According to a study by PwC , “the central theme of [the] growing world of media is that it’s personal and increasingly digital. And it is one that is constructed by the individual for his or her own enjoyment and gratification, and delivered through personal devices.”
It comes as no surprise then when we think about the rapid shift that the media and entertainment industry is making from simpler content recommendation systems to an entire AI-driven personalized content experience, using advanced data science and machine learning.
So how can the M&E industry continue to improve their customer experience and personalization, and exploit AI and machine learning at their full potential?
It all comes down to a gradual evolution into a truly data-powered organization. For this, how we communicate the value of data to influence business stakeholders, democratize the use of data, and make sure the insights are widely adopted and leveraged, is becoming key to the success of any AI project.
It’s the tale of data storytelling, which is becoming increasingly important not only as a trend, but also as a skill.
What exactly is data storytelling?
We interviewed Alex Simonoff, Data Scientist at Spotify, one of the biggest media & entertainment brands pioneering advanced AI-driven solutions. Here's what she had to say on the topic of data storytelling:
Data storytelling is using data mining and domain knowledge to draw connections between trends in data to real-world outcomes or circumstances. By focusing on the customer's experience, data scientists are able to communicate with their teams the usage of their product or feature in a way that builds empathy towards customers. Using data storytelling to build empathy often results in more impactful work on the user experience being prioritized."
Good visual representations of datasets or models are important for data scientists to uncover patterns or outliers during the model development. But turning datasets into beautiful – and useful – visuals is critical to making data more understandable and actionable for end consumers, empowering people to use data in new and creative ways.
How can M&E companies leverage it to understand their customers’ needs and emotional drivers to deliver better end-to-end user experiences?
Everyone on a product team has their role and my role is to be a data specialist who not only understands what the data says but also translates those trends into takeaways and product recommendations. Spotify embraces data storytelling in a way that allows data scientists to have an important seat at the table when it comes to deciding ‘What’s Next?’.
Data storytelling can bridge the gap between business and technical leaders: sharing a common language builds trust and confidence, ultimately leading to a healthy relationship for an inclusive data-driven culture and scalable AI.
When we look at digital transformation in M&E from this perspective—and communicate about the value of democratizing AI within the organization— we can see the real impact of data science, AI, and machine learning: an ability to understand customers on granular level, deliver powerful, personalized content, one-on-one user experience and storytelling, as well as attract, retain and empower data talent, and create a new sustainable business model.