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AI Academy Instructor Digs into AI & Data Science

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As part of the AI Academy for Marketers membership, we offer monthly Ask Me Anything (AMA) sessions with leading industry experts. We chat about everything from technology trends, use cases, lessons learned, to much more.

This post offers an inside look into the latest, exclusive members-only session with Cal Al-Dhubaib (@caldhubaib), CEO, Pandata. During the session, he discusses all things data, from data science, data bias, to the use of data in pilot projects—all questions stemming from his certification offered in the AI Academy for Marketers (linked below).

Below is a quick video from our chat, followed by top takeaways from the conversation.


What is a common barrier for organizations getting started with AI? 

Available talent. If you don't have the talent, ask: how can we help a marketer understand what goes under the hood without understanding the nitty gritty and programming side of things? If you can ask better questions, you can create better solutions when you partner with data scientists. 

What is data science? 

AI and data science are closely related. Data science is as a broad discipline as being able to solve challenges by analyzing data at scale and making sense of it. It’s the process from which you create value from data. 

How do define AI? 

It’s a product of data science. AI and machine learning go hand-in-hand. Starting broadly, AI is software that excels at recognizing and reacting to complex patterns, automating human-seeming tasks (not jobs).  

What do you mean by tasks?

One of the biggest mistakes we’ve made as a field is calling it AI … perhaps augmented intelligence would have been better. If you think about the utility of AI and what it can mean, it allows you to do higher-level thought. AI changes the nature of what you do, but allows you to focus on higher-level questions (so you don’t have to do the mundane, specific tasks).

Can you talk about how Pandata works with the Cleveland Museum of Art?

Because it’s free, the museum has a unique problem. They have a hard time counting visitors in their space and how they engage with the exhibits. Pandata partnered with them to look at WiFi data and location data. We found this really fascinating nugget. CMA has gamified how you engage with the art in a high-tech exhibit, and they found individuals who spend 5+ mins in that space spent an hour more on average in the museum. So now, CMA knows that more interactive exhibits are more engaging. This can help curators decide which installments to input in the future. 

What is data bias? 

Bias and data should terrify us. We should really be thinking about it as we are conducting AI experiments. Ask the question: who is missing? What groups are not represented and what that might mean for expectations? Stopping to ask these questions makes a world of difference. 

How do you find a pilot project? 

Simpler is always better. To understand where AI can provide value: think about your workflow. What are the parts that have the highest human consistency? And also, what takes the most time? That’s usually a good way to identify a use case for AI. There’s probably some nugget in there that is worth automating. 

What are the next steps?

In Cal’s certification course, you will learn to ask the right questions of data scientists. You don’t need to be a data scientist to get started. You just need to look at problems differently and know there are better ways to solve them! 

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