Revolutionizing Media & Retail Measurement: Nielsen's AI Transformation

Revolutionizing Media & Retail Measurement: Nielsen's AI Transformation

Table of Contents:

1. Introduction
2. Nielsen: A Brief Overview
3. The AI-First Strategy
4. Challenges Faced by Nielsen Data Science
5. Embracing Tech: Cloud and Open Source
6. Partnering with Databricks
7. The Data Science Pipeline at Nielsen
8. The Impact of Databricks
9. Next Steps for Nielsen Data Science
10. Conclusion

Introduction

Today, I want to share a transformation story that is unlike any other You've heard. It's a narrative about cultural change driven by technology, data, machine learning, and AI. As a proud leader at Nielsen, I've witnessed and led this change within our data science organization. In this article, I will discuss why Nielsen had to fully embrace cloud, open source, and spark, and how partnering with Databricks has helped us benefit from this transformation.

Nielsen: A Brief Overview

Before diving into the specifics of our transformation, let's start with a brief overview of Nielsen. Founded in 1920, Nielsen is a well-known company that invented market share as a metric. Over the years, we have made significant contributions to the field of data science, including the development of star schema and the use of data in media measurement. With our watch and buy businesses, we provide valuable insights to media companies, advertisers, agencies, manufacturers, and retailers. Our data sets serve as the foundation for advertising transactions, helping buyers and sellers make informed decisions Based on audience demographics, content preferences, and reach frequency.

The AI-First Strategy

To stay ahead of the data revolution, Nielsen adopted an AI-first strategy. This means that machine learning and AI are at the Core of our business challenges and opportunities. By leveraging cloud, open source technologies, and mobile devices, we have democratized data access and improved computational power. Our goal is to solve complex business problems using machine learning and AI frameworks like TensorFlow, PyTorch, and more. This strategy has enabled us to stay agile, adapt to changing consumer behavior, and provide Better Insights to our clients.

Challenges Faced by Nielsen Data Science

Before embracing technology and AI, Nielsen data scientists faced several challenges. The first was limited access to data. Access was often based on personal networks and tenure within the company, leading to a lack of transparency and collaboration. Additionally, working with big data posed significant hurdles, including metadata issues, manual partitioning, slow performance, and the need for proprietary software.

Embracing Tech: Cloud and Open Source

To address these challenges, Nielsen made the decision to double down on technology. We transitioned our data to the cloud, using AWS for our watch business and Azure for our buy business. We adopted open source platforms like Spark, Python, and notebooks, which eliminated manual partitioning and improved performance. By embracing cloud and open source, we democratized data access and fostered collaboration between data science, data engineering, and application development teams. This shift also allowed us to treat data as an enterprise asset, with better metadata and scalability.

Partnering with Databricks

In 2016, Nielsen partnered with Databricks to further accelerate our transformation. Databricks helped us democratize the use of Spark across our organization, providing performance improvements, scalability, and Simplified infrastructure management. With Databricks' support, approximately 80% of our data scientists are now using Spark. This partnership has allowed us to focus on building the best algorithms and leveraging machine learning and AI to understand consumer behavior. The Python support provided by Databricks has been particularly beneficial, enabling complex ETL processes and seamless collaboration.

The Data Science Pipeline at Nielsen

At Nielsen, we have multiple data science pipelines in place. These pipelines involve data collection, input data processing, and the use of Nielsen's labeled data to train machine learning models. We have a learning platform where data scientists and engineers collaborate to build and deploy models. The use of Databricks has simplified these pipelines, reducing cycle time and improving performance. We Continue to explore and incorporate new tools and technologies, such as MLflow and Delta, to enhance our data science capabilities.

The Impact of Databricks

The impact of utilizing Databricks in our data science organization has been significant. We have seen improvements in performance, with tasks that previously took 12 hours now completing in just 30 minutes. Cycle times have been reduced from one week to less than two hours, representing an 80x improvement. These achievements are monumental, considering the Scale of our data science team and the complexity of our business problems. Additionally, Databricks has fostered collaboration, improved workflows, and created a culture of innovation within our organization.

Next Steps for Nielsen Data Science

While we have made remarkable progress, our Journey is far from over. Our next challenge is to move our data science and machine learning models into production pipelines. We aim to further integrate data science and data engineering, adopting principles of model lifecycle management and continuous deployment. By focusing on productionization, we can ensure that the benefits of our AI-first strategy are fully realized across the organization.

Conclusion

In conclusion, Nielsen's transformation story is a testament to the power of technology, data, and collaboration. By embracing cloud, open source, and partnering with Databricks, we have revolutionized our data science organization. We have overcome challenges, improved performance, and fostered a culture of innovation. As we continue on this journey, We Are confident that Nielsen will remain at the forefront of data-driven insights, empowering businesses across the media and retail industries.

Highlights:

• Nielsen has a long history in the field of data science, and their transformation story is driven by technology, data, machine learning, and AI.
• The AI-first strategy focuses on leveraging cloud, open source technologies, and mobile devices to solve business challenges and provide better insights to clients.
• Challenges faced by Nielsen data science include limited access to data, working with big data, and the need for proprietary software.
• Embracing cloud and open source has democratized data access, improved performance, and fostered collaboration between teams.
• Partnering with Databricks has further accelerated Nielsen's transformation, providing performance improvements and simplified infrastructure management.
• The impact of Databricks on Nielsen's data science organization includes significant performance improvements and a culture of innovation.
• Next steps for Nielsen involve moving machine learning models into production pipelines and further integrating data science and data engineering.
• Nielsen's transformation story showcases the power of technology, data, and collaboration in driving innovation and success in the media and retail industries.

FAQ:

Q: What is Nielsen's AI-first strategy? A: Nielsen's AI-first strategy focuses on using machine learning and AI to solve business challenges and provide better insights to clients. It leverages cloud, open source technologies, and mobile devices to democratize data access and improve computational power.

Q: What challenges did Nielsen data science face before embracing technology and AI? A: Nielsen data science faced challenges such as limited access to data, working with big data, and the need for proprietary software. Access to data was often based on personal networks and tenure within the company, leading to a lack of collaboration and transparency.

Q: How has partnering with Databricks benefited Nielsen? A: Partnering with Databricks has provided significant performance improvements, scalability, and simplified infrastructure management for Nielsen. Approximately 80% of Nielsen's data scientists are using Databricks, which has fostered collaboration and allowed the focus to be on building the best algorithms and leveraging machine learning and AI.

Q: What is the impact of using Databricks in Nielsen's data science organization? A: The impact of using Databricks in Nielsen's data science organization has been significant. Performance has improved, with tasks completing in significantly less time. Cycle times have been reduced, resulting in an 80x improvement. Collaboration has increased, and a culture of innovation has been fostered.

Q: What are the next steps for Nielsen data science? A: The next steps for Nielsen data science involve moving machine learning models into production pipelines and further integrating data science and data engineering. The focus is on model lifecycle management and continuous deployment to ensure the benefits of the AI-first strategy are fully realized.

Q: How has cloud and open source technology transformed Nielsen's data science organization? A: Cloud and open source technology have democratized data access, improved performance, and fostered collaboration within Nielsen's data science organization. By transitioning data to the cloud and adopting open source platforms like Spark and Python, Nielsen has eliminated manual partitioning, improved scalability, and treated data as an enterprise asset.