Introduction
Many law firms and legal departments lack the ability to provide well-grounded guidance based on historical data, measurement, and statistics. They often rely on guesswork and personal experience, leading to difficulties in advising clients on market trends for contract terms, selecting the best firm for a class action lawsuit, and identifying strategic training areas to reduce employment claims. To succeed in an increasingly competitive legal marketplace, firms must leverage data-driven decision-making and embrace the use of data, dashboards, and trends, similar to other data-immersed businesses. Alternative legal service providers and accounting firms are already utilizing data efficiently, and forward-thinking legal organizations can meet or exceed client expectations by adopting similar data awareness and analytics.
Today, legal organizations can leverage data to allow lawyers to formulate strategies and budgets based on the paradigm underpinning the very practice of law: gathering evidence, studying the rules or applicable framework, and drawing conclusions.
Signs of change
Lawyers are increasingly interested in using data and have seen a sharp rise in data-driven pursuits, including AI, data management, analysis, and visualization. Law firms have tripled their hiring of data scientists in the last two years to enhance their capacity for data collection and analysis. Organizations that invest in data-driven insights can meet client and business expectations, enhance existing practices, and create new quantitative legal services. It's crucial for legal organizations to start now and gradually advance their analytics maturity to stay competitive and avoid falling behind in the rapidly growing data-driven landscape.
Analytics maturity levels
Legal organizations' analytics maturity varies widely. As a frame of reference, consider the following maturity levels.
Level 1: Foundational analytics
At the foundational level, law firms and in-house teams focus on planning and gaining a basic understanding of their data and its potential applications.
They strategically assess data inventory, collection, management, governance, capability and their tools. They work on filling gaps in their data systems and begin capturing and storing matter information for various types of cases.
Data analytics programs often start small, focusing on a single practice area before expanding to others. Detailed data collection in the early stages fuels future advancements in legal analysis as organizations progress to more advanced levels.
Level 2: Descriptive analytics
Descriptive analytics explain past events or offer current snapshots and are often captured in non-actionable reports.
Improving descriptive analytics provides a foundation for deeper data analysis and can be achieved through spend-analytics solutions. Regular descriptive analytics with helpful insights can improve transparency and generate interest in further data exploration.
Interactive visualizations, such as dashboards, make legal concepts more accessible and educate firm and business leaders. Basic tools like Excel and SQL, along with visualization tools like Power BI, Tableau, or Qlik, can support descriptive analytics in law firms and departments. Customized legal-oriented solutions like CounselCommand are optimal for conveying insights to busy lawyers and facilitating data exploration.
Level 3: Diagnostic analytics
Diagnostic analytics goes beyond descriptive data to explain the factors and causes behind certain outcomes or events.
Law departments can use diagnostic analytics to assess variables that may lead to litigation and study metrics to determine the likelihood of case success. Law firms can utilize diagnostic analytics to understand the reasons behind changes in utilization and assess a prospective client's risk profile, providing an early warning system for potential legal issues and fostering proactive solutions for recurring client relationships.
Diagnostic analytics involves analyzing factors that explain specific results and requires advanced tools like statistical programming languages (R, Python, SAS) for regression, classification, and feature analysis.
Diagnostic data takes descriptive analytics to the next level, drilling down into the basic assumptions and outcomes revealed by descriptive data to explain the factors that contributed to or caused a certain outcome.
Level 4: Predictive analytics
Predictive analytics represents a shift from reactive to proactive data analysis and relies on historical data to estimate future events and outcomes.
Data collection and aggregation are crucial at this level, with larger and more diverse data sets leading to more accurate predictions. A good predictive model should aim for at least 80% accuracy, and law firms can use predictive analytics to offer valuable insights to clients and allocate resources more effectively.
Predictive analytics can help in predicting deal closures, settlement amounts, and matter outcomes, providing a unique and valuable service to clients. Law firms that master predictive analytics can demonstrate their expertise and gain a competitive edge by offering proprietary insights unavailable from competitor firms.
To apply predictive analytics, organizations require access to machine learning-based regression, classification, and clustering tools like Python and R through integrated development environments or web-based analytics services available on cloud platforms, which may involve custom work, technical expertise, and mathematical fluency to create ensemble models combining multiple approaches.
Level 5: Prescriptive analytics
Prescriptive analytics suggest actions legal organizations should take to influence outcomes, focusing on narrower practice areas or specific contexts.
Applications of prescriptive analytics have been seen in online dispute resolution, traffic ticket challenges, and automated exchanges for simple transactions. Law departments can use prescriptive analytics to optimize resource allocation and select outside counsel for risk mitigation and cost reduction.
Implementation of predictive and prescriptive analytics will face challenges, including algorithm aversion, but can benefit from customized applications and advanced techniques like neural networks and deep learning.
The data collection imperative
Effective analytics strategies in legal organizations rely on purposeful data collection and well-organized practice-oriented data, emphasizing the need for thoughtful data models (beyond spend data) to support strategic goals. Collecting public and proprietary data about the characteristics of legal matters allows for higher-quality insights to inform and support lawyers' recommendations and advice.
Organizations must shift from capturing data based on functional need to strategically collecting data that aligns with their business objectives and supports the practice of law.
To develop analytics maturity, legal organizations need to develop thoughtful data models—beyond spend data—that describe particular classes of legal work or risks that they regularly handle, that are important to their business, and that they expect to continue supporting in the foreseeable future.
What corporate law departments should keep in mind
Law departments often collect data for operational rather than strategic purposes, requiring creative approaches to capture data for meaningful long-term key performance indicators and metrics. Investing in tools and technologies to process, analyze, synthesize, and transform data is essential, and creating practice-specific or risk-specific strategies for purposeful data collection is crucial for enhancing data analytics capabilities and influencing law firms and service providers to optimize their own performance.
What law firms should keep in mind
Law firms should plan strategically for data collection, analysis, insights, and visualization, being ready to capture new proprietary data to fill gaps and fully describe their services and outcomes for each matter. Analytics strategies should be tailored to specific practice areas to improve client engagement and meet their future expectations, and firms should proactively find ways to capture and share client-centric analytics and data-based advice to prove their value and differentiate themselves from competitors.
Conclusion
Progressive law departments and firms recognize the need for meticulous data collection and data-driven decision-making in the future practice of law, catching up with other industries. Organizations should invest in the early levels of analytics maturity before exploring predictive and prescriptive analytics, establishing a solid foundation for desired results.
These investments include talent and technology. Successful analytics projects involving legal service delivery require a multidisciplinary team and strategic efforts. Accessing the latent value in legal data also requires tools and technologies, with potential benefits including more informed decisions, improved client experience, and a stronger value proposition for firms.
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