By Kian Poon, Director, Intapp Strategic Consulting
More and more legal firms are relying on AI-based technology to unburden their professionals from substantial amounts of manual work, especially labor-intensive data analysis. However, prior to implementing AI, firm leaders and professionals must take critical steps to ensure they truly understand the state of their underlying data. Firms must consider the structure — or lack thereof — of key data, their sources, approaches to data extraction and data collection, and how to organize and analyze the data.
Collecting and analyzing data is a daunting task for many firms, especially as the volume, velocity, and complexity of data increases in line with client expectations for firms to work smarter. By setting the proper processes in place and utilizing AI-powered insights, firms can successfully achieve both goals and gain a competitive advantage in the market.
Understand the Challenges of Collecting Data
When collecting data, firms need to sort through the data and determine which information is relevant. Paul Wilner, Data and Analytical Consultant and Statistical Analysis Instructor, spoke about the challenges of data mining during a recent ILTA podcast episode, “Eureka! Effectively Using Data Mining and Analytics to Influence Decisions.”
“The idea that you’re going to come across a pure data center or that you’re going to design some perfect data collection isn’t going to happen,” Wilner said. “It’s just like mining gold: You’re going to have to sift through a lot of rock. Even when you find gold, you're going to have to clean it and shine it before you can show it off and have it be useful to you.”
Gathering the right data is easier said than done, and can often require trial and error. Firms are constantly collecting massive amounts of operational data from clients, partners, and professionals. The sheer volume of data and various data sources can present a challenge, as can the quality of that data.
Recognizing these constraints while attempting to identify correlations between data dimensions can help surface meaningful patterns of interest. If, for example, a firm wants to enhance matter profitability, it may wish to explore correlations between matter type, client type, resourcing model, client satisfaction score, and how the work was won. The volume, recency, and quality of data within each of these dimensions will inform the accuracy and granularity of any derived insights.
Our own experience at Intapp has shown that firms that tackle these data mining constraints early on make better and more informed decisions and predictions, especially when utilizing AI-based tools to provide insights on client matters, potential risks, and budgets and profitability.
Update Your Data Mining Process
Meeting data-related standards across the organization is critical, and firms must ensure all their processes reflect that urgency. To bolster data integrity, firm leaders must gather input from all appropriate teams before enacting any sort of process change.
“The people who are often not in the room until the end [of the process planning] are the analysts themselves,” Wilner pointed out. “You need to bring them in the room first.” Meeting with the appropriate individuals before updating or creating new processes lets firms discover and address potential problems right away, saving them time and effort.
Wilner also suggested that all firm teams work together to create cross-silo data identifiers which thread disparate data sets together. Not being able to unite relevant data runs the risk of overlooking the full picture and missing important insights.
Implement AI-Based Technology
Once firms have successfully organized their data, they can use automated AI-based technology to help analyze it. AI can take on a variety of human-like cognitive functions — such as reasoning, learning, and problem solving — while lowering the risk of human error and increasing data analysis accuracy. AI complements data analytics by augmenting or accelerating pattern identification within the data and providing data-based insights. Teams can then review the AI-generated results, identify potential problems, and determine next steps to improve overall functionality within the firm.
Relying on AI to provide data-based insights — as opposed to trying to figure them out manually — returns valuable time to professionals, helping them serve their clients quickly and efficiently. The costs associated with data analysis also drop over time; firms will no longer need to bill clients for hours of manual labor. Between a higher quality of service and lower costs, firms will improve client satisfaction for both new and current accounts.
Learn more about best practices for legal industry AI, or find out about Intapp solutions for legal firms.
[bio]
Kian Poon serves as Director at Intapp Strategic Consulting, where he leads transformation programs focused on delivering enhanced client-centric services. With his deep experience in operations transformation, Poon works closely with chief officers and their teams to upgrade their business operating models to meet changing client and market demands. He’s worked with some of the world’s largest professional services firms to develop their approaches to clients and market development, front-office services modernization, and business-enabling support services.
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