Best Machine Learning Data Catalog Software
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.
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. Additionally, these tools differ from master data management software due to their enhanced governance, collaboration, and machine learning functionality.
To qualify for inclusion in the Machine Learning Data Catalog category, a product must:
Featured Machine Learning Data Catalog Software At A Glance
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- Overview
- Pros and Cons
- User Satisfaction
- Seller Details
Built by a data team, for data teams, Atlan is THE Active Metadata platform for enterprises to find, trust, and govern AI-ready data, and a leader in The Forrester Wave™: Enterprise Data Catalogs, Q3
- Financial Services
- Information Technology and Services
- 54% Mid-Market
- 40% Enterprise
9,589 Twitter followers
- Overview
- Pros and Cons
- User Satisfaction
- Seller Details
AWS Glue is a serverless data integration service that makes it easier for analytics users to discover, prepare, move, and integrate data from multiple sources for analytics, machine learning, and app
- Data Engineer
- DevOps Engineer
- Information Technology and Services
- Computer Software
- 48% Enterprise
- 28% Mid-Market
2,234,689 Twitter followers
- Overview
- User Satisfaction
- Seller Details
A fully managed and highly scalable data discovery and metadata management service.
- Computer Software
- 46% Small-Business
- 29% Mid-Market
32,788,922 Twitter followers
- Overview
- User Satisfaction
- Seller Details
Cloudera Navigator is a complete data governance solution for Hadoop, offering critical capabilities such as data discovery, continuous optimization, audit, lineage, metadata management, and policy en
- Information Technology and Services
- 48% Enterprise
- 38% Small-Business
107,628 Twitter followers
- Overview
- Pros and Cons
- User Satisfaction
- Seller Details
Sifflet is a comprehensive data observability solution designed to assist data engineers and data consumers in gaining complete visibility into their data stacks. This platform can be deployed as a So
- Data Engineer
- Information Technology and Services
- Financial Services
- 64% Mid-Market
- 32% Enterprise
390 Twitter followers
- Overview
- Pros and Cons
- User Satisfaction
- Seller Details
Appen collects and labels images, text, speech, audio, video, and other data to create training data used to build and continuously improve the world’s most innovative artificial intelligence systems.
- Information Technology and Services
- 58% Small-Business
- 26% Mid-Market
- Overview
- Pros and Cons
- User Satisfaction
- Seller Details
Decube is the all-in-one Data Trust Platform designed for the modern data stack. Our mission is to make your data reliable, easily discoverable, and constantly monitored across your entire organizatio
- Information Technology and Services
- 36% Small-Business
- 32% Mid-Market
113 Twitter followers
- Overview
- Pros and Cons
- User Satisfaction
- Seller Details
Secoda is an AI-powered data governance platform designed to help organizations explore, understand, and utilize their data effectively. By providing a comprehensive platform that connects to 75+ data
- Computer Software
- Financial Services
- 65% Mid-Market
- 18% Small-Business
934 Twitter followers
- Overview
- User Satisfaction
- Seller Details
Each entry in the dataset consists of a unique MP3 and corresponding text file. Many of the 1,368 recorded hours in the dataset also include demographic metadata like age, sex, and accent that can hel
- 64% Small-Business
- 27% Mid-Market
269,532 Twitter followers
- Overview
- Pros and Cons
- User Satisfaction
- Seller Details
Try Collibra for free @ Collibra.com/tour Collibra is for organizations with complex data challenges, hybrid data ecosystems—and big ambitions for data and AI. We help organizations who are trying
- Financial Services
- Banking
- 73% Enterprise
- 20% Mid-Market
5,760 Twitter followers
- Overview
- Pros and Cons
- User Satisfaction
- Seller Details
Select Star is a modern data governance platform that helps organizations manage and understand their data at scale, enabling AI, analytics, and self-service across the business. It automatically c
- Information Technology and Services
- Real Estate
- 51% Mid-Market
- 38% Enterprise
388 Twitter followers
- Overview
- User Satisfaction
- Seller Details
A machine-learning-based data catalog that allows to classify and organize data assets across cloud, on-premises, and big data. It provides maximum value and reuse of data across enterprise.
- Information Technology and Services
- Computer Software
- 53% Enterprise
- 26% Mid-Market
100,810 Twitter followers
- Overview
- User Satisfaction
- Seller Details
IBM Watson® Knowledge Catalog is a unified data catalog that can help your data users quickly find, curate, categorize and share data, analytical models and their relationships with other members of y
- 42% Enterprise
- 32% Small-Business
714,643 Twitter followers
- Overview
- Pros and Cons
- User Satisfaction
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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
- 67% Small-Business
- 25% Mid-Market
5,572 Twitter followers
- Overview
- Pros and Cons
- User Satisfaction
- Seller Details
Coalesce Catalog is a collaborative, automated data discovery & catalog tool. We believe that data people spend way too much time trying to find and understand their data. Coalesce Catalog
- Information Technology and Services
- Financial Services
- 59% Mid-Market
- 27% Enterprise
Learn More About Machine Learning Data Catalog 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.