Dataiku Case Studies Scaling a Small Data Team with the Power of Machine Learning

Edit This Case Study Record

	Scaling a Small Data Team with the Power of Machine Learning Dataiku

Scaling a Small Data Team with the Power of Machine Learning

Dataiku

Technology Category	Analytics & Modeling - Machine Learning Analytics & Modeling - Natural Language Processing (NLP) Analytics & Modeling - Predictive Analytics
Applicable Functions	Sales & Marketing
Use Cases	Predictive Maintenance
Services	Data Science Services
Challenge	DAZN, a subscription sports streaming service, was looking to grow their business in existing and new markets. They wanted to enable their small data team to run predictive analytics and machine learning projects at scale. They also wanted to find a way to allow data analysts who were not necessarily technical or experienced in machine learning to contribute in meaningful ways to impactful data projects. The goal was to support an underlying data culture with advanced analytics and machine learning at the heart of the business. Read More
About Customer	DAZN is a subscription service owned by Perform Group dedicated to live and on-demand streaming of worldwide sporting events. It offers access to more than 8,000 sporting events a year across a wide range of devices to customers in Austria, Germany, Japan, Switzerland, and Canada, with more markets coming soon. In an effort to continue to grow their business in existing and new markets, DAZN wanted a fast, low-maintenance way to enable their small data team to run predictive analytics and machine learning projects at scale. Read More
Solution	DAZN turned to Amazon Web Services (AWS) and Dataiku Data Science Studio (DSS) for their simplicity in setup, connection, integration, and usability. They were able to get up and running in under one hour. With AWS and Dataiku, the small data team built and now manages more than 30 models in parallel, all without needing to do any coding so that the processes are completely accessible to non-technical team members. They use these models as the basis for a variety of critical processes throughout all areas of the business, including content attribution, advanced customer segmentation, propensity modeling, survival analysis, and natural language processing on social networks for market research. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Analytics & Modeling - Natural Language Processing (NLP)

Analytics & Modeling - Predictive Analytics

Applicable Functions

Sales & Marketing

Use Cases

Predictive Maintenance

Services

Data Science Services

Challenge

DAZN, a subscription sports streaming service, was looking to grow their business in existing and new markets. They wanted to enable their small data team to run predictive analytics and machine learning projects at scale. They also wanted to find a way to allow data analysts who were not necessarily technical or experienced in machine learning to contribute in meaningful ways to impactful data projects. The goal was to support an underlying data culture with advanced analytics and machine learning at the heart of the business.

About Customer

DAZN is a subscription service owned by Perform Group dedicated to live and on-demand streaming of worldwide sporting events. It offers access to more than 8,000 sporting events a year across a wide range of devices to customers in Austria, Germany, Japan, Switzerland, and Canada, with more markets coming soon. In an effort to continue to grow their business in existing and new markets, DAZN wanted a fast, low-maintenance way to enable their small data team to run predictive analytics and machine learning projects at scale.

Solution

DAZN turned to Amazon Web Services (AWS) and Dataiku Data Science Studio (DSS) for their simplicity in setup, connection, integration, and usability. They were able to get up and running in under one hour. With AWS and Dataiku, the small data team built and now manages more than 30 models in parallel, all without needing to do any coding so that the processes are completely accessible to non-technical team members. They use these models as the basis for a variety of critical processes throughout all areas of the business, including content attribution, advanced customer segmentation, propensity modeling, survival analysis, and natural language processing on social networks for market research.

Impact #1	Each Data Team Member 2.5x More Efficient in Putting Models in Production
Impact #2	Data team has more impact thanks to the ability to quickly deploy to production
Impact #3	Point-and-click features enable non-technical data team members to do machine learning

Impact #1

Each Data Team Member 2.5x More Efficient in Putting Models in Production

Impact #2

Data team has more impact thanks to the ability to quickly deploy to production

Impact #3

Point-and-click features enable non-technical data team members to do machine learning

Benefit #1	Hired two data analysts who are doing as much work as five analysts in the pre-Dataiku team
Benefit #2	Empowered a non-technical team to create more models than ever before and get them into the production environment quickly
Benefit #3	Plans to grow the team to three data scientists and 6-10 analysts to exponentially increase the number of machine learning models in production

Benefit #1

Hired two data analysts who are doing as much work as five analysts in the pre-Dataiku team

Benefit #2

Empowered a non-technical team to create more models than ever before and get them into the production environment quickly

Benefit #3

Plans to grow the team to three data scientists and 6-10 analysts to exponentially increase the number of machine learning models in production

Download PDF Version

Overview

Scaling a Small Data Team with the Power of Machine Learning

Operational Impact

Quantitative Benefit