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BigID Next vs SAP Data Hub 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 Governance
6th
Average Rating
8.2
Reviews Sentiment
7.0
Number of Reviews
15
Ranking in other categories
Data Loss Prevention (DLP) (7th), Data Privacy Management Software (2nd), Data Security Posture Management (DSPM) (4th), AI Data Analysis (2nd)
SAP Data Hub
Ranking in Data Governance
32nd
Average Rating
7.6
Reviews Sentiment
6.8
Number of Reviews
3
Ranking in other categories
Metadata Management (15th)
 

Mindshare comparison

As of January 2026, in the Data Governance category, the mindshare of BigID Next is 5.2%, down from 7.8% compared to the previous year. The mindshare of SAP Data Hub is 1.1%, up from 1.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Governance Market Share Distribution
ProductMarket Share (%)
BigID5.2%
SAP Data Hub1.1%
Other93.7%
Data Governance
 

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.
VM
GTM Lead at Capgemini
The solution is seamless, but the database sometimes leads to confusion
We used to have multiple different kinds of databases, which internally, had different compliance levels. Retention management is very different now. If the policy is live and the claim has been completed, I couldn't archive the claim. I needed to keep a reference integrity of that claim and understand which policy paid out the claim. With this solution, the policy came in six months ago and qualified for archiving. The claim had been paid and in every environment, the claim had been closed, including the reporting system, the claims system, etc. With the payment set gateway, I can just go and archive. But, we had a hard time during this process. I rate the overall solution a seven out of ten.

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."
"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 most valuable feature of BigID is its large number of classifiers, which allow us to scan for specific data such as SSN numbers."
"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."
"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 provides a unified view across different databases and supports a wide range of data source types, including cloud and on-premises systems."
"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."
"Its connection to on-premise products is the most valuable. We mostly use the on-premise connection, which is seamless. This is what we prefer in this solution over other solutions. We are using it the most for the orchestration where the data is coming from different categories. Its other features are very much similar to what they are giving us in open source. Their push-down approach is the most advantageous, where they push most of the processing on to the same data source. This means that they have a serverless kind of thing, and they don't process the data inside a product such as Data Hub. They process the data from where the data is coming out. If it is coming from HANA, to capture the data or process it for analytics, orchestration, or management, they go to the HANA database and give it out. They don't process it on Data Hub. This push-down approach increases the processing speed a little bit because the data is processed where it is sitting. That's the best part and an advantage. I have used another product where they used to capture the data first and then they used to process it and give it. In Data Hub, it is in reverse. They process it first and give it, and then they put their own manipulations. They lead in terms of business functions. No other solution has business functions already implemented to perform business analysis. They have a lot of prebuilt business functions for machine learning and orchestration, which we can use directly to get an analysis out from the existing data. Most of the data is sitting as enterprise data there. That's a major advantage that they have."
"SAP is one of the most seamless ERPs that have integrated SAP archiving within Excel. I have not seen this with any other database."
"The most valuable feature is the S/4HANA 1909 On-Premise"
 

Cons

"There are some shortcomings when it comes to Calvirus authentication, which is not yet supported by BigID."
"BigID's user interface was problematic as it was not very user-friendly, though I believe it improved over time."
"There are some shortcomings when it comes to Calvirus authentication, which is not yet supported by BigID."
"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."
"One improvement I would suggest is addressing the intermittent failures of BigID scans, as there are times when some errors occur."
"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."
"BigID is expensive. I prefer McAfee."
"Improvement could be made in data consent management and data privacy impact assessment."
"In 2018, connecting it to outside sources, such as IoT products or IoT-enabled big data Hadoop, was a little complex. It was not smooth at the beginning. It was unstable. It took a lot of time for the initial data load. Sometimes, the connection broke, and we had to restart the process, which was a major issue, but they might have improved it now. It is very smooth with SAP HANA on-premise system, SAP Cloud Platform, and SAP Analytics Cloud. It could be because these are their own products, and they know how to integrate them. With Hadoop, they might have used open-source technologies, and that's why it was breaking at that time. They are providing less embedded integration because they want us to use their other products. For example, they don't want to go and remove SAP Analytics Cloud and put everything in Data Hub. They want us to use SAP Analytics Cloud somewhere else and not inside the Data Hub. On the integration part, it lacks real-time analytics, and it is slow. They should embed the SAP Analytics Cloud inside Data Hub or support some kind of analysis. They do provide some analysis, but it is not extensive. They are moreover open source. So, we need a lot of developers or data scientists to go in and implement Python algorithms. It would be better if they can provide their own existing algorithms and give some connections and drop-down menus to go and just configure those. It will make things really quick by increasing the embedded integrations. It will also improve the process efficiency and processing power. Its performance needs improvement. It is a little slow. It is not the best in the market, and there are other products that are much better than this. In terms of technology and performance, it is a little slow as compared to Microsoft and other data orchestration products. I haven't used other products, but I have read about those products, their settings, and the milliseconds that they do. In Azure Purview, they say that they can copy, manage, or transform the data within milliseconds. They say that they can transform 100 gigabytes of data within three to five seconds, which is something SAP cannot do. It generally takes a lot of time to process that much amount of data. However, I have never tested out Azure."
"The company has everything offshore."
"Nowadays there are some inconsistencies in data bases, however, they upgrade and release the versions to market."
 

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."
"I think that BigID's pricing is very reasonable."
"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"
"The solution is expensive."
"The Cloud is very expensive, but SAP HANA previous service is okay."
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Top Industries

By visitors reading reviews
Financial Services Firm
22%
Insurance Company
9%
Computer Software Company
8%
Manufacturing Company
6%
Manufacturing Company
19%
Financial Services Firm
13%
Government
10%
Computer Software 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...
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Overview

 

Sample Customers

Home Depot, Grant Thornton LLP, Cimpress, Fidelity Investments
Kaeser Kompressoren, HARTMANN
Find out what your peers are saying about BigID Next vs. SAP Data Hub and other solutions. Updated: December 2025.
881,114 professionals have used our research since 2012.