Data discovery may seem like a no-brainer, but the massive growth of data use and assets have made it a much more complicated practice than just a decade ago. Manual processes can no longer efficiently keep up with data discovery and management, and the downstream effects could spell trouble for data teams. Just as ignoring your mail would inevitably lead to unpaid bills and overlooked documents, failing to implement a data discovery solution to manage incoming data may end in penalties and legal ramifications.
To help avoid these potential pitfalls and navigate data discovery, here’s everything you need to know about choosing a data discovery solution.
What is Data Discovery?
When discussing data discovery, it’s important to distinguish between its use for business intelligence (BI) and analytics, versus its role in data security and access management.
Data discovery for BI and analytics involves finding and inspecting data from various sources to identify trends and patterns within it. Typically, this will involve tools like Looker or Tableau. This type of data discovery empowers non-technical stakeholders to easily make sense of data so they can make informed decisions, without having to rely on IT.
Similarly, sensitive data discovery for security and access management identifies sensitive information in a data set, but with the goal of ensuring that the appropriate access control policies can be put in place to protect it. Sensitive data encompasses personally identifiable information (PII) like social security or driver’s license numbers, as well as protected health information (PHI) such as medical history and insurance information. Commercially sensitive data also falls in this category, and includes information like business strategies and intellectual property.
Whether it’s being used to understand trends that drive decision-making or to maintain compliance with data regulations, it’s clear that data discovery is a fundamental component in the modern data stack.
Use Cases for Data Discovery Solutions
Both data volumes and users are continuing to grow at exponential rates across industries. In the five-year period ending in 2025, the amount of data created and consumed globally is expected to nearly triple. Keeping tabs on what data your organization is collecting, storing, and using is essential to ensuring that you’re maximizing its value while maintaining its security. A data discovery solution is the best way to do this efficiently.
To take the abstract and make it tangible, let’s look at some of the top use cases for data discovery solutions:
Accelerating Data Workflows
Inefficient data workflows don’t just cause headaches – they can actually impact bottom line results. In a survey of 600 data professionals, nearly 90% said their organization had missed business opportunities due to data access obstacles. What’s more, 55% of respondents in a separate report found said data is stale or outdated by the time they gain access to it. Inefficient data access is hindering workflows and in turn, stifling insights – but the root of the problem may be traced back to data discovery.
Manual approaches to sensitive data discovery impede data workflows even further. Individual human inspection requires substantial time and attention, not to mention that it is highly error-prone. The aforementioned survey of 600 data professionals found that data professionals spend up to 10 hours a week – 480 hours a year – responding to, managing, and resolving data access issues.
Incorporating sensitive data discovery into standard workflows helps to quickly identify, tag, and classify information so that the appropriate policies can be enforced with minimal human intervention. As a result, data consumers get faster access to data, while governance, risk, and compliance stakeholders can rest assured that all sensitive information is being properly handled.
Performing Audits for Regulatory Compliance
Like manual inspection, auditing for compliance laws and regulations can be a burdensome and time consuming process. With the rapid growth of data localization and regulatory requirements, keeping track of which data is subject to which laws is complex and runs the risk of expensive penalties. This is particularly true for highly regulated data-driven organizations in industries like financial services and healthcare.
Sensitive data discovery helps simplify the auditing process by tagging data that contains sensitive information and classifying it accordingly. This way, governance, risk, and compliance teams can determine what types of data are subject to specific regulations, and data engineering teams can build and enforce policies that translate relevant regulatory requirements into access controls. If an organization is ever audited, this helps provide a holistic view of the data in its possession and the mechanisms in place to protect it.
Assessing Risk Within Your Data Ecosystem
Auditing can provide insight into what has already happened in your data environment so you can address and remediate any issues. But using data discovery to assess risk can help proactively uncover what might happen in the future so you can avoid it in the first place.
By automatically discovering, tagging, and classifying all data – including sensitive data – you can more easily gain an understanding of how much sensitive data you have, where it resides, who owns it, and what controls are in place to protect it. At a baseline, knowing what percentage of your data is considered sensitive may help determine the potential level of risk you face. However, combining that with the other contextual factors provides a better snapshot of the level and types of risks, including residual risks that are entirely out of an organization’s control.
Each organization will have a different risk tolerance, or the amount of risk deemed acceptable in pursuit of long-term objectives. Risk tolerance is often a function of both environmental elements, like regulations pertinent to an organization’s industry, and organizational conditions, such as culture and technology. For instance, a large bank will likely have a much lower risk tolerance than a small startup due to its more established processes and high degree of regulation. That said, risk tolerance can only be determined once the organization has identified exactly what data it holds.
Feeding BI Dashboards for Real-Time & Predictive Analytics
The ability to find, aggregate, and train models on data from across an organization’s ecosystem is an enormous benefit for real-time and predictive analytics. Data discovery powers business intelligence (BI) dashboards by doing just that. Still, the data that feeds BI and analytics tools must be only available to users who are authorized to access it.
Combining data discovery and classification with the appropriate access controls helps streamline this process by integrating BI data sources directly into data security solutions that feature sensitive data discovery and access control. For instance, enabling Tableau row-level security allows data teams to build and share dashboards without having to request and wait to be manually granted access to data. In practice, this helped save one health data company more than $1 million in data engineering costs, and reduced its time-to-data from 90 days to three.
What to Look For in a Data Discovery Solution
Sensitive data discovery tools have made substantial strides in recent years to adapt to the evolving data use landscape. When looking to adopt such a tool, modern organizations should prioritize a few key features:
Automation – The speed and scale of data use make automated data discovery essential – going about it manually will no doubt lead to data slipping through the cracks. Today’s organizations can’t afford to be penalized for overlooking sensitive data because of error-prone human inspection. Automation makes such a scenario entirely avoidable, not to mention much more efficient.
Customizable Classification – Pre-built classifiers that can be automatically applied to data types like PII and PHI are must-haves. But the best data discovery solutions also allow you to build customized classifiers that can be tailored to your organization’s specific data. This puts more flexibility and control in your hands, and avoids defaulting to a one-size-fits-all approach.
Catalog and Metadata Integration – The global data catalog market is expected to soon surpass $2 billion annually. It’s therefore reasonable to assume that data catalogs will be integral to organizations’ data stacks going forward. The metadata that can be used to discover, classify, and create policies for the information housed within those platforms can greatly simplify access control implementation. Teams looking to build streamlined data environments should prioritize integration with their data catalog of choice.
Next Steps for Adopting a Data Discovery Solution
Sensitive data discovery is a central component of a strong data security strategy, and choosing the right solution should not be taken lightly. Taking your data needs and objectives into account, along with assessing your current and future tech stack, will help lay the foundation for identifying which tool is right for you. To find out how these platforms work and which are the most popular, check out our blog on sensitive data discovery tools for modern data stacks.