Case Studies: DocuSign



By migrating over to Snowflake and integrating Matillion ETL to create dimensional models, DocuSign reduced the time required for long-running jobs from over 22 hours to just 6 hours.

Their success

1

Reduced the time needed for long-running jobs from over 22 hours to just 6 hours

2

Launched in minutes, with their entire infrastructure running in Matillion in 2 days

3

Rapidly onboarded team, which became proficient in Matillion in less than 14-days

About

DocuSign helps organizations connect and automate how they prepare, sign, act-on, and manage agreements. As part of its cloud-based System of Agreement Platform, DocuSign offers eSignature—the world’s number one way to sign electronically on practically any device, from almost anywhere, at any time. Today, more than 400,000 customers and hundreds of millions of users in over 180 countries use DocuSign to accelerate the process of doing business and to simplify people’s lives.

Company: DocuSign

Location: San Francisco, CA

Industry: Digital Transaction Management

Employees: 3000+

Product: Matillion ETL for Snowflake

Use Case: Data Warehouse Modernization / Reporting Analytics / Security

Website: https://www.docusign.com/

The challenge

As the leader in its space, DocuSign had developed a strong business intelligence infrastructure and robust set of analytic solutions to help the company make data-driven decisions. As Docusign continued to grow, however, it became apparent that it’s legacy infrastructure lacked the scale and performance it needed to keep pace with the demands of the business. The legacy solution was also becoming costly to maintain, pushing DocuSign to consider moving its business intelligence infrastructure to more modern and flexible options.

The solution

While selecting a cloud data warehouse (CDW) provider was a key piece of its data modernization strategy, DocuSign also needed a broader solution that could:

  • Move data from its SQL Server environment into the chosen CDW.
  • Quickly process large amounts of data to meet performance objectives.
  • Ensure that data in transit remained secure.

After an in-depth evaluation, DocuSign selected Snowflake as its CDW provider and Matillion ETL for Snowflake to best facilitate DocuSign’s transition to the cloud, aggregate its various data sources, and create the dimensional models needed for downstream consumption.

The benefits

Once the decision was made to move forward with Snowflake and Matillion, the Snowflake set-up was very straightforward, with simple APIs to easily move initial data sets into the CDW. Docusign deployed Matillion from the AWS Marketplace in a few clicks, launched the product directly into DocuSign’s EC2 instance, and then seamlessly integrated it with Snowflake.

To ensure Matillion was properly deployed in order to meet the complexities of DocuSign’s environment, the Matillion team came onsite to work with DocuSign engineers. The teams worked tirelessly for several days to resolve key issues, allowing the project to move forward. The thorough, hands-on support provided by Matillion continues to be instrumental in helping DocuSign manage its large, complicated data environment. Since implementation, Snowflake and Matillion have helped DocuSign meet its challenging goals around performance, scale, security and cost savings.

The results

  • Launch in minutes, with their entire infrastructure running in Matillion in 2 days.

  • Rapidly onboard their data engineering team, which became proficient in Matillion in less than 14-days.

  • Achieve greater extensibility, allowing for more integrations, dimensional modeling, and dispersed analytical modeling approach.

  • Decrease latency to improve bandwidth and performance.

  • Achieve greater value for IT spend, relative to its ETL processes.

  • Reduce the time needed for long-running jobs from over 22 hours to just 6 hours.

What's next

To continually harness the power of its data in order to enable intelligent business decisions as it continues to grow, DocuSign plans to add new data source integrations and new dimensional models to support strategic decision making. For example, the company hopes to develop a customer scoring framework to better understand its customers and measure both usage and adoption over the customer lifecycle and hopes to develop an index to understand customer churn.

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