Rely on Automation for Scalability
Customer Company Size
Large Corporate
Region
- America
Country
- United States
Product
- Dataiku’s collaborative data science and analytics platform
Tech Stack
- R
- Scala
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Productivity Improvements
- Customer Satisfaction
Technology Category
- Analytics & Modeling - Big Data Analytics
- Analytics & Modeling - Predictive Analytics
Applicable Functions
- Sales & Marketing
Services
- Data Science Services
About The Customer
The customer is a large national media organization that produces both news and public interest (cultural) pieces. They serve as a syndicator to a network of more than 500 stations across the United States. Additionally, their content is available directly from a variety of sources, including on the web and via their mobile application (which they develop and maintain in-house). The organization has a small team and limited resources, and they wanted to build a recommendation system for their app in a scalable way.
The Challenge
A large national media organization wanted to provide high-quality recommendations for users of their app. Their goal was to target consumers with content that they would actually be interested in based not only on what they previously consumed, but how exactly they interacted with topics in which they previously expressed interest. For example, if someone chose to listen to a report on Topic A but then fast forwarded through much of the piece (as opposed to actually listening to the piece in its entirety), the app should take that activity into account for future recommendations. However, with a very small team and limited resources, the organization wanted to accomplish this in a scalable way. Not only would the system have to be mostly or entirely automated, but the team itself would have to be able to build the recommender easily in a way that would allow for quick tweaks and adjustments in the future.
The Solution
The team behind the national media organization’s app leveraged Dataiku’s collaborative data science and analytics platform to efficiently and easily string together data cleaning operations and processes and then ultimately use that prepared data in the built-in supervised learning algorithms to build their recommender system. Out-of-the box without having to write code, the team was able to seamlessly try out different models and choose the one that produced the best recommendations for the app according to their business goals. At the same time, when they did want to use code for data processing, Dataiku enabled them to use a combination of languages (mainly R and Scala), choosing the tool that would be easiest for the task at hand. And with all of the data processing required (sometimes more than 25 recipes for a single processing operation), if any part of their process failed, it was simple and efficient with Dataiku for the small team to tell exactly where the issue was and to fix it quickly.
Operational Impact
Quantitative Benefit
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