Atlan Case Studies Scaling Postman's Data Team: A Case Study on Rapid Growth and Process Improvement

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	Scaling Postman's Data Team: A Case Study on Rapid Growth and Process Improvement Atlan

Scaling Postman's Data Team: A Case Study on Rapid Growth and Process Improvement

Atlan

Technology Category	Analytics & Modeling - Big Data Analytics Application Infrastructure & Middleware - Database Management & Storage
Applicable Industries	Buildings Construction & Infrastructure
Applicable Functions	Sales & Marketing
Use Cases	Construction Management Time Sensitive Networking
Services	Data Science Services System Integration
Challenge	Postman, an API collaboration platform, experienced rapid growth, with its valuation reaching $5.6 billion and its user base expanding to over 17 million people from 500,000 companies globally. However, the company's data team was not growing at the same pace. In April 2020, the data team consisted of only six or seven people. Over the next year, the team expanded by 4-5x to 25 people. This rapid growth presented challenges in terms of onboarding new hires, handling requests from the rest of the company, and planning their work. The data team was also grappling with the decision between a centralized and decentralized team structure, with the former leading to conflicting data systems and metrics. Additionally, the team faced difficulties in prioritizing work and allocating projects fairly among team members. Read More
About Customer	Postman is an API collaboration platform that aims to simplify and expedite API processes. The company, which started as a side project six years ago, has grown to become one of India's latest unicorns with a valuation of $5.6 billion. Postman's platform is used by over 17 million people from 500,000 companies globally. The company has a 400-member team spread across two offices in Bangalore, one in San Francisco, and distributed remotely across four continents. The data team, which is based in Bangalore, consists of 25 members divided into two sub-teams: the Data Engineering Team and the Data Science Team. Read More
Solution	To address these challenges, Postman's data team implemented several strategies. They moved towards a more centralized team structure, hiring analysts into the Data Science Team and training them centrally. After a several month onboarding process, some new hires would move to more data-mature teams as embedded or decentralized analysts. The team also introduced a hierarchy to distinguish between different levels of data analysts. To manage work prioritization, they implemented a ticketing system on Jira, where anyone in the organization could create a ticket detailing their data request. The team would then follow up on each ticket, gathering more information and assigning each ticket to a sprint based on its priority and impact. To allocate projects, the team introduced weekly Issue Grooming sessions, where they would assess and assign new tasks. The team also adopted the sprint methodology, breaking down projects into smaller tasks and conducting sprint retrospectives to continuously improve their processes. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Big Data Analytics

Application Infrastructure & Middleware - Database Management & Storage

Applicable Industries

Buildings

Construction & Infrastructure

Applicable Functions

Sales & Marketing

Use Cases

Construction Management

Time Sensitive Networking

Services

Data Science Services

System Integration

Challenge

Postman, an API collaboration platform, experienced rapid growth, with its valuation reaching $5.6 billion and its user base expanding to over 17 million people from 500,000 companies globally. However, the company's data team was not growing at the same pace. In April 2020, the data team consisted of only six or seven people. Over the next year, the team expanded by 4-5x to 25 people. This rapid growth presented challenges in terms of onboarding new hires, handling requests from the rest of the company, and planning their work. The data team was also grappling with the decision between a centralized and decentralized team structure, with the former leading to conflicting data systems and metrics. Additionally, the team faced difficulties in prioritizing work and allocating projects fairly among team members.

About Customer

Postman is an API collaboration platform that aims to simplify and expedite API processes. The company, which started as a side project six years ago, has grown to become one of India's latest unicorns with a valuation of $5.6 billion. Postman's platform is used by over 17 million people from 500,000 companies globally. The company has a 400-member team spread across two offices in Bangalore, one in San Francisco, and distributed remotely across four continents. The data team, which is based in Bangalore, consists of 25 members divided into two sub-teams: the Data Engineering Team and the Data Science Team.

Solution

To address these challenges, Postman's data team implemented several strategies. They moved towards a more centralized team structure, hiring analysts into the Data Science Team and training them centrally. After a several month onboarding process, some new hires would move to more data-mature teams as embedded or decentralized analysts. The team also introduced a hierarchy to distinguish between different levels of data analysts. To manage work prioritization, they implemented a ticketing system on Jira, where anyone in the organization could create a ticket detailing their data request. The team would then follow up on each ticket, gathering more information and assigning each ticket to a sprint based on its priority and impact. To allocate projects, the team introduced weekly Issue Grooming sessions, where they would assess and assign new tasks. The team also adopted the sprint methodology, breaking down projects into smaller tasks and conducting sprint retrospectives to continuously improve their processes.

Impact #1	The implementation of new processes and structures significantly improved the efficiency and effectiveness of Postman's data team. The move towards a more centralized team structure eliminated the issue of conflicting data systems and metrics. The introduction of a hierarchy provided clarity on roles and responsibilities within the team. The ticketing system on Jira and the weekly Issue Grooming sessions helped in managing work prioritization and project allocation, ensuring that tasks were distributed fairly and based on their impact and priority. The adoption of the sprint methodology encouraged the team to break down projects into smaller tasks, promoting continuous output and enabling the team to identify and address issues in a timely manner. These improvements have made the data team more comfortable with onboarding new hires, handling requests from the rest of the company, and planning their work.

Impact #1

The implementation of new processes and structures significantly improved the efficiency and effectiveness of Postman's data team. The move towards a more centralized team structure eliminated the issue of conflicting data systems and metrics. The introduction of a hierarchy provided clarity on roles and responsibilities within the team. The ticketing system on Jira and the weekly Issue Grooming sessions helped in managing work prioritization and project allocation, ensuring that tasks were distributed fairly and based on their impact and priority. The adoption of the sprint methodology encouraged the team to break down projects into smaller tasks, promoting continuous output and enabling the team to identify and address issues in a timely manner. These improvements have made the data team more comfortable with onboarding new hires, handling requests from the rest of the company, and planning their work.

Benefit #1	Postman's data team grew by 4-5x to 25 people in just over a year.
Benefit #2	The company's valuation reached $5.6 billion, with its user base expanding to over 17 million people from 500,000 companies globally.
Benefit #3	Nearly one quarter of the company is active on Looker every week, using the data processed by the Data Science Team.

Benefit #1

Postman's data team grew by 4-5x to 25 people in just over a year.

Benefit #2

The company's valuation reached $5.6 billion, with its user base expanding to over 17 million people from 500,000 companies globally.

Benefit #3

Nearly one quarter of the company is active on Looker every week, using the data processed by the Data Science Team.

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Overview

Scaling Postman's Data Team: A Case Study on Rapid Growth and Process Improvement

Operational Impact

Quantitative Benefit