Insights · 10 min read

What is data discovery? Process, methods & why it matters

The Fullstory Team
Posted April 26, 2024
What is data discovery? Process, methods & why it matters

What exactly is data discovery and why should you care?

By simplifying the torrent of business data into clear, actionable insights, data discovery effectively acts as the lipchin for decisive data-driven action.

This article offers a straightforward guide on the methods and advantages of data discovery and is an essential resource for organizations looking to boost their competitive edge and foster innovation.

Key takeaways

  • Data discovery is the process of exploring and analyzing diverse data sources to uncover patterns and drive actionable insights, enhancing business decisions and performance.

  • The methods of data discovery include manual processes, which require extensive knowledge of data organization, and automated processes that utilize AI and machine learning to streamline analysis and ensure data reliability.

  • Data discovery is a cyclical, iterative process comprising goal setting, aggregation, preparation, visualization, analysis, and repetition, enabling continuous refinement and relevance to business objectives.

What is data discovery?

Data discovery is a dynamic process that involves exploring, classifying, and analyzing data from diverse sources to uncover patterns and extract actionable insights. It allows users to visually navigate data relationships and apply advanced analytics to optimize business decisions and performance.

Data discovery identifies what data exists and how it can be strategically used to drive value and efficiency within an organization.


Think of data discovery as a treasure hunt. It’s a process that involves:

  • Collecting, cataloging, and sifting through data to unearth valuable insights

  • Driving informed decision-making and improving business performance

  • Visually navigating through data

  • Applying advanced analytics to detect patterns and gain insights

Data discovery empowers teams by facilitating what Forbes highlights as the leading trend in data analytics: democratization. This trend allows everyone in an organization to explore and comprehend data easily as well as:

  • Bridging all areas of your business

  • Uncovering insights hidden within their data

  • Fostering a culture of collaboration and teamwork

Why data discovery matters

Data discovery plays an essential role in various facets of a business. These include:

  • Optimizing business processes

  • Identifying opportunities for cost savings

  • Ensuring data security and accuracy

  • Adhering to privacy regulations

Furthermore, data discovery is iterative, allowing businesses to refine their insights by repeatedly gathering and analyzing data continually.

Better customer experiences

Companies have begun leveraging Voice of Customer programs to improve customer experiences. Some benefits of analyzing customer data include:

  • Revealing friction points in user journeys

  • Providing insights for more personalized experiences

  • Identifying areas for improvement in products or services

By implementing these programs, companies can create more enjoyable customer experiences.

For instance, browsing and purchase history insights can help tailor product recommendations, making shopping feel more personalized and efficient.

Product innovation

Data insights can be a guiding star for product innovation. By understanding user needs and market trends, businesses can:

  • Develop new products or updates that align with customer expectations

  • Ensure innovations are timely and resonate with what customers genuinely want

  • Keep your business competitive

This data-driven approach ensures your business stays ahead of the curve and delivers products that meet customer needs.

Optimized operations

Data discovery highlights operational inefficiencies, enabling companies to streamline processes for cost reduction and enhanced performance.

For example, AI and machine learning automate the creation of metadata repositories, accelerating the understanding of where and how data is utilized across an organization.

Data-informed strategy

A deep dive into market and consumer behavior data can guide strategic choices that resonate with business goals and meet consumer needs. Decisions backed by data are more likely to yield success, from market entry strategies to targeted marketing campaigns. The result? Efforts that are well-directed and effective.

Methods of data discovery

There are two main methods of data discovery: manual and automated. Each method has its strengths, and the choice between the two often depends on the complexity of your data and the resources at your disposal.


Manual data discovery involves collecting and consolidating data from various sources into a single location. This method is crucial for transforming different data structures into a consistent format for analysis.

Data profiling, the initial step in manual data discovery, identifies which data sources are worth investigating, saving time and improving accuracy.

Visualizations such as charts, graphs, and heatmaps are key to manual and visual data discovery. These visual aids enable the identification of trends and patterns at a glance. However, manual discovery requires extensive data categorization and lineage knowledge to map and organize data effectively.


Automated data discovery, on the other hand, leverages AI and machine learning to streamline the analysis process. During the preparation phase, automated tools have the capability to:

  • Erase outliers

  • Unify data formats

  • Detect null values

  • Standardize data quality

These processes are essential for ensuring the reliability of the data being used. This method enables sales teams and business users to find relevant data insights without needing extensive data expertise.

Automated process discovery tools use machine learning and process mining algorithms to track activities within a system, offering an accurate view of business operations.

These tools can automate key steps such as data collection, process mapping, and providing automated insights, significantly accelerating the discovery process and making data discovery processes more efficient.

6 steps in the data discovery process: how is data discovered?

Now that we’ve explored the methods, let’s dive into the mechanics of data discovery.


Although the process might vary slightly between organizations, it typically comprises six main steps:

1. Set goals

Clear goal setting ensures the data discovery process aligns with business objectives and focuses on uncovering relevant insights. Without well-defined goals, data discovery efforts risk losing relevance and delivering insignificant business value. Think of this step as setting the compass for your data treasure hunt!

2. Aggregate

The next step is to aggregate data, which involves collecting raw data from multiple sources and presenting it in a summarized format for higher-level analysis. The collection phase extracts data from sources and stores it as atomic data, the smallest unit of data.

3. Prepare

Data preparation is like prepping ingredients before cooking. This step involves cleaning the extracted data, applying formatting rules, and discarding irrelevant data. Standardizing data during this phase ensures uniform formatting, which aids in subsequent analysis and visualizations.

4. Visualize

Data visualization is the process of transforming complex data into charts, graphs, and maps, providing broader insights and convenient platforms for visual analysis. This step is essential in making insights easily understandable and actionable, fostering effective decision-making.

5. Analyze

Now comes the analysis phase. Here, data is summarized, organized, and applied with descriptive statistics to highlight patterns, trends, and insights.

Advanced analytics and reporting tools summarize data into comprehensible reports that aid businesses in making informed decisions.

6. Repeat

The final step is to repeat the process. Data discovery is cyclical and requires constant updating to provide relevant insights based on the latest data, making it an ongoing task rather than a one-off event.

As business objectives evolve over time, so should your data discovery process.

The role of AI in enhancing data discovery

Artificial Intelligence (AI) is the secret sauce that takes data discovery to the next level. AI plays a vital role in:

  • Automating data processing tasks

  • Allowing for intelligent exploration

  • Advanced analytics and modeling

  • The generation of automated insights.

Smart data discovery tools leverage AI to automate and enhance the process of analyzing and documenting data. They enable natural language interactions and provide a more intuitive data discovery experience. These automated tools streamline the discovery process by:

  • Cleansing data

  • Detecting and correcting outliers

  • Standardizing data quality

  • Facilitating the integration of disparate data sources

Data governance strategies in data discovery

Safeguarding data is essential. Data governance strategies ensure data security, compliance, and risk management by implementing policies and practices aligned with privacy laws and ethical data handling guidelines. These strategies play a critical role in maintaining compliance during data discovery, from classifying data to ensuring data quality.

AI and machine learning tools complement manual stewardship by automating:

  • Metadata management

  • Data classification

  • Governance

  • Enforcing data lineage tracking and access controls

Thus, integrating data governance practices into data discovery enhances the trust and transparency of data across your organization.

Use cases and examples of data discovery

Data discovery isn’t confined to a specific industry; its applications are as diverse as the businesses that use it. Here are some examples of industries that benefit from data discovery:

Optimizing retail strategies

Retailers apply data discovery to:

  • Understand customer behavior

  • Refine marketing strategies

  • Refine inventory strategies

  • Craft personalized marketing strategies

  • Make informed decisions about inventory management

Insights into customer preferences and buying patterns derived from data discovery can guide retailers in these areas.

Streamlining financial operations

In the financial sector, institutions implement data discovery for tasks like analyzing client behavior to predict mortgage defaults and strengthen risk management.

The detection of non-compliance through data discovery can significantly improve risk management strategies in financial services.

Advancing healthcare outcomes

In healthcare, data discovery plays a pivotal role in enhancing the accuracy of diagnoses, the effectiveness of treatments, and the overall improvement of patient care. Life sciences companies apply data discovery to analyze genomic data, clinical trial results, and drug interactions, thereby accelerating drug discovery, advancing personalized medicine, and contributing to healthcare research.

Achieving data literacy across the organization

Achieving data literacy across an organization is akin to equipping every employee with a superpower. Here are some strategies to enhance data literacy:

  1. Organize educational workshops to provide personalized guidance and hands-on experience.

  2. Implement online courses to allow employees to learn at their own pace.

  3. Discuss a broad range of data-related topics to foster a deeper understanding. Implementing these strategies can enhance data literacy and empower employees with valuable skills.

AI data catalogs foster collaboration and facilitate access to data analysis for non-IT professionals, promoting a data-driven culture within organizations. Assessing literacy skills through tests and encouraging practice with real-world data sets helps individuals appreciate data’s complexities and nuances.

Discover your data with Fullstory

Now we understand data discovery is an iterative process involving collecting, analyzing, and visualizing data to identify patterns and insights. These insights are crucial for improving customer experiences, driving product innovation, optimizing operations, and guiding strategic decisions. Enhanced by AI, effective data discovery is key to informed decision-making and robust business performance, positioning your organization for success.

But how do we leverage this process successfully? Fullstory’s behavioral data platform offers a sophisticated solution that further enhances your discovery capabilities. By emphasizing the detailed sentiment behind every user interaction on your website or application, Fullstory extends the reach and depth of data analysis.

Fullstory excels in capturing extensive user behavior data. This capability, in turn, enriches analytics and provides invaluable insights beyond traditional data examination methods.

Frequently asked questions

What is data discovery?

Data discovery involves collecting, analyzing, and visualizing data to uncover insights that inform decision-making and enhance business performance.

Why is data discovery important?

Data discovery is important because it enhances customer experiences, drives product innovation, optimizes operations, and informs strategic decisions. It's crucial for overall business success.

What are the methods of data discovery?

The two main methods of data discovery are manual and automated. Automated methods use AI and machine learning for more efficient analysis.

What is the role of AI in enhancing data discovery?

AI enhances data discovery by automating tasks, enabling intelligent exploration, and providing advanced analytics and insights. This can lead to more efficient and effective data analysis.

How can data discovery improve healthcare outcomes?

Data discovery can improve healthcare outcomes by enhancing diagnosis accuracy, treatment effectiveness, and overall patient care.

The Fullstory TeamContributor

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