data discovery

A Guide to Data Discovery: Definition, Benefits, & Methods


Table Of Contents

Organizations have understood the power of data for a long time, but harnessing it effectively is another conversation altogether. Data silos within departments and systems that prevent decision-makers and leaders from seeing the whole picture to reliance on IT teams and data scientists to find, extract, and interpret data so insights can be gleaned are common issues that organizations face.

The idea behind data discovery is to democratize data so that everyone who needs access to data can easily access and understand it. As you can imagine, reducing data silos and making data easily interpretable, even if someone isn’t a data expert, can open up many positive opportunities for an organization.

But before you can get started, you’ll need to:

  1. Understand what data discovery is and how it works.
  2. Know the benefits of data discovery and be able to apply those benefits to specific aspects of your business, from operations to finances.
  3. Learn how to find and identify the best datasets from secure and reliable sources that you can trust

Let’s get started.

What is Data Discovery?

The data discovery definition is the process of “discovering” the data an organization has, and using that data to glean insights and inform business decisions.

There are really two ways that data discovery occurs within an organization.

The first is gathering data from various sources to augment internal organizational data. The process begins with extracting data from multiple sources and consolidating, classifying, and otherwise organizing the retrieved data into a single area for evaluation.

By incorporating outside data with internal data, organizations can get a holistic view of their business performance by comparing it to an entire industry or vertical rather than only internal information.

In other words, data discovery allows organizations to get a big picture and contextual, unbiased, and detailed understanding of where their business sits within an industry, rather than the opposite. It’s like having the whole storyline of your business rather than just a few chapters.

But that’s not all data discovery is used for.

Data discovery can also be used internally within organizations to have full visibility into their data to determine which datasets need the highest levels of security and access privileges versus which datasets can require low security and everything in between. With the greater level of understanding of organizational data that data discovery offers, organizations can manage regulatory and legal compliance like GDPR and CCPA much easier and more effectively.

Benefits of Data Discovery Service

Understand data movement within your organization

Data discovery permeates every aspect of an organization’s data and the movement of it. By understanding how the data discovery process applies to each point regarding how data moves in your organization, it’s easier to see where data discovery fits in and the information it can give you.

Shopify provides a good summary of each point in their data process (at a high level), and outlines how data discovery and management addresses questions at each point:

  • Where is the data coming from?
  • What is the quality of this data?
  • Who owns the data source?
  • What transformations are being applied?
  • How can this data be accessed?
  • How often does this process run?
  • What business logic is being applied?
  • Is the model stale or current?
  • Are there other similar models out there?
  • Who are the main stakeholders?
  • How is this data being applied?
  • What is the provenance of these applications?

Chances are that by viewing the above data process, you can see similarities with how data movement occurs within your own organization. Making your own table and highlighting what questions you want a data discovery service to answer will help you choose the most effective tool for the job.

Revelate, as a complete data fulfillment platform, has full activity tracking built into the system. This means that any action that takes place is recorded, so you can have full transparency into how data moves in and out of your organization, including who has access to what data.

Meet regulatory requirements

There are strict rules for data use in specific industries that extend beyond national or international data handling requirements. In the United States, for instance, HIPAA, or the Health Insurance Portability and Accountability Act, is one of them. Healthcare organizations that store and use patient medical data need to ensure the safety and confidentiality of these records or face severe fines and other penalties. Alternatively, PCI DSS is a set of industry-regulated standards in the finance industry that sets out rules regarding how organizations handle and protect cardholder data from transactions.

The complexity and ever-changing nature of regulations and best practices with data handling demand that organizations have complete visibility into the discoverability of their data, including the ability to ensure security at every turn.

Revelate, for instance, allows you to apply data automation to the movement of your data to ensure security measures are being followed every single time.

Metadata management

Effective metadata management is part of scalable data governance, where new data that are added to an ecosystem has the appropriate business terms, data classes, and quality assessments so it can be discovered, used, and governed effectively.

There are a few reasons why you’d want to do this beyond just straight organization:

  • To make it easier for people to find your data sets on a data marketplace, such as your data web store. The more specific and relevant information you can provide on a dataset, the easier it will be for your customers and stakeholders to gain the necessary information.
  • To meet regulatory compliance requirements. Metadata does more than just describe what data is, it can also describe source to target mapping from, let’s say, a data warehouse. Enhanced understanding of what the data is, what it can be used for, where it comes from, and what’s included within it is essential for regulatory bodies to understand whether it’s compliant with set-out security and access rules or not.

Data preparation tools to improve data quality

The typical data supply chain has a “data preparation” category for a reason: raw data needs to be organized, understood, and refined before it can be used by people. Revelate enables turning raw data into data products automatically, which can then be placed on your customized web store, ready to be downloaded by your customers. The refinement process takes raw data from a source, prepares it according to the customer’s needs while following your security and access requirements, and produces a finished data product.

Democratize insights and decision-making

Data discovery makes it possible for stakeholders across your organization to analyze and use data to improve their processes without needing high data literacy. Instead, using a platform like Revelate, anyone can download relevant data sets in a format that makes sense for their use case, and use that information to save on departmental expenses, identify how leads move through the sales funnel, or identify if sales activities align with customer touchpoints.

Identify new business opportunities

One of the other benefits of data discovery is the potential to identify business opportunities that you didn’t even know existed or that you didn’t think were possible before. Here are a few examples:

  • Many businesses use web chatbots powered by AI and ML. By augmenting internal data with outside data sources, the effectiveness of these technologies is increased, allowing you to provide a better experience for your customers.
  • Just like autocorrect on your phone gets better over time by analyzing your typing patterns, both internal and external data can be used to improve your business applications.
  • When you build a web store with Revelate, the data and analytics from purchases, downloads, searches, and more are captured for you to capitalize on. This could mean understanding the most popular data products and placing them at the top for easy access or reworking your listing to make it easier for those from a specific industry to find certain data products.
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Why is Smart Data Discovery Important?

smart data discovery importance

Think about the sheer volume of data that are copied, transformed, downloaded, and otherwise interacted with every single day. This astronomical amount of data can make it difficult to find relevant and high-quality data sources that actually provide the information you’re looking for.

An expert does traditional data discovery manually (which we’ll discuss more in the next section). As you can imagine, is not the most unbiased or error-free way to gain essential business insights and can also take a lot of time and effort.

Smart data discovery allows:

  • The data discovery process to be automated, which eliminates the time and effort that needs to be done by humans to interpret data, but it also ensures that the final product is error-free.
  • The democratization of data is another important part of smart data discovery. It means that everyone can read, understand, and get value from information in the form of smart data without requiring the assistance of a professional for interpretation.
  • Automatic classification of data based on context
  • Easier compliance with regulatory requirements
  • Better ability to handle data risk management, including implementation of security controls in real-time based on policies and contextual factors

With businesses needing to move quickly to stay relevant and continue to gain revenue in ever-changing markets and industries, more efficient and accurate data insights that can be understood by all stakeholders is imperative.

Data Discovery Methods

Manual Data Discovery Automated Data Discovery
Requires a professional (not necessarily a data analyst) to gather and interpret data to glean insights. Open to human error and bias. Uses advanced technologies such as AI and ML and set automations to gather, interpret, and present actionable insights.

Steps to Accomplish Smart Data Discovery

The smart data discovery process involves extracting data from multiple sources, preparing and cleaning the data for use, sharing data with internal and external stakeholders and customers, and performing analysis.

At a high level, the steps for smart data discovery can be summarized as follows:

  • Preparing data

Data preparation involves cleaning and rearranging data so that it can be used for visualization and analysis. This is essential when you’re getting data from multiple sources to ensure that the quality and integrity of the data remains intact, and that it follows your organization’s policies. With Revelate, data is refined and prepared according to criteria that you set, including security and access privileges.

  • Data visualization

Once data is in a readable format, it can be transformed into visualizations that make sense for the use case. Visual data discovery tools display prepared data into different formats, like charts, graphs, maps, and more.

  • Advanced analysis

Data discovery tools can take data and essentially summarize it so that it can be easily understood for whoever needs to use it. This is done through descriptions and metadata.

Data Classification and Discovery

If you don’t know where your data lives, you can’t protect it.

Data discover allows you to find data, while data classification allows you to organize that data into categories using tags, metadata, file types, content, and more.

Even smaller organizations have huge amounts of data stored in various areas, including cloud-based systems, on-premise locations, and more. All of this data needs to be tracked and maintained, but it’s also constantly moving and changing, adding to the complexity.

For data classification and discovery to be beneficial for organizations, they must have an effective process in place. This means understanding what data you have, ensuring that security and compliance is met, and finally understanding the scope of your data discovery needs.

Data visibility

With data discovery, organizations gain complete visibility and control of their data, including where it lives and who can access it. Data classification makes locating and retrieving sensitive data easier because it’s categorized and tagged according to your organization’s criteria.

Security and compliance

If there are any gaps in security or compliance that may affect the integrity of your data, it can be identified using data discovery. Because data discovery gives you a bird’s eye view of what’s happening with your organization’s data, you can quickly identify and laser-focus on areas of concern to eliminate vulnerabilities.

Narrowing the scope of your data discovery

Understanding the scope of your data is important for data discovery to work effectively. You need to know all the areas of your organization where data is stored, including the structured (e.g. information within a database) and unstructured (e.g., files and emails) that exist on cloud and on-premises storage networks.

Tips for effective data discovery and classification

Tip Description
Automate your processes Automating data classification eliminates errors, ensures consistency, and saves time when compared to manual classification.
Understand your business’s goals Understand where your pain points are and what you want to be solved. Enhanced security, better meeting of regulatory compliance, and better protection of PII are good examples of business goals that data discovery and classification can solve.
Look for flexible solutions Over time, your business’s needs will change as it grows and your data scales. Your data discovery and classification plan should be flexible enough to accommodate both predicted and unforeseen changes so you can pivot your strategies as needed.
Continue to iterate your processes as needed Data discovery and classification isn’t a one-off project. Instead, you should view it as something that can be continually improved upon over time. Examples include revisiting processes and identifying gaps and roadblocks and optimizing automations to accommodate changes to your organization’s data structures.

Solving Business Problems with Data Discovery

solving problems with data discovery

One of the main purposes of data discovery is to democratize data insights, allowing individuals in any department in an organization to understand insights without having to rely on IT or data experts to interpret those insights for them.

This allows business problems to be solved faster and easier, where every stakeholder can get a holistic and consistent view of insights and apply them directly to business strategies, regardless of their level of expertise with data analysis.

Although data discovery can help in many business scenarios, here are a few examples of common business problems that data discovery can help solve:

  1. Loss of market share

    Keeping track of competitors, pricing trends, and new entrants to a market is no longer an option for organizations to ensure they remain competitive.

    Markets are constantly changing, a new entrant could come into the market and gain market share pretty much overnight, or an aggressive pricing strategy from a competitor could result in a significant loss of existing and potential customers. By utilizing the most most up-to-date data available, organizations can make proactive decisions so they can continue to secure their position in a target market.

  2. Poor customer experience

    Providing a consistent and positive customer experience across all product and service offerings is key to keeping customers long-term, but it’s difficult to keep up as an organization scales. Using data discovery, business leaders and decision makers can better identify where gaps are causing a poor customer experience, and work to close those gaps with policy or procedure changes, touchpoint changes, and more.

  3. Stagnant revenue

    Being able to target the right customers with the right offering is one of the keys to continued growth. Data discovery contributes to better understanding of your customers to improve their experience with your organization, aids in finding new markets, and can improve existing products or services.

    For potential customers, data discovery can be used to zero-in on prospects that are looking for solutions that match the product and/or service offering perfectly, making it easier to close a sale and turn a prospect into a long-term customer.

    With business these days moving so fast, especially for SaaS businesses or eCommerce, its invaluable to use data discovery to access understandable data available that business leaders and decision-makers can easily interpret.

  4. Being reactive instead of proactive

The future can’t be entirely predicted, but data can help identify what is likely to happen to help organizations make educated business decisions. For instance, new channels and regulatory changes can throw organizations for a loop—especially when they seemingly come out of nowhere and leave leaders scrambling to accommodate them.

Data discovery can help organizations ensure they have access to the latest data and insights and create strategies that not only allow them to be more flexible to changes but anticipate potential changes before they happen and adjust accordingly.


The importance of data discovery cannot be understated. As data continues to be one of the most valuable and important resources that we have access to, it’s important that organizations take their data seriously. This means implementing the tools, policies, and procedures to classify, categorize, and control access to their data and also understanding the endless possibilities that democratizing data and augmenting internal data with external data can bring to their organization.

Data discovery is an important part of this process and should be key for any successful business.

If you’re interested in an all-in-one data fulfillment solution that can help you fully democratize data within your organization and with your stakeholders and customers and is fully scalable as your organization grows, then look no further than Revelate.

Interested in learning more? Contact us today.

Simplify Data Fulfillment with Revelate

Revelate provides a suite of capabilities for data sharing and data commercialization for our customers to fully realize the value of their data. Harness the power of your data today!

Get Started