Data is growing in volume and also spreading across multiple clouds. With the exponential growth of data, it's difficult for businesses to know what data exists and where. Lack of data visibility leads to heightened risk of data exposure, necessitating a robust data security posture management strategy to curb evolving data security risks.
Gaining insights into data assets, reducing risks, or deducing assumptions for meaningful results requires a critical step: discovering data residing on-premises, in the cloud, in hybrid cloud environments, and beyond. Data discovery does exactly that, enabling organizations to transform raw, disconnected data into a strategic advantage.
Despite its growing importance, many executives still view data discovery as a nebulous or overly technical concept. In this guide, we aim to demystify data discovery, explain its business value, and highlight why it should be a priority in your overall data strategy.
What is Data Discovery?
At its core, data discovery is the process of discovering meaningful patterns and insights from massive volumes of data. It entails identifying correlations, anomalies, and hidden trends in your data that might not be immediately noticeable.
Consider it a treasure hunt for important insights hidden in your databases. Making sense of the data you already have and turning it into competitive advantage information that can direct your business strategy.
Multiple goals could be behind discovering data across an organization’s data ecosystem. For instance, knowing what data exists in the environment, where it exists, and its access permission enables a business to manage risks related to security, governance, and compliance obligations.
The Critical Steps Involved in Data Discovery
Every organization leverages varying discovery and classification tools to identify and categorize data. However, the critical steps of discovering data involve some common yet critical steps.
a. Cataloging Known and Shadow Data Assets
To discover all data across an organization’s infrastructure, it is imperative first to identify and catalog all the data assets. While discovering data assets, it is also critical to identify non-cloud native assets as well that make their way to the cloud during the lift-and-shift of on-premise applications.
These may be unmanaged cloud databases running on top of generic compute instances. Since these assets are not managed by the cloud provider, they generally don’t show up as part of your data asset inventory in the cloud console. Such data assets are also known as shadow data assets, as they are usually not visible to IT teams.
Organizations may have dozens or hundreds of shadow assets across geographies, accounts, or cloud environments. Discovering all these assets is essential to prevent security risks and get complete visibility of all the assets and the data within.
The second step in data discovery is identifying sensitive data across the environment, along with its metadata. The metadata could be associated with varying business, technical, or security attributes. For instance, tags can assist in understanding the purpose of the collected data, such as its purpose of processing or purpose limitation.
Similarly, security metadata allows teams to determine whether the data is properly protected, such as whether it is encrypted, masked, or obfuscated. In other words, metadata is the basis of determining if the data is appropriately protected and governed.
c. Enabling Broader Data Initiatives Through Discovery
Data discovery isn’t the end goal, but it is a stepping stone that helps organizations reach their ultimate objectives. For example, data classification is carried out based on data discovery, where teams categorize personal and sensitive data across structured and unstructured assets. Similarly, data discovery is critical in various organizational initiatives, such as cloud migration. Businesses need to fully understand their data assets, security gaps, and dependencies to ensure a seamless migration.
Importance of Data Discovery for Business Value
When data is viewed or analyzed in silos, it doesn’t provide any meaning since it lacks complete context. A comprehensive data discovery process is essential to gain the complete data context for extracting meaningful results. There are many benefits of data discovery:
Data discovery promotes data literacy across the organization. In other words, data discovery helps increase the understanding and awareness of data amongst teams, enabling them to effectively use it for decision-making or problem-solving.
b. Enabling Effective Data Governance
It can help establish a robust governance framework by giving teams insights into data ownership, data lineage or quality, retention policies, access policies, etc. Effective governance ultimately helps optimize risk management and enables compliance.
c. Enhancing Data Mapping Capabilities
Data mapping is a critical governance and compliance component. It gives organizations a complete overview of where the data is stored, how it moves across systems, and the associated processing activities.
d. Supporting Security Risk Assessment
Data discovery also effectively contributes to the assessment of security risks. By classifying and cataloging data, organizations can assess potential security and compliance risks, such as unauthorized access, cross-border restrictions, etc.
e. Fueling Data Classification and Management
Data discovery helps fuel data classification, which is a necessary component of data management. It allows organizations to create policies, procedures, and controls for safeguarding data based on its sensitivity.
f. Protecting Intellectual Property Assets
Intellectual Property (IP) assets can also be properly protected by identifying and classifying IP data across an organization’s infrastructure, such as trade secrets, patents, etc.
g. Enabling Smarter Cloud Migrations
Discovery and classification are critical in cloud migration projects, enabling teams to ensure that all important data is migrated. With data discovery and classification, teams can efficiently identify and remove duplicate or obsolete data before migrating it to the cloud.
h. Facilitating Global Regulatory Compliance
There are varying data privacy laws and compliance standards across the globe. Data discovery and classification help comply with those regulations and standards by classifying data based on regulatory context. With properly classified data, organizations can implement appropriate security controls, compliance standards, or retention policies.
Challenges That Hinder Data Discovery
The process of discovering and classifying data isn’t a seamless one, even with discovery tools. There are several thorns and bumps that come along the way, making the discovery process a lot more challenging.
a. The Challenge of Distributed Data Environments
Data isn’t limited to traditional data centers or on-premise systems. It is spread across data centers, SaaS applications, and multi-cloud environments. The distributed nature of data makes it fairly difficult to identify and unite under one roof.
b. Shadow Data Assets Create Visibility Gaps
Shadow data assets also tend to create a huge blind spot for data teams. Cloud service providers (CSPs) offer data visibility in managed or cloud-native services. But when third-party or generic applications are moved to the cloud, they tend to bring shadow assets that remain unindexed or hidden, creating data blind spots and associated risks.
c. Rapid Data Growth Complicates Asset Tracking
The exponential growth of new data assets across an organization’s environment adds more complexity. Cloud platforms like AWS offer rich capabilities, allowing rapid innovation and integration across systems. Consequently, monitoring and keeping track of all the assets becomes difficult.
d. Uncontrolled Data Flows Lead to Sprawl
Data flow sprawl is yet another challenge that organizations face. As data has become the source of all innovations and strategic decision-making processes, it is continuously shared and accessed across varying systems and applications. Due to the high volume & velocity of data, it is challenging to trace data spread across different storage systems, geographies, departments, etc.
Data is now available in varying types and formats. Structured data, which is available in tabular format, is easier to discover and classify than unstructured data. Unstructured formats include images, documents, and other types of media. Keeping track of all these datasets and classifying them appropriately is difficult.
Automate Data Discovery with Securiti
The sheer volume, variety, and velocity of data require fresh thinking and a modernized approach to gain complete visibility of data and mitigate risks associated with data security and privacy.
Securiti uses its data command graph to build a catalog of all shadow & managed data assets. The platform helps discover & classify data across any structured and unstructured data system. By leveraging these insights, organizations can proactively minimize data risks and strengthen their data security posture.
Data discovery is one critical aspect, and data security posture is another. Securiti Data Command Center (rated #1 DSPM by GigaOM) provides a built-in DSPM solution, enabling organizations to secure sensitive data across multiple public clouds, private clouds, data lakes and warehouses, and SaaS applications, protecting both data at rest and in motion.
Schedule a demo to learn how Securiti addresses your organization’s unique data security, privacy, and governance needs with a unified Data + AI Command Center.
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