The ultimate guide to data discovery

A strong data discovery strategy helps businesses make informed decisions, enhance their data governance, improve efficiency, and provide a better customer experience. Learn how to build an excellent strategy, and essential tools for enacting it.

Written by

Belinda Walsh

Reviewed by

Adam Roberts
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The volume of information held by businesses continues to grow exponentially, and it’s making it more complex for organizations to understand and manage their data. Without this transparency, it’s difficult to use data to reduce organizational risk or identify insights, trends and patterns from it.  

Unlocking these insights and exposing opportunities for risk reduction starts with data discovery.  

A strategic data discovery process helps businesses reduce risk, make informed decisions to enhance their data governance, improve efficiency, and provide a better customer experience.

Let’s dive in and learn more about data discovery and the valuable insights it provides.

What is data discovery?

Data discovery is the process of systematically identifying, classifying, and exploring data sets to help detect patterns, trends, and insights. It’s like transforming thousands of puzzle pieces into a complete picture to uncover valuable information to inform your strategies.

Businesses possess vast amounts of data in siloed, disparate data sources. While each data set may be useful on its own, the real benefit of data discovery is its ability to bring these data stores together in a cohesive data warehouse.  

Why is data discovery important?

We speak to organizations all the time about their data management challenges, and lately, we’re witnessing a growing realization among leadership teams that they need to understand all the data they possess.  

Direction is coming from above, with executives and boards having moved their focus from securing the perimeter to a more data-centric approach: worrying more about what to do with the data their organization possesses.

Why? The increase in number of privacy regulations, plus the frequency of data breaches, are forcing companies to understand – under threat of fine or data exposure.  

Data privacy regulations increase obligations  

Across the globe, modern data privacy laws place the requirement on companies to understand their data. They also require companies to remove data once it’s no longer required.  

The General Data Protection Regulation (GDPR) requires companies to know where their data is stored. Companies also need this understanding to ensure data accuracy, and appropriately respond to both Data Subject Access Requests (DSAR), and to requests for data to be removed (also known as “the right to be forgotten”).

A failure to adhere to these rules could result in fines of 4% of a company’s global annual revenue, or €20 million (about $22.4 million), whichever is greater.

In the wake of the GDPR’s passing in 2016, many countries implemented their own privacy laws, often copying elements of the law.

While the United States federal government has yet to pass a national privacy law, a growing number of US states are implementing their own, many of which take inspiration from the GDPR. By 2026, 13 states will have enacted comprehensive privacy legislation, with many more considering bills of their own (the International Association of Privacy Professionals has an excellent analysis of the laws).

No matter where your business is based, it’s becoming increasingly likely that you need to comply with relevant privacy laws to remain operational, and compliance starts with knowing your data.

Data breaches are inevitable

In the security space, data discovery can play a similarly important role. Companies that lack an understanding of their data won’t be able to secure it, appropriately manage access, or remove data once it is no longer needed. This means a potential data breach will have a larger area of impact, and more customers will be affected.  

When breached, companies that don’t understand their data will struggle to respond and won’t be able to tell what data has been accessed. This often results in a lengthy delay in informing affected customers.

A 2017 hack on credit monitoring firm Equifax, which impacted 147 million Americans and a further 15.2 million United Kingdom residents, went unreported for six weeks. Equifax did not disclose the reason for this delay, which was widely criticized at the time, and the case has been cited in calls for mandatory breach notifications.

In 2022, a major data breach of Australian insurance provider Medibank impacted 9.7 million customers, but customers had to wait more than three weeks to find out whether they had been impacted. During this time, the insurer provided a variety of explanations for which systems and data had been impacted by the breach, with the hackers often posting “drops” of data that appeared to contradict the official story.

Companies that experience a data breach often lose customers, revenue, and reputation – but this isn’t inevitable. A company that understands its data can come through a data breach without losing customers.

One of the key recommendations in IBM’s Cost of a Data Breach Report 2023 was that companies need to modernize data protection across hybrid cloud. Gaining visibility and control of data spread across systems is an essential step to achieving this.

Data discovery enables better strategies and decisions

As well as allowing for improved risk management, data governance and security, data discovery allows business leaders and key stakeholders to take a step back and view the bigger picture. This makes it easier to find patterns and make informed decisions.  

Picture a retail business that wants to improve its sales performance. To do so, it needs to gather information on sales, customer demographics, inventory levels, marketing campaigns, and dozens of other contributing factors.  

Typically, this data is siloed on point-of-sale systems, cloud data stores, customer relationship management (CRM) solutions, email databases, and marketing tools. While viewing one system independently may provide some insights, the real value comes from evaluating all the data at once.  

Data discovery helps businesses organize this information in an accessible way, making it easier to form cohesive strategies that consider every piece of information.  

Data discovery challenges

Locating your data is the right problem to focus on. But addressing it can be more complicated than it might seem, especially at scale.  

Often, data is distributed throughout an enterprise, with data silos and poor management practices making it hard to gain visibility on data.  

Data sprawl increases complexity

Rapid adoption of cloud platforms and Software as a Service (SaaS) platforms means data is held in more places than ever before. Data initially created in one app can make its way to others, while limited oversight of teams and pressure to complete tasks make it more likely teams will take shortcuts.

In one survey of 650 organizations, 54% of respondents say a decentralized data environment that spans on-premises and multiple cloud environments has contributed to a “data wild west.” The survey found that “44% of organizations cannot maintain governance and automate policy controls around data, and 42% cannot enforce consistent security measures – a clear vulnerability.

Data hoarding makes the haystack bigger  

Many organizations have a culture of data hoarding – holding onto data “just in case” it has value and is needed later. On top of slowing down teams, who must search multiple data sources for the information they need, the volume of data is much higher than required, making finding the "needle in the haystack” much more difficult. This can also result in companies holding onto legacy applications, which can represent a security threat in itself.  

Unstructured data makes automated analysis difficult

Security and privacy teams often struggle to understand what data they have. Maybe they understand what’s in their structured data sources—databases and applications like customer relationship management (CRM) platforms – but it’s much more challenging to understand what information is held in an unstructured format, such as data held in documents, PDFs, or media files. This data represents 80-90% of enterprise data, and is growing three times faster than structured data, with research firm IDC predicting growth from 33 zettabytes in 2018 to 175 zettabytes by 2025.

We’re not the only company that has seen an increase in customers seeking solutions to their data discovery challenges.

In a recent interview, Votiro VP of product management Eric Avigdor, whose company offers  a zero-trust platform to manage cybersecurity, said the biggest problem he heard from Chief Information Security Officers (CISO) was that they did not know where their data was.

“It's not that we know what the problem is and now let's figure out a way to remediate. We're not even sure where the data is."

“We know we have a bunch of stuff in OneDrive and a lot in SharePoint and we may have some on Google Drive and a lot of unstructured data in an S3 bucket in Amazon. How do we even go about finding the problem, and then how do we remediate once we know where the problem lives?”

So if organizations are sitting on a massive volume of data, most of it unstructured, and often lack the knowledge and tools to gain visibility and understanding of the data, what can they do?  

In many cases, they simply don’t know where to start. Fortunately, you don’t need to do it all at once.  

Data discovery can act as the catalyst for overcoming the challenges associated with poor data understanding and will allow you to make better decisions for risk and security, as well as inform your company strategy.

How to conduct data discovery

Effective data discovery requires a comprehensive strategy for mapping, tagging, classifying, and analyzing information. Here are the five critical steps to building this strategy:  

Step 1: Define the objectives  

You’ll need to clearly define the goal you want the process to achieve, the data required to reach that goal, and the resources you have to make that goal possible.  

  • Is your aim to speed up your business’s operational efficiency?  
  • Are you looking to discover a new target market?  
  • Do you need to fulfill a Data Subject Access Request (DSAR) or tag your sensitive data to prepare for evolving regulation?

A clear objective – or set of objectives – ensures you know which types of data you need to discover and the analytical processes you need to follow to obtain the required insights.  

Step 2: Data preparation

To address the challenge of data sprawl, before discovering patterns, create a data map across your organization’s platforms, devices, and departments to know where everything lies and prepare it for analysis. This is where you turn the chaos into order:

  • Map the lifecycle of each piece of data you possess
  • Eliminate any irrelevant or obsolete data you no longer require
  • Locate the relevant data that really matters to your organization

Given the vast number of systems in the average organization’s ecosystem, mapping your data may seem daunting. Using RecordPoint’s data inventory capability allows you to collect, store, and discover all your data, making it easier to create a roadmap to guide your discovery.

Step 3: Data profiling and data classification

Once you’ve compiled all your data points, classify your information to better understand your data insights.  

It’s crucial to classify data types based on sensitivity and any relevant regulatory requirements. You can do this by analyzing metadata, including metadata such as:

  • The data type stored, such as personal information, financial records, intellectual property, or health data
  • The format of the data. Is it structured, semi-structured, or unstructured?
  • The data store owner, for example, a specific department, an external vendor, or an internal stakeholder
  • The number and type of data fields in the data store, including the expected classification of data they contain  
  • Data store connectivity. For example, where does it link to, why does it link to it, and what is the nature of the data flow?  

Indexing your metadata can be a long, laborious task, but artificial intelligence (AI) can automate it. RecordPoint can help you map your data, index sensitive information, and discover trends faster.

Step 4: Data exploration

With your data mapped, tagged, and classified in a central data catalog, locating the necessary information should now be simple.

Now, you can transform your data into accessible formats with insights.  

  • Explore your data using data analytics tools such as descriptive data analysis, diagnostic tools, or predictive analytics to unlock insights
  • Experiment with different ways of displaying your data, like charts, graphs, or heat maps, to visualize and analyze it

Step 5: Record your findings and repeat

Once you’ve made and recorded your discoveries, the entire process can begin again.

Data discovery isn’t a one-time business process. Data constantly evolves, and businesses need to ensure ongoing compliance and a consistent strategy to maximize the benefits.  

While this task can seem overwhelming, by being proactive and using a platform to automate your data processes, you’ll have everything you need to reap the rewards of data discovery.  

Data discovery best practices

Implementing best practices at every touchpoint is crucial to effective data discovery. Here are seven key factors to consider.

✅ Ensure data quality

The effectiveness of your data discovery strategy varies by the quality of your data. Each piece of data you process should be:  

  • Accurate  
  • Complete
  • Standardized  

Data profiling ensures data integrity and allows data cleansing protocols to rectify errors. Establishing data quality policies increases the reliability of your data.  

✅ Regularly update metadata

Keep your metadata up to date by regularly updating the information you possess about data sources. This will support data discovery processes and allow you to dispose of obsolete records.  

Your unstructured data sources are particularly challenging in this regard. Learn more about the risks that come from unstructured data, and how to overcome them.

✅ Prioritize collaboration

Encourage collaboration between departments, stakeholders, business users, and data teams to leverage diverse perspectives and insights. Open communication is the key to ensuring everyone is on the same page about the objectives of your data discovery efforts.  

✅ Put data privacy at the forefront

Implementing data access governance, encryption, and other security measures is crucial to protect sensitive information from falling into the wrong hands.

✅ Take an iterative approach

Manage data responsibly and proactively. Allow for continuous improvement and ongoing refinement. Regularly review and update your strategies based on your business needs, goals, and evolving compliance.  

✅ Document data lineage

Always document the lineage of data to understand its origin and transformations throughout your organization. This helps you troubleshoot issues, prove compliance when required, and maintain transparency throughout the data discovery process.  

✅ Conduct risk management

Perform comprehensive data risk assessments, such as data concentration and data sensitivity tests, to assess your organization’s exposure to cyber threats. Evaluate risks, proactively implement safeguards, strengthen your security posture, and better protect against unauthorized access.

The importance of a data discovery platform

Manual data discovery is an incredibly tedious, error-prone task. Especially when sourcing data from multiple siloed sources. This problem doesn’t just apply to large-scale corporations.  

Small businesses with only a few systems might struggle to perform data discovery effectively and forget crucial data sets. Consider using a data discovery platform like RecordPoint to automate the process of mapping, tagging, and classifying your data.  

Artificial intelligence makes maintaining compliance, confidently handling DSARs, and discovering valuable insights easier.

What should I look for in a data discovery solution?

A data discovery tool assists with managing, automating, and controlling data. Here are some important features to look out for:

  • Machine learning capabilities to locate sensitive data and automate data discovery
  • Embedded analytics and visualization tools for data discovery
  • Automated classification based on data risk and value
  • Data management functions that encompass the entire data lifecycle
  • Compliance support for essential data standards and regulations

How can RecordPoint help?

If you’re looking to uncover valuable insights without the headache of manual discovery, RecordPoint’s data discovery platform can help. Our platform provides businesses with complete visibility of their data’s touchpoints.  

RecordPoint can connect to 900+ essential business systems and apps businesses rely on, allowing businesses to easily manage their data in one central location. With our ML-powered data discovery model, you’ll never have to worry about manually indexing and categorizing data assets again.

Why trust our expertise? As the only SaaS records management platform to formally complete a third-party IRAP assessment, we’ve demonstrated our commitment to robust data security and strict compliance. There’s a reason we’re the trusted data solution for dozens of heavily regulated organizations.  

Want to find out more? Explore our industry-leading data discovery platform solutions:

Want to see more? Schedule a demo to day to see how RecordPoint can help you overcome your data discovery challenges.