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BigID Next vs Cube comparison

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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
9th
Average Rating
8.2
Reviews Sentiment
7.0
Number of Reviews
15
Ranking in other categories
Data Loss Prevention (DLP) (13th), Data Governance (7th), Data Privacy Management Software (2nd), Data Security Posture Management (DSPM) (4th), Data Security Platforms (DSP) (1st)
Cube
Ranking in AI Data Analysis
13th
Average Rating
8.6
Reviews Sentiment
5.8
Number of Reviews
6
Ranking in other categories
Embedded BI (10th)
 

Mindshare comparison

As of November 2026, in the AI Data Analysis category, the mindshare of BigID Next is 0.6%, down from 24.0% 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 Next0.6%
Cube0.3%
Other99.1%
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

"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."
"BigID is more advanced than Microsoft Purview when it comes to machine learning and AI development tools."
"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 integrates well with our other products."
"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's scanning feature is its most valuable component."
"Data classification is highly effective due to its automatic capabilities."
"BigID has one of the best technical support teams."
"Cube's caching mechanism impacts my database query loads and response times significantly."
"Cube completes my tasks very easily and takes less time, allowing me to deliver any project in a timely manner to our clients."
"Implementation was super smooth, and within two weeks we were up and running and the metrics were exposed in our app."
"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."
"The security provided by Cube has helped my organization by allowing us to use NATed IP on Cube to send outside the public IP while keeping the internal IP safe in our on-premise environment."
 

Cons

"BigID's user interface was problematic as it was not very user-friendly, though I believe it improved over time."
"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."
"Some users find catalog navigation challenging due to the lack of a search-by-column feature, which makes it difficult to locate specific data quickly."
"The tool currently lacks security features."
"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."
"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."
"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 did not see any return on investment from Cube."
"Cube's interface can be challenging for non-technical users, needing clearer use-case examples to ease integration into workflows."
"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."
"Cube can be improved as it lacks some features."
"There is no way to create a real template that is not exposed directly in the UI."
 

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

By visitors reading reviews
Financial Services Firm
17%
Manufacturing Company
11%
Comms Service Provider
8%
Insurance Company
7%
Construction Company
19%
Comms Service Provider
15%
Financial Services Firm
13%
Manufacturing Company
10%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business5
Large Enterprise11
By reviewers
Company SizeCount
Small Business1
Midsize Enterprise2
Large Enterprise4
 

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?
My experience with pricing, setup cost, and licensing is that we have to order Cube from Cisco, which takes some time. After that, we have to deploy it in the data center and work with the carrier ...
What needs improvement with Cube?
Cube can be improved as it lacks some features. I think we could include some more features like Ribbon and Codec so that we can use Cisco Cube as well, because Cisco Cube is only CLI, and nowadays...
What is your primary use case for Cube?
My main use case for Cube is call routing and SIP session management between our enterprise and the carriers. A specific example of how I use Cube for call routing in my day-to-day work is that we ...
 

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: September 2026.
920,769 professionals have used our research since 2012.