Top 15 Data Discovery Tools in 2024

In recent years, data has become the most significant asset for any company. It is rightly called the new oil. According to research, nearly 3.5 quintillion bytes of data are generated every day. To put this into perspective, one quintillion bytes equals one billion gigabytes! That’s a lot. As companies grow, the datasets they work with also grow. That is what makes data discovery a vital component in performing data analysis successfully. This has given birth to data discovery tools. 

Companies around the world are doubling down on data-driven decision-making. All major market strategies, product development, HR management, and everything else related to business are now based on data. Through BI and data analytics services, companies uncover business insights, patterns, and trends that enable them to understand their target personas, competitors, and the larger enterprise landscape. All these data discovery tools can unearth facts and trends hidden within data sets.  

Data discovery and data discovery tools have therefore become indispensable components has therefore become an indispensable component of modern enterprise success. With the right tools, platforms, and strategies in place, you can use data discovery and data discovery tools to maximize the potential of data.

This article explains data discovery and the advantages of using data discovery tools. Most importantly, it explores the 15 best data discovery tools you need to know about in 2024.

Let’s begin!

What is Data Discovery?

To understand the significance of data discovery tools, one must understand data discovery first. Data discovery is all about finding hitherto unknown insights and patterns in data. It is a business-user-oriented process that uses visual navigation and advanced analytics to identify patterns, gain insights, and answer highly specific business questions. Data discovery tools find patterns within data.  

The goal of smart data discovery is to reveal relevant data insights, communicate these insights to business users in a way that is accessible to non-technical users, and ultimately improve business processes. A key aspect of data discovery is that it breaks down data silos by bringing together data from multiple sources. This integrated data is more accurate, thus driving improved business outcomes through data-driven decision-making.

Data discovery tools lead to improved and informed decision-making. Now, we will learn about data discovery.  

Recognizing the pivotal role of data discovery in streamlining operations, major tech giants such as Netflix, Lyft, Uber, LinkedIn, and Facebook have taken the initiative in the development of internal data discovery solutions. Notably, these solutions, once confined to in-house applications, have evolved into open-source tools accessible to a wider audience. 

Prominent examples include Lyft’s Amundsen, LinkedIn’s DataHub, and Netflix’s Metacat, which exemplify the industry’s commitment to encouraging collaborative innovation in data cataloging. This shift towards open-source initiatives not only promotes transparency but also sets a precedent for driving advancements in data management on a global scale. 

Important Aspects of Data Discovery Platform

• Data Sources Identification: Data can exist in various forms, including structured databases, spreadsheets, unstructured files, cloud storage, data lakes, and more. Data discovery tools can really help with unstructured data. The first step in data discovery is to identify all potential data sources within an organization or stored in external repositories. You will also need relevant data discovery tools. Don’t forget that external data can be a very valuable source of information for companies. Unstructured data discovery tools can dish out positive insights.  
• Data Profiling: Once you have identified the data sources, data profiling comes into play. This involves examining the data to understand its structure, quality, completeness, and potential issues. Data profiling helps you determine whether the data is suitable for analysis and what kind of preprocessing may be required. This pii data discovery software also play a part in data profiling.  
• Data Cataloging: A data catalog is a centralized repository that provides metadata information about the available data assets. This includes details like data source, schema, data types, data owner, last update date, and any relevant documentation. A well-organized data catalog makes it easier for data users to search for and access the data they need.
• Data Exploration and Visualization: Data discovery often involves exploring data through data visualization tools and techniques. Data visualization helps users comprehend the data’s patterns, trends, and outliers, making it easier to identify potential insights and correlations.
• Data Relationships and Dependencies: Understanding the relationships and dependencies between different data elements is crucial in data discovery. Sensitive data discovery tools help to identify these relationships and dependencies. This helps data analysts and data scientists know how various data points are interconnected and how changes in one dataset might impact others.
• Data Security and Compliance Considerations: During data discovery, it’s essential to identify sensitive data and ensure compliance with relevant data protection regulations like the General Data Protection Regulation (GDPR). GDPR data discovery tools adhere to compliance standards. This step helps safeguard data privacy and ensures that data usage adheres to legal and industry standards.
• Data Access and Governance: Data discovery also involves defining access control policies and governance mechanisms to regulate who can access the data, what they can do with it, and how data is managed throughout its lifecycle.
• Data Quality Assessment: Assessing data quality is crucial. Poor data quality can lead to erroneous conclusions and decisions. Data discovery should identify data quality issues and initiate corrective measures.
• Iterative Process: Data discovery is often an iterative process as new data sources and requirements may emerge over time. It requires continuous updates and improvements to the data catalog as new data is discovered and becomes available.

Advantages of Using Top Data Discovery Software

Data discovery tools offer numerous advantages that empower you to efficiently explore, analyze, and derive insights from your data. These data discovery tools can be of immense value. These data discovery tools can be of immense value. Here are some key benefits of using data discovery tools:

• User-Friendly Interface: A good data discovery tool typically comes with an intuitive and user-friendly interface, enabling business users and analysts with varying technical skills to interact with the data easily. This reduces the dependency on IT specialists for data access and analysis.
• Rapid Insights: The goal of a data discovery tool is to enable rapid data analysis, leading to data-driven decision-making. A good tool therefore allows users to access and explore data, facilitating faster decision-making quickly. Your team can perform ad-hoc queries and visualize data in real-time, reducing the time you need to discover insights and identify patterns.
• Visual Data Exploration: Data discovery tools often include data visualization capabilities that present data in charts, graphs, and other graphical formats. Visual representations help users grasp complex relationships and trends more easily. A good data discovery solution must possess visualization capabilities.
• Data Integration: Good data discovery solutions can connect to a wide range of data sources, including databases, cloud storage, data lakes, spreadsheets, and more. Data integration enables you to access and analyze data from various sources in one centralized platform.
• Self-Service Analytics: Also known as self-service BI, self-service analytics allows users to create custom reports, conduct ad-hoc analyses, and answer their data-related questions without relying on IT or data specialists. This increases overall productivity and agility.
• Data Quality and Profiling: Data discovery tools often include data profiling features, helping users assess the quality of data and identify potential issues. This ensures that the data used for analysis is accurate and reliable.
• Predictive Analytics and Machine Learning: Some advanced data discovery tools integrate predictive analytics and machine learning capabilities. This allows users to perform predictive data modeling and gain deeper insights from their data.
• Collaboration and Sharing: A good tool promotes collaboration among teams by allowing users to share their analysis, insights, and reports with others securely. This facilitates knowledge-sharing and informed decision-making across the organization.
• Scalability and Performance: The best data discovery tools can handle large datasets and deliver efficient performance, ensuring that users can work with significant amounts of data without experiencing slowdowns.
• Support and Training: Excellent customer support and training resources help users get the most out of the data discovery tool and resolve any issues effectively.
• Integration with Existing Ecosystem: Integration with other data tools and systems, such as business intelligence platforms and data warehouses, enhances the tool’s value within the organization’s existing data ecosystem.

15 Best Data Discovery Tools

1. Tableau

Tableau is one of the most popular BI and data analytics platforms  out there. Known for its interactive data visualization, Tableau lets you evaluate your business data in real time, develop and share visualizations, and perform data analysis with others. It is considered one of the widely used data discovery tools. 

You can use Tableau to gather data from multiple sources, including spreadsheets, SQL databases, and the cloud, and create a unified dataset. This helps make the data analysis process easier.

Pros

• Provides various visualization options to choose from (dashboards, charts, maps, etc.)
• Offers features like calculated fields, grouping, and filtering to make data analysis easier.
• An excellent collaboration tool that lets you share dashboards and visualizations with team members, even on mobile devices.
• Offers cloud and on-premises deployment.
• Easy integration with other tools and programming languages like Alteryx, Python, and R.
• Secure and reliable.

2. Informatica

Informatica is an AI-powered cloud data management software that lets you set up and maintain data warehouses. It is best known for offering ETL (extract, transform, load), data integration, and enterprise data cataloging features. Informatica is one of the top data discovery tools.

Pros

• Scanning and indexing metadata.
• Data discovery, profiling, and tracking dataset lineages.
• Automatic scanning across cloud platforms, BI tools, and external or third-party metadata catalogs.
• Manages data privacy problems efficiently and ensures regulatory compliance.
• Analyses data applications to provide business insights.

Cons

• Complex Setup: Informatica’s setup and configuration are typically complex and time-consuming, resource-intensive.  
• High Licensing Costs: Informatica is expensive when it comes to large-scale enterprise deployments. 
• Limited Flexibility for Smaller Data Sets: It is better suited for larger datasets. 
• Steep Learning Curve: Similar to Tableau, it has some advanced features that require significant training and technical knowledge. 

Price

Informatica does not offer a transparent pricing model but quote could be established upon request.  

3. ManageEngine

Endpoint DLP Plus is a data loss prevention (DLP) software by ManageEngine aimed at securing critical enterprise data on managed endpoint devices. It protects your data against theft and unauthorized disclosure. This is included in the list of fascinating and most useful data discovery tools. 

It is an on-premises software that allows you to uncover important business data and categorize it. Its other features include data access control and file protection. You can easily control data transfer, stop insider threats, and respond to data alerts.

Cons

• Limited to On-Premises Deployment: Endpoint DLP Plus is restricted to on-premises deployment and it normally less flexible for businesses in pursuit of cloud-based solutions. 
• Limited Data Discovery Features: It lacks advanced data discovery and visualization capabilities.  
• Complex Implementation: Setting up and configuring the software can be tricky and demands specialized knowledge.  

Price

• Endpoint DLP Plus Standard Edition is priced at $15 per device per year with a bar on 50 devices at least. 

Pricing may vary based on deployment size and other add-ons.  

4. Aggua

A data fabric platform, Aggua allows businesses to access their data and generate insights. It is designed as a collaborative data management solutions for BigQuery, Redshift, and Snowflake users. Moreover, Aggua has an automated data catalog that provides users with a bird’s eye view of their data, including column-level lineages across systems.

Pros

• Comprehensive data mapping capabilities from source to target.
• Catalog of your organization’s data assets and enterprise jargon.
• Tracking and reporting of granular level job costs.
• Anomaly detection for data observability.

Cons

• Limited customization: It comes with very few options to customize and for very specific user needs. 
• Performance Issues: Some users report performance issues because it usually deals with large-scale datasets. 
• Learning Curve: It requires specialized training as it could complicated for new users.  

Price

Pricing varies based on data volume and usage. It does offer custom pricing for enterprise clients. 

5. Equalum

Such data discovery solutions can be extremely helpful in fetching real-time analytics. Equalum offers real-time analytics across every target and source and allows you to stream data to the cloud services. This is what enables data discovery software to provide data warehouse modernization, real-time analytics, and more. Moreover, this data discovery software real-time visibility of your data storage enables you to optimize costs and improve performance.

Pros

• Change data capture (CDC).
• Batch and real-time ETL.
• Data ingestion.
• Excellent for hybrid data stacks and B2C organizations leveraging vast amounts of data.

Cons

• Setup: Initial setup can be complex, especially smaller teams find it hard to come by.  
• Support Options: It offers very less and limited support, especially for new beginners and non-technical users. 
• Price and Package: Equalum is priced higher when it comes to employing extensive data sources. 

Price

It averages around $500/month and depending on the amount and extent of usage. 

6. Osano

Osano is a cloud-based data privacy software designed to facilitate enterprises in adhering to data governance and privacy laws like GDPR. It offers a data discovery capability that automatically finds, classifies, and evaluates enterprise-grade data, irrespective of where the data resides.

Pros

• Easy to install and implement.
• Uses artificial intelligence solutions to discover and classify personal and non-personal data.
• Offers over seventy types of personal data classification options.
• Maps organizational data to ensure compliance with privacy laws.
• Has a visually appealing and intuitive UI (User Interface), enabling users to quickly access and track important data.

Cons

• Effectiveness: It is not always as effective for complex data structures. 
• High Cost: For some users pricing could be steep.  
• Limited scalability: It has limited flexibility and adaptability for large-scale operations. 

Price

It begins at $300/month and could be scaled for enterprise requirements. 

7. Looker

Another interesting data discovery tool is Looker. Now a part of GCP (Google Cloud Platform), Looker for Business Intelligence is a robust enterprise platform for business intelligence, embedded analytics, and data apps that enables users to explore business insights in real time and share the same with others.

It uses efficient data visualizations for data discovery tool, connecting directly to live, row-level data. Looker for BI has an intuitive, easy-to-use interface.

Pros

• Offers a wide range of data sources.
• Seamless integration with GCP and BigQuery.
• Cost-effective.

Cons

• It could be expensive for smaller firms and businesses. 
• This data discovery tool needs expertise and knowledge of SQL to solve complex queries. 
• This tool has very limited offline features and accessibility.  

Price

It starts around $5,000/annum for small teams along with custom pricing most for enterprise users.

8. Qlik Sense

Qlik Sense is an easy-to-use data analytics product or data discovery platform by Qlik. It allows users with various skill levels to gain business insights and act upon them to improve business outcomes.

In terms of data discovery, Qlik Sense automatically finds and determines relationships between datasets and across various analytics tools.

Pros

• Highly interactive dashboards and visualizations.
• Integrated augmented analytics for automated insight generation, data prep, natural language interaction, and more.
• Data lineage pipeline that ensures automatic construction of all data assets from source to target, with built-in construction time tracking to monitor the process efficiently.

Cons

• Steeper learning curve because of its difficulty for new users and non-technical folk.  
• Pricing can be prohibitive for smaller and startup organizations. 
• Limited real-time data processing. 

Price

Its initial quote is at $30 per user per month for standard plans. 

9. Atlan

Atlan is an excellent data discovery tool that offers user-friendly, Google-like search capabilities to find data, dashboards, and databases in no time. The fact that this data discovery platform has a single search window shows that the developers of this platform wanted it to be easy for non-technical people to use.

Pros

• Automatic data profiling to identify anomalies, outliers, etc.
• Search data using natural language, SQL syntax, or business context.
• Column-level relationship visualizations.
• Role-based access controls that ensure compliance.
• Automated metadata enrichment.

Cons

• With usage and increasing volume its pricing escalates quickly. It could be expensive for new comers.  
• It often lacks advanced features needed for highly technical users. 
• Some users find the UI cluttered and confusing at first. 

Price

It usually operates on custom pricing structure based on data size and team size. 

10. Nightfall

Nightfall is one of the best data discovery solutions out there. It is a robust data discovery tool designed to prevent sensitive data leaks to SaaS apps and generative AI tools like ChatGPT and Bard, etc. It is essentially a DLP (data loss prevention) tool – however, it uses AI/ML to discover sensitive data, which you can then use to identify and classify data.

Pros

• Easy integration using APIs.
• Prevents data and compliance risks by letting you discover, classify, and protect your data.
• Offers multiple features like alerts, deletion, quarantines, etc., for enabling collaboration.

14. SAP Lumira

Being an intuitive data visualization platform, SAP Lumira is meant for exploring data and SAP integration.  

Pros

• SAP Integration: Works and incorporates well with SAP HANA and SAP BW by employing existing data.
• User-Friendly Interface: Has the intuitive Drag-and-drop features that bodes well with non-technical users.
• Flexible Data Connections: It connects and operates effortlessly with databases, flat files, and cloud services.
• Storyboards: It has options for stunning visuals with narratives for storytelling.

Cons 

• Limited Advanced Analytics: Focuses on visualization, lacking deep statistical tools. 
• Customization Limits: It offers a lesser extent of customization in comparison to other pii data discovery tools.  
• Cost: SAP is an expensive affair for smaller organizations. 

Price

Typically starts around $185 on annual basis. 

15. Oracle Analytics Cloud (OAC)

Oracle Analytics Cloud (OAC) is a world-renowned analytics tool kit or suit that works well and integrates with Oracle products. It AI-powered insights and visualization skills are the catch.  

Pros 

• All-in-One Platform: It has everything covered like data preparation, visualization, and advanced analytics.
• Augmented Analytics: It employs ML algorithms for insights.
• Scalability: Cloud-based and highly scalable when it comes to adjusting and accommodating new needs.
• Security: It offers high-level security and features.

Cons 

• Higher Costs: Pricing can be a concern for smaller organizations.
• Learning Curve: One must acquire in-depth understanding and knowledge of Oracle technologies to use its advanced features and faculties.
• Setup Complexity: Initial setup is quite steep and difficult.

Price

It commences around $16 per user/month, with enterprise options available. 

Conclusion

Data discovery software are essential for maintaining, securing, and harnessing modern IT infrastructure. Each and every member of your organization, be it the CTO of your company or your Flutter developer, stands to benefit from such a tool.

Data is the critical element that drives digital transformation across a company. That said, think of data discovery tools as the enablers and facilitators of such transformation. Choose a data discovery tool that serves your business interests.

If you’re not sure about which tool will work best for you, drop us a line at [email protected] to book a free consultation session with our BI & data analytics team.