As legal departments and law firms continue to digitize their operations, the integration of advanced technologies into legal workflows has introduced a new layer of complexity – and with it comes a new layer of terminology. Understanding the jargon behind legal technology is essential for professionals navigating this evolving space.

This guide serves as a concise reference for key legal tech terms, designed for legal ops teams, law firm support teams, and forward-thinking legal professionals who need to bridge the gap between legal expertise and technical implementation.

Whether you’re evaluating new platforms, collaborating with IT, or assessing your legal tech stack, fluency in this vocabulary will help you make informed decisions and communicate more effectively across teams.

Let’s unpack the foundational terms shaping legal technology today. 

Common Legal Technology Terms

Big Data
Extremely large and complex sets of digital information collected from sources like social media, websites, IoT devices, and more. Because of its size and complexity, big data is difficult to manage and analyze using traditional tools.

Data Analytics
The process of collecting and turning raw data into actionable insights. It involves using tools, technology, and techniques to find patterns and trends. In legal tech, data analytics supports litigation analysis, e-discovery, contract review, and time and billing insights.

Data Cleansing
The process of finding and correcting errors in a dataset by removing duplicates, fixing inconsistencies, and filling in missing information. This improves the accuracy and consistency of the data.

Data Integration
The process of combining data from different sources into a single system or platform for easier access, analysis, and use. The goal is to create a unified dataset that supports better insights and decision-making.

Data Lineage
The process of tracking data as it moves through a system – from its source to its destination. It shows how the data was created, changed, and used, helping ensure accuracy throughout its lifecycle.

Data Mapping
The process of linking a data field from one system to another to show how information moves between them. It helps keep data accurate by tracking where it comes from and where it goes.

Data Mining
A computer-assisted process used to sort through and analyze large datasets to discover patterns or information. It shows insights that would be hard to recognize manually.

Data Quality
The measure of how accurate, clear, and reliable data is for specific needs, such as reporting or analysis. High data quality standards support businesses who make informed, data-driven decisions.

Data Visualization
The process of using visual representations such as charts, maps, and graphs to simplify complex data and information. It helps make data easier to understand by revealing patterns, trends, and insights.

Data Warehouse
A system that stores data from multiple sources on a single platform. It is designed to support data analysis and support better, more informed decision-making.

Machine Learning
A form of artificial intelligence that analyzes data and makes decisions on its own, without being directly programmed. It helps identify trends, predict outcomes, and improve decision-making over time.

Metadata
Descriptive information about data that helps identify, understand, and manage it. It may include details such as the source, format, date created, and how the data should be used.

Normalization
The process of organizing data in a database to reduce duplication and improve consistency. It includes creating tables and setting up relationships between them to keep the data clean and easy to manage.

Predictive Analytics
The use of data and statistical models such as data mining techniques and machine learning to predict future outcomes. By analyzing past data and trends, it helps anticipate what’s likely to happen next and supports better decision-making.

Query Optimization
In Structured Query Language (SQL), the process of improving the performance of database queries by finding the most efficient way to execute them. It helps deliver faster, more efficient data insights.

Structured Data
Information that is organized into a set format, like rows and columns in a database or spreadsheet. Its consistency makes it easy to search, filter, and analyze.

Unstructured Data
Information that does not follow a set format or structure. Examples include emails, PDF files, audio files, and videos. It often requires advanced tools to organize, process, and analyze.

Customized Legal Data Management Solutions

Complex litigation provides an extraordinary amount of data and having the right tools in place is no longer optional, they are strategic assets. But technology alone isn’t the answer. Success lies in aligning the right solution with the right technological partner.

With LMI’s 40+ years of experience establishing industry-leading standards and procedures, our data collection and analysis processes have been proven to maintain data integrity for trustworthy reporting and decision-making. In matters ranging from single cases to mass torts, LMI’s litigation management software provides a secure environment to manage and access data, documents, and processes.

“Data integrity – that is, the accuracy and completeness of a dataset – is a critical component of legal data management and analytics. Maximized data integrity leads to actionable insight. It ensures the best results for reliable and informed decision making throughout the claims and litigation lifecycle.” – Megan Pizor, GC & Chief Data Officer

Questions? Contact us today.