Back to resources

Build V.S. Buy: Empowering Teams with a Data-Driven Approach

Innovation / Experiment
Personal Growth
Company Culture
Leadership
Coaching / Training / Mentorship

11 March, 2022

Paramita Bhattacharjee
Paramita Bhattacharjee

Vice President of Product Management at Early Warning

Paramita Bhattacharjee, Vice President of Product Management at Early Warning, recounts when she let her team focus on experimentation to develop a data-driven solution.

Engineers Want to Build

In large organizations, when initiatives come up, there are build v.s. buy decisions that need to be made. Inherently all engineers and data scientists have the notion that it should be done in-house. Why create a partnership when we can do it ourselves? It’s very important for product leaders to assess the build v.s. buy, and how to make the right decision.

Around ten years ago, my company was putting a novel product into production – involving artificial intelligence and modeling. We partnered with two vendors who were leaders in these industries, specifically modeling and machine learning. As I mentioned, there were team members that were incredibly interested and thought the machine learning model could be developed in-house.

Experimentation Within a Team

I was leading the product at that time and understood that these team members wanted to take an opportunity to grow and learn. Similar to many hi-tech companies such as Google, I encouraged experimentation and implemented a schedule that allowed team members to focus on new innovation and research, 20% of the time, for professional development and had high value. In combination with this, if any individuals came up with something high impact, my team would bring it to life.

There was a very smart engineer on my team who spent a significant amount of time coming up with a machine learning model for the product we were working on. I had a few conversations with him regarding machine learning performance and set a threshold of 80% accuracy before it could be launched into production. The idea was that once it was in development, the team could continue to improve upon it and gain a higher accuracy.

He continued to work on his model, working nights and on his 20% time. Finally, he created a model, and put it in a sandbox, only to realize it was performing with only 60% accuracy. Obviously, this was not the threshold that we wanted, and we could not bring it to life with such a low accuracy. The team could have spent time enhancing the threshold but it would be at a cost.

Empowering the Team with Data

This experience taught me that instead of pushing back on team members’ ideas, it’s important to allow them to pursue their passions while being data-driven. In this example, I gave my team member the runway to experiment with his machine learning model. Even after the accuracy was only 60% effective, he continued to work on it for a few weeks to try and improve the score. Eventually, he came back to me and said that we should leverage our vendors to build the machine learning model – as they were the experts in the field.

Since I built trust with him by providing him with opportunities to experiment, his data was telling enough for him to come up with a different recommendation. While keeping the goal in mind, I empowered my team to make decisions with a data-driven approach.

Discover Plato

Scale your coaching effort for your engineering and product teams
Develop yourself to become a stronger engineering / product leader


Related stories

How to Maximize Employee Retention in Engineering Teams

25 May

Vimal Patel, Founder and CTO at iMORPHr, shares how he retained all of his employees since beginning his software development company in 2019.

Building A Team
Company Culture
Hiring
Retention
Psychological Safety
Vimal Patel

Vimal Patel

Director of Engineering at iMORPHr

The Art of Asking Why: Narrowing the Gap Between Customers and Users

24 May

Jord Sips, Senior Product Manager at Mews, shares his expertise on a common challenge for product managers – finding root causes and solutions.

Customers
Innovation / Experiment
Product
Personal Growth
Leadership
Stakeholders
Users
Jord Sips

Jord Sips

Senior Product Manager at Mews

Creating a Company Culture That Balances Helpfulness and Productivity

16 May

Alexis Philippe, Vice President, Product & Engineering at Amilla, describes his one simple rule for creating a culture of helpfulness that doesn't disrupt productivity.

Mission / Vision / Charter
Company Culture
Collaboration
Cross-Functional Collaboration
Alexis Philippe

Alexis Philippe

Vice President, Product & Engineering at Amilla

Streamlining Product Processes After a Reorganization

16 May

Snehal Shaha, Lead Technical Program Manager at Momentive (fka SurveyMonkey), details her short-term technical strategy to unify processes among teams following an acquisition.

Acquisition / Integration
Product Team
Product
Building A Team
Leadership
Internal Communication
Collaboration
Reorganization
Strategy
Team Processes
Cross-Functional Collaboration
Snehal Shaha

Snehal Shaha

Senior EPM/TPM at Apple Inc.

Navigating Disagreements When It Comes to Priorities

9 May

Pavel Safarik, Head of Product at ROI Hunter, shares his insights on how to deal with disagreements about prioritization when building a product.

Innovation / Experiment
Product Team
Product
Dev Processes
Conflict Solving
Internal Communication
Collaboration
Convincing
Strategy
Prioritization
Pavel Safarik

Pavel Safarik

Head of Product at ROI Hunter

You're a great engineer.
Become a great engineering leader.

Plato (platohq.com) is the world's biggest mentorship platform for engineering managers & product managers. We've curated a community of mentors who are the tech industry's best engineering & product leaders from companies like Facebook, Lyft, Slack, Airbnb, Gusto, and more.