The Art and Science of Data Reuse: Unlocking the Potential of Discovery Datasets 

Written by Jeanne Somma

In an era where data is both a commodity and a catalyst for innovation, the art and science of data reuse stand out as a strategic imperative for businesses aiming to stay ahead of the curve. There is a growing importance for the reuse of eDiscovery data in legal and compliance fields, as harnessing these insights is a catalyst for innovation and strategic decision-making within organizations. Further, the evolution of eDiscovery in the face of data disruption, has created the critical need for innovative approaches to manage the exponential growth in data types, volume, and complexity. This blog explores how organizations can transform discovery-focused datasets into dynamic tools that predict trends, inform strategies, and optimize operations. 

The Art of Seeing Beyond the Surface 

Data reuse is an art form that requires a visionary mindset. Data reuse is about seeing beyond the initial analysis and understanding the broader implications and potential applications of datasets. In relation to legal matters, identifying potentially relevant, privileged, protected, or interesting data for legal cases or compliance audits is the ultimate goal. It also closely links to the skill of navigating new kinds of data (e.g., asynchronous chats, multi-author documents, outputs from structured data systems like a CRM) and the necessity of creative problem-solving to address these evolving challenges. 

This perspective involves: 

Creative Thinking: Envisioning new ways to apply existing data to solve different problems or identify opportunities. In the context of eDiscovery, this could mean envisioning new ways to apply data for solving legal challenges or improving compliance strategies. 

Contextual Awareness: Understanding the data’s origin, including how, why, and when it was collected, to assess its suitability for new applications. The need for contextual awareness allows you to understand the legal relevance of data and its implications in litigation or regulatory inquiries. 

Collaborative Synergy: Engaging with stakeholders across different departments to gather fresh insights and perspectives on the data’s potential uses. In the litigation and regulatory framework this means working with legal, IT, and compliance teams to uncover hidden insights within eDiscovery data. 

The Science of Data Transformation 

Turning the vision of data reuse into reality is where science comes into play. This process involves a series of methodical steps to ensure that the data is not only relevant but also reliable and robust enough for further analysis. 

Data Cleaning: Ensuring the accuracy and completeness of the data by removing errors or inconsistencies. This is fundamental to the process and, yes, can be very difficult if you are only accessing data in separate legal matters lacking visibility into its treatment outside of the specific matter at hand.  

Data Integration: Combining data from various sources to create a comprehensive dataset that provides deeper insights. It is important to ensure a wide array of data to ensure all topics, communication styles, and categories are accounted for. For example, ensuring you are capturing traditional sources like email and e-docs alongside chat and structured data outputs will make for a robust pool of data and better insights. 

Data Analysis: Employing statistical methods and machine learning algorithms to analyze the data and uncover patterns or trends. 

Data Visualization: Using graphical representations to make the data accessible and understandable to non-technical stakeholders.

Along with the above elements, innovative strategies like using “fingerprints” and “recipes” to manage eDiscovery data efficiently are also imperative. Fingerprints are unique identifiers created using hashing algorithms for data subsets, allowing eDiscovery teams to track and manage data across various cases. Recipes are predefined procedural steps for handling data that enable teams to forecast data relevance and manage it consistently across different matters. These approaches help in efficiently processing, reviewing, and analyzing vast datasets by linking insights back to source data, thus enabling data reuse and reducing eDiscovery costs and complexities. 

(NOTE: “fingerprints” and “recipes” are explained in greater detail in our recent white paper “Equipping eDiscovery for Data Disruption”

Predicting Trends 

One of the most compelling uses of reused data is trend prediction. By analyzing historical data, organizations can identify patterns that are likely to continue or emerge. This predictive power can inform strategic planning, from market entry strategies to product development and beyond. It is even more important as the changing landscape of data affects eDiscovery trends, as we shift from traditional data types to modern, complex datasets that require innovative handling techniques. 

Informing Strategies 

Strategic decisions are best made on solid data foundations. Reused data can provide that foundation, offering insights into market dynamics, customer behavior, and competitive landscapes. This information can be a game-changer in shaping business strategies that are both proactive and responsive to market needs. Insights gained from advanced eDiscovery processes inform strategic decisions, and it is important to adapt strategies to accommodate the rapid pace of data evolution. The shift from traditional data types to modern, complex datasets requires innovative handling techniques but can reap massive rewards if done correctly.  

Optimizing Operations 

Operational efficiency is crucial for any organization’s bottom line. Data reuse can play a key role in streamlining processes, identifying bottlenecks, and enhancing productivity. For instance, analyzing data from past operational cycles can highlight areas for improvement, from supply chain management to customer service. In the realm of eDiscovery data, optimization focuses on legal processes and workflows, improving efficiency in document review, and enhancing productivity in legal teams. Strong eDiscovery teams are moving past the funnel approach, using data reuse strategies to enhance operational efficiency and reduce costs in these areas and more. 

Overcoming Challenges 

While the benefits are clear, the path to effective data reuse is not without its challenges. Issues of data privacy, security, and quality must be addressed. Moreover, the cultural shift towards data-driven decision-making can be a hurdle for organizations not accustomed to leveraging data in strategic ways. The great news is that the legal department can lead this paradigm shift in how companies view data examination and reuse. 

Conclusion: A Strategic Imperative 

The art and science of data reuse are not just about leveraging existing assets; they’re about fostering a culture of innovation and continuous improvement. By converting eDiscovery-focused datasets into dynamic tools for predicting trends, informing strategies, and optimizing operations, organizations can position themselves as leaders in their fields. Further, the transformation of eDiscovery from a cost center to a strategic value driver can take shape within organizations, with data reuse bridging the gap between traditional, siloed eDiscovery practices and a holistic, value-creating approach to data management across the company. As we move forward in the digital age, the ability to creatively and scientifically reuse data will become a defining trait of a successful legal team. 


About the Author 

Jeanne Somma is the Chief Client Officer and General Counsel at Lineal. She has over a decade of experience in the legal industry, with strong expertise in eDiscovery, analytics application, and consultation regarding defensible uses of technology in document review and production.  

Jeanne is a licensed attorney and has studied law both in the US; receiving her LLM in International Business and Trade from Fordham University School of Law and her J.D. from Hofstra University School of Law; as well as abroad at both the University of Sydney Law School and the University of Nairobi School of Law. She is admitted to practice in New York and New Jersey.  

Jeanne writes and speaks frequently on topics such as best practices for incorporating analytics into discovery workflows, developments in the laws around data privacy and cross-border discovery, and strategies for reducing cost and improving efficiency in discovery.  

About Lineal 

Lineal is an innovative eDiscovery and legal technology solutions company that empowers law firms and corporations with modern data management and review strategies. Established in 2009, Lineal specializes in comprehensive eDiscovery services, leveraging its proprietary technology suite, Amplify, to enhance efficiency and accuracy in handling large volumes of electronic data. With a global presence and a team of experienced professionals, Lineal is dedicated to delivering custom-tailored solutions that drive optimal legal outcomes for its clients. For more information, visit