Top 5 eDiscovery Data Transformation Techniques
Written by Jeanne Somma
In the realm of eDiscovery, the efficient handling and transformation of data are paramount. The concept of data reuse as explored in our blog “The Art and Science of eDiscovery Data Reuse: Unlocking Discovery Datasets” delves into the methodical steps required to make case data not only relevant but also reliable and robust enough for further analysis. This post will take a deeper look at these elements, focusing on innovative strategies like “fingerprints” and “recipes,” and their broader implications for eDiscovery professionals.
- Data Cleaning: Challenges and Best Practices
Data cleaning is often the first critical step in ensuring the usability of data in eDiscovery. This involves the elimination of errors and inconsistencies, which can be particularly challenging when data comes from multiple legal matters without clear visibility into how it has been handled previously. Best practices in data cleaning for eDiscovery include:
Automation Tools: Employing software that can automatically detect and correct errors in data to save time and reduce human error. At Lineal, we leverage Amplify Workflow to automatically flag problematic or bottleneck areas in data and process flows.
Consistent Standards: Developing and adhering to data cleaning protocols that apply across all data, regardless of its source, to maintain consistency. At Lineal, these protocols are integrated into our systems automatically through the Amplify suite. This integration ensures that data is normalized across all types, and in the same way across all matters.
2. Data Integration: Creating a Unified View
Integrating disparate data sources is crucial in building a comprehensive dataset that offers deeper insights. In eDiscovery, where data may come from various
cases and time frames, integration allows professionals to piece together information that forms a more complete narrative of the case.
Technology Use: Leveraging advanced data integration tools that can handle large volumes and varieties of data. At Lineal we provide custom applications that enhance the Relativity platform. These enhancements are designed to provide users with unparalleled access to AI-powered capabilities across large volumes of diverse data.
Collaboration Between Teams: Ensuring that IT and legal teams work closely to align on the data needs and the context necessary for accurate integration. Having the right information to feed back into the cycle is key to ensure knowledge across teams.
Part of influencing better collaboration between teams is keeping data in one accessible platform such as Relativity. RelativityOne data migration is a seamless process with Lineal’s expertise. Our team ensures that data transitions smoothly from legacy systems to RelativityOne, maintaining data integrity and security. This migration facilitates enhanced data handling capabilities and supports robust eDiscovery processes, empowering legal teams to manage data more effectively.
3. Data Analysis: Leveraging Advanced Techniques
The analysis phase involves applying statistical methods and machine learning algorithms to identify patterns or trends that inform case strategies. For eDiscovery, this means:
Predictive Coding: Using machine learning to more accurately predict which documents are relevant to a case, thereby reducing the time spent on manual review.
Trend Analysis: Analyzing data trends over time to predict future occurrences or to understand past behaviors within a dataset. Lineal’s Command Center powers understanding of individual matter metrics and extrapolation of that data across an entire portfolio. This allows for better matter and practice management.
4. Data Visualization: Enhancing Stakeholder Understanding
Data visualization is a powerful tool in making complex data accessible to non-technical stakeholders involved in legal matters. Effective visualization techniques for eDiscovery include:
Interactive Dashboards: Allowing users to explore data through interactive tools that can drill down into specifics, offering a hands-on way to understand the data.
Graphical Summaries: Utilizing charts, graphs, and other visual aids to summarize large datasets for quick comprehension during presentations or meetings.
5. Data Strategies: Fingerprints and Recipes
The use of “fingerprints” and “recipes” in eDiscovery are two main strategies that significantly enhance data management and reuse:
Fingerprints: By creating unique identifiers for data subsets using hashing algorithms, eDiscovery teams can efficiently track and manage data across cases. This ensures that data is not redundantly processed and helps maintain the integrity of data through its lifecycle.
Recipes: These are predefined procedural steps for handling data. By standardizing data handling, recipes help teams forecast data relevance and manage consistency across different matters. This approach not only saves time but also reduces the complexity and cost associated with data management.
Conclusion
As eDiscovery continues to evolve, embracing the art and science of data transformation is crucial. By understanding and implementing the discussed techniques—data cleaning, integration, analysis, visualization, and reuse tools like fingerprints and recipes—eDiscovery professionals improve efficiency, reduce costs, and enhance the overall effectiveness of their data management strategies. This deep dive into these five protocols reaffirms the importance of each component in the broader context of data reuse and the value they bring to legal proceedings.
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 lineal.com