Data discovery tools: How to choose the right one

Data discovery tools are the key to your organization's data. They provide a pathway for categorizing and understanding the data points your company collects for decision-making and analysis.

However, with so many tools on the market, deciding which are the most effective and tailored to your specific business can be difficult. That's why we've assembled this list of the top data discovery tools on the market and advice on selecting the right one.

But first, let's define what we mean by data discovery.

What is data discovery?

Data discovery is a process that data scientists and other data professionals use to find data, uncover relationships between data points, and gain insights via deep dives for specific business use cases. For example, check out how we helped T-Mobile scale its data discovery, scanning over 5,000 apps and 22,000 databases continuously.

It is one of the primary tools of business intelligence, and decision-makers use it to uncover relevant data for their initiatives.

Some of the immediate benefits of data discovery include:

• Consolidating data from multiple sources.
• Cleaning and preparing data so it's ready for use.
• Making data accessible to relevant stakeholders.
• Visualizing data assets.
• Single-entry platform with customizable dashboards for different use cases.

10 Top data discovery tools on the market

The following is a list of the ten most popular data discovery tools on the market today.

1. Ataccama

Ataccama's data catalog software emphasizes data quality as integral to the user experience and efficient data management, offering native DQ capabilities and AI-powered automation for descriptions, rule implementation, and more. Ataccama ONE Data Quality & Catalog enables companies to effortlessly build, maintain, and scale a knowledge base of their data landscape. Their AI-powered platform combines integrated data discovery and profiling, metadata management and data catalog, and robust data quality management built on top of it.

The platform’s flexibility in deployment and technology-agnostic nature further ensures it can integrate smoothly with various systems, making it a versatile tool for diverse data environments. As such, they empower organizations to maximize the transformative potential of data and AI by enabling collaboration on high-quality, reusable, and scalable data while maintaining its accuracy, control, and governance.

Key functionalities:

• Automated data cataloging; metadata scanning, profiling, classification, issues, and change detection in source systems.
• AI-based data classification without complex configuration setup.
• Advanced profiling.
• Automated algorithmic rule suggestion and business terms assignment.
• Built-in data quality to understand data's quality and context.
• Comprehensive lineage with a wide range of scanners and advanced visualization layer.
• Advanced search and collaboration capabilities.

Learn everything you need to know about data cataloging here!

2. Informatica

Informatica's Data Catalog is a centralized repository for all your enterprise data assets, offering a comprehensive overview of their lineage, relationships, and quality. It empowers users to quickly discover, understand, and trust their data, facilitating better decision-making and data-driven insights.

Key functionalities:

• Locate data across the cloud and on-premises.
• AI-powered data enrichment.
• End-to-end data lineage for asset relationship analysis.
• Measures data quality with scorecards.

3. Atlan

Atlan's data catalog specializes in delivering trusted data to other members of your organization. It highlights low-effort usage and the ready adoption of data and data-driven culture across the board. You can use one control plane for your entire data stack, and it cites its collaboration with "industry leaders" as evidence of its platform's success.

Key functionalities:

• Optimized search engine
• Column-level data lineage.
• One control plane for the entire data stack.
• Business glossary compatible with GitHub, Slack, Jira, and more.

4. Collibra

Collibra's data catalog can create a "unified view" of your data assets, leading to comprehensive data discoverability in your systems. They chose simplicity as a means of easing the discovery and understanding of data, allowing companies to deliver faster insights across disparate data sources.

Key functionalities:

• Unified view of data assets.
• Self-service portal.
• Automated data curation and enrichment.
• Putting data within business context.

5. Microsoft Purview

Stemming from its prestigious parent company, Microsoft Purview uses its vast technological resources to provide data discovery software that drives a data-driven culture. The tool's main mission is to "uncover powerful insights and turn them into impact." It also boasts built-in data visualization and self-service capabilities.

Key functionalities:

• Scalable and self-service interface.
• Immediate setup and start.
• Embedded BI reports for customers.
• Interoperation with existing apps.

6. Oracle

Oracle offers a cloud infrastructure-based data catalog that functions as a metadata management service, aiding in data discoverability. It supports data professionals and their journeys with data governance and other data-related tasks, providing an inventory of assets, a business glossary, and metadata storage for data lakes.

Key functionalities:

• Metadata harvesting.
• Accelerated analytics.
• Data governance support.
• Unified meta store.

7. Select Star

Select Star's main slogan for their data catalog is "single source of truth for data," and that's exactly what it provides. The data discovery tool offers an automated data catalog, data lineage, and usage insights so data teams don't need to debate where to find the best data for their tasks.

Key functionalities:

• Data catalog organization.
• Schema change analysis.
• Usage insights.
• Swift integrations.

8. OneTrust

OneTrust's data catalog specializes in sensitive data, ensuring you know what sensitive data your company has in its database and that it's handled appropriately. They emphasize visibility into your data to help with compliance, classifying data immediately to guarantee its proper treatment.

Key functionalities:

• Data discovery in unstructured file shares.
• Specializes in sensitive data.
• Discovery and classification scanning capabilities.
• Automated consumer rights requests.

9. Alation

Alation labels its data discovery tool with the keyword "data intelligence," highlighting data context as a primary resource stored in its centralized catalog. Their tool fuels a robust understanding of data assets, leading to more confident usage.

Key functionalities:

• Social networking capabilities.
• Interoperability with other systems.
• Intelligent SQL editor.
• Cloud services.

10. Talend

Talend's data discovery tool allows you to crawl, profile, organize, link, and enrich all your data in a timely fashion. With these capabilities, you can deliver trusted data across your organization and fully automate data discovery.

Key functionalities:

• Accelerate ETL processes.
• Data privacy support.
• Open contribution to business glossary.
• Compliance tracking.

Why are data discovery tools important?

Now that we've examined the most popular data discovery software, let's discuss why your business needs a data discovery tool.

These tools make handling, understanding, and accessing your organization's data easier, leading to faster and enhanced decision-making and analytics that can grow your most important initiatives.

More specifically, these tools are valuable for four main reasons:

• More accurate information
• Enhanced decision-making
• Scalability of data systems
• Security and compliance

More accurate information

Data discovery provides information about the data you're working with via data profiling, such as its quality, so that you can ensure its accuracy and reliability for your specific project/task. It also helps you access the best possible data for each assignment, leading to better results.

Enhanced decision-making

With data discovery tools, your organization's most important decision-makers will have the data they need readily available to make their choices faster based on better information.

Scalability of data systems

As data volumes increase, it gets harder to keep track of what's available to your team, what data is built for what purpose, what is sensitive, what isn't, etc. With data discovery tools, you can automatically organize new data as it arrives, keeping everything organized and easy to work with.

Security and compliance

New regulations and requirements have led companies to assume increased responsibility for collecting data. A data discovery tool keeps this data safe by ensuring access permissions, keeping it updated, and making it available if regulatory entities request access.

How to choose the right data discovery tools: 8 must-have features

Before you choose your data discovery software, you should know the features they need to function to the best of their ability. Some software can perform exceptionally at data profiling but leave much to be desired in the business glossary, and vice versa.

Below, you'll find the eight must-have features of data discovery software:

1. Integration

Data discovery software must seamlessly connect and interact with other software applications and data sources within an organization's ecosystem. This allows it to access, process, and analyze data from various platforms, breaking down information silos and providing a unified view of the data assets.

Your tool must be integrable with all your existing systems and processes. This will streamline workflows by eliminating the need to manually transfer data between different systems, saving time and reducing errors. It also combines insights from multiple sources and comprehensively explains their business landscape.

2. Data cleansing

Data cleansing identifies and corrects errors, inconsistencies, and inaccuracies in datasets. It involves tasks like fixing typos, handling missing values, standardizing formats, and removing duplicates, ultimately enhancing the overall quality of the data.

Learn more about the importance of data quality in our other blogs.

Data cleansing plays a vital role in data discovery software, ensuring the accuracy and reliability of insights derived from the data. By proactively addressing data quality issues, the tool enables users to explore, analyze, and visualize information confidently, making informed decisions based on trustworthy data.

3. Supports a robust data governance framework

Supporting data governance and management in a data discovery tool means facilitating the implementation and enforcement of an organization's data policies and standards. This encompasses aspects such as data lineage (tracking the origin and transformations of data), access control (managing who can view and use specific data), and adherence to data quality guidelines.

This way, your tool can help with the responsible, secure, and compliant use of data with internal and external regulations. This fosters trust in the data, promotes transparency, and empowers organizations to make informed decisions while mitigating risks associated with data misuse or breaches.

4. Offers data visualization

Data visualization is the graphical representation of data and information using visual elements like charts, graphs, maps, and dashboards. It transforms complex datasets into intuitive and interactive visuals that facilitate understanding, exploration, and communication of insights.

Data discovery software can help users quickly identify trends, patterns, and outliers within their data. Interactive visualizations allow users to delve deeper, filter information, and uncover hidden relationships, ultimately empowering them to make data-driven decisions with confidence and clarity.

5. Gathers analytical insights

Gathering analytics insights in a data discovery tool involves employing advanced analytics techniques, such as predictive modeling and machine learning, to extract meaningful patterns and trends from data. This goes beyond basic descriptive analytics, enabling users to anticipate future outcomes, identify potential risks, and uncover hidden opportunities.

By incorporating analytics capabilities, a data discovery tool transforms into a strategic asset, empowering users to make informed decisions based on data-driven predictions and actionable insights. This fosters proactive decision-making, enhances operational efficiency, and supports organizations in achieving their goals by leveraging the full potential of their data.

Is your organization ready for AI? Learn more about AI readiness now.

6. Enhanced data security

Enhanced data security implements measures to protect sensitive information from unauthorized access, breaches, and misuse. This encompasses features like granular access controls (limiting who can see and interact with specific data), encryption (scrambling data to prevent unauthorized decryption), and compliance support (adhering to industry and regulatory data protection standards).

In the realm of data discovery, where sensitive information is often handled, prioritizing enhanced data security is paramount. Confidential data remains confidential, building user trust and safeguarding the organizations from the potential financial and reputational damage associated with data breaches.

7. Efficient data sharing

Efficient data sharing refers to the rapid and secure distribution of data and insights across an organization, ensuring that relevant information reaches the right people at the right time. This involves features like collaborative workspaces, customizable reports, and integration with communication platforms.

It is crucial for unlocking the full potential of information. By facilitating collaboration and knowledge exchange, a data discovery tool empowers teams to make informed decisions collectively, identify new opportunities, and drive innovation.

8. Quickly access the data sets you need

You need to locate, retrieve, and utilize relevant information without delays or cumbersome navigation. This involves intuitive search capabilities, filtering options, and intelligent recommendations that guide users to the data they seek.

Data discovery software that streamlines data retrieval empowers users to respond swiftly to emerging trends, seize opportunities, and proactively address challenges. It eliminates the frustration of searching for information, allowing users to focus on analysis and extracting valuable insights.

How do data discovery and data observability work together?

While distinct, data discovery and data observability are pivotal in establishing a comprehensive data management framework.

Data discovery empowers users to locate, understand, and utilize relevant data, enabling informed decision-making. Data observability, on the other hand, monitors the health and data quality dimensions of pipelines, ensuring data reliability and proactively identifying issues.

Data discovery benefits from data observability by ensuring the accuracy and completeness of data used for analysis. Data observability, in turn, leverages data discovery insights to prioritize monitoring efforts on critical datasets and identify potential areas of improvement.

Identify, classify, and protect your data with data discovery tools

With plenty of options available, choosing the right tool for your specific needs is crucial. You need a tool that enables data professionals to find data, identify relationships, and gain insights for business use cases. Businesses that choose the right tool will benefit from more accurate information, enhanced decision-making, scalability of data systems, and security and compliance.

Consider the key features and the importance of these tools. By evaluating these factors and factoring in your specific requirements, you can choose the data discovery software that best aligns with your organization's goals and empowers you to harness the full potential of your data assets.

For example: our data discovery tool. At Ataccama, we strive to provide the best control over your data possible, and it all starts with understanding what you have available. Schedule a call with us today to learn more.

Data discovery FAQ

Eager to learn more? Here are some frequently asked questions about data discovery software.

1. What are the challenges of data discovery?

Companies can encounter challenges when implementing data discovery tools. It's important to be aware of them so you can navigate them proactively. Here are some of the top challenges:

• Data quality. It can be challenging to use data discovery if data is poorly organized, inaccurate, or incomplete.
• Multiple sources and data overload. The larger the volume of data you're working with (and the number of sources it comes from), the more complex it is to set up a data discovery tool, assign metadata, recognize duplicates, etc.
• Lack of skilled professionals and funding. While data is your company's most valuable resource, decision-makers may not see it that way, leading to gaps in the number of skilled professionals and resources necessary for successful implementation.
• Outdated technology. Legacy or outdated tools might not synchronize with modern data systems, causing errors and incompatibility issues.
• Data culture and governance. To effectively conduct data discovery and gain organizational buy-in, you need a data-driven culture with adequate data governance measures.
• Preparation. Remember that your tool needs to fit your systems. Always understand your goals and requirements in-depth before selecting a tool.

2. Which technology or tools are commonly used in data discovery?

Data discoverability is a complex process requiring several key data management capabilities: data profiling, pattern recognition, automated metadata labeling, anomaly detection, data storage, visualization, and a data catalog.

3. What's the difference between data search and discovery?

Data search is typically a more targeted action, focused on finding specific pieces of information known or assumed to exist within a dataset. It relies on keywords, filters, and metadata to pinpoint the required information.

Data discovery is a broader and more exploratory process that aims to uncover hidden patterns, relationships, and insights within data. It involves not just finding specific information, but also understanding the context, quality, and potential uses of data. Data discovery software often employs visualization, advanced analytics, and machine learning to facilitate exploration and understanding.