More data means more complexity and more time needed for analysis and ultimately results, right? Everyone involved in litigation and regulatory action lives this problem every day. But thankfully, new AI-driven tools can help cut through this complexity and provide meaningful insights in hours, rather than the days to which we’ve become accustomed.

A recent, extremely complex client matter made this new reality clear. We were tasked with analyzing large transactional datasets to generate intelligence reports critical for regulatory response. When using traditional Structured Query Language (SQL) systems, this process was cumbersome and time-consuming, likely taking days to complete due to the volume of the data and the complexity of the analyses. 

By migrating this process to Databricks on Azure, leveraging its distributed computing structure, and rewriting the logic as user-defined functions (UDF) in Python, we achieved a remarkable transformation. The same process that was estimated to take days to compute was completed overnight. Distributed computing technology, which dramatically reduces processing time, has revolutionized our operational efficiency, allowing us to rapidly deliver insights to stakeholders and enable faster, more informed decision-making.

Accelerated value, by the numbers:

  • More than 120 million transactions analyzed overnight for interdependent relationships for more than 140,000 accounts ($4 billion transaction volume).
  • Performance improvements led to a timely response to the Securities and Exchange Commission, with a defensible analytical approach.

While perhaps more complex than usual, this case is hardly unique. Legal projects often drown in mountains of data and documents, and the limitations of traditional SQL systems are increasingly bogging down efforts to swiftly extract crucial information for litigation and case preparation. 

We have found, though, that AI-powered solutions—in particular, Databricks on Azure—are emerging as a game-changing answer to this problem, revolutionizing data processing with unprecedented power, speed, and efficiency in handling even the most demanding workloads. For legal professionals, this could make data management significantly easier and streamline workflows for enhanced productivity.
 

Three key benefits for law firms

1. Efficiency and cost effectiveness

Efficiency in data processing goes beyond mere speed: It encompasses intelligent resource utilization and cost management. On-premises SQL systems require substantial upfront investments and ongoing maintenance, which can strain a client’s financial resources. In contrast, Azure Databricks offers a pay-as-you-go model, allowing firms to pay only for the compute and storage resources they use.

This flexible resource allocation means additional compute power can be provisioned instantly during peak processing times and scaled down during quieter periods. This not only enhances operational efficiency but also significantly reduces costs, making Databricks a financially prudent choice for clients looking to optimize their data processing capabilities.


2. Seamless collaboration for enhanced project workflows

Close collaboration with clients is critical when managing complex, detail-specific data, to ensure alignment and precision throughout the process. Databricks’ collaborative notebooks and integration with Azure Active Directory enable teams to work together seamlessly. Multiple users can share, collaborate, and iterate on the same notebook in real time, facilitating faster insights and more cohesive data strategies. This level of collaboration accelerates case preparation and ensures all team members are aligned, fostering a unified approach to data management and litigation.
 

3. Simplified integration of machine learning and AI

In today’s data-driven world, machine learning and artificial intelligence are invaluable tools. Databricks simplifies the integration of these advanced technologies into data pipelines. With native support for popular machine-learning libraries like TensorFlow, PyTorch, and scikit-learn, data analysts can develop, train, and deploy machine-learning models effortlessly.

This seamless integration allows professionals to turn raw data into actionable insights quickly. Predictive analytics models that once took weeks to develop and deploy can now be operationalized in days, enhancing the ability to make data-driven decisions swiftly and accurately, which is crucial for litigation and case strategy development.
 

Key takeaways

Cumbersome and slow processes are quickly yielding to incredibly fast time-to-results in the age of AI. What we’re witnessing is not just technological advancement—it’s a pivotal shift in how data is managed and leveraged. The power, speed, and efficiency of Databricks on Azure, for example, far exceed traditional on-premises SQL systems, enabling professionals to handle larger datasets, perform more complex analyses, and derive insights faster and more cost-effectively.

Databricks on Azure has been transformative in streamlining our data processing, enhancing collaboration, and integrating advanced machine learning models into our workflows. Given how early we are in the AI revolution, this promises to be just the tip of an enormous productivity-driving iceberg.

In a legal landscape where data and precision are paramount, embracing cloud-based solutions like Azure Databricks is essential. Stay ahead of the curve, unlock the full potential of data, and transform your client’s operations with the unparalleled capabilities of these new solutions. 

As is the case with many disruptive technologies, you need a team of experienced experts to fully realize the benefits associated with Databricks or other AI tools. Contact us today to discover how we can partner together to help your clients.