Citizen Data Scientist

AT&T

Democratizing Data Science Across the Enterprise

In 2023 I was working as Staff Product Strategist at Formula.Monks. We were approached by a Telecom client with an opportunity that had the potential to drive major impact across the entire 150,000+ employee organization.

Our client owned a massive amount of data, which presented a valuable opportunity to enhance business processes at all levels by enabling non-data scientist employees to work with artificial intelligence (AI) and machine learning (ML) models. By becoming Citizen Data Scientists, these employees were able to leverage the power of AI and ML to improve their work.

Challenge

An over-reliance on a handful of data scientists shared across business units makes it costly to those business units that utilize the data scientists and they aren’t typically available on demand.
The people who were trying to solve difficult problems with big data had no way of knowing if a solution already existed or if others were working on the same thing.
The organization promoted many internal applications that had redundant functionality, each with a unique UI and interaction model, leading to confusion and frustration among the users who were supposed to adopt and benefit from them.

Solution

We interviewed stakeholders, product owners, data scientists, data analysts, and the many people who would be affected by and potentially benefit from our work.

After our first round of interviews and multiple Q&As at monthly Town Hall meetings hosted by the Chief Data Office, I led a series of focused workshops, including a GV-style Design Sprint with key stakeholders and would-be users. Coming out of the workshop we decided to take a multi-pronged approach.

We audited internal applications in the AI/ML ecosystem that people expressed frustration with during our interviews, and areas of functional redundancy and conflict were identified.
Working with the product owners we created a common UI and interaction model that would be used across this suite of tools and immediately started to publish these new patterns, decreasing user confusion and increasing literacy across tools.
We designed, tested, and engineered a functional prototype, named “Data Stories”, to serve as the data and AI collaboration hub for the suite of tools we audited, equipping employees from all business units, regardless of experience, with user-friendly yet advanced AI and machine learning tools.
We designed, prototyped, and tested a web app that takes queries and data sets and recommends existing ML models or guides non-data scientists through their creation. The prototype was extremely successful with our audience.

This initiative is empowering the client’s users to more easily discover meaningful and trustworthy AI-driven insights, enhancing their ability to serve their customers effectively.

By democratizing data science, organizations drive innovation and stay ahead of the competition, driving consistency, reuse, and collaboration.

Responsibilities

Product Management

Stakeholder Alignment

Interview Facilitation

Design Sprint Facilitation

Design Direction

User Research

Usability Testing