Customer Story

Digital Legal Services Company Builds an Analytics-Ready Foundation on Databricks

What we did

  • Designed a Databricks medallion architecture to transform raw operational data into trusted, curated datasets.
  • Unified customer data across marketing, application, and behavioral systems into a single customer view.
  • Developed analytics data models to support marketing attribution, funnel analysis, executive reporting, and ROAS measurement.
  • Delivered self-service analytics with Databricks AI/BI and Genie, enabling business users to explore data and answer questions without SQL.

Industry

  • Services

Technologies

  • Databricks

5 Weeks to Deliver

Databricks data foundation

90% Faster

Dashboard performance

~33% Lower

compute costs

The Challenge

This fast-growing online legal services company helps create legally valid personal agreements through a streamlined digital platform. With more than 120,000 users and approximately 90,000 legal documents generated, the company was growing quickly and expanding into new offerings.

As the business scaled, the team needed better visibility into customer acquisition, marketing performance, and user behavior.

The Solution

Analytics8 partnered with the company to design an analytics-ready architecture in Databricks and establish the foundational data models needed for reporting and decision-making.

Over a five-week engagement, Analytics8 delivered:

Analytics-Ready Data Architecture

We reviewed the existing Databricks environment and designed a validated medallion architecture aligned to analytics best practices. Using Databricks Auto Loader, Spark Declarative Pipelines (formerly Delta Live Tables), and Delta Lake architecture patterns, the team established a framework for transforming raw source data into curated analytical datasets.

Unified Customer Identity Resolution

To connect customer activity across systems, we designed and implemented a centralized user dimension that stitched together data from:

  • The company’s application database
  • Google Ads
  • RudderStack event tracking
  • Hubspot

This unified customer view allowed the organization to connect anonymous marketing interactions with known customers and track user journeys across channels.

Dimensional Data Models for Analytics

We developed the logical data model, dimensional design, and transformation patterns needed to support:

  • Multi-touch marketing attribution
  • Customer funnel analysis
  • Leadership reporting
  • Return on ad spend (ROAS) measurement

The team also delivered documentation, lineage diagrams, data models, and implementation guidance so that the company could continue expanding the framework independently.

Self-Service Analytics with Databricks AI/BI and Genie

To help business users access insights without writing SQL, we built:

  • Executive leadership dashboards using Databricks AI/BI
  • Marketing attribution dashboards focused on campaign performance and ROAS
  • A Databricks Genie space that enabled marketing and business users to ask questions in natural language and explore data independently

Business Impact

In just five weeks, the company established the foundation for scalable analytics and self-service reporting on Databricks.

Key outcomes include:

  • Created an analytics-ready Databricks architecture to support future growth and new product offerings.
  • Unified customer data across application, marketing, and event-tracking systems through a centralized identity model.
  • Enabled multi-touch attribution and customer funnel analysis for the first time.
  • Rebuilt leadership reporting on curated analytical datasets, reducing dashboard load times from 30 seconds to 3 seconds and simplifying query logic from ~ 500-line raw CTEs to concise 2- to 20-line gold-layer queries.
  • Reduced compute costs by approximately one-third by moving from near-real time streaming pipelines to 6-hour batch processing.
  • Delivered self-service analytics capabilities that reduced dependence on engineering resources for business reporting.
  • Provided a documented framework and implementation roadmap that allows the company’s team to continue expanding its analytics environment independently.

By combining Databricks best practices, dimensional modeling, AI/BI dashboards, and Genie-powered self-service analytics, the legal services company gained the foundation needed to understand customer behavior, optimize marketing investments, and support continued business growth.

More stories like this

Related articles

Talk with a data expert

Schedule a meeting with one of our data analytics experts who will review your data struggles and map out steps to achieve data-driven decision making.

Request a session