Data discovery: Definition, importance, and trends

What is data discovery?

Data discovery is the process of identifying, collecting, and analyzing data across an organization. An important part of data management, data discovery plays a crucial role in enabling organizations to understand what data is stored in various systems and repositories.

Data discovery is also essential for effective data governance, compliance, and analytics. Through data discovery, organizations gain insights into the types, locations, and relationships of data, which is crucial for making informed decisions, enhancing data security, and optimizing data usage.

Why data discovery is important

Data accessibility

Data discovery tools often provide intuitive, visual interfaces that help make complex datasets understandable for non-technical users, enabling them to explore and analyze data. This democratization of data makes insights accessible to a broad range of users.

Better data quality and integrity

Data discovery processes help identify data quality issues and inconsistencies, duplicates, and errors to improve the reliability and usefulness of their data, ensuring that analytics and business intelligence efforts are based on accurate and trustworthy information.

Competitive advantage

Data discovery enables organizations to rapidly adapt to market changes, customer preferences, and emerging trends by making information easily accessible and ready for analysis, which can provide a significant competitive edge.

Data management streamlined

Data discovery simplifies data management by automating the labor-intensive processes of data collection, organization, and analysis. This streamlines the management of data storage, access, and lifecycle policies in complex data environments and reduces the time and resources required for data-driven projects.

Enhanced compliance

Data discovery facilitates compliance with data protection regulations such as GDPR (General Data Protection Regulation), CCPA (California Consumer Privacy Act), and HIPAA (Health Insurance Portability and Accountability Act) by identifying and classifying sensitive data. Understanding where sensitive data resides and how it is used helps teams apply the appropriate data protections to comply with regulatory requirements. This not only minimizes the risk of noncompliance but also helps improve overall data security and privacy.

Identification of insights and trends

Data discovery tools can analyze patterns and relationships within the data, uncovering insights that might otherwise remain hidden. These insights can help identify new opportunities, optimize existing products and services, and discover ways to increase efficiencies in operations, as well as drive creative solutions and strategic growth.

Improved data governance

Data discovery helps organizations implement more effective data governance programs by providing visibility into data assets and their distribution, usage, and quality across the enterprise.

Increased security

Data security is enhanced by providing visibility into what sensitive data is stored, where it is stored, and how it is accessed, helping teams refine and target data protection practices.

Informed decision-making

Data discovery allows organizations to identify and access relevant, high-quality data quickly to inform decision-making processes.

Opportunities for data integration

By providing a complete view of data across an organization, data discovery helps break down silos enabling teams to identify opportunities for data integration. This integration can provide a unified view of information and support a more holistic approach to data analysis and utilization.

Risk assessment and management

Data discovery can help identify and assess risks related to data storage, processing, and compliance. This allows teams to remediate issues that protect data in accordance with regulatory and operational requirements.

Types of data discovery

Several types of data discovery address different use cases, from identifying sensitive information and ensuring compliance to enhancing business intelligence initiatives. The decision about which kind of data discovery is best depends on the complexity of the data environment and the data analysis and handling objectives.

Advanced analytics

Advanced analytics is sometimes referred to as augmented data discovery. Artificial intelligence and machine learning algorithms power advanced analytics, including predictive analytics, prescriptive analytics, and data mining. Augmented data discovery is used to identify deeper insights, forecast future trends, and identify patterns. This type of data discovery requires specialized skills and tools but can be used to predict future trends, recommend actions, and identify complex relationships within the data.

Automated

Automated data discovery uses software tools to scan, identify, and classify data across various storage systems and databases automatically. It leverages algorithms and machine learning to recognize patterns, relationships, and sensitive data without manual intervention.

Mobile

Mobile data discovery allows users to access and analyze data anytime, anywhere. It includes mobile business intelligence applications and platforms that are designed for optimal viewing and interaction on smaller screens.

Self-service

Self-service data discovery provides tools and interfaces to empower non-technical business users to access, explore, and analyze data without relying on IT departments. Self-service tools include intuitive interfaces, drag-and-drop functionalities, pre-built analytical models, and visual data exploration features to facilitate access to and interpretation of data.

Social

Social data discovery analyzes data from social media platforms to gain insights into customer sentiments, trends, and behaviors. Large volumes of unstructured data are collected from posts, comments, and other user data.

Visual

Visual data discovery uses graphical representations of interactive dashboards, charts, graphs, and maps to explore and analyze data sets. It helps users quickly identify patterns, trends, and anomalies and drill down into specifics.

Visual data discovery makes complex data more accessible and understandable to a broader group of non-technical users.

Visual data discovery tools are commonly used in business intelligence and analytics to present the results of analytics.

Steps in data discovery

1. Discovery and collection
   Data discovery begins with scanning databases, data lakes, file systems, and other internal and external data storage systems to catalog available data. This step helps organizations understand what data they possess, where it is stored, and how it is structured.
2. Cataloging
   In data discovery, cataloging is the process of organizing collected data into a searchable and manageable format. This step often involves documenting the metadata associated with each data element to make it easier to understand the data's origin, structure, and purpose.
3. Classification and tagging
   Once data is identified and cataloged, it needs to be classified based on its type, sensitivity, and importance. Data can be classified into various categories (e.g., personal data, financial information, or operational data) as well as its sensitivity level (e.g., public, private, or confidential) based on predefined criteria. These classifications and associated tagging ensure that the appropriate data protections are applied to data according to policies and regulations.
4. Evaluation and analysis
   Data discovery tools are used to analyze the data to determine its quality and usage patterns. During the evaluation and analysis step, any inconsistencies, duplicates, or errors that may affect data analysis and decision-making are detected.
5. Data profiling
   Data profiling provides clarity about the structure, content, and relationships within data, as well as data patterns, anomalies, and integrity constraints.
6. Mapping data lineage
   Data lineage needs to be mapped to understand how data flows from its source through various processes and transformations before it is used for analysis or reporting. This includes tracking data's origins, movements, and where it is consumed across an organization to help data managers understand the impact of changes in data.
7. Ensure compliance and security
   All discovered data should be evaluated in the context of compliance requirements and security policies. Sensitive data needs to be appropriately protected according to regulatory standards (e.g., GDPR, HIPAA, and CCPA).
8. Establish governance and stewardship
   Develop a governance framework to manage data discovery and ongoing data management practices. Assign data stewards to oversee data assets, ensuring data remains accurate, accessible, and secure. Set up policies and procedures for data usage, access, and quality control.
9. Integration and accessibility
   Facilitate the integration of data across systems to break down silos and ensure data is accessible to authorized users. This might involve ETL (extract, transform, load) processes, data virtualization, or API (application programming interface) management. Self-service tools and platforms help non-technical users to access and analyze data without IT support.
10. Continuous monitoring and improvement
    Continuously monitor the data environment for new or changed data assets. Regularly revisit data discovery processes to ensure they adapt to changes in data sources, business needs, and regulatory requirements.

Data discovery and business intelligence

Data discovery and business intelligence (BI) are closely linked parts of data analytics. While data discovery focuses on identifying, understanding, and organizing data, business intelligence leverages this groundwork to analyze data and support decision-making.

Data discovery provides the initial step in the BI process by ensuring that the data used for analysis is accurate, comprehensive, and relevant. By identifying and cataloging data across various sources, organizations gain a solid foundation for their BI initiatives. The data discovery process enables effective business intelligence by:

1. Enabling advanced analytics
2. Enhancing data quality
3. Facilitating self-service BI
4. Supporting data governance

Data discovery use cases

Horizontal data discovery use cases

1. Business intelligence and reporting
   Data discovery facilitates the development of comprehensive business intelligence (BI) and reporting solutions by ensuring that all relevant data sources are identified and accessible.
2. Compliance and risk management
   Organizations in regulated industries use data discovery to identify and classify sensitive data, such as personal identifiable information (PII), protected health information (PHI), and financial details to facilitate compliance with regulations like GDPR, HIPAA, and CCPA.
3. Customer insights and market analysis
   Data discovery helps organizations collect, organize, and analyze customer data from various touchpoints, such as sales transactions, social media, and customer feedback to provide deeper customer insights, support personalized marketing strategies, and inform product development decisions.
4. Data governance and quality
   Data discovery supports comprehensive data governance programs by providing visibility into data assets across the organization and establishing a clear inventory of data, assessing its quality, and identifying redundancies or inconsistencies.
5. Data lake optimization
   Organizations that use data lakes employ data discovery to catalog and classify structured and unstructured data to enable more efficient data management, improve the accessibility of data for analytics, and enhance the overall value derived from data lakes.
6. Mergers and acquisitions
   During mergers and acquisitions, data discovery is used to assess the data landscape of the entities involved. It helps provide clarity around the scope, quality, and compatibility of these assets to streamline integration processes and uncover potential issues early in the transition.
7. Operational efficiency optimization
   Organizations leverage data discovery to identify and analyze data across different systems and departments, uncovering bottlenecks, redundant processes, and opportunities for optimization.
8. Security enhancement
   By identifying where sensitive or critical data is stored, data discovery enables organizations to implement targeted security measures.

Vertical data discovery use cases

1. Education
   Educational institutions use data discovery to track student performance, attendance, and engagement, identifying areas for intervention and support. It is also used to facilitate analysis of academic outcomes and course feedback to refine curricula and teaching methods to improve student learning experiences.
2. Energy and utilities
   Utilities use data discovery to analyze consumption patterns and forecast energy demand, enabling more efficient energy production and distribution. Data discovery is also used to analyze grid performance data to identify inefficiencies and plan for infrastructure improvements to ensure a reliable energy supply.
3. Finance
   Financial institutions leverage data discovery to analyze transaction patterns and identify anomalous behavior indicative of fraud. In addition, by examining market data, credit histories, and customer profiles, banks can assess and manage credit risk more effectively.
4. Healthcare
   In healthcare, data discovery is utilized to aggregate and analyze patient data, research findings, and clinical trial data to support advanced research, disease surveillance, personalized patient care plans, and better outcomes analysis.
5. Manufacturing
   Data discovery enables manufacturers to analyze supply chain data to identify inefficiencies, streamline operations, and reduce costs. It is also used to predict issues with equipment before they occur to reduce downtime and maintenance costs.
6. Public sector
   Data discovery supports smart city initiatives, analyzing traffic patterns, utility usage, and public service demand to improve urban planning and citizen services. Government agencies also use data discovery to assess the efficacy of policies and programs.
7. Retail and e-commerce
   By analyzing customer data, retailers can identify purchasing patterns, preferences, and behaviors to tailor marketing messages and offers, as well as increase customer engagement and loyalty. Data discovery is also used to help retailers understand sales trends and customer demand.
8. Telecommunications
   Telecom companies use data discovery to monitor network traffic, identify congestion points, and optimize network performance to enhance customer experience.

Tools used in data discovery

Data discovery utilizes a variety of technologies, including tools for:

1. Advanced analytics and machine learning platforms
2. Automated scanning
3. Data analytics and visualization
4. Data cataloging
5. Data governance and compliance
6. Data integration
7. Data lineage
8. Data preparation
9. Data profiling
10. Data quality
11. Search-based data discovery
12. Self-service data discovery

Trends in data discovery

Several technology and operational changes are pushing data discovery in new directions. Like so many other areas, advancements in artificial intelligence capabilities are being incorporated into data discovery to automate pattern recognition, power predictive analytics, and enable deeper data-driven insights. In addition, the adoption of self-service data discovery platforms empowers end-users to perform sophisticated analyses without extensive technical knowledge. Finally, a shift towards integrating data discovery capabilities directly into business processes is delivering real-time insights that drive immediate decision-making.

Data discovery: Foundational to operations

Organizations across industries leverage data discovery processes and tools to navigate the vast amounts of data they generate and collect, transforming this data into actionable insights and strategic assets. By effectively identifying, classifying, and analyzing data, organizations can enhance their data governance, improve data quality, meet compliance requirements, and extract value from assets. With the volume, variety, and velocity of data creation only increasing, data discovery should be a top priority for all organizations.

Assess the strength of your identity security program

Research-backed, industry-specific benchmark data and a roadmap for driving business value