Data surpassed oil as the most valuable resource in the world several years ago. But like oil, data isn’t meaningful in and of itself—it’s what an organization does with its data (i.e. how it performs data discovery) that creates its value.
Unfortunately, most organizations aren’t realizing the full value of their data. A Seagate and IDC survey from 2019 to 2020 found that organizations fail to leverage 43% of the data they capture and a whopping 68% of the total data available to them. How can corporations get more from their data?
What businesses need is a mechanism for locating, understanding, and drawing insight from their data. Data discovery—the process of collecting, analyzing, and organizing information from multiple sources—is that mechanism.
Organizations can use data discovery reactively, to monitor their performance, or proactively, to drive innovation. Data discovery also has many applications in the legal context, from detecting risk and ensuring compliance to making data more accessible in the event of a data subject access request, litigation, or investigation.
This post will discuss the purpose, process, and benefits of data discovery and look at how the right tools can help organizations get more from their data.
- What is data discovery?
- What is the purpose of data discovery?
- What are the challenges associated with data discovery?
- How can data discovery be useful for legal teams?
- The data discovery process
- Common data discovery techniques
- The benefits of data discovery
- Tools for smart data discovery
- How the right data discovery tools can help corporations leverage their data
What is data discovery?
Data discovery is the process of exploring and analyzing data to gain insights and identify patterns, trends, relationships, and other relevant information. It involves using various tools and techniques to extract meaningful information from large volumes of data that may be structured or unstructured.
Data discovery is used in various industries and contexts, including legal, scientific, and business applications. The goal is to help users gain insights from the data, make informed decisions, and improve their understanding of the topic at hand.
What is the purpose of data discovery?
The purpose of data discovery is to give organizations an understanding of their data as a whole and a means to extract valuable information from that data for business and legal purposes.
Data discovery helps organizations identify and understand the data they have and facilitates easy access to specific data sets. It does this by centralizing data to reveal patterns and outliers that would normally be difficult to see. These insights can then inform business strategy, data security, and legal and regulatory compliance.
Without data discovery, organizations can quickly get stuck micromanaging individual data streams, causing them to miss out on their data’s full potential and overlook security gaps.
With this purpose in mind, let’s take a look at how data discovery applies in the legal context.
What are the challenges associated with data discovery?
The most common challenges related to data discovery include:
- Volume and complexity of data: Legal cases often involve a large volume of data, which can be structured or unstructured. This data can come from multiple sources, such as emails, social media, and databases, and can be difficult to manage and analyze.
- Privacy and confidentiality concerns: Legal professionals need to be careful to protect the privacy and confidentiality of the data they collect during data discovery. This can be particularly challenging when dealing with sensitive or confidential information, such as trade secrets or personal information.
- Cost and time implications: Data discovery can be time-consuming and expensive. Legal professionals need to allocate sufficient resources to the process, including personnel, software, and equipment. Failure to properly manage these resources can result in delays and increased costs.
- Legal requirements and regulations: There are various legal requirements and regulations that must be followed during data discovery, including rules of evidence and data privacy laws. Failure to comply with these requirements can result in sanctions or penalties.
How can data discovery be useful for legal teams?
When legal teams use data discovery to prepare and understand their organization’s data, they can better ensure compliance and improve the way they handle data subject access requests, investigations, and eDiscovery matters.
Data discovery can show legal teams what personal and sensitive data an organization has and reveal the potential risks associated with that data. Legal teams can then use that information to improve security, ensure compliance, and mitigate risk.
Through data discovery, legal teams can also learn where highly regulated or sensitive data is stored and how it can be accessed. That enables organizations to find critical data quickly and easily when needed and avoid costly consequences such as fines, sanctions, lawsuits, and enforcement actions.
Additionally, data discovery tools can help legal teams to reduce the time and costs associated with gathering and analyzing data. Automated data discovery techniques can quickly and efficiently scan large volumes of data, freeing up legal professionals to focus on higher-level tasks such as case strategy and analysis.
So, how does data discovery work? Let’s walk through the full process.
The data discovery process
While the data discovery process is fairly simple in the abstract, it can quickly become complicated in practice. The process involves four distinct steps, which we’ll consider one by one.
To understand your data, you need to know what it includes. That requires surveying every bit of your organization’s data. Look through every possible place where organizational data could be stored and audit it, leaving no stone unturned. If you fail to consider one or more of your organization’s data streams, you risk forming an incomplete picture of your data, failing to recognize major security and compliance issues, and missing valuable business information.
Fully preparing data is a prerequisite to its proper analysis. Before reviewing any data, you must classify, cleanse, and deduplicate it to ensure that it is readable and that redundancies have been minimized. This preparation step can also reduce your organization’s data storage costs and prevent compromised data from clouding your operations or impairing future analyses.
Once you’ve surveyed all your data and prepared it, you must review and analyze that data through the lenses of business strategy, risk management, and compliance.
If that sounds daunting, never fear: this is where artificial intelligence (AI) technology comes in. AI can detect patterns and issues in data sets faster and more thoroughly than any human reviewer can, handling otherwise intimidating volumes and varieties of data with ease.
What did you learn from your analysis? That’s what you’ll compile and organize into a readable format, fashioning a story that offers actionable insights into your organization’s business operations and highlights potential risks. You may be able to create an automated report from your analysis software, or you may create an official report with specific recommendations, depending on what best serves your organization’s purposes in conducting data discovery.
Bear in mind that data discovery is not a one-and-done process. Organizations create new data constantly, so they must perform data discovery at regular intervals to gain new insights, detect new risks, and remain compliant.
Why exactly should corporations spend valuable time and resources performing data discovery? Let’s turn now to the benefits.
Common data discovery techniques
There is a variety of data discovery techniques that can be useful in helping legal professionals to identify and analyze relevant data in legal cases. That said, it’s important to choose the appropriate technique based on the specific needs of the case and the type of data being analyzed. Some of the most common data discovery techniques include: keyword searches; metadata analysis, concept searching, email threading, data visualization.
- Keyword searches: This technique involves searching for specific keywords or phrases in data sources to identify relevant information. Keyword searches can be performed manually or using automated tools.
- Metadata analysis: Metadata is information about data, such as file creation date, author, and location. Analyzing metadata can help to identify relevant data sources and provide context for the data.
- Concept searching: This technique involves using natural language processing (NLP) algorithms to identify relevant concepts and themes in the data. Concept searching can help to identify relevant data that may not be captured by keyword searches.
- Email threading: Email threading involves grouping related emails together to provide a more complete picture of a conversation or event. Email threading can help to identify important communications and key players in a legal case.
- Social media analysis: Social media platforms can be a rich source of information in legal cases. Techniques such as sentiment analysis and network analysis can be used to identify relevant information and key players.
- Data visualization: Data visualization tools can be used to create graphical representations of data, such as charts and graphs. Data visualization can help to identify patterns and relationships in the data that may not be apparent from raw data.
The benefits of data discovery
Data discovery provides a host of business and legal benefits. By thoroughly and regularly performing the process described above, organizations can:
- eliminate dirty and duplicate data and prevent it from disrupting business operations and future analyses;
- zoom out from individual data streams to see the big picture, thereby gaining valuable insights into their business and data management practices;
- learn what data they collect, where it is stored, who has access privileges, and when and how it is processed;
- identify gaps in their data so they can determine what additional data they may need to collect in the future;
- detect security risks so they can better manage those risks and comply with data privacy laws and regulations, such as the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA);
- organize their data and improve its accessibility so they can timely comply with data subject access requests, eDiscovery demands, and internal and regulatory investigations;
- inform business decisions and strategy and gain a competitive advantage; and
- share insights with individual departments and business units to help close information gaps and improve internal processes.
With all these benefits, data discovery sounds like an obvious choice. But remember what we said above: it can quickly become complicated. Organizations create and store more data now than ever before. Add to that the difficulty of dealing with different data formats and locating data stored across various locations, and what initially seemed like a straightforward process can turn out to be quite overwhelming.
Luckily, certain tools can do the heavy lifting for you.
Tools for smart data discovery
The average organization stores vast amounts of data across multiple repositories, making a manual data discovery process unrealistic. Even organizations with modest data stores can easily overlook important details unless they use a data discovery tool to facilitate the process.
Data discovery tools are widely variable. Some are limited to reviewing data for compliance, while others can perform data discovery for nearly any purpose.
Effective data discovery tools have features to prepare data through deduplication, identification of outliers, and other functions. Data preparation is essential for proper analysis, so it’s crucial that a data discovery tool adequately addresses the preparation stage.
During the analysis stage, most tools employ machine learning and visual analytics to generate and then display insights through charts or an interactive dashboard. This approach can make it easier for those who lack a background in data science to understand the relationships between data streams and the information they contain.
Other data discovery tools use guided advanced analytics, which produces descriptions and visualizations of data for an even more general overview.
By adopting smart tools that include these and other data discovery functions, organizations and legal teams can perform more thorough data discovery in less time, saving money and unlocking actionable information sooner.
How the right data discovery tools can help corporations leverage their data
An effective data discovery tool can help you extract value from your organization’s data while minimizing the risks of collecting and storing such data.
Tools designed for legal discovery, like IPRO’s eDiscovery suite, are perfectly suited to managing data discovery too. Our comprehensive AI-driven platform quickly eliminates unnecessary data through deduplication and other processing, reducing the volume of data that must be analyzed. It then uses machine learning to analyze large volumes of data gleaned from multiple sources, all from one dashboard.
More so, IPRO’s in-place search solution – Live EDA – allows teams to conduct Early Data Assessments on data sources in their original location, before any collection, thus enabling smarter data discovery. This lets legal teams search, filter and query all data from one interface with greater efficiency, which helps reduce review time and costs by 80% or more.
With IPRO complete eDiscovery suite, you’ll easily see what data your organization has and where it lives so you can retrieve it on demand. You’ll also learn what information your data contains, gaining valuable insights that you can use to inform business strategy and maintain the highest level of security and compliance.
To learn more about IPRO’s eDiscovery and other solutions, contact our team or schedule a demonstration today.