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Your business data can work better for you. Here are the top 8 data discovery tools, designed to streamline and enhance your entire data discovery process. Which one will help transform your organization's data management strategy?
Data discovery is the process of how businesses gather, integrate, and analyze data to find patterns and insights that help in informed decision-making. In this process, data analysts use data discovery tools to derive actionable insights from raw and scattered datasets quickly.
In this blog, we will explore the significance of data discovery and the 8 best tools to make your data discovery process more efficient and accurate.
Download the Data Discovery Without Limits Whitepaper to learn how a knowledge graph can transform your data catalog into a powerful search engine that goes beyond traditional data and metadata.
A data discovery tool is a software application that assists users in searching and analyzing large datasets to identify patterns, trends, and anomalies. They support each stage of the data discovery process by providing a user-friendly interface and automating many of the complex tasks involved.
Here are 8 best data discovery tools to improve data discovery procedures regardless of your business size and type.
data.world
Qlik
Tableau
Informatica
SpectralOps
Atlan
IBM Cognos Analytics
Collibra
data.world is one of the best data discovery tools. It uses the power of AI and a knowledge graph architecture to map and show data discovery results within its enterprise data catalog. This increases data discovery speed and provides results 10 times faster than manual research.
Data.world empowers organizations by streamlining the data discovery process through automation, flexibility, and scalability. It automates data preparation and frees employees for analysis. Besides this, it also automates data integration from various sources to ensure a smooth flow of information.
This flexibility allows organizations to adapt their data workflows as their needs evolve, and it can handle both structured and unstructured data for more comprehensive insights.
Learn more about its key features below and the power of AI for data analytics and search.
AI-assisted data delivery: Uses AI to generate natural language responses for users, making data discovery 10x faster
Democratized delivery: Makes it easier for any level of technical expert to search through data with a chat-like experience
Generative AI search: Auto enriches data and metadata assets with NLP descriptions through its Archie bots to provide context
Knowledge graph: Uses an AI-powered knowledge graph architecture that displays the relationship between different entities
Ideation and powerful exploration: Provides natural language responses with suggestions to improve business decisions and solve problems with data-driven insights
Learning Care Group used data.world’s friendly UI to simplify their searchability and analysis. One senior manager from the company said, “As we continue on our journey toward self-service analytics, wherever that journey leads us, I do believe we’re a much stronger company today because we are providing our employees and colleagues an opportunity to understand who we are as we grow and develop.”
Pros
User-friendly platform for even non-technical experts
Provides a powerful quality data catalog to organize, search, and discover data
Built on a cloud-native platform which makes it easier to use SaaS for data analysis, governance, and Dataops
Central repository provides a reliable way of sharing data
Cons
Requires knowledge of SQL for more accessibility
data.world's pricing currently has four subscription plans: Essentials, Standard, Enterprise, and Enterprise+.
data.world is rated 4.3 out of 5 stars on the review platform G2. One reviewer noted, "Data.world provides a quality data catalog for search and discovery. Their cloud-native platform Saas was very helpful in data analysis and governance, data ops and creation of any knowledge related graphs." Another said, "Their catalog creates a unified body of knowledge that anyone in my team can easily find, understand, and use. The capability enhances collaboration, accelerates decision-making, and promotes data-driven insights across our department, ultimately leading to better business outcomes."
Ready to try data.world’s exclusive data dicsovery features? Book a demo with data.world today.
Qlik is a business intelligence platform with a simple UI that provides real-time data profiling, cleansing, and enrichment features. It enables users to make selections and view associations across complex datasets through color coding.
Let’s explore Qilk in more detail.
Direct discovery: Allows the analysis of large SQL data sets by associating them with in-memory data
Data quality & governance: Provides tools to discover, remediate, and share trusted data to ensure regulatory compliance among business users
AI & machine learning integration: Integrates with AI and machine learning platforms such as Open AI, Amazon Bedrock, Azure ML, and Databricks ML
On-demand apps: Provides alternative methods like on-demand apps to manage big data to reinforce its adaptability and power in data discovery
Augmented analytics: Supports no-code model generation, predictions, and what-if scenario testing to identify key data drivers and provide unlimited experimentation with data models
Qlik helped Save the Children create a scalable solution for collecting data and searching through it. They used Talend—A Qlik tool – to optimize this process and organize their financial data effectively. One data engineer noted, "If we canceled Talend right now, we would have to hire at least two full-time employees so that they can just type in the data and bring it all from A to B — and the data accuracy could suffer."
On Gartner, one customer noted, "I've been able to monitor many of our data connections and get immediate alerts when things break or are no longer functioning as expected." However, not all customers are fully satisfied. Another on the same review platform said, "It takes a long time to get any support issues resolved. Contract renewal process was time consuming, the renewal license keys were provided just a day before the expiry without providing any temporary license incase of delay, this could lead to a risk of our ETL failing."
Pros
Can integrate with multiple tools for data preparation to perform data profiling in real-time
Allows users to operate on substantial datasets with full Qlik Sense functionality
Highly interactive and context-rich dashboards for data visualization
Supports loading and analyzing extensive data from SQL sources in combination with in-memory data
Cons
Premium plans for enterprises are expensive for SMBs as compared to other data discovery platforms
Data preparation is complex when integrating multiple sources or unstructured data
Qlik offers three different pricing solutions. In the "Data Integration and Quality" category, the tiers are: Stitch Data Loader, Qlik Data Integration, or Talend Data Fabric. In the "Cloud Analytics" category, the Standard plan is $20 per user, per month. The Premium plan is $2,700 per month. For the Enterprise plan, one must request a custom quote. The "AI/ML" category offers Premium and Enterprise tiers, whose specific pricing tiers are not publicly disclosed.
From the 667 reviews on G2, Qlik has gathered a 4.5/5 stars rating. Customers consider it a powerful tool for data access and discovery. One user noted, "The platform is self-service. Users can easily build their own dashboard on top of governed measures and dimensions. The platform is open, so you can integrate insights into your own applications."
Informatica is an AI-powered data discovery tool that enhances the data management lifecycle. Its data discovery process helps organizations understand data intricacies and ensure data quality to make informed business decisions.
Let’s explore its data discovery features in a bit more detail.
Data quality: Provides tools for data observability and ensuring high-quality data standards
AI-powered cloud data management: Uses artificial intelligence for enhanced data management and governance in the cloud
Cloud data governance and catalog: Provides teams with the ability to locate, comprehend, and use governed data efficiently
Modern data architecture expertise: Provides resources and tools to become adept in modern data architectures quickly
Cloud data marketplace: Facilitates data democratization with an easy-to-use platform for accessing and sharing trusted data
Informatica helped CVS Health automate tedious tasks with its advanced data analysis features, which make it easier to search through data. A CVS executive advisor noted, “In the past, it took 6 months to generate files that are used for client reporting that can now be done in 2-3 days—a 95% reduction in manual effort to analyze data—allowing us to expand the scope of our project effort for critical clinical operations.”
Pros
Has high speed in data transformation and efficiency in handling large datasets
User-friendly graphical interface simplifies the creation of complex data integration workflows
Uses artificial intelligence to make it easier for users to locate, understand, and trust the data they need
Provides several capabilities for end-to-end data integration and ETL processes
Cons
Can be complex to learn and use, particularly for those new to data integration or ETL tools
Requires additional hardware for optimal performance, which can increase the total cost of ownership
Informatica provides 3 flexible pricing options for cloud services. You can get a custom quote based on your subscription plan.
Informatica has earned a 4.4/5 star rating on G2 with tens of customer reviews. One satisfied reviewer noted that "It is a great ETL tool that can ingest data from multiple sources, perform transformations, and then load it to target systems with drag-and-drop options. Different tools are available for monitoring and repository management and workflow designing."
SpectralOps (also known as "Spectral") is an advanced data discovery tool that monitors, classifies, and protects code, assets, and infrastructure. It uses exposed API keys, tokens, credentials, and high-risk security configurations. This helps developers find harmful security errors in code and configurations in real time to protect their sensitive data.
Learn more about its functionality below.
Data cleaning: Uncovers and monitors supply chain gaps and proprietary code assets across multiple data sources to effectively address public blind spots
Cloud security: Reduces noise and simplifies securing sensitive information across various platforms
Integration with cloud services: Works with public Github, Gitlab, Dockerhub, and over 30 other cloud services
Shadow resource detection: Identifies and manages shadow resources and security blindspots
Real-time protection: Provides continuous monitoring and protection against data leaks and security vulnerabilities
Spectral helped Perion prevent security leaks by automatically identifying flaws through its advanced discovery features. One product manager noted, "Spectral have automatically identified and surfaced security flaws that our company was not aware of, it helped us be more secure and helped us avoid operational risks."
Another customer, as cataloged on the Capterra review site, noted, "Spectral is easy to set up and use, and it provides valuable insights into sensitive issues. But, the reports can be better, with more options to slice and dice the issues."
Pros
Handles large volumes of datasets with machine learning and predictive analysis
Helps fix important security issues in code with automated features instead of manual discovery
Provides daily scan of repositories for security issues
Easy integration with developer assets which speeds up daily operations
Cons
Slow UI performance which can hinder the workflow
Limited options to slice & dice the issues in reports
After setting up an account, you have to request SpectralOps pricing information from their team.
Unfortunately, there are no reviews about SpectralOps on G2 to guide your purchasing decision. So data.world recommends checking out its popular alternatives in this list.
IBM Cognos Analytics provides advanced data search and analysis capabilities powered by AI. You can leverage a natural language assistant to describe the data you need, and Cognos Analytics will build stunning data visualizations based on that.
IBM’s data fabric architecture simplifies data access and enhances self-service data consumption. This smooth data integration across a flexible, secure, and high-quality data system helps organizations use data effectively for analytics and decision-making.
Advanced data exploration: Allows users to explore data through natural language queries and machine learning-powered pattern detection to uncover hidden insights
Interactive dashboards: Provides interactive dashboards and reports that you can easily customize and share to draw quicker insights
Smart data discovery: Provides AI-assisted insights where the system automatically highlights significant patterns and anomalies in data
Integrated data management: Easily integrates with IBM's data management and governance solutions with access to reliable and governed data across multiple sources
Scalable architecture: Supports scalability to handle large volumes of data which accommodates the needs of both small businesses and large enterprises
Users have praised IBM Cognos Analytics's UI design on Gartner's review platform, saying the design makes it easier to use this platform for data discovery and data management. One user said, "It is user friendly, and it is making it easy to access a wide range of features, but new users should require some knowledge to deal with it. I appreciate the depth of functionality and advanced features that you are offering."
Pros
Connects with multiple data sources, including structured and unstructured data
Flexible and enables deeper analysis of various data from different sources
Easy-to-use drag-and-drop interface for creating visualizations
Its dashboard gives real-time monitoring options for key performance indicators
Cons
Time-consuming initial setup and customization
Limited self-service capabilities for data integration
Require assistance from the IT development team
IBM Cognos Analytics has three pricing options: on demand, hosted, or on-premises. The "On-Demand" tier starts at $10.60 per user, per month. However, the other pricing is private, so you must contact the Sales team to learn more.
IBM Cognos Analytics has 363 reviews on G2 and a 4-star rating. One less-than-satisfied customer noted there's a high cost, a steep learning curve, and limited customizability. Another reviewer said, "It is highly flexible and enables deeper analysis of various data which comes from different sources. The drag and drop interface for creating visualizations is also very intuitive and easy to use, even for those who are not technically savvy."
Tableau supports data discovery by showing patterns, trends, and correlations between data assets and enhancing collaboration among data teams. This helps data teams share findings and insights quickly with the team to speed up decision-making processes.
Let’s learn more about how Tableau helps with data discovery.
Interactive visualizations: Creates dynamic visualizations that make data exploration insightful and productive
Drag-and-drop interface: Simplifies the analysis process to quickly manipulate and display data without advanced technical skills
Real-time data monitoring: Monitor data changes and updates in real-time
Advanced integration tools: Facilitates easy data integration from various sources to enrich the data discovery process with broader context and deeper insights
Trend identification: Identifies trends and patterns to help users focus on the most relevant data points and insights
Tableau has made it easier for organizations to drill down important data from unstructured raw form. One customer from Juniper Networks noted, "Tableau allows us to drill down to the profitability at the customer level, providing valuable insight when constructing large, complex deal pricing and delivery structures. In the past few years, we’ve broken records in overall margin in the services business with these insights."
Pros
Integrate with various data sources, such as Bigquery and Google Sheets
Provides a mobile version so you can track your data operations on the go
Has a drag-and-drop interface which makes it very easy to build visualizations through data
Easily handles large amounts of raw datasets which are then used to extract information from various viewpoints and requirements
Cons
Slow processing times
Limited free availability and requires a license for full use
Tableau has three pricing tiers:
$75 per user, per month for the "Tableau Creator" plan
$42 per user, per month for the "Tableau Explorer" plan
$15 per user, per month for the "Tableau Viewer" plan
A custom plan for large enterprises, based on your needs
Tableau has an amazing 4.4-star rating on G2. One customer did note, "Tableau is one of the most expensive BI tools among all others available in the market currently. Due to this, some small businesses may think twice before integrating tableau in their organization. This is something that they can work upon. Also sometimes, it gives problems with large datasets. This is something that tableau should work upon on priority basis."
Collibra's AI-driven discovery features allow users to search for data and remediate anomalies quickly so that data is trustworthy and accurate. By doing so, it prevents redundancy, inaccuracy, and duplication of data to maintain data integrity. This helps ensure that data is reliable and actionable.
Collibra also delivers context-specific results through its custom-level lineage technology in its data catalog. So, you can use these results to curate relevant data from a well-cataloged repository with end-to-end visibility.
Key features
Data quality monitoring: Monitors data quality to ensure data remains accurate and reliable for decision-making processes
Observability: Provides comprehensive visibility into data health and usage to help data teams identify and resolve issues quickly
Data cataloging: Provides a robust data catalog to find and understand data assets efficiently
Impact analysis: Allows users to assess the potential impact of data quality issues on business operations and analytics
Integrated data quality rules: Enables organizations to define and enforce data quality rules to ensure consistency and compliance across data sources
Many of Collibra’s customers consider it a one-stop shop for data solutions. One customer from UCare Minnesota said, "We needed to provide data dictionaries to our regulators in which we had some gaps in field level descriptions. It was as simple as the push of a button to generate and a few minutes to review and approve. We got the request done in a matter of hours vs. weeks all thanks to generative AI descriptions from Collibra."
Pros
Protect data from security risks to improve data accuracy and quality
Automates governance processes to maintain data integrity across the organization
Gives comprehensive visibility into data health, usage, and lineage to give a better understanding of data flows and dependencies
Integrates with different types of data sources like on-premise, data lakes, cloud data, or other storage platforms
Cons
Slow to engage in product enhancements
Requires considerable technical expertise to implement
Collibra does not have transparent pricing available. So you have to contact their team to get more information on this—which can consume a lot of your valuable time.
Collibra is rated 4.2/5 on G2 with over 76 reviews. One happy user said, "As a tool that is also expected to be used by business users in addition to technical users, collibra is one of the most user-friendly tools in the market providing 360-degree coverage of data."
One less satisfied customer said, "Unfortunately, there are several weak points of Collibra. Very technical and not intuitive User Interface heavily impacts the end user's experience. All people without technical knowledge have many issues at the beginning of their Collibra journey which gives a really bad first impression. Very often without any training, nobody in the organization is able to use the tool and understand how metadata is structured there. Those aspects prevent especially business users from the adoption which impacts overall governance programmes in many enterprises."
Atlan’s data discovery feature leverages NLP to extract metadata from databases, data lakes, and data warehouses. This metadata includes information about the data assets, such as their names, descriptions, schemas, and lineage. By having this metadata centralized and cataloged, you can quickly search for and locate the data assets you need.
Natural language search: Easy search for synonyms related to keywords in metadata
Data governance: Simplifies governance with community-centered enablement and privacy at its core
Customized security: Custom masking and hashing policies for securing data
Intelligent metadata automation: Activate metadata for DataOps to enable an always-on, intelligent, and action-oriented data ecosystem
Search using SQL: Makes it easier for data teams to search through data with SQL tools like ‘db.schema’
Atlan helped Autodesk activate its data mesh with Snowflake. Autodesk's Chief Data Architect noted, "We needed something that could help bridge the gap between publishers and consumers, so we adopted a data catalog. Atlan is the layer that brings a lot of the metadata that publishers provide to the consumers, and it’s where consumers can discover and use the data they need."
Pros
Reduces effort required to maintain and consume metadata
Minimizes the need for user training and enables user engagement with its friendly UI
Provides a central repository for data definitions and catalogs
Allows creating and enforcing specific data quality rules with SQL
Cons
Expensive AI and ML features for smaller organizations
Complex setup for personas and roles which limits user access controls
Atlan’s pricing is available on request. You have to share your requirements with their team, and then they’ll recommend a suitable package.
Atlan has a 4.6-star rating on G2. One happy customer wrote, "I am most impressed by Atlan's ability to demystify complex data sets and provide actionable insights.It was very easy to set up, and we quickly found it useful on a daily basis. Its user-friendly interface allows team members of varying technical abilities to interact with our data more confidently."
A less-happy customer critiqued, "Documentation [needs work], especially the API documentations provided. Additionally, Support needs to ease up more." 3
While different data discovery tools have unique features, here are three key attributes found in every data discovery tool:
Data scanning refers to the process of automatically identifying and cataloging data sources within an organization's IT infrastructure.
This includes scanning databases, data warehouses, data lakes, file servers, and other storage locations to discover and extract metadata about the data assets. Doing so helps organizations gain visibility into their data environment, understand what data they have, and where it resides.
Data mapping is the process of establishing relationships and connections between different data sources and data elements. It involves understanding:
how data flows through the organization
how various data sources are interconnected
how data elements are related to one another
Data mapping helps organizations comprehend the data lineage, identify redundancies, and establish a unified view of their data assets.
Data cataloging is the creation and maintenance of a centralized repository that stores metadata about an organization's data assets.
The catalog serves as a comprehensive inventory of all data sources, their descriptions, data types, data owners, access controls, and other relevant metadata. It promotes data governance, enables data discoverability, and facilitates data understanding and reuse across the organization.
Data discovery tools have become essential assets for organizations that want to fully utilize their data assets. When you decide to work with such a tool, data discovery and classification becomes 10 times easier.
So, here are some most common benefits you will get by working with data discovery tools:
Eliminates data silos: Data discovery tools collect and merge data from disparate sources while reducing data silos
Improve data accessibility: These tools ensure that all relevant stakeholders can easily access the needed data
Central data management: They provide an automated and centralized platform for managing and discovering data with easy to use interface
Data cleaning: Automatically separates useful information from raw data and prepares it for analysis
Informed decision-making: By using advanced analytics and visualization capabilities, data discovery tools help businesses translate raw data into actionable insights
Faster and reliable processing: They streamline data processes which reduces the time and effort required to analyze and interpret data
There’s no one-size-fits-all solution for data discovery. Each business has unique requirements, goals, and challenges, and the tool that works perfectly for one company may not be the best fit for another.
To identify and select the most suitable data discovery tool, you need to carefully assess your organization's specific needs and priorities.
You can do this by asking yourself the following questions:
Does your organization need a data discovery tool that can connect and integrate disparate data sources?
Will you need support for other data functions, such as migration, management, governance, etc?
Should your discovery tool have an intelligent search interface?
Would it help your organization to have visualization capabilities (e.g., charts, graphs, etc.)?
Does your organization need a data discovery tool with specific integration capabilities?
Do you need a tool that can grow and scale your business?
Once you have answers to these questions, you'll have the context to select the right tool for your organization.
Are you searching for a faster data discovery tool with a user-friendly interface? If so, data.world is your solution because it has an AI-powered knowledge graph architecture that makes data discovery 10X faster. Ask questions about your data and give comprehensive answers with advanced data discovery capabilities.
Want to learn more? Book a demo with data.world today.
Download the Data Discovery Without Limits Whitepaper to learn how a knowledge graph can transform your data catalog into a powerful search engine that goes beyond traditional data and metadata.
