Artificial intelligence in the Enterprises is revolutionizing the way businesses operate. AI is being integrated into company operations with the goal of increasing efficiency and producing useful insights. It's often seen as a way for companies to have a competitive advantage in the market. This is because it allows them to utilize their data to drive their business forward.
Enterprise AI software enables organizations to collect data, analyze it, and use the insights they gain to make better decisions. Machine learning algorithms can also be used to automate processes and minimize operational costs of repetitive tasks.
Carlos Ariza, Chief Data Scientist at Creative Artists Agency (CAA), talked about leveraging Enterprise AI to improve overall business performance in these rapidly evolving times at the Miami Machine Learning and AI Meetup Week earlier this month. Read this recap post to learn how the talents and sports agency built a data team that came up with a platform able to measure and predict content performance in order to achieve better results.
Trends and challenges in the content landscape
There are some major disruptive trends in the consumption and valuation of content.
One of the biggest changes is that streaming content became very popular compared to the traditional "sitting in front of the television". Disney announced a few months back that they have about 116 million subscribers while Spotify has about 28 million podcast listeners per month.
A major challenge of streamed content is finding a way to value it. A lot of payments for content are done upfront by streaming platforms, which leads to a lot of guessing of how much a title is worth in perpetuity.
Also, content valuation based on performance is just as challenging. There's a lack of performance metrics such as plays, views, searches and it’s hard to tell who’s watching what in Hollywood.
Building an enterprise data team
In order to be able to answer some essential questions about content performance based on data, CAA built an organizational data team. The development of this team was based on the DELTA Framework, derived from business guru and co-author Tom Davenport. The DELTA framework ensures companies have the right capabilities in five areas: Data, Enterprise, Leadership, Targets and Analysts.
The data team is subdivided into four remote, globally distributed teams and this includes Data Science, Data Analytics, Data Insights and Data Engineering.
- Data Science team. Focuses on algorithms and models for prediction and performance and trends.
- Data Analytics and business intelligence team. Deals with the building of interactive dashboards that tell compelling visual data stories.
- Data Insights team. An internal consulting team that works with agents and clients and is involved in the delivery of reports and analyses that provide actionable insights.
- Data Engineering team. Supports computing, storage and production pipelines.
Creating a content analytics platform
The data team at CAA came up with the idea of creating a platform able to show trends on how content is consumed globally and called this solution Intell Platform.
The Intell Platform has a feature known as the Intell TV, which analyzes content across streaming and analytic platforms and shows global digital demand. For example, it is able to track the top shows over time, based on the digital activity on streaming platforms.
Another use of the platform is to try to understand buyers and know which kind of shows appeal most to them, based on models able to analyze similarities between different actors for example.
Also, CAA created a service with the ability to quickly build custom models for different business functions, such as the financial planning around the music business of box offices. The team was involved in building predictive models to explore how different scenarios of the Coronavirus pandemic (creation of the vaccines, vaccine resistance etc.) will affect the music touring and venue business and how fast these businesses would come back after venues have been shut down for a while.
Conclusion
By building an enterprise data team and adopting machine learning and data analytics, CAA was able to come up with a platform capable of measuring content performance and predicting its value. Ultimately, this allows the company to make predictions on how a particular show with a particular cast will be a hit or not on a streaming platform.
