What Is Data Discovery?
Data discovery refers to the process of searching, identifying, and analyzing large amounts of data within an organization to uncover hidden patterns, relationships, and insights that can inform better decision-making. This process involves using various techniques such as data visualization, machine learning, and statistical analysis to gain a deeper understanding of the data and extract meaningful information from it.
Organizations implement data discovery because it helps them to better understand what data they have, and how it can be used to drive business value. It also allows them to assess the accuracy, completeness, and consistency of their data and identify potential risks associated with the data, such as data privacy and security threats, data duplication, and data obsolescence.
This is part of a series of articles about data security.
Why Is Data Discovery Important?
Data discovery is important for compliance because it helps organizations to identify the types of data they have, where it is stored, and who has access to it. This information is necessary for ensuring compliance with data protection regulations and industry standards, which often have specific requirements for the handling of sensitive data.
For example, the General Data Protection Regulation (GDPR) requires organizations to implement appropriate technical and organizational measures to protect personal data. By discovering and classifying personal data, organizations can ensure that they have the necessary controls in place to meet these requirements.
Similarly, other regulations and standards, such as the Payment Card Industry Data Security Standard (PCI DSS) and the Health Insurance Portability and Accountability Act (HIPAA), have specific requirements for the protection of sensitive data. Data discovery is an important step in ensuring compliance with these requirements by helping organizations to identify and classify sensitive data and implement appropriate controls to protect it.
The Data Discovery Process
The data discovery process can be broken down into several key steps:
- Establishing objectives: The first step in the data discovery process is to establish the objectives of your discovery effort. This includes understanding the purpose of the discovery, the business problem that needs to be addressed, and the type of insights that are desired from the data.
- Determining the data storage scope: The next step is to determine the scope of the data storage systems that will be included in the discovery process. You need to cover all potential data storage locations to ensure you don’t miss any data points. This may include databases, data warehouses, data lakes, and other data storage systems.
- Choosing the best approach for discovering data: The next step is to choose the most suitable data discovery approach for your organization. This can be done through manual discovery, where a team of data analysts reviews the data, or by using an automated data discovery solution. Automated solutions use algorithms and machine learning models to automatically scan and analyze large amounts of data to identify patterns and insights.
- Data collection and preparation: Once you’ve identified all your data, you can collect and prepare it for analysis and other uses. This includes cleaning and transforming the data, removing duplicates, and ensuring that the data is in a format that can be easily analyzed. You might consider visualizing the data, conducting statistical analysis, or using machine learning models to uncover patterns and relationships in the data.
5 Key Capabilities of Data Discovery Tools
Data discovery platforms offer a range of capabilities to help organizations manage, analyze, and gain insights from their data.
Some of the key capabilities of data discovery platforms include:
- Visual front-end for managing data: Many data discovery platforms provide a visual front-end for managing data, which makes it easy for users to explore, visualize, and interact with data. This allows users to quickly identify patterns and relationships in the data, without having to write complex queries or perform manual data analysis.
- In-memory data processing: Many data discovery solutions use in-memory data processing to analyze large data sets in real-time. This allows users to quickly query, visualize, and interact with the data, without having to wait for results.
- Data source connections for big data: Data discovery platforms offer a range of data source connections for collecting large amounts of data, including support for Hadoop, Spark, and NoSQL databases. This allows organizations to analyze data from a variety of sources, including structured and unstructured data, to gain a more complete understanding of their data assets.
- Data preparation and cleaning: Most platforms also provide a range of tools for data preparation and cleaning, which is an important step in the data discovery process. These tools allow users to clean and transform the data, remove duplicates, ensure the data is in the right format, and preserve data integrity and accuracy.
- Machine learning and statistical analysis: Many data discovery platforms also include advanced analytics capabilities, such as machine learning and statistical analysis, to help users uncover patterns and relationships in the data. This can be used to gain a deeper understanding of the data and inform better decision-making.
Best Practices for Effective Data Discovery and Classification
Create a Discovery Strategy
Planning the data discovery strategy helps organizations identify the most important data assets and prioritize their efforts accordingly. By taking the time to understand your organization’s data landscape, goals, and requirements, you can make informed decisions about which data to analyze and how to analyze it. This leads to a more efficient data discovery process and a greater return on investment, as the organization can focus its efforts on the data that is most critical to your business.
A well-planned data discovery strategy also ensures that organizations have a clear understanding of the data discovery process and what they hope to achieve, which can help mitigate the risk of data breaches and other security incidents. It also helps you incorporate specific considerations such as data protection requirements to comply with regulations like GDPR.
Automate the Discovery Processes
Automating data discovery processes helps organizations save time and resources, reduce errors and inconsistencies, and gain a more comprehensive understanding of their data assets. Automated discovery solutions use algorithms to scan, classify, and analyze data in real-time, which makes it possible to quickly identify patterns and relationships in the data.
Automated data discovery also provides a more efficient and scalable solution for managing and utilizing data, compared to manual discovery methods. By automating the data discovery process, organizations can more easily monitor and manipulate their data, ensuring that they have accurate and up-to-date information.
Enforce Data Governance
Implementing data governance policies helps to ensure that data is managed in a consistent, controlled, and secure manner throughout its lifecycle. Data governance policies provide a framework for defining who is responsible for managing data, how data is collected, stored, used, and shared, and what data is considered sensitive or confidential.
This allows you to improve data quality, reduce the risk of data breaches, and ensure that data is being used in a manner that is consistent with legal and regulatory requirements. Additionally, data governance policies help to ensure that data is discoverable, accessible, and usable, which is essential for effective data discovery and classification.
Repeat the Process
Data discovery is an ongoing process because your organization’s data assets are constantly growing and changing, and new data sources are continually being added. As new data is generated and old data becomes obsolete, you must continually update your data discovery and classification efforts to ensure that the data is up-to-date and accurately classified.
A continuous discovery process helps you ensure that the organization is fully utilizing all data assets, mitigating risks associated with sensitive data, and making informed business decisions based on accurate and up-to-date information.
Data Security Fabric with Imperva
Imperva Data Security Fabric protects all data workloads in hybrid multicloud environments with a modern and simplified approach to security and compliance automation. Imperva DSF flexible architecture supports a wide range of data repositories and clouds, ensuring security controls and policies are applied consistently everywhere.
