# Best Machine Learning Data Catalog Software - Page 4 *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 ## How Many Machine Learning Data Catalog Software Products Does G2 Track? **Total Products under this Category:** 90 ### Category Stats (May 2026) - **Average Rating**: 4.38/5 (↑0.01 vs Apr 2026) - **New Reviews This Quarter**: 10 - **Buyer Segments**: Small-Business 44% │ Enterprise 38% │ Mid-Market 19% - **Top Trending Product**: Cloudera Data Platform (+0.155) *Last updated: May 18, 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) --- **Sponsored** ### QuerySurge QuerySurge is an enterprise-grade data quality platform that leverages AI to continuously automate data validation across your entire ecosystem ‐ from data warehouses and big data lakes to BI reports and enterprise applications. With AI-powered test creation, scalable architecture, and the leading DevOps for Data CI/CD integration, QuerySurge ensures data integrity at every stage of the pipeline. Automated Data Validation Use Cases: QuerySurge provides a smart, AI-driven, data validation & ETL testing solution for your automated testing needs. - Data Warehouse / ETL Testing - DevOps for Data / Continuous Testing - Data Migration Testing - Business Intelligence (BI) Report Testing - Big Data Testing - Enterprise Application Data Testing What QuerySurge Provides: - Automation of your manual data validation and testing process - Ease-of-use, low-code/no-code features - Generative AI capabilities for test creation - Testing across 200+ data platforms - Integration into your CI/CD DataOps pipeline - Acceleration of your data analysis - Ensurance of regulatory compliance Key Features: - Data Connection Wizard provides an easy way to link to your data stores - Visual Query Wizard builds table-to-table and column-to-column tests without writing SQL - Generative AI module automatically creates transformation tests in bulk - DevOps for Data provides a RESTful API with 110+ calls and Swagger documentation and integrates into CI/CD pipelines - Create Custom Tests and modularize functions with snippets, set thresholds, stage data, check data types & duplicate rows, full text search, and asset tagging - Schedule tests to run immediately, at a predetermined date & time, or after any event from a build/release, CI/CD, DevOps, or test management solution - Multi-project support in a single instance, new Global Admin user, assign users and agents, import and export projects, and user activity log reports - Webhooks provide real-time integrations with DevOps, CI/CD, test management, and alerting tools - Ready-for-Analytics provides seamless integration with QuerySurge and your BI tool or open-source Metabase to create custom reports and dashboards and gain deeper, real-time insights into your data validation and ETL testing workflows - Data Analytics Dashboards and Data Intelligence Reports track, analyze, and communicate data quality [Visit website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list&secure%5Bcategory_id%5D=1383&secure%5Bdisplayable_resource_id%5D=108&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=neighbor_category&secure%5Bplacement_resource_ids%5D%5B%5D=2686&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=54942&secure%5Bresource_id%5D=1383&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fmachine-learning-data-catalog%3Fpage%3D2%26source%3Dsearch&secure%5Btoken%5D=fa4dec6c48e283c6214ef8a5c089dfc031441d2c710d350c5884b02b13c4e126&secure%5Burl%5D=https%3A%2F%2Fwww.querysurge.com%2Fget-started%2Fprivate-demo%3Futm_source%3DG2%26utm_medium%3Dcpc%26utm_campaign%3DG2-reviews&secure%5Burl_type%5D=book_demo) --- ## What Are the Top-Rated Machine Learning Data Catalog Software Products in 2026? ### 1. [Key Ward](https://www.g2.com/products/key-ward/reviews) Key Ward is a deep-tech company specializing in engineering data management and AI adoption, particularly within the automotive and aerospace industries. Their flagship products, Key Ward HUB and Key Ward FLOW, leverage artificial intelligence to streamline and enhance engineering design processes. Key Ward HUB automates the extraction and conversion of data from various CAE/CAD file formats into AI-ready datasets, eliminating the need for manual data preparation. Key Ward FLOW utilizes pre-trained AI models to predict engineering design evaluations, such as computational fluid dynamics (CFD) assessments, enabling engineers to explore more design variations in less time. This approach not only accelerates the design evaluation cycle but also delivers more accurate results compared to traditional simulation methods. By integrating these tools, Key Ward empowers engineering teams to optimize designs efficiently, reduce the risk of late-stage failures, and improve overall product performance without requiring prior data science expertise. **Who Is the Company Behind Key Ward?** - **Seller:** [Key Ward](https://www.g2.com/sellers/key-ward) - **Year Founded:** 2021 - **HQ Location:** Berlin, DE - **LinkedIn® Page:** https://www.linkedin.com/company/keyward (13 employees on LinkedIn®) ### 2. [Metaphor Data](https://www.g2.com/products/metaphor-data/reviews) Metaphor is a modern, comprehensive data catalog that excels in making data management accessible and efficient for both data producers and consumers. Its strengths include robust data governance and lineage, ensuring data integrity and traceability, crucial for maintaining high-quality data management. Metaphor also facilitates effective collaboration, allowing team members across various departments to easily access, understand, and utilize data. Popular use cases for Metaphor include data documentation, where it aids in creating a clear and comprehensive understanding of data assets. Its search and discovery features enable users to quickly find the data they need, enhancing productivity and decision-making efficiency. The lineage feature of Metaphor is particularly valuable, as it provides insights into the data’s origin and transformation, crucial for data analysis and compliance. By balancing technical capabilities with user-friendly features, Metaphor is an ideal choice for organizations aiming to leverage their data more effectively, fostering a data-informed culture across the company. **Who Is the Company Behind Metaphor Data?** - **Seller:** [Metaphor Data](https://www.g2.com/sellers/metaphor-data) - **Year Founded:** 2020 - **HQ Location:** Menlo Park, California - **Twitter:** @MetaphorData (393 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/metaphor-data (23 employees on LinkedIn®) ### 3. [Pentaho Data Catalog](https://www.g2.com/products/pentaho-data-catalog/reviews) Pentaho Data Catalog changes how your business discovers and manages data, ensuring seamless scalability across all data types and volumes. Simplify data observability with a unified business glossary and advanced metadata management to enhance lineage, trust, and quality. Embrace a smarter way to handle your data, making it easier to search, validate, and derive insights, all tailored to your unique business needs. **Who Is the Company Behind Pentaho Data Catalog?** - **Seller:** [Pentaho](https://www.g2.com/sellers/pentaho-d1c9c8d5-c72c-42b5-967d-4a0985833684) - **Year Founded:** 2004 - **HQ Location:** Santa Clara, CA - **LinkedIn® Page:** https://www.linkedin.com/company/pentaho/ (151 employees on LinkedIn®) ### 4. [Privacera Data Security Platform](https://www.g2.com/products/privacera-data-security-platform/reviews) Privacera, based in Fremont, CA, was founded in 2016 by the creators of Apache Ranger™ and Apache Atlas. Delivering trusted and timely access to data consumers, Privacera provides data privacy, security, and governance through its SaaS-based unified data security platform. Privacera’s latest innovation, Privacera AI Governance (PAIG), is the industry’s first AI data security governance solution. Privacera serves Fortune 500 clients across finance, insurance, life sciences, retail, media, consumer, and government entities. The company achieved AWS Data and Analytics Competency Status, and partners with and supports leading data sources, including AWS, Snowflake, Databricks, Azure and Google. Visit www.privacera.com for more information. **Average Rating:** 4.3/5.0 **Total Reviews:** 4 **How Do G2 Users Rate Privacera Data Security Platform?** - **Ease of Use:** 7.5/10 (Category avg: 8.6/10) **Who Is the Company Behind Privacera Data Security Platform?** - **Seller:** [Privacera Inc](https://www.g2.com/sellers/privacera-inc) - **Company Website:** https://privacera.com/ - **Year Founded:** 2016 - **HQ Location:** Newark, California, United States - **Twitter:** @privacera (467 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/privacera/ (110 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 75% Enterprise, 25% Small-Business #### What Are Privacera Data Security Platform's Pros and Cons? **Pros:** - Data Storage (2 reviews) - Integrations (2 reviews) - Access Control (1 reviews) - Access Management (1 reviews) - Backup Ease (1 reviews) **Cons:** - Complex Coding (1 reviews) - Complex Implementation (1 reviews) - Complexity (1 reviews) - Complexity Management (1 reviews) - Complex Setup (1 reviews) ### 5. [Reltio Connected Data Platform](https://www.g2.com/products/reltio-connected-data-platform/reviews) Reltio offers the industry's first cloud-native, multi-domain MDM SaaS solution. By offering next-generation master data management, the Reltio Connected Data Platform leverages a cloud-native, multi-tenant architecture and our ecosystem to enable speed, agility and flexibility at scale, and facilitate successful digital transformation projects. Companies across industries rely on Reltio to deliver mission-critical, secure, unified, reliable and real-time data at scale to create connected experiences for their customers, partners, prospects, users and employees. If you are a Data Innovator and want to position your company to win in the experience economy, let's talk! **Average Rating:** 3.3/5.0 **Total Reviews:** 6 **How Do G2 Users Rate Reltio Connected Data Platform?** - **Ease of Use:** 7.0/10 (Category avg: 8.6/10) **Who Is the Company Behind Reltio Connected Data Platform?** - **Seller:** [Reltio](https://www.g2.com/sellers/reltio) - **Year Founded:** 2011 - **HQ Location:** Redwood City, California, United States - **Twitter:** @Reltio (1,489 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/reltio-inc (582 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 50% Enterprise, 33% Mid-Market ### 6. [Scalifi AI - Unified Model Catalogue](https://www.g2.com/products/scalifi-ai-unified-model-catalogue/reviews) Our model catalog is a game-changing experiment tracker for managing machine learning operations. It’s a centralized hub where you can discover, explore, streamline, and manage your individual or organizational ML models at a single place. By keeping track of key model metadata and performance metrics in machine learning, this tool significantly improves knowledge sharing between teams. It enables better informed decision-making, and results in boosting productivity. For teams heavily reliant on machine learning or leading NLP projects this catalog is a must-have for driving efficiency, collaboration, and responsible AI practices. **Who Is the Company Behind Scalifi AI - Unified Model Catalogue?** - **Seller:** [Scalifi AI](https://www.g2.com/sellers/scalifi-ai) - **HQ Location:** Gurugram, IN - **LinkedIn® Page:** https://www.linkedin.com/company/scalifiai (4 employees on LinkedIn®) ### 7. [Sequentum Cloud](https://www.g2.com/products/sequentum-cloud/reviews) Sequentum Cloud - the ultimate low code web scraper for trusted data. Sequentum Cloud is a fully cloud-based SaaS web scraping solution to help any user access high-quality, trusted, custom web data, when and how they want, on a pay-as-you-go basis. This new SaaS solution leverages Sequentum's industry leading expertise in web data extraction and process automation and requires no downloads, dedicated servers or software. Our Cloud-based SaaS solution eliminates the need for software installation, firewall configuration and browser extensions. The result is a fully integrated, low-code, point-and-click environment for ingesting, transforming, AI-enriching, structuring and delivering data designed for use across small businesses and large corporations. **Average Rating:** 5.0/5.0 **Total Reviews:** 2 **How Do G2 Users Rate Sequentum Cloud?** - **Ease of Use:** 10.0/10 (Category avg: 8.6/10) **Who Is the Company Behind Sequentum Cloud?** - **Seller:** [Sequentum Inc.](https://www.g2.com/sellers/sequentum-inc) - **Year Founded:** 2008 - **HQ Location:** New York, US - **LinkedIn® Page:** https://www.linkedin.com/company/sequentum/ (108 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Small-Business #### What Are Sequentum Cloud's Pros and Cons? **Pros:** - Customization (2 reviews) - Ease of Use (2 reviews) - Easy Setup (2 reviews) - Features (2 reviews) - Helpful (1 reviews) ### 8. [Strategy Mosaic](https://www.g2.com/products/strategy-mosaic/reviews) Strategy Mosaic, from Strategy (formerly MicroStrategy), is an enterprise-grade universal semantic layer solution designed to enhance the capabilities of AI and Business Intelligence (BI) within organizations. It addresses critical challenges such as data fragmentation and inconsistent metrics, which lead to untrusted AI answers, compliance risks, and runaway cloud costs. The universal semantic layer that Mosaic provides serves as a centralized repository for business definitions, hierarchies, and security rules, ensuring that all users access consistent metrics and KPIs regardless of the tools they employ. This single source of truth is actively monitored by our integrated Sentinel layer, which moves you from reactive audits to proactive, real-time governance. Sentinel provides immediate intelligence on potential data breaches, compliance risks, and cost-saving opportunities, helping you optimize cloud spend and prevent violations before they happen. Additionally, Mosaic empowers organizations to build an auditable foundation for AI. By providing a layer of rich business context and consistent, human-readable definitions, Mosaic gives AI models the deep understanding required to provide more accurate and verifiable answers. This accelerates time to insight, allows you to end vendor lock-in, and dramatically reduces the total cost of ownership (TCO) by eliminating costly data rework and optimizing data management processes. In summary, Strategy Mosaic stands out by addressing the fundamental issues of data fragmentation and governance. Its robust connectivity, centralized semantic layer, and focus on delivering trusted data make it an invaluable tool for organizations aiming to enhance their analytics capabilities and leverage AI effectively. **Average Rating:** 4.5/5.0 **Total Reviews:** 15 **How Do G2 Users Rate Strategy Mosaic?** - **Ease of Use:** 8.6/10 (Category avg: 8.6/10) **Who Is the Company Behind Strategy Mosaic?** - **Seller:** [Strategy (formerly MicroStrategy)](https://www.g2.com/sellers/strategy-formerly-microstrategy) - **Company Website:** https://www.strategy.com/software - **Year Founded:** 1989 - **HQ Location:** Tysons Corner, VA - **Twitter:** @MicroStrategy (303,022 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/strategy/ (3,444 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 53% Enterprise, 40% Mid-Market #### What Are Strategy Mosaic's Pros and Cons? **Pros:** - Ease of Use (2 reviews) - Features (2 reviews) - Reporting (2 reviews) - Data Analysis (1 reviews) - Data Modeling (1 reviews) **Cons:** - Bugs (2 reviews) - Bug Issues (1 reviews) - Debugging Issues (1 reviews) - Expensive (1 reviews) - Learning Curve (1 reviews) ### 9. [Structurify](https://www.g2.com/products/structurify/reviews) Structurify creates value from your untapped unstructured data. **Who Is the Company Behind Structurify?** - **Seller:** [DscvryAI](https://www.g2.com/sellers/dscvryai) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 10. [Teleskope](https://www.g2.com/products/teleskope-teleskope/reviews) Teleskope is a data security platform (DSPM/DLP) that helps security teams discover, classify, and automatically remediate sensitive data exposure across cloud, SaaS, collaboration, database, and AI environments — without manual triage or external remediation tools or ticketing. Designed primarily for CISOs, security operations, and GRC teams at mid-market and enterprise organizations, Teleskope addresses a gap that exists across the data security category: most platforms identify where sensitive data lives but provide no native mechanism to act on what they find. Teleskope combines deep classification with a built-in remediation engine, allowing organizations to move from finding risk to closing it in the same platform. At the core of the platform is a proprietary Data Reasoning Layer — an intelligence architecture that understands business context rather than matching against predefined patterns. This allows Teleskope to classify sensitive documents based on what they are and what they mean in a given environment, not just what regulated fields they contain. A draft M&A term sheet, a proprietary engineering formula, or a sealed legal record can each be identified as sensitive without a rule that says look for it. Key capabilities include: - Automated native remediation — inform, redact, quarantine, revoke access, relocate, or delete, enforced directly within the platform based on your policies, with no handoff to a ticketing system or external tool - Flexible deployment — air-gapped, single-tenant SaaS, or multi-tenant SaaS, covering regulated government and defense environments through to cloud-first enterprises - Broad connector coverage — cloud infrastructure (AWS, GCP, Azure), SaaS (Salesforce, ServiceNow, Workday), collaboration (Slack, Google Drive, Microsoft 365), databases (Snowflake, BigQuery, PostgreSQL), and AI tools (Microsoft Copilot, OpenAI, Claude) - AI data governance — classifies data before it reaches AI agents and copilots, blocks sensitive transfers to external LLMs, and governs what internal AI systems are trained on - Compliance-ready reporting — automated audit evidence for SOC 2, ISO 27001, HIPAA, PCI-DSS, GDPR, CCPA, and EU AI Act, with a full remediation log for every automated action. Key value propositions include: - Reduced risk exposure — high-confidence data exposure is remediated automatically and continuously, shrinking the attack surface in seconds rather than days. - Lower operational burden — security teams are freed from manual alert triage; customers report approximately one hour per week of active platform management after initial deployment. - Reduced legal and compliance liability — enforced retention policies eliminate data that has outlived its purpose, reducing what is discoverable in litigation, subject to breach. notification requirements, and exposed in regulatory inquiries. - Responsible AI adoption — security teams gain visibility and control over what data AI tools can reach before deployment, enabling organizations to adopt AI without creating ungoverned data exposure. - Faster time to value than legacy platforms — a crawl, walk, run deployment model brings customers from initial data map to governed automation in approximately six months. Teleskope is used by organizations in fintech, professional services, healthcare, hospitality, manufacturing, and the public sector, including Ramp, GoFundMe, The Atlantic, Aprio, Alloy, and Chevron Phillips. **Average Rating:** 5.0/5.0 **Total Reviews:** 2 **How Do G2 Users Rate Teleskope?** - **Ease of Use:** 10.0/10 (Category avg: 8.6/10) **Who Is the Company Behind Teleskope?** - **Seller:** [Teleskope](https://www.g2.com/sellers/teleskope-26ab78ed-b7c8-479a-8a36-e73014f85de1) - **Year Founded:** 2022 - **HQ Location:** New York, US - **LinkedIn® Page:** https://www.linkedin.com/company/teleskopeai/ (30 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 50% Mid-Market #### What Are Teleskope's Pros and Cons? **Pros:** - Automated Classification (1 reviews) - Collaboration (1 reviews) - Comprehensive Coverage (1 reviews) - Connectivity (1 reviews) - Content Management (1 reviews) ### 11. [TextQL](https://www.g2.com/products/textql/reviews) TextQL is a platform that simplifies the data-to-insight process for organizations. The platform indexes BI tools and semantic layers, documents data in dbt, and uses OpenAI and language models to provide self-serve power analytics. With TextQL, non-technical users can easily and quickly work wit ... see more **Who Is the Company Behind TextQL?** - **Seller:** [TextQL](https://www.g2.com/sellers/textql) - **LinkedIn® Page:** https://linkedin.com/company/textql ### 12. [Traceye](https://www.g2.com/products/traceye/reviews) Traceye is an Enterprise-grade data indexing infrastructure platform to build and deploy subgraphs with best-in-class performance, security and scalability. Experience Faster and Seamless Access to Indexed Blockchain Data with Traceye Subgraphs. **Who Is the Company Behind Traceye?** - **Seller:** [Traceye](https://www.g2.com/sellers/traceye) - **LinkedIn® Page:** https://www.linkedin.com/company/traceyeio/ ### 13. [Untrite](https://www.g2.com/products/untrite/reviews) Complex organisations with legacy systems and specialised knowledge suffer from data silos and fragmented information. Untrite Intelligence™ AI platform helps leveraging your existing data to improve internal operations and customer service efficiency by up to 42%. Unlike other large corporations where you need to purchase an out of box full solution, we provide a modular platform with building blocks which you pick as you need. The more modules and areas of your business are connected, the more synergy effects can be achieved. Untrite Intelligence™ empowers your teams to use the data to increase transparency and efficiency of your business workflows; improve first time customer query resolution, optimise service performance, assess and mitigate risks and drive exceptional client experience. **Who Is the Company Behind Untrite?** - **Seller:** [Untrite](https://www.g2.com/sellers/untrite) - **Year Founded:** 2015 - **HQ Location:** London, GB - **LinkedIn® Page:** https://www.linkedin.com/company/untrite (7 employees on LinkedIn®) ### 14. [Vectice](https://www.g2.com/products/vectice/reviews) Vectice, an auto-documentation platform, empowers data scientists and model developers to build trust faster in AI models. By continuously cataloging AI assets and knowledge during development, model documentation, compliance, and governance becomes easier. Integrating with your existing tools, Vectice generates comprehensive and consistent documentation effortlessly. This typically results in model-to-production time being cut by 25% or more. Vectice works with global Data Science and AI/ML leaders to boost productivity while improving their ability to control risk and governance. Vectice integrates seamlessly with popular AI/ML tools like Python, R, Snowflake, Databricks, MLflow, and more. For more information, visit www.vectice.com **Who Is the Company Behind Vectice?** - **Seller:** [Vectice](https://www.g2.com/sellers/vectice) - **Year Founded:** 2020 - **HQ Location:** San Francisco, US - **LinkedIn® Page:** https://www.linkedin.com/company/vectice/ (19 employees on LinkedIn®) ### 15. [Vyapin Dockit Metadata Manager](https://www.g2.com/products/vyapin-dockit-metadata-manager/reviews) Dockit Metadata Manager for SharePoint allows you to take control of all aspects of metadata management in your SharePoint. As everyone knows, SharePoint content without proper metadata renders SharePoint to be just a storage repository resulting in very poor user adoption and gross under-utilization of the powerful capabilities of SharePoint. Irrespective of whether you have a well settled, on-going production SharePoint environment or you have just begun to streamline your SharePoint metadata before or after your SharePoint migration, you need to have complete control on how your SharePoint metadata is organized and how it needs to be monitored and managed. **Who Is the Company Behind Vyapin Dockit Metadata Manager?** - **Seller:** [Vyapin](https://www.g2.com/sellers/vyapin) - **Year Founded:** 1996 - **HQ Location:** Chennai, Tamil Nadu - **Twitter:** @vyapinsoftware (93 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/661868/ (29 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 Governance Tools](https://www.g2.com/categories/data-governance-tools) - [DataOps Platforms](https://www.g2.com/categories/dataops-platforms) - [Active Metadata Management Software](https://www.g2.com/categories/active-metadata-management) --- ## 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.