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Data Discovery

Data Discovery Definition

Data discovery refers to the processes involved in identifying, classifying, and providing visibility into the location, volume, and context of structured and unstructured data. Data discovery helps answer the questions: What data exists in our environment? What data repositories are storing that data? Which users and applications have access to this data? and What are these users and applications doing with this data? In answering these questions, data discovery has become a top priority for enterprise security teams that manage enormous volumes of disparate data throughout various repositories and need a clear view of every angle of their enterprise’s data.

Data discovery is also an increasingly valuable component of business intelligence. The discovery process helps business professionals answer specific, relevant questions and exploit the full value of their data. The discovery process typically involves visually navigating data in order to discover patterns and insights that would otherwise be hidden throughout vast, heterogeneous databases. These insights can be used to reveal valuable business opportunities and, more importantly, to identify sensitive data that may be vulnerable to compromise and to ensure data integrity and data confidentiality. Advanced data discovery tools enable businesses to easily execute activities such as continuous data governance, data privacy management, and database activity monitoring, which all contribute to data protection efforts.

Data Discovery FAQs

What is Data Discovery?

Data discovery is relevant to continuous governance, as centralized data storage is a key component of data governance strategies. As such, discovering data is highly valuable to security teams, aiding in data activity monitoring by enabling better discovery and classification of data from various sources, and faster identification of anomalies before they become real threats. Artificial Intelligence-powered activity monitoring is an increasingly crucial component of data security and data privacy management as our global information economy continues to experience exponential growth.

With data discovery, data from disparate databases is collected and consolidated into a single centralized place, making it easier to identify and classify sensitive data, investigate and uncover patterns, evaluate potential points of vulnerability, and communicate these insights to security teams. This can be accomplished with the use of data visualization and business intelligence tools.

Another popular goal of the data discovery process is empowering non-technical business leaders to uncover otherwise hidden patterns and anomalies, improving their understanding of the potential insights within their data. This democratizes data and fosters a culture of independence and data literacy where anyone in the company, not just data scientists, can fully understand and realize the potential of the data available to the business.

An integral part of discovery is interactive visual analysis, where users are able to interactively and clearly view data from all angles. Visual data discovery is the subsequent step after data exploration has first refined the data sets. Data preparation, data visualization, and analysis result in data that is cleaner, easier to understand, and more user-friendly.

What is the Data Discovery Process?

How is data discovered? There are three main data discovery process steps, which include data preparation, data visualization, and advanced analytics and reporting:

  • Data Preparation: To get high quality, consistent data that’s easy to use, a preprocessing step often needs to be taken. This step uses statistical techniques to clean and transform high-noise data obtained from different sources, so that it is easier to use.
  • Data Visualization: In data visualization, humans instantly recognize patterns and relationships in graphs or images. It is a critical process for analyzing big data, so important insights and messages would otherwise be lost, and for displaying the results of machine learning and predictive analytics.
  • Advanced Analytics and Reporting: With advanced analytics, you can tap into the power of descriptive statistics to organize and analyze data in order to make better business decisions.

Types of Data Discovery

There are two main data discovery techniques:

  • Manual Data Discovery: Manual discovery is the manual management of data before machine learning advances. Data specialists would map and prioritize data, monitor metadata and categorize information, document rules, and conceptualize all available data with critical thinking.
  • Smart Data Discovery: Also known as automated data discovery, smart data discovery solutions have created an automated experience. With advanced machine learning, data discovery software has been developed using AI to automate data preparation, conceptualization, integration and presentation of hidden patterns and insights through visualizations.

What is Data Discovery Used For?

Big data discovery is a major component of data security. In order to protect data from threats, ensure data accuracy, and comply with privacy and security regulations, you must first be well acquainted with what data you have, where it’s located, how it is accessed, who is accessing it, how they are using it, and its context.

Some common uses include:

  • 360° View of Your Data: Data discovery provides a broad, high-level view of all the data streams in your organization, allowing you to combine these streams and analyze them thoroughly. Seeing all of your data from every angle helps with data privacy management.
  • Compliance and Risk Management: Increasingly data is growing in size and regulations around them are changing, risk management and compliance are increasingly top priorities. Data discovery helps detect patterns that are anomalous within those datasets so business users can proactively address them.
  • Automated Classification: Data discovery enables automatic classification of information so that data from disparate sources can be clearly organized based on the context in which it is collected. Data governance simplifies the data preparation phase of discovery as it ensures that most data has been classified and categorized according to the company’s designated formats.
  • Real-Time Data Access Controls: Data discovery enables using predefined controls or contextual factors, which help businesses ensure data practices are compliant and storage is secure. Granular access controls are essential for continuous activity monitoring, data masking, and filtering.
  • Democratized Decision-Making: IT expertise shouldn’t be necessary to gain insight in business. Data discovery makes it possible for people across the business – regardless of their technical background – to analyze data.

Why is Data Discovery Important?

Knowing what data you have and where it’s located is the first step in protecting your data. Data discovery platforms help enterprises with exploring data and understanding their data better. The discovery process helps identify what sensitive data consists of, where it lives, and who has access to it. Discovery then enables data classification, which identifies the data that is the most important to the company, and ensures those pieces of data carry that identification throughout the pipeline, no matter where they end up or how many times they are copied.

Understanding data better also helps with compliance. Sensitive data discovery helps with establishing data policies that can be enforced and monitored so that the company can comply with regulatory obligations. Creating a culture of data awareness will help protect data wherever it resides. Data discovery and classification are the foundation of data security.

The benefits of data discovery also lie in its ability to democratize data and create a more agile, accessible workflow. Agility is a key component of business growth, and enterprise data discovery is the cornerstone of that.

Does Cyral Offer a Data Discovery Solution?

With Cyral’s discovery solution, enterprise leaders can see who has access to what data, where the data is located, and the pathways taken to access it. Features like multi-factor authentication and user-specific access help keep bad actors out of cloud-based databases. If a breach still occurs beyond those initial protections, sensitive data remains secure since the database will only permit limited access.

The Cyral platform provides data activity monitoring, policy-based cloud access control, least privilege, and identity federation, all of which have data discovery at their core. Discovery and auditing are designed to be database-centric so that information security teams can easily get a full view of the data from every angle. This approach greatly reduces the number of data breach instances and the reach of a successful breach.

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