Who Are We?

Loom Analytics is a Canadian data analytics company that builds SaaS platforms for Legal and Enterprise use. The company was founded in 2015 and began with a single goal: to bring an analytics-driven approach to Canadian legal research. As founder Mona Datt likes to say, we wanted to create a Moneyball System for the Law.

Since then, Loom has expanded, creating analytics products for a variety of industries. Through it all, we haven’t lost sight of the belief at the centre of our company: Data Makes a Difference.


So What is Analytics Anyways?

Analytics is a simple word encompassing a massive undertaking—the process by which data is collected, organized, interpreted, and applied in strategic problem solving. Put more simply, analytics lets you learn from the past in order to better the future.

Analytics can be broken down into descriptive, predictive, and prescriptive analytics. Descriptive analytics is the summary of historical data in an organized, quantitative manner. Predictive analytics uses statistical analysis and predictive modelling to extrapolate from past and present data to make predictions about future performance.

Prescriptive Analytics is where the interesting things happen, marrying numerical insight with business sense, experience, and a healthy splash of creativity. Once you understand where you’re at and have the tools to predict outcomes, how do you leverage that data to make better strategic decisions? Loom can get you to the point of asking this question; what happens next is up to you.


Data Makes a Difference

 Your industry is rapidly becoming more complex, but strategies for making sense of that complexity aren’t always adopted at the same speed. Our clients are intelligent, experienced, and have great gut instincts, but gut instincts can lead you astray if you’re missing key facts.  Ask yourself: how do you make an informed business decision without access to all available information? The answer is simple, of course—you can’t. Which is why finding ways to make sense of your business data is not only important, but increasingly crucial.

In a Big Data world, seeing the Big Picture requires Big Solutions.

Data analytics provide you the opportunity to move at the speed of data, becoming smarter, more efficient, and better able to respond to setbacks or challenges. It’s how you make your business data tell a story, then change the next part of the story for the better.

With Data Analytics, you can:

  • Put your professional outcomes in context.
  • Predict future outcomes and plan accordingly.
  • Respond faster to new challenges.
  • Select strategies based on empirical evidence.
  • Gain a competitive edge.

The Loom Analytics Team

Mona Datt

Founder and CEO

Siddarth Menon

Legal Data Analysis Lead

Helen Marukh

Content Creation and Design Lead

Varaha Pudipeddi

Product Architect

Rajdeep Ghosh

Legal Data Analyst

Steven Shen

Full Stack Developer

Arjun Vidhu

Customer Product Manager


Analytics Solutions from Loom

Court Analytics is Loom’s analytics platform for Canadian case law. Our legal team carefully reads through case law, then classifies and encodes it, allowing users to look up case data by specific desired parameters such as parties, decision practice areas, the judge or master presiding over the decision, and more. Users can choose from multiple report types that cover a range of aspects of the legal process. Court Analytics lets you do everything from looking up historic success rates in cases similar to yours, to researching opposing counsel track records in your practice area, to finding out the average damages your client can expect to receive based on historic figures.

Structura is a web-based analytics and data management platform that provides an intuitive, customisable environment for users to interact with all of their of data. The platform takes data points from multiple sources, combining them in reports that the user designs. With easy-to-use statistical modelling capabilities, any data available to Structura can be plugged into a predictive model, allowing users to mitigate risk and anticipate outcomes without a data science team.