th), Data Security Platforms (DSP) (1st)
Cube
Ranking in AI Data Analysis
19th
Average Rating
8.4
Reviews Sentiment
6.2
Number of Reviews
5
Ranking in other categories
Embedded BI (10th)
 

Mindshare comparison

As of August 2026, in the AI Data Analysis category, the mindshare of BigID Next is 0.7%, down from 23.3% compared to the previous year. The mindshare of Cube is 0.3%. It is calculated based on PeerSpot user engagement data.
AI Data Analysis Mindshare Distribution
ProductMindshare (%)
BigID Next0.7%
Cube0.3%
Other99.0%
AI Data Analysis
 

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.
Amir Hassan - PeerSpot reviewer
Data Scientist / Data Analytics Consultant at Fiverr International Ltd
Managing complex data hierarchies has improved analytics but still needs richer hierarchy types
To evaluate Cube's efficiency in handling large data volumes, I use metrics including F1 scores, AUC (Area Under the Curve), and RMC (Root Mean Squares), along with RSE (Root Square Errors). These factors help me gauge the efficiency of the algorithms clearly. Regarding improving Cube, I believe that when we are building hierarchies, I should enhance the type of hierarchies. For instance, in a few countries lacking state systems, there could be a hierarchy without proper categorization—such as continent, country, and city. Therefore, it would be beneficial if I could improve the hierarchies to retrieve data as quickly as possible from Cube.

Quotes from Members

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

Pros

"Although I was serving the client rather than my own organization, BigID has made scans faster and more efficient, and the DSR results are much more accurate."
"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 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."
"The most valuable feature of BigID is its large number of classifiers, which allow us to scan for specific data such as SSN numbers."
"The best feature that BigID offers is data discovery and classification, which is the most powerful engine, allowing connection to many different data sources ranging from cloud to on-premises and from structured to unstructured data, with the ability to build your own classifiers if no connector is available."
"The tool's most valuable feature is correlation. Using BigID's data classification capabilities has strengthened our data security. It lets me classify and connect data, which helps me manage data at various classification levels."
"BigID is more advanced than Microsoft Purview when it comes to machine learning and AI development tools."
"NPS improved to approximately eight out of ten for our feature, and internally ticket handling times decreased, allowing reallocation of resources to higher-impact projects."
"Cube's caching mechanism impacts my database query loads and response times significantly."
"Implementation was super smooth, and within two weeks we were up and running and the metrics were exposed in our app."
"Cube completes my tasks very easily and takes less time, allowing me to deliver any project in a timely manner to our clients."
 

Cons

"There are some shortcomings when it comes to Calvirus authentication, which is not yet supported by BigID."
"One area where BigID can be improved is the UI, which has a lot of bugs."
"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."
"Improvement could be made in data consent management and data privacy impact assessment."
"The challenge we encountered was with data connection across multiple databases. We struggled with configuring the data connection successfully. However, with the assistance of dynamic teams, we resolved this issue."
"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."
"The tool currently lacks security features."
"BigID needs improvement in terms of automation."
"I did not see any return on investment from Cube."
"Cube can be improved by enhancing data refresh over multiple tabs."
"When it comes to the initial setup of Cube, I faced some challenges, including server issues when uploading data from local sources to the Unipi servers."
"There is no way to create a real template that is not exposed directly in the UI."
"Cube's interface can be challenging for non-technical users, needing clearer use-case examples to ease integration into workflows."
 

Pricing and Cost Advice

"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 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 solution is expensive."
"The product is expensive, but so are all competitor tools"
"I think that BigID's pricing is very reasonable."
Information not available
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Top Industries

By visitors reading reviews
Financial Services Firm
18%
Manufacturing Company
11%
Insurance Company
8%
Comms Service Provider
7%
No data available
 

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 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 advice do you have for others considering BigID?
I have covered information regarding data scanning, data classification, and the DSAR module, as these are the parts I have worked on, apart from developing custom connectors for a few data sources...
What is your experience regarding pricing and costs for Cube?
The cost is around $1,500 per month. The exact number is not coming to my mind, but it is approximately $1,500 or $200 per month.
What needs improvement with Cube?
To evaluate Cube's efficiency in handling large data volumes, I use metrics including F1 scores, AUC (Area Under the Curve), and RMC (Root Mean Squares), along with RSE (Root Square Errors). These ...
What is your primary use case for Cube?
In my recent projects with Cube, I was tasked with finding crashes and the reasons behind them using three datasets: people, crash, and vehicles. I had to merge them and preprocess them, clean and ...
 

Comparisons

 

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. Cube and other solutions. Updated: June 2026.
909,725 professionals have used our research since 2012.