What is data discovery?

During data discovery, data professionals identify and extract raw data from across an organization’s databases, applications, internal files and other repositories. They examine the data’s characteristics, format, lineage, quality and potential uses of the data—a process called data profiling—laying the groundwork for successful data ingestion. Insights uncovered during the data discovery process are used to inform and streamline business decisions in areas such as marketing strategies, customer experiences and supply chain operations.

Exploratory data analysis (EDA) is a widely used approach for data discovery. In EDA, statistical methods and algorithms are deployed to investigate datasets and summarize their main characteristics. These findings help data scientists determine how best to manipulate data sources to get valuable insights.

Besides helping organizations identify and leverage all their data sources, data discovery also enhances data security, improves data accuracy and supports compliance with certain data privacy regulations. When augmented by artificial intelligence (AI) and machine learning (ML) techniques, it can give organizations even greater visibility into and control over their data assets.

At first glance, data discovery and data analysis may seem synonymous. However, they are distinct data management processes that work best when used together.

Data discovery often occurs first. It’s an exploratory phase that helps organizations locate and understand all available data—including information that is siloed or hidden. Analysts may not know exactly what data they are searching for at this stage.

Once they find the data, they can begin data analysis. This process involves using specific techniques and queries to interpret the data and uncover meaningful insights.

Consider this analogy: Data discovery is similar to searching your kitchen for ingredients, including those hidden in the back of the cabinet. Data analysis is using the ingredients you found to create a nutritious, high-quality meal. The more thorough your discovery, the better your outcome.

Data is critical to modern businesses. Every day, they collect massive amounts of information from an expanding ecosystem of sources spanning departments, business units and geographies. This data is handled by various users and stored across disparate data repositories and employee devices.

But when data is everywhere, it becomes harder to find, access and use. In fact, it’s estimated that 68% of enterprise data goes unused. Failure to analyze all types of data leads to missed insights and unexplored opportunities. For example, what if the key to improving customer retention is hidden in meeting notes and email threads, but the sales team relies only on data from their customer relationship management (CRM) system?

Not knowing what data you have and where it resides also exposes the organization to risk, such as noncompliance with the growing list of data privacy regulations governing personal data. However, data discovery is both a data privacy and a data security concern. If you don’t know where your sensitive data is, you also can’t properly protect it.  

Data discovery helps organizations explore and leverage all available data, supporting the following benefits:

• Enhanced decision-making
• Improved data accuracy and quality
• Strengthened data security
• Thorough compliance

Undiscovered and unmanaged data—often referred to as shadow data—poses a significant security risk, especially when it contains sensitive information. According to IBM’s 2024 Cost of a Data Breach Report, data breaches involving shadow data account for one-third of all incidents, and cost USD 5.27 million on average—16% more than the average breach cost calculated in the report.

Core to securing all organizational data is understanding how and where it enters the network, and how and where it is shared and stored. Robust data discovery processes are therefore crucial elements of both data security and data protection. The use of AI and ML to train systems to automatically identify files containing sensitive data can further boost these efforts.

Data discovery practices can also help reduce an organization’s overall attack surface. An attack surface is all of an organization’s vulnerabilities, pathways or methods that hackers can use to gain unauthorized access to sensitive data or launch a cyberattack. Through data discovery, unused or duplicate data is eliminated, leaving only the most necessary sensitive data. Organizations can then prioritize and tailor data security measures to these critical assets.

Data discovery is a combination of technical processes, tools and strategies that can be grouped into the following steps:

• Goal scoping
• Data collection and integration
• Data preparation
• Data visualization
• Data analysis

This first step typically involves defining the goals of the data discovery process. These objectives should align with the organization’s overall data strategy. Here, C-suite and business unit leaders work together to determine what insights they want to find, which helps guide data exploration.

Next, data is collected from various sources using extraction methods such as querying databases, pulling remote files or retrieving data through application programming interfaces (APIs). Collected data is ingested, integrated and transformed into a unified, consistent format to reside in a data catalog (a detailed inventory of data assets within an organization).

Once collected and combined, data undergoes various quality assurance processes to help ensure data is free from errors, inconsistencies and other data integrity issues. This preparation may include data validation, data cleansing and standardization techniques.

Data teams can create visual representations of the prepared data—such as graphs, charts, dashboards and infographics—that display complex data relationships in user-friendly interfaces.

Data visualization tools may even support self-service analytics. These tools allow non-technical users to access and analyze visualizations, helping drive data-driven decision-making. Advanced analytics may also be applied at this stage, which uses predictive modeling and other sophisticated techniques to generate forecasts.

Throughout the process, strong data governance helps ensure data integrity and data security. It defines and implements the policies, standards and procedures for data collection, ownership, storage, processing and use.

Using AI, ML and natural language processing (NLP) in data discovery adds both speed and intelligence to the process. These technologies give organizations greater visibility and control over their data. Key examples and use cases include:

• Automated data discovery: These tools automatically scan network devices and data storage systems, indexing new data and metadata in near real time for faster asset identification.
  
  
• Automated data classification: This functionality automates the tagging of new data based on predefined rules, such as sensitivity levels, data access controls and compliance rules.
  
  
• Intelligent search: AI-powered search uses NLP to interpret user search queries, understand intent and then deliver relevant data results. AI assistants can provide intuitive natural language guidance.
  
  
• NLP for unstructured data: NLP tools, including large language models (LLMs), can extract structured data from unstructured data sources such as documents, emails and chat transcripts.

Integrating AI, ML and NLP into data discovery workflows accelerates time-to-insights, increases accuracy and can help strengthen regulatory compliance. As data volumes continue to grow, AI-powered data discovery will become an essential capability and competitive advantage.