Storytelling for more impactful data science

As data science teams look to scale their impact across the organization, data scientists often spend a long time optimizing and refining models. All too often, they end up neglecting what may be the most important selection criterion for scaling data science: acceptance by the end-user.

When presenting results, data teams often fall into the common pitfalls of communicating the impact of their work and are often met with skepticism towards the models they build and how they fit within the organization. This translates into decreased trust in data science, hyperfocus on edge-cases when scoping machine learning projects, and resistance to change or adopting data solutions. Getting everyone on board and earning a wide range of approval is often a critical step in successfully scoping, implementing, and evaluating models and their results.

In this webinar, we outline how storytelling enables data teams to bring the same facts to the table, but provide a clear framework that helps formulate the most impactful aspects of their work. We cover some tips and tricks using real case examples of how to convince the most skeptical end-users.

Key takeaways:

• Storytelling is a core skill in the data teams' skillset

• Storytelling in data science and machine learning can be just as factual as other presentation forms for communicating impact

• Crafting audience-specific data stories increases the impact of data science within the organization

Gert De Geyter

Machine Learning Lead at Deloitte

Bhavya Dwivedi

Data Scientist at Deloitte

Adel Nehme

Data Science Evangelist at DataCamp