# Best Machine Learning Data Catalog Software - Page 3

*By [Shalaka Joshi](https://research.g2.com/insights/author/shalaka-joshi)*


Machine learning data catalogs allow companies to categorize, access, interpret, and collaborate around company data across multiple data sources, while maintaining a high level of governance and access management. Artificial intelligence is key to many features of machine learning data catalogs, enabling functionality such as machine learning recommendations, natural language querying, and dynamic data masking for enhanced security purposes.

Companies can utilize machine learning data catalogs to maintain data sets in a single location so that searching for and discovering data is simple for everyday business users and analysts alike. Users have the ability to comment on, share, and recommend data sets so colleagues can have an immediate understanding of what they are querying. Additionally, IT administrators can put into place user provisioning to ensure unauthorized employees are not accessing sensitive data.

Machine learning data catalogs are most frequently implemented by companies that have multiple data sources, are searching for one source of truth, and are attempting to scale data usage company-wide. These products are generally administered by IT departments, who can maintain organization and security, but data can be accessed by data scientists or analysts and the average business user. The data can then be transformed, modeled, and visualized either directly in the machine learning data catalog or through an integration with [business intelligence software](https://www.g2.com/categories/business-intelligence).

It should be noted that not all machine learning data catalogs provide data preparation capabilities and may require an integration with a [business intelligence platform](https://www.g2.com/categories/business-intelligence-platforms). Additionally, these tools differ from [master data management software](https://www.g2.com/categories/master-data-management-mdm) due to their enhanced governance, collaboration, and machine learning functionality.

To qualify for inclusion in the Machine Learning Data Catalog category, a product must:

- Organize and consolidate data from all company sources in a single repository
- Provide user access management for security and data governance purposes
- Allow business users to search and access the data from within the catalog
- Offer collaboration features around data sets, including categorizing, commenting, and sharing
- Give intelligent recommendations based on machine learning for quicker access to relevant data 






## G2 Grid® for Machine Learning Data Catalog Software
![G2 Grid® for Machine Learning Data Catalog Software plotting products by satisfaction and market presence](https://www.g2.com/categories/machine-learning-data-catalog/grids.png?focus%5B%5D=108031&focus%5B%5D=40849&focus%5B%5D=35126&focus%5B%5D=111980&focus%5B%5D=113991&focus%5B%5D=91379&focus%5B%5D=39669&focus%5B%5D=1224334)
Highlighted products: Atlan, AWS Glue, Alation, erwin Data Modeler, Google Cloud Data Catalog, Appen, Collibra, and decube.
Underlying data: [Grid® JSON](https://www.g2.com/categories/machine-learning-data-catalog/grids.json?focus%5B%5D=atlan&focus%5B%5D=aws-glue&focus%5B%5D=alation&focus%5B%5D=quest-software-erwin-data-modeler&focus%5B%5D=google-cloud-data-catalog&focus%5B%5D=appen&focus%5B%5D=collibra&focus%5B%5D=decube)


## How Many Machine Learning Data Catalog Software Products Does G2 Track?
**Total Products under this Category:** 90

### Category Stats (Jul 2026)
- **Average Rating**: 4.38/5 The average rating of products in this category, based on all submitted ratings
- **Top Trending Product**: erwin Data Modeler (+0.77%) - Among all products in this category, erwin Data Modeler recorded the largest rating increase compared to last month
*Last updated: July 09, 2026*


## How Does G2 Rank Machine Learning Data Catalog Software Products?

**Why You Can Trust G2's Software Rankings:**

- 30 Analysts and Data Experts
- 1,800+ Authentic Reviews
- 90+ Products
- Unbiased Rankings

G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category.


## Which Machine Learning Data Catalog Software Is Best for Your Use Case?

- **Leader:** [Atlan](https://www.g2.com/products/atlan/reviews)
- **Highest Performer:** [Collibra](https://www.g2.com/products/collibra/reviews)
- **Easiest to Use:** [AWS Glue](https://www.g2.com/products/aws-glue/reviews)
- **Top Trending:** [Atlan](https://www.g2.com/products/atlan/reviews)
- **Best Free Software:** [Alation](https://www.g2.com/products/alation/reviews)


---

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---

## What Are the Top-Rated Machine Learning Data Catalog Software Products in 2026?
### 1. [Solidatus](https://www.g2.com/products/solidatus/reviews)
Solidatus gives you trust in your data and confidence in your decisions. Get fast insights with dynamic discovery, game-changing visualization and the ability to sustainably govern your complex data landscape. By revealing hidden opportunities, threats and the impact of change, your Solidatus data blueprint will help you make the unknown known, so you can optimize your infrastructure, operate more efficiently and minimize risk.


**Average Rating:** 4.2/5.0
**Total Reviews:** 10
**How Do G2 Users Rate Solidatus?**

- **Ease of Use:** 7.9/10 (Category avg: 8.6/10)
- **Business and Data Glossary:** 7.8/10 (Category avg: 8.6/10)
- **Metadata Management :** 7.8/10 (Category avg: 8.3/10)
- **Data Lineage:** 10.0/10 (Category avg: 8.7/10)

**Who Is the Company Behind Solidatus?**

- **Seller:** [Solidatus](https://www.g2.com/sellers/solidatus)
- **Year Founded:** 2011
- **HQ Location:** London, GB
- **Twitter:** @Solidatus_com (335 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/solidatus/ (65 employees on LinkedIn®)

**Who Uses This Product?**
- **Company Size:** 60% Small-Business, 30% Mid-Market



#### What Are Recent G2 Reviews of Solidatus?

**"[Game-changer platform for data governance initiatives](https://www.g2.com/survey_responses/solidatus-review-8258738)"**

**Rating:** 4.5/5.0 stars
*— CA Prashant S.*

[Read full review](https://www.g2.com/survey_responses/solidatus-review-8258738)

---

**"[Manage Your All Data And Visualize It With Solidatus](https://www.g2.com/survey_responses/solidatus-review-8651431)"**

**Rating:** 4.5/5.0 stars
*— Kavya K.*

[Read full review](https://www.g2.com/survey_responses/solidatus-review-8651431)

---



### 2. [Spectrum Discovery](https://www.g2.com/products/spectrum-discovery/reviews)
Spectrum Discovery, a powerful predictive analytics solution, helps create a clear picture of your client and identify areas rife with opportunity - and it does it in 3D.


**Average Rating:** 4.5/5.0
**Total Reviews:** 12
**How Do G2 Users Rate Spectrum Discovery?**

- **Ease of Use:** 9.2/10 (Category avg: 8.6/10)
- **Business and Data Glossary:** 10.0/10 (Category avg: 8.6/10)
- **Metadata Management :** 7.5/10 (Category avg: 8.3/10)
- **Data Lineage:** 10.0/10 (Category avg: 8.7/10)

**Who Is the Company Behind Spectrum Discovery?**

- **Seller:** [Precisely](https://www.g2.com/sellers/precisely-0b25c016-ffa5-4f51-9d9e-fcbc9f54cc55)
- **HQ Location:** Burlington, Massachusetts
- **Twitter:** @PreciselyData (3,963 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/64863146/ (3,006 employees on LinkedIn®)

**Who Uses This Product?**
- **Company Size:** 50% Small-Business, 42% Mid-Market


#### What Are Spectrum Discovery's Pros and Cons?

**Pros:**

- Data Management (1 reviews)
- Data Quality (1 reviews)



### What Do G2 Reviewers Say About Spectrum Discovery?
*AI-generated summary from verified user reviews*

**Pros:**

- Users appreciate the **effective data management** capabilities of Spectrum Discovery for educator and student information.
- Users appreciate the **data quality** of Spectrum Discovery, effectively managing educator and student information seamlessly.


#### What Are Recent G2 Reviews of Spectrum Discovery?

**"[Data handing was never so easy.](https://www.g2.com/survey_responses/spectrum-discovery-review-7512999)"**

**Rating:** 4.0/5.0 stars
*— Amrit A.*

[Read full review](https://www.g2.com/survey_responses/spectrum-discovery-review-7512999)

---

**"[Great Data management](https://www.g2.com/survey_responses/spectrum-discovery-review-10751685)"**

**Rating:** 5.0/5.0 stars
*— Tony W.*

[Read full review](https://www.g2.com/survey_responses/spectrum-discovery-review-10751685)

---



### 3. [Global IDs Data Governance Platform](https://www.g2.com/products/global-ids-data-governance-platform/reviews)
Global IDs is an innovative software company delivering purpose-built solutions for data-centric organizations. Global IDs is committed to helping organizations of any size solve business problems with core metadata management techniques in an automated and scalable approach. Our integrated platform delivers key capabilities that enable transparency, trust and traceability of your data assets. A highly automated approach to implementing a Data Governance methodology that drives cost optimization and revenue growth by uncovering hidden insights and opportunities.


**Average Rating:** 4.2/5.0
**Total Reviews:** 3
**How Do G2 Users Rate Global IDs Data Governance Platform?**

- **Ease of Use:** 6.7/10 (Category avg: 8.6/10)

**Who Is the Company Behind Global IDs Data Governance Platform?**

- **Seller:** [Global IDs](https://www.g2.com/sellers/global-ids)
- **Year Founded:** 2001
- **HQ Location:** Princeton, US
- **Twitter:** @GlobalIDs (3,943 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/global-ids (90 employees on LinkedIn®)

**Who Uses This Product?**
- **Company Size:** 33% Enterprise, 33% Mid-Market



#### What Are Recent G2 Reviews of Global IDs Data Governance Platform?

**"[Degital store data in system](https://www.g2.com/survey_responses/global-ids-data-governance-platform-review-7825968)"**

**Rating:** 5.0/5.0 stars
*— Verified User in Financial Services*

[Read full review](https://www.g2.com/survey_responses/global-ids-data-governance-platform-review-7825968)

---

**"[Transparency, Traceability, Trust along with Analytics for your enterprise data](https://www.g2.com/survey_responses/global-ids-data-governance-platform-review-9083309)"**

**Rating:** 4.0/5.0 stars
*— Sumeet J.*

[Read full review](https://www.g2.com/survey_responses/global-ids-data-governance-platform-review-9083309)

---



### 4. [Informatica Enterprise Data Preparation](https://www.g2.com/products/informatica-enterprise-data-preparation/reviews)
Informatica Enterprise Data Preparation empowers data scientists and data analysts to rapidly discover, enrich, cleanse, and govern data pipelines for faster insights.


**Average Rating:** 4.3/5.0
**Total Reviews:** 9
**How Do G2 Users Rate Informatica Enterprise Data Preparation?**

- **Ease of Use:** 7.2/10 (Category avg: 8.6/10)
- **Business and Data Glossary:** 8.3/10 (Category avg: 8.6/10)
- **Metadata Management :** 8.3/10 (Category avg: 8.3/10)
- **Data Lineage:** 8.3/10 (Category avg: 8.7/10)

**Who Is the Company Behind Informatica Enterprise Data Preparation?**

- **Seller:** [Informatica](https://www.g2.com/sellers/informatica)
- **Year Founded:** 1993
- **HQ Location:** Redwood City, CA
- **Twitter:** @Informatica (99,643 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/3858/ (2,802 employees on LinkedIn®)
- **Ownership:** NYSE: INFA

**Who Uses This Product?**
- **Company Size:** 50% Mid-Market, 30% Small-Business



#### What Are Recent G2 Reviews of Informatica Enterprise Data Preparation?

**"[Informatica, Old but still Gold in market](https://www.g2.com/survey_responses/informatica-enterprise-data-preparation-review-7630831)"**

**Rating:** 4.0/5.0 stars
*— Verified User in Cosmetics*

[Read full review](https://www.g2.com/survey_responses/informatica-enterprise-data-preparation-review-7630831)

---

**"[Informatica Enterprise Data Preparation](https://www.g2.com/survey_responses/informatica-enterprise-data-preparation-review-7950459)"**

**Rating:** 5.0/5.0 stars
*— Himaja Y.*

[Read full review](https://www.g2.com/survey_responses/informatica-enterprise-data-preparation-review-7950459)

---


#### What Are G2 Users Discussing About Informatica Enterprise Data Preparation?

- [What is Informatica Enterprise Data Preparation used for?](https://www.g2.com/discussions/what-is-informatica-enterprise-data-preparation-used-for)

### 5. [PHEMI Health DataLab](https://www.g2.com/products/phemi-health-datalab/reviews)
The PHEMI Trustworthy Health DataLab is a unique, cloud-based, integrated big data management system that allows healthcare organizations to enhance innovation and generate value from healthcare data by simplifying the ingestion and de-identification of data with NSA/military-grade governance, privacy, and security built-in. Conventional products simply lock down data, PHEMI goes further, solving privacy and security challenges and addressing the urgent need to secure, govern, curate, and control access to privacy-sensitive personal healthcare information (PHI). This improves data sharing and collaboration inside and outside of an enterprise—without compromising the privacy of sensitive information or increasing administrative burden. Built on privacy-by-design principles, the software gives researchers, scientists, and clinicians faster access to more information while ensuring that they only see data on a need-to-know basis. Responsible data sharing and a governance framework facilitate compliance with privacy regulations. PHEMI Trustworthy Health DataLab can scale to any size of organization, is easy to deploy and manage, connects to hundreds of data sources, and integrates with popular data science and business analysis tools. For more information, visit https://www.phemi.com/ and follow us on Twitter @PHEMISystems, Linkedin, Youtube, and Facebook


**Average Rating:** 3.9/5.0
**Total Reviews:** 6
**How Do G2 Users Rate PHEMI Health DataLab?**

- **Ease of Use:** 9.6/10 (Category avg: 8.6/10)

**Who Is the Company Behind PHEMI Health DataLab?**

- **Seller:** [PHEMI Systems](https://www.g2.com/sellers/phemi-systems)
- **Year Founded:** 2013
- **HQ Location:** Vancouver, CA
- **Twitter:** @PHEMIsystems (744 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/3561810 (6 employees on LinkedIn®)

**Who Uses This Product?**
- **Company Size:** 67% Small-Business, 33% Enterprise



#### What Are Recent G2 Reviews of PHEMI Health DataLab?

**"[Trustworthy datalab](https://www.g2.com/survey_responses/phemi-health-datalab-review-7866495)"**

**Rating:** 4.5/5.0 stars
*— Verified User in Computer Software*

[Read full review](https://www.g2.com/survey_responses/phemi-health-datalab-review-7866495)

---

**"[Great!](https://www.g2.com/survey_responses/phemi-health-datalab-review-6670863)"**

**Rating:** 5.0/5.0 stars
*— Danielle H.*

[Read full review](https://www.g2.com/survey_responses/phemi-health-datalab-review-6670863)

---


#### What Are G2 Users Discussing About PHEMI Health DataLab?

- [What is PHEMI Health DataLab used for?](https://www.g2.com/discussions/what-is-phemi-health-datalab-used-for)

### 6. [Zaloni Data Governance](https://www.g2.com/products/zaloni-data-governance/reviews)
At Zaloni, we believe in the unrealized power of data. Our data management software, Arena, provides an augmented catalog that enables self-service data enrichment and consumption. We work with the world's leading companies, delivering exceptional data governance built on an extensible, machine-learning platform that both improves and safeguards enterprises’ data assets. To find out more visit www.zaloni.com.


**Average Rating:** 4.0/5.0
**Total Reviews:** 1

**Who Is the Company Behind Zaloni Data Governance?**

- **Seller:** [Zaloni](https://www.g2.com/sellers/zaloni)
- **Year Founded:** 2007
- **HQ Location:** Research Triangle Park, US
- **Twitter:** @zaloni (1,289 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/859448 (62 employees on LinkedIn®)

**Who Uses This Product?**
- **Company Size:** 100% Small-Business



#### What Are Recent G2 Reviews of Zaloni Data Governance?

**"[Great product](https://www.g2.com/survey_responses/zaloni-data-governance-review-4419904)"**

**Rating:** 4.0/5.0 stars
*— Jackie H.*

[Read full review](https://www.g2.com/survey_responses/zaloni-data-governance-review-4419904)

---


#### What Are G2 Users Discussing About Zaloni Data Governance?

- [What is Arena by Zaloni used for?](https://www.g2.com/discussions/what-is-arena-by-zaloni-used-for)

### 7. [BigID](https://www.g2.com/products/bigid/reviews)
BigID’s data intelligence platform enables organizations to know their enterprise data and take action for privacy, protection, and perspective. Customers deploy BigID to proactively discover, manage, protect, and get more value from their regulated, sensitive, and personal data across their data landscape. By applying advanced machine learning and deep data insight, BigID transforms data discovery and data intelligence to address data privacy, data security, and data governance challenges across all types of data, at petabyte-scale, on-prem and in the cloud. Get actionable data intelligence with BigID: one platform, infinite possibility.


**Average Rating:** 4.3/5.0
**Total Reviews:** 16
**How Do G2 Users Rate BigID?**

- **Ease of Use:** 7.8/10 (Category avg: 8.6/10)

**Who Is the Company Behind BigID?**

- **Seller:** [BigID](https://www.g2.com/sellers/bigid)
- **Year Founded:** 2016
- **HQ Location:** New York, New York
- **Twitter:** @bigidsecure (2,763 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/bigid/ (646 employees on LinkedIn®)

**Who Uses This Product?**
- **Top Industries:** Marketing and Advertising, Computer Software
- **Company Size:** 44% Small-Business, 38% Mid-Market


#### What Are BigID's Pros and Cons?

**Pros:**

- Cookie Management (1 reviews)

**Cons:**

- Banner Issues (1 reviews)
- Cookie Management (1 reviews)
- Data Management Issues (1 reviews)
- Expensive (1 reviews)
- Limited Functionality (1 reviews)


### What Do G2 Reviewers Say About BigID?
*AI-generated summary from verified user reviews*

**Pros:**

- Users value the **enhanced cookie management** of BigID, appreciating its robust capabilities for compliance and efficiency.

**Cons:**

- Users experience **banner issues** with BigID, facing account deletions and login problems after the takeover.
- Users express frustration over **account deletion and lack of support** after BigID took over, complicating cookie management.
- Users report **data management issues** with BigID, facing account deletions and access problems after the takeover.
- Users are frustrated with the **expensive cost** after BigID took over and ignored existing LTD agreements.
- Users are frustrated with BigID's **limited functionality** , including account access issues after the takeover of Illow.

#### What Are Recent G2 Reviews of BigID?

**"[BigID](https://www.g2.com/survey_responses/bigid-review-9867530)"**

**Rating:** 4.5/5.0 stars
*— Dolly B.*

[Read full review](https://www.g2.com/survey_responses/bigid-review-9867530)

---

**"[Say hello to Illow.io](https://www.g2.com/survey_responses/bigid-review-8233082)"**

**Rating:** 5.0/5.0 stars
*— Carla S.*

[Read full review](https://www.g2.com/survey_responses/bigid-review-8233082)

---



### 8. [eQube® Analytics Suite](https://www.g2.com/products/eqube-analytics-suite/reviews)
The eQube® Analytics Suite is an Analytics and Business Intelligence platform designed to empower end users with analytics capabilities. It processes streaming data, Big Data, and Data Lakes, providing a unified view across various data sources and core business systems (PLM, ERP, MRO, Supply Chain Asset Management, Logistics, ALM, etc.) to derive critical insights. The platform incorporates advanced analytics, including Machine Learning and Advanced Statistical techniques, along with Automated Data Discovery, to expedite data discovery, identify patterns, similarities, and enhance data quality. The eQube® Analytics Suite consists of 3 product offerings: eQube®-BI (Business Intelligence), eQube®-ADA (Augmented Data Analytics), and eQube®-DP (Data Profiler).


**Average Rating:** 4.3/5.0
**Total Reviews:** 2
**How Do G2 Users Rate eQube® Analytics Suite?**

- **Ease of Use:** 6.7/10 (Category avg: 8.6/10)
- **Business and Data Glossary:** 8.3/10 (Category avg: 8.6/10)
- **Metadata Management :** 6.7/10 (Category avg: 8.3/10)
- **Data Lineage:** 8.3/10 (Category avg: 8.7/10)

**Who Is the Company Behind eQube® Analytics Suite?**

- **Seller:** [eQ Technologic](https://www.g2.com/sellers/eq-technologic)
- **Year Founded:** 2000
- **HQ Location:** Costa Mesa, US
- **Twitter:** @1eQTechnologic (58 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/eq-technologic (916 employees on LinkedIn®)

**Who Uses This Product?**
- **Company Size:** 50% Mid-Market, 50% Small-Business



#### What Are Recent G2 Reviews of eQube® Analytics Suite?

**"[Serves the role of powerful BI](https://www.g2.com/survey_responses/eqube-analytics-suite-review-8759299)"**

**Rating:** 5.0/5.0 stars
*— Abhishek P.*

[Read full review](https://www.g2.com/survey_responses/eqube-analytics-suite-review-8759299)

---



### 9. [Precisely Data 360 Govern](https://www.g2.com/products/precisely-data-360-govern/reviews)
Now part of Precisely's Data Integrity Suite, Data360 Govern is an enterprise data governance, catalog, and metadata management solution that gives you confidence in the quality, value, and trustworthiness of your data. It automates governance and stewardship tasks to help you answer essential questions about your data’s source, use, meaning, ownership, and quality. With Data360 Govern, you can make faster decisions on data usage and management, build collaboration across your entire organization, and allow users to get the answers they need – when they need them.


**Average Rating:** 3.3/5.0
**Total Reviews:** 2
**How Do G2 Users Rate Precisely Data 360 Govern?**

- **Ease of Use:** 8.3/10 (Category avg: 8.6/10)

**Who Is the Company Behind Precisely Data 360 Govern?**

- **Seller:** [Precisely](https://www.g2.com/sellers/precisely-0b25c016-ffa5-4f51-9d9e-fcbc9f54cc55)
- **HQ Location:** Burlington, Massachusetts
- **Twitter:** @PreciselyData (3,963 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/64863146/ (3,006 employees on LinkedIn®)

**Who Uses This Product?**
- **Company Size:** 50% Mid-Market





### 10. [Accurity Software Suite](https://www.g2.com/products/accurity-software-suite/reviews)
With Accurity, the all-in-one data intelligence platform, you get company-wide understanding and complete trust in your data - speed up business-critical decision making, increase your revenue, reduce your costs, and ensure your company’s data compliance. Accurity supports multiple solutions across the entire project life cycle from the definition of data requirements to data quality checks by data stewards. Accurity offers comprehensive solutions covering data harmonization, quality and lineage including business glossary, data catalog, and reference data management. You can describe and provide a company-wide and company-specific common language of business terms and get a complete overview of all technical metadata related to your business “projects” that covers all layers of your data architecture. And break down your business terms to a structured, consistent, and more granular model that allows you to achieve data lineage. Accurity platform is available on-premises or as a SaaS. It is built in a way to help beginners easily start managing their data with the ability to scale up the range of services according to your needs, up to large-scale enterprise environments with specialist requirements. Anyone can get started with Data Catalog and Business Glossary SaaS right now, absolutely free.


**Average Rating:** 5.0/5.0
**Total Reviews:** 1
**How Do G2 Users Rate Accurity Software Suite?**

- **Ease of Use:** 10.0/10 (Category avg: 8.6/10)

**Who Is the Company Behind Accurity Software Suite?**

- **Seller:** [Simplity](https://www.g2.com/sellers/simplity)
- **Year Founded:** 2010
- **HQ Location:** Prague, CZ
- **LinkedIn® Page:** https://www.linkedin.com/company/simplity/ (25 employees on LinkedIn®)

**Who Uses This Product?**
- **Company Size:** 100% Mid-Market


#### What Are Accurity Software Suite's Pros and Cons?

**Pros:**

- Accessibility (1 reviews)
- Automation (1 reviews)
- Collaboration (1 reviews)
- Compliance Management (1 reviews)
- Data Accuracy (1 reviews)

**Cons:**

- Difficult Setup (1 reviews)
- User Adoption Difficulty (1 reviews)


### What Do G2 Reviewers Say About Accurity Software Suite?
*AI-generated summary from verified user reviews*

**Pros:**

- Users benefit from **intuitive accessibility** in Accurity, enhancing collaboration and enabling efficient metadata management across teams.
- Users value the **automation capabilities** of Accurity, enhancing data management efficiency and collaboration across teams.
- Users value the **collaboration features** of Accurity, which enhance usability and unite business and technical teams effectively.
- Users value the **flexibility and usability** of Accurity Software Suite, enhancing compliance management and supporting data governance effectively.
- Users value the **data accuracy** of Accurity Software Suite, enhancing trust and compliance in data management processes.

**Cons:**

- Users find the **difficult setup** process requires careful planning, though support helps streamline the initial configuration.
- Users face **user adoption difficulty** due to complex initial configuration, despite helpful support from Accurity’s team.

#### What Are Recent G2 Reviews of Accurity Software Suite?

**"[ETCB roadtrip to Data Excellence — Our Journey with Accurity](https://www.g2.com/survey_responses/accurity-software-suite-review-11771426)"**

**Rating:** 5.0/5.0 stars
*— Verified User in Financial Services*

[Read full review](https://www.g2.com/survey_responses/accurity-software-suite-review-11771426)

---



### 11. [Binary Demand](https://www.g2.com/products/binary-demand/reviews)
Binary Demand provides custom solutions to prevent wastage of your data by making up for its natural degradation. Its customised data solutions include standardisation, de-duping, cleansing, verification etc. This helps in creating a list of probable customers based of criterias such as geography, company size, job titles, industry, etc.



**Who Is the Company Behind Binary Demand?**

- **Seller:** [Binary Demand](https://www.g2.com/sellers/binary-demand)
- **HQ Location:** Dubai, AE
- **Twitter:** @BinaryDemand (41 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/binary-demand/ (57 employees on LinkedIn®)






### 12. [circlewise](https://www.g2.com/products/datazone-circlewise/reviews)
Chat with your Enterprise Knowlange/Databases/Documents From HR policies to sales data, provide your team instance access to consistent company information. Just ask our AI finds what you need, explains it clearly, and helps everyone stay aligned.



**Who Is the Company Behind circlewise?**

- **Seller:** [Datazone](https://www.g2.com/sellers/datazone)
- **Year Founded:** 2021
- **HQ Location:** London, GB
- **LinkedIn® Page:** https://www.linkedin.com/company/datazoneco (10 employees on LinkedIn®)






### 13. [Data Collection](https://www.g2.com/products/sapien-data-collection/reviews)
Sapien's Data Collection Services provide high-quality, structured datasets tailored to fuel AI and machine learning projects. Addressing the challenges of sourcing and preparing data for AI training, Sapien utilizes real-time data collection software and automated techniques to gather relevant data for both supervised and unsupervised training models.



**Who Is the Company Behind Data Collection?**

- **Seller:** [Sapien](https://www.g2.com/sellers/sapien)
- **HQ Location:** New York City, US
- **LinkedIn® Page:** https://www.linkedin.com/company/getsapien (8 employees on LinkedIn®)






### 14. [Dataland](https://www.g2.com/products/dataland/reviews)
Dataland is a unified data workspace specifically intended for frontline, operational users – like supply chain, logistics, operations, and customer support. Dataland centralizes all data into one place, so business users can ask AI, search, analyze, and take actions against data in one place. Business teams use Dataland to get a 360-degree view of all the data they need for their day-to-day workflows. Here’s how Dataland works: Real-time Data Sync: Dataland has built-in connectors to databases, data warehouses, SaaS APIs, and custom sources. Unlike traditional data integration tools, Dataland live-updates in real-time from its source systems, which is necessary for operational use cases. Universal Search: Dataland indexes the data in real-time and enables operational users to quickly search for up-to-date information across all attributes in all data sources. This enables users to quickly get the full context they need without having to know where to look or to use inflexible tools that can only look up information in specific pre-defined ways. 360-Degree Views: Dataland combines information from fragmented data sources into unified views that provide the complete 360-degree picture of every business entity. This eliminates the need for users to jump across multiple tools. AI Data Assistant: Users can ask AI to analyze data for them, without knowing SQL. Automations and Alerts: Custom business actions can be made available to operational users (e.g. triggering a Zapier automation, hitting an internal API, executing SQL updates, etc.) so they can complete end-to-end workflows in Dataland without ever having to leave the platform. Users can also define alerts through a simple interface without knowing code.



**Who Is the Company Behind Dataland?**

- **Seller:** [Dataland](https://www.g2.com/sellers/dataland)
- **Year Founded:** 2021
- **HQ Location:** N/A
- **LinkedIn® Page:** https://www.linkedin.com/company/datalandio (8 employees on LinkedIn®)






### 15. [DataMarket](https://www.g2.com/products/datamarket/reviews)
DataMarket is a data marketplace and data discovery solution within the RightData platform. It provides a centralized place where datasets and data products are organized, documented, and made available for use across teams, with defined access controls. Users can search and browse available data, view metadata, and understand what the data represents before requesting access. This helps them assess whether a dataset is relevant for their use case without relying on back-and-forth with data teams. Access requests follow approval workflows based on organizational policies, and records are maintained for audit and tracking purposes. DataMarket brings together data cataloging, data discovery, and access management in a single interface. Datasets are structured and categorized so they can be located and understood more easily. By standardizing how data is published and accessed, it reduces inconsistencies in how different teams interact with data. The platform supports data democratization by allowing more users across the organization to find and use data independently, while still operating within governance and security boundaries. This reduces dependency on data engineers for routine access and helps teams move faster on reporting and analysis tasks. DataMarket is used by business users, analysts, and data scientists who need access to data for reporting, analysis, and decision-making. It is also used by data engineers and data owners to publish, organize, and manage datasets so they can be accessed by others in a controlled manner.



**Who Is the Company Behind DataMarket?**

- **Seller:** [RightData](https://www.g2.com/sellers/rightdata)
- **Year Founded:** 2016
- **HQ Location:** Atlanta, US
- **Twitter:** @GetRightData (121 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/getrightdata (87 employees on LinkedIn®)






### 16. [Data World](https://www.g2.com/products/data-world-data-world/reviews)
data.world is the most-adopted data catalog and governance platform on the market. Built on a unique knowledge graph foundation, data.world seamlessly integrates with your existing systems. We set the standard for swift, people-centric governance. We don't just manage data; we unlock its potential, paving the way for responsible AI adoption and data-driven decision-making at scale. data.world is a Certified B Corporation and public benefit corporation and home to the world’s largest collaborative open data community with more than two million members, including ninety percent of the Fortune 500.



**Who Is the Company Behind Data World?**

- **Seller:** [data.world](https://www.g2.com/sellers/data-world)
- **Year Founded:** 2016
- **HQ Location:** Austin, Texas
- **Twitter:** @datadotworld (5,498 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/data.world/ (100 employees on LinkedIn®)






### 17. [Deasie](https://www.g2.com/products/deasie/reviews)
Deasie provides an automated labeling workflow to rapidly label, catalog and filter unstructured data better than any human.



**Who Is the Company Behind Deasie?**

- **Seller:** [Deasie](https://www.g2.com/sellers/deasie)
- **HQ Location:** N/A
- **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®)






### 18. [Dsense](https://www.g2.com/products/dsense/reviews)
Empowers non-tech users to converse with data using any LLMs, whether deployed in VPC or through external APIs, ensuring rapid retrieval with exceptional speed, accuracy, and precision. Fully managed and for tified with robust security guardrails to expose data on a need-to-know basis, thereby complying with info-sec protocols. It enables organizations to accelerate analytics and build AI apps securely with their enterprise data while safeguarding their invaluable assets.



**Who Is the Company Behind Dsense?**

- **Seller:** [Dview](https://www.g2.com/sellers/dview)
- **Year Founded:** 2021
- **HQ Location:** Bengaluru East, IN
- **Twitter:** @DviewTech (19 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/dview-io/ (16 employees on LinkedIn®)






### 19. [Enterprise Data Catalog Advanced Scanners](https://www.g2.com/products/enterprise-data-catalog-advanced-scanners/reviews)
With Enterprise Data Catalog Advanced Scanners, you can automatically extract the most granular metadata and track data dependencies across data sources.



**Who Is the Company Behind Enterprise Data Catalog Advanced Scanners?**

- **Seller:** [Informatica](https://www.g2.com/sellers/informatica)
- **Year Founded:** 1993
- **HQ Location:** Redwood City, CA
- **Twitter:** @Informatica (99,643 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/3858/ (2,802 employees on LinkedIn®)
- **Ownership:** NYSE: INFA






### 20. [Entity Catalog](https://www.g2.com/products/entity-catalog/reviews)
Entity Catalog is a comprehensive database developed by Qloo, encompassing over 575 million entities across various domains such as entertainment, dining, travel, and consumer products. This extensive repository includes detailed information on movies, music, books, restaurants, hotels, and more, enabling businesses to gain deep insights into consumer preferences and trends.



**Who Is the Company Behind Entity Catalog?**

- **Seller:** [Qloo](https://www.g2.com/sellers/qloo)
- **Year Founded:** 2012
- **HQ Location:** New York, US
- **LinkedIn® Page:** https://www.linkedin.com/company/qloo/ (56 employees on LinkedIn®)






### 21. [Erisna](https://www.g2.com/products/erisna/reviews)
Erisna is an enterprise data catalog and discovery platform that enables data analysts, data engineers, data scientists, and data managers to get the most out of their data. Connect Erisna to various data sources such as Amazon Redshift, Google BigQuery, Microsoft Azure Synapse, Snowflake, PostgreSQL, and SQL Server to build your data dictionary, auto-detect sensitive data, automate data discovery, gather data pipeline requirements and improve data governance, all in one place. Our platform helps organizations increase productivity, reduce regulatory risks, make better decisions, and reduce costs significantly. Create your Erisna account and request a demo today!



**Who Is the Company Behind Erisna?**

- **Seller:** [Erisna](https://www.g2.com/sellers/erisna)
- **Year Founded:** 2021
- **HQ Location:** London, GB
- **LinkedIn® Page:** https://www.linkedin.com/company/erisna (5 employees on LinkedIn®)






### 22. [Forcepoint Data Classification](https://www.g2.com/products/forcepoint-data-classification/reviews)
Forcepoint Data Classification redefines data classification with AI-driven precision and automation, eliminating manual errors and enhancing DLP efficacy. We use an advanced AI Mesh to deliver highly accurate data classification. Its networked AI architecture leverages a Small Language Model and advanced AI components to improve efficiency and reduce false positives. Through continuous learning and improvement. it delivers confident recommendations, enhancing policy enforcement and compliance for organizations.



**Who Is the Company Behind Forcepoint Data Classification?**

- **Seller:** [Forcepoint](https://www.g2.com/sellers/forcepoint)
- **Year Founded:** 1994
- **HQ Location:** Austin, TX
- **Twitter:** @Forcepointsec (65,335 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/forcepoint/ (1,652 employees on LinkedIn®)






### 23. [Foursquare Spatial H3 Hub](https://www.g2.com/products/foursquare-spatial-h3-hub/reviews)
FSQ Spatial H3 Hub eliminates traditional barriers to geospatial data adoption in traditional ML models by providing data scientists with analysis-ready datasets that do not require specialized geospatial tools or expertise. Datasets containing information in raster and vector formats are converted to tabular form and indexed to H3 cells. This allows data scientists to easily enrich their own datasets, containing attributes like lat/long coordinates, city names, or zip codes, by joining on a common H3 index. Built on DataHub‘s enterprise metadata management system, the platform ensures data lineage tracking, versioning, and governance capabilities that enterprise data teams require. This foundation enables the first offering in the FSQ Spatial H3 Hub: an Iceberg Catalog that offers 20+ open datasets pre-indexed to H3 cells at resolution 8, made available in a free preview. Data scientists can access this catalog from their framework of choice (Spark, Python, DuckDB) and augment their ML models with a rich array of spatial features.



**Who Is the Company Behind Foursquare Spatial H3 Hub?**

- **Seller:** [Foursquare](https://www.g2.com/sellers/foursquare)
- **Year Founded:** 2009
- **HQ Location:** New York, NY
- **Twitter:** @foursquare (22,906 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/foursquare (523 employees on LinkedIn®)






### 24. [Herus](https://www.g2.com/products/herus/reviews)
Herus is a modern data catalog designed to help teams organize, discover, understand and govern their data faster. The platform integrates with your data stack to import metadata, semantic definitions, lineage, usage insights and data processing code. It can also push field descriptions back to databases as native SQL comments. Users can explore data through an intuitive interface, advanced SQL-like filters or AI-powered search. They can navigate table and field lineage end-to-end, trace data back to its sources, and understand how it flows into downstream dashboards and analytics. Herus uses AI to reduce manual documentation work by suggesting business definitions, inferring lineage and enabling natural language interactions within the catalog. All AI-generated suggestions remain fully reviewable before validation, keeping teams in control. Beyond cataloging existing assets, Herus includes a collaborative data board where analysts and engineers can visually design transformations. Based on these designs, AI can automatically generate complete written specifications, reducing duplicated work across documentation tools and wikis.



**Who Is the Company Behind Herus?**

- **Seller:** [Herus](https://www.g2.com/sellers/herus)
- **HQ Location:** Paris, FR
- **LinkedIn® Page:** https://www.linkedin.com/company/106522400 (1 employees on LinkedIn®)






### 25. [IQ Metadata Manager](https://www.g2.com/products/iq-metadata-manager/reviews)
IQ Metadata Manager helps enterprises unlock their data by uniting metadata domain categories in a single place to create a real-time view of all of your information. Create a standardized glossary & data catalog. Improve data visibility for data preparation and analysis.



**Who Is the Company Behind IQ Metadata Manager?**

- **Seller:** [InQuisient](https://www.g2.com/sellers/inquisient)
- **Year Founded:** 2004
- **HQ Location:** Reston, US
- **LinkedIn® Page:** https://www.linkedin.com/company/inquisient (8 employees on LinkedIn®)







## What Is Machine Learning Data Catalog Software?

[IT Infrastructure Software](https://www.g2.com/categories/it-infrastructure)

## What Software Categories Are Similar to Machine Learning Data Catalog Software?

- [Data Quality Tools](https://www.g2.com/categories/data-quality)
- [Data Governance Tools](https://www.g2.com/categories/data-governance-tools)
- [Sensitive Data Discovery Software](https://www.g2.com/categories/sensitive-data-discovery)
- [Data Fabric Software](https://www.g2.com/categories/data-fabric)
- [DataOps Platforms](https://www.g2.com/categories/dataops-platforms)
- [Active Metadata Management Software](https://www.g2.com/categories/active-metadata-management)
- [Data Observability Software](https://www.g2.com/categories/data-observability)


---

## How Do You Choose the Right Machine Learning Data Catalog Software?

### What You Should Know About Healthcare Claims Management Software

### What is a Machine Learning Data Catalog?

Machine learning data catalog (MLDC) is an automated data catalog that carries out tasks like crawling metadata, cataloging, and classifying personally identifiable information (PII) data. Machine learning data catalogs organize the dataset inventory using metadata.

Data catalogs help companies know where the data is stored, thus reducing the time taken to identify data and making it easily accessible for analytics. They are inventories of assets like tables, schema, files, and charts in organizations, aiding in solving a company's data discovery, quality, and governance challenges.

### What does MLDC Stand For?

MLDC is an acronym for Machine Learning Data Catalog. 

### What are the Common Features of Machine Learning Data Catalogs?

Machine learning data catalogs simplify the manual functions of a data catalog. A data catalog is an essential part of the data management strategy of any organization. Some of the features of machine learning data catalogs are:

**Data ingestion and discovery:** Machine learning data catalogs must have prebuilt adapters to connect to different company systems like applications, databases, files, and external APIs. These adapters help in discovering metadata from systems. Metadata can be table names, attribute names, and constraints. The feature helps build native connectivity like integrations for data sources, business intelligence (BI) solutions, and data science tools.

**Business glossary:** Although a good amount of data is stored in the repository, it is also essential for the users to understand what the stored data means. The glossary feature links this data to business terms giving it more meaning. 

**Automated data labeling:** Data labeling is a prerequisite for machine learning algorithms. Automated data labeling is more accurate than manual since it eliminates human errors. Data labeling usually involves annotators identifying objects in images to build quality artificial intelligence (AI) training data. Automated labeling eliminates the challenges posed by the tedious annotation cycles.

**Data lineage:** Data lineage is the process that helps the users know who, why, when, and where changes are made to the data. It is a part of metadata management. MLDCs automate the data lineage process. Data lineage helps determine when new or changed data require retraining machine learning models. MLDCs usually parse through query logs into data lakes and other data sources automatically to create a data lineage map.

**Data quality monitoring and anomaly detection:** Data quality monitoring helps users understand if the data came from a trusted source. The machine learning data catalog also has a feature to identify sudden changes in data using machine learning algorithms. The users are immediately alerted to any changes or anomalies that are detected. 

**Semantic search for data sets:** Machine learning data catalogs provide users with visual and intuitive searches like search engines. Almost every user in any organization is a data user, but not everyone can use SQL queries to use data. The semantic search feature makes it easier for all users to discover data sets.

**Compliance capabilities:** This feature ensures that sensitive data is not exposed and that the user can trust the data. It further helps keep data governance policies in place and strengthen data management in the organization. Data stewards can identify low-quality data and restrict access to sensitive data, thus helping comply with regulations such as the General Data Protection Regulation (GDPR).

**Data profiling:** Data profiling helps check the data from the data source and collects information about it. This process helps in knowing data quality issues much better, thus making the data management process more efficient.

### What are the Benefits of Machine Learning Data Catalogs?

A machine learning data catalog provides several benefits to different types of users in the organization. These include:

**Ease in data curation:** Data curation is a process of collecting, organizing, labeling, and cleaning data. Machine learning data catalogs validate metadata and organize insights into correct repositories using machine learning algorithms.

**Ease of search:** Because of semantic search, it becomes easier for non-technical users to search and discover data for use since they do not have to use SQL queries every time to access data.

**Ease in data collaboration:** Machine learning data catalogs help the users collaborate, use, and share data sets because machine learning data catalogs ease finding and storing siloed data.

### Who Uses Machine Learning Data Catalogs?

Machine learning data catalogs centralize metadata for various data assets. By organizing the metadata, MLDCs help organizations to govern data access.

**Data analysts:** Data analysts use MLDC to discover, classify, and manipulate data for their analytics processes. They can also discover AI or machine learning models, understand how they work, and import them into their BI tools. Data catalogs help data analysts make companies into self-service organizations. Self-service analytics is important for any organization that wants to be driven by insights. Machine learning data catalogs help the users know the means to find, understand, and trust data.

**Marketers:** Marketing teams use the machine learning data catalog more commercially. They obtain insights for making better decisions using data catalogs.

**Data scientists:** Data scientists usually publish their models for reuse. Data scientists always look for one platform that centralizes data for different projects. 

### Challenges with Machine Learning Data Catalogs

Although machine learning data catalogs help solve major challenges in traditional data catalogs like data discovery and data lineage, MLDCs also come with challenges.  

**Scalability:** It is tricky for all MLDCs to support a huge metadata volume. Sometimes, the data catalogs break down due to performance issues when overloaded with enormous amounts of metadata. Initially, data used to be stored in the company's mainframe data center. However, due to today's big data, machine learning data catalogs must keep track of data in both cloud and data lakes.

**Fragmentation in evaluating a product:** If a data catalog is too bulky, it causes fragmentation in the user's journey of evaluating a product. Too much data makes users use too many tools, thus breaking a seamless experience into fragments.

### How to Buy Machine Learning Data Catalogs

#### Requirements Gathering (RFI/RFP) for Machine Learning Data Catalogs

The machine learning data catalog offers many features to help users identify usable data. A buyer can choose the right MLDC software depending on the organization's needs. RFP/RFIs help the organization look for pricing, product features, and guidelines.

#### Compare Machine Learning Data Catalog Products

**Create a long list**

The first step is to look for all the possible players in the space. This gives an advantage of evaluating the vendors for the price, product features, and customer service. 

**Create a short list**

After evaluating the potential vendors, the company can narrow the list to those who check all their boxes.

**Conduct demos**

Demos help in understanding the product as a whole. A team of IT professionals and data scientists should join these demos to understand the product's functionality, whereas the marketing team can join in to analyze the business use of the software in the projects.

#### Selection of Machine Learning Data Catalogs

**Choose a selection team**

A team of marketing professionals with data scientists and IT professionals can communicate any queries related to the MLDC product with the vendors. A data scientist would be more interested in knowing the technical features of the software. A marketing manager would be curious to know how the marketing team could use MLDC for any project. An IT professional would want to understand the software installation procedure.

**Negotiation**

Once the vendor quotes the price, the negotiations begin. The price is fixed based on the cost of other similar products available in the market and the extent to which the product can solve the challenges.

**Final decision**

The final decision is based on agreements between the vendor and the buyer.