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.

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, companies uncover business insights, patterns, and trends that enable them to understand their target personas, competitors, and the larger enterprise landscape.

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

This article explains data discovery and the advantages of using good tools. Most importantly, it explores the top 10 data Discovery Tools you need to know about in 2023.

Let’s begin!

What is Data Discovery?

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.

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.

Important Aspects of Data Discovery

• Data Sources Identification: Data can exist in various forms, including structured databases, spreadsheets, unstructured files, cloud storage, data lakes, and more. The first step in data discovery is to identify all potential data sources within an organization or stored in external repositories. Don’t forget that external data can be a very valuable source of information for companies.
• 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.
• 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. 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). 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 Tools

Data discovery tools offer numerous advantages that empower you to efficiently explore, analyze, and derive insights from your data. 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 quickly access and explore data, facilitating faster decision-making. 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.

Top 10 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.

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.

Here are some of the main features and advantages of using Tableau.

• 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.

Its data discovery features and benefits include the following:

• 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.

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.

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.

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.

Some of its major features include:

• 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.

5. Equalum

Equalum offers real-time analytics across every target and source and allows you to stream data to the cloud services. This is what enables it to provide data warehouse modernization, real-time analytics, and more. Moreover, the real-time visibility of your data storage enables you to optimize costs and improve performance.

Its leading features include:

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

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.

Its major features are:

• 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.

7. 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, connecting directly to live, row-level data. Looker for BI has an intuitive, easy-to-use interface.

Its main features are:

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

8. Qlik Sense

Qlik Sense is an easy-to-use data analytics product 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.

Some of its important features include the following:

• 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.

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 it has a single search window shows that the developers of this platform wanted it to be easy for non-technical people to use.

Its primary features include:

• 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.

10. Nightfall

Nightfall 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.

Its main features are:

• 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.

Conclusion

Data discovery tools 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.