8. Naras Eechambadi, Chief Global Data and Analytics Officer, Universal Music Group
Describe your career to date
I currently lead global data and analytics efforts for Universal Music Group, the largest music company in the world, helping artists in more than 60 countries better understand and build relationships with their fans.
Prior to joining UMG, I founded, built, and sold two companies focused on customer data and analytics software. I also built the Knowledge Based Marketing department at Wachovia Bank (now part of Wells Fargo) and helped launch an analytics practice at McKinsey.
How are you developing the data literacy of your organization, including the skills of your data teams and of your business stakeholders?
We have built a diverse team of data strategists, architects, engineers, analysts, scientists, and product managers who build and distribute data and analytical products to all our business units and partners. We have weekly brown bag sessions where our team shares knowledge and developments with each other; this has been extremely effective. We have also brought on external companies to help train our team in effective communication of data and analytics insights.
What role do you play in building and delivering conventional AI solutions, including machine learning models? Are you involved in your organization’s adoptions of generative AI?
We manage about 5,000 websites and close to 1,000 stores on behalf of our artists, selling music and merchandise. As a result, we manage hundreds of thousands of SKUs across these stores.
Our artificial intelligence (AI) and machine learning models are deeply embedded in sales forecasting and inventory and supply chain management across these stores, using streaming as well as other engagement data from fans.
We are working with our artists as well as leading generative AI (genAI) companies like Google and YouTube to ensure that our artists can leverage genAI effectively, while also protecting their copyrights from being infringed by AI companies that use their music and lyrics for training their models, often illegally.
Have you been able to fix the data foundations of your organization, particularly with regard to data quality?
As a music company, we are led by creative people who have historically focused on artist relationships. As a result, fan data has not been a priority.
My journey over the past few years has been to build a robust data foundation across all labels, artists, and territories that provides a standard, reliable, accessible data foundation for analyzing fan behavior and using it to improve the artist-fan dynamic and relationship.
Our team has done a great deal to help our finance and supply chain teams improve their data quality, which has led to tremendous increases in our forecasting accuracy, which is critical for us as a public company.
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