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Telling Stories in Data Science1

Communication is a key skill in any field. Without communication, we would be hard-pressed to drive change in an organization. We need to communicate ideas well to ensure that the people that help us do what we do, can help us do what we do. One of the key functions of any data scientist is to encourage data-driven change to effect some positive collective outcomes (increasing revenues, reducing costs, improving resiliency). If you can’t communicate the work you’ve done in a concise, meaningful, contextualized way,…you may fail in making your work actionable. The right people responsible for driving change won’t get to onboard your train, and your data science will go nowhere. No person is an island. We need the help of thousands of people to do what we do effectively, and to do that, communication is an absolute key.

Data science communication often takes one of two forms: (1) evangelizing an idea that doesn’t yet exist (“The Art of the Possible”), or (2) explaining a result of an outcome of a data science process in such a way that it (a) sheds light on or increases transparency around, a hidden process, (b) it improves understanding of the system (how it works, how it fails, and how it can be exploited or brought under a certain discipline or control to promote our objectives), and finally, (c) results in the creation of new value through greater understanding or more in-depth insight into the workings of the system.

While data science as a profession is arguably “new,” it frequently makes use of many matured techniques that we can readily borrow from other domains. We shouldn’t be afraid to borrow from other domains – that is how innovation happens, often. It is also one of the reasons why data science can be so insightful. We can take ideas from one area (say, manufacturing), and apply it to a completely different area (say, banking).

One simple technique that I readily make use of is how the film industry uses storyboards to plan a movie or to tell a story. I was always fascinated by the elaborate process and tremendous collaboration required in the production of epic movies such as the Lord of the Rings or Star Wars or Jurassic Park, among many others. Watching the bonus features, you learn a lot about what it really takes to tell a story, emotionally, critically, substantively, and effectively. You also appreciate the thousands of people behind the scenes who somehow magically collaborate, like a swarm of bees (a hive mind), to produce something with the continuity of a single mind. That scale of human collaboration, for me, is extremely fascinating. Often, it is hard enough to get 5-10 people to do something the way you want, let alone getting 1000s of costume designers, stagehands, actors, extras, etc., to work towards a shared vision that comes across seamlessly to the unsuspecting audience. After all, the purpose of a movie is to forget that you’re watching a movie. Movie producers seem to be able to do that time and time again. I imagine there’s a lot we can learn from them that we can bring into our discipline.

I have, in many recent blogs, expressed the importance of ontologies in how we view, understand, and interpret the world, and how we interpret data in particular. However, putting the concept in practice is rather complex. So, how do we simplify this? How do we communicate what we’re envisioning to a broader audience? I thought to myself, why not storyboard it?

In the movie production process, before anything is captured on film, the story is first scripted, and then storyboarded. The writers, producers, directors, actors, and many other key personnel I am not aware of, hash, and re-hash the storyboard for weeks or months on end. Until they arrive at a point where they think they can collectively visualize the story, without having shot a single minute of actual footage. Once the actual shooting begins, they can focus on the lighting, textures, emotions, among others, that are trying to convey in each shot. Before they ever arrive at the shot, they think through what each shot means at a macro-level (philosophically, emotionally, mentally, etc.). And then down to the micro-level (plot, staging, textures, sounds, backgrounds, etc.).

So, in my quest to copy their technique, I watched several educational YouTube videos about how to storyboard (references provided as links at the end of this blog). It was followed by putting together about 13 slides that successively conveyed a sequence of images/concepts/ideas about ontologies.

Through this, we basically hope to educate/inspire our audience to think about a few key questions which should be “top of mind” as they look to operationalize their data

What is a knowledge strategy?
What is our knowledge strategy?
How can we use ontologies to capture knowledge?
How can we exploit that knowledge to drive insights?
What value do such insights have in how we go about our day to day business?

Let’s take a gander through these storyboard slides, and then see the finished product that our marketing team has put together.

The Storyboard







The Final Production - Lights, Camera, Action!

Video: The Final Production - Lights, Camera, Action!

Some Links to Useful YouTube Content on Storyboarding:

  1. Intro to Storyboarding Intro to Storyboarding
  2. How to draw A-grade storyboards (even if you can’t draw!) How to draw A-grade storyboards (even if you can't draw!)
  3. Storyboarding - Tomorrow’s Filmmakers Storyboarding - Tomorrow’s Filmmakers

Prad Upadrashta

Senior Vice President & Chief Data Science Officer (AI)

Prad Upadrashta, as Senior Vice President and Chief Data Science Officer (AI), spearheaded thought leadership and innovation, rebranded and elevated our AI offerings. Prad's role involved crafting a forward-looking, data-driven enterprise AI roadmap underpinned by advanced data intelligence solutions.