Case Study

Pitney Bowes Accelerates Growth and Innovation

Pitney Bowes Backstory

Coined as the Craftsmen of Commerce, Pitney Bowes (PB) continuously innovates to keep pace with the evolving complexities in commerce, from launching its Model M Postage Meter in the 1920s to introducing cross-border solutions more recently. To continue setting new records in commerce, PB relies on data to make business decisions with precision.

The Challenge

In 2015, the business embarked on its digital transformation journey with the goal of giving frequent data access to stakeholders to uncover new opportunities and areas for improvement to drive business growth. Previously, employees spanning across departments used and shared data that turned stale as soon as they downloaded it onto their local machines. Without continuous frequent data, making critical business decisions at the right moment was virtually impossible at PB.

Digital transformation at a tactical level required the PB Big Data team to store and back up data from all their disparate data sources on a data lake, and host curated data in a cloud data warehouse for users to access the data directly. By allowing users to access data via a cloud data warehouse, the Big Data team was able to minimize data security risks, provide data to authorized users, and curate the data on the data lake before it’s shared with users.

The Big Data team initially built a home-grown integration tool to move data from disparate on-premises and cloud sources & systems into the data lake built on Amazon S3. The integration tool, however, could not support integrations for many of the new cloud-native applications they adopted to support their digital transformation journey. Instead, the team spent an excruciating amount of time coding custom integrations and built workarounds to cover the tool’s shortcomings.

The Big Data team had to re-evaluate their strategy on how to move data from cloud applications into their data lake in the long-term. “We spent so much time coding ETL pipelines instead of focusing on our data lake project and enabling users to get access to the data they need,” said Vishal Shah, Solutions & Deployment Architect at PB. “We weren’t scaled to support the business strategy at the pace we needed to.”

The Solution

Shah and his team sought an enterprise-grade data integration platform and cloud data warehouse. They evaluated integration tools based on the following criteria: the ability to support existing on-premises and cloud application integrations, the ability to load and update large volumes of data at the frequency the business requires into their cloud data warehouse and data lake, all while requiring minimal maintenance. They were so meticulous in their evaluation phase that they even identified and tested out integration use cases that weren’t required yet to eliminate the need to build or purchase other integration tools in the future. That’s when they found SnapLogic, the leading enterprise integration platform as a service (iPaaS), who was able to prove out all the integration use cases.

Once deployed in their environment, SnapLogic sped up their ETL/ELT and data and application integrations. They connected key endpoints — including Salesforce, SAP HANA, MongoDB, Oracle, SQL Server, MySQL, and many more — into Amazon S3 using SnapLogic’s intelligent connectors, to build out their data lake. The team was also able to connect virtually any cloud application thanks to SnapLogic’s REST connectors. “SnapLogic has made our data lake implementation and enterprise data warehouse a reality,” said Shah, “Only SnapLogic has been able to support our company-wide initiatives including big data implementations and machine learning projects that require multi-source integrations.”

After careful consideration, Shah chose Snowflake as the company’s enterprise cloud data warehouse which could very easily integrate with SnapLogic. Shah knew that more and more users would access and share data from the cloud data warehouse and Snowflake was more cost-effective than others in the market. Together, Snowflake and SnapLogic enabled the Big Data team to effortlessly centralize data and give users access to real-time information.

Business Outcomes

Since implementing SnapLogic to store data on their data lake and Snowflake, the Big Data team has helped users speed up time to make business decisions, from launching products to rolling out targeted marketing campaigns. Over 500 users from Sales, Marketing, Finance, E-Commerce and Business Operations can now share and analyze data. E-Commerce, for example, analyzed Shipping & Mailing data to uncover Performance bottlenecks, Delivery Prediction, Return Volume Analysis and more. While Marketing analyzed trends to understand customer behavior based on campaigns and offers, learn how their portfolio is faring, and create separate targeted offers based on customer spend and usage.

“As more business users get access to Snowflake, there will always be a growing desire to connect more business applications,” said Shah, “We have connected more than 25 applications, completed more than 1,000 integration projects within our data lake implementation, and processed over 900 billion documents using SnapLogic. We look forward to continued partnership with SnapLogic to propel growth at Pitney Bowes.”