3 Themes I Hear Often as the Host of an Analytics Podcast

Editor’s note: This edition was written by Megan Bowers , host of the Alter Everything Podcast.

As the Alter Everything Podcast host, I’ve interviewed guests from diverse industries (sports teams, healthcare companies, financial companies, and everything in between) and data roles. Despite their different backgrounds, our conversations often revolve around similar opportunities in data and analytics.

Let’s examine these trends that thought leaders in the data space have expressed on the show.

#1: Analysts get writer’s block, too – and genAI is helping

In our data storytelling episode, Kat Greenbrook shared how she uses generative AI as a collaboration tool in data storytelling to understand her audience and craft her message:

“I'll ask Chat GPT, ‘What is this audience struggling with? What are the challenges that they would face in that role?’ It's a great way to get an initial understanding of an audience that you don't necessarily know much about. And I think it's helpful in terms of reframing narratives and writing.”

Another use of generative AI for analysts came up in our episode with my Alteryx colleague, Dr. Erica Reuter, Ph.D. . She discussed how this technology is embedded into our Auto Insights product and how it enables users to jump straight to the first draft of a data analysis presentation:

“That's where AiDIN comes in—we already have Magic Documents out, so you do an analysis, or you upload some data, and the computer says, ‘This is what I see in your data, your patterns, your trends. Let me generate a PowerPoint message for you...’ And it creates that first draft… Then, you can go through and tweak it. For people like me, that's amazing. Because I feel like starting that email, starting that text, that's the hardest part, right? So it gets you past that blank canvas where you can just go through and edit from there and make [your analysis] look the way you want.” 

Building trust in AI models is key to ensuring that AI is a successful tool for data workers. As Krishna Gade put it in our episode with Fiddler,

“How do we build that trust with the machine? And how do we build that transparency? That is the whole game that we need to solve in the future for AI to be successful. So it can be controlled, can still work for us, not against us, but it can make us productive.”

As we continue to see more AI-powered features rolled out in software across industries, we have an increasing need for responsible, explainable AI—ensuring that AI makes us more productive (and not leading us down the wrong path)

Intrigued yet hesitant about generative AI?

Watch a panel of AI specialists as they cut through the hype surrounding AI and analytics, offering practical insights and actionable advice on integrating AI into your business strategy.

Watch the discussion on demand.

#2: The most important part of data projects is the “why.”

 Simon Sinek famously believes everything you do should start with your “why.” Similarly, when Mara Cairo, P.Eng., PMP joined us to talk about driving AI adoption, she was clear about the following pitfall in adopting machine learning:      

“I think there can be a misconception that machine learning is this magical dust that you sprinkle on a problem to get a better result and be more competitive, and I think that can sometimes be a problem… We always want to start with a business problem. And sometimes companies want to start with the solution, which is machine learning, but there actually isn't a clear business problem to work on.” 

When there is a push from the top of an organization to deliver on AI or any data project, we want to jump straight to a solution. This is problematic, leading to unused work and frustrated stakeholders. We want to lead with the business problem rather than the technology. Take Akshay Swaminathan 's example below in our winning with data science episode: 

“The worst thing is when you let the data team run free, and they come back with something that's really cool… But either it's solving the wrong problem, or the business isn't ready to put it into action. This happens in healthcare all the time… There are a ton of models that people build to predict some disease outcome… and they have decent performance, but they never end up having any impact because it's not enough to just build a model that model needs to align with a business use case and a health system use case. The hospital has to be ready to implement that model within its care workflows. If no doctor wants that model… No one's going to use the model.”

How do we ensure that our projects solve real business problems? We empower the line of business workers and work collaboratively toward a shared goal. As Akshay put it,

“It's not enough to have a skilled data team that can build performant tools. The building needs to align with the business problems, needs to align with the capabilities of the business, the readiness, the willingness of the business to adopt those solutions. And that's why we need to work on empowering the business folks, right? [Empowering] the domain experts to become better collaborators.”

 Without a clearly defined business problem, the business is unlikely to adopt your solution. Our guests highlight the importance of starting projects correctly by articulating the problem and taking steps to solve it.

#3: There is need and opportunity for analytics in non-profits and the public sector 

Several guests on the podcast have discussed their analytics work in non-profits and education. Dr. Oscar Rico , the executive director of technology services at the Canutillo Independent School District, summed up his attitude toward data in education like this:

“… Data really gives us an understanding of who we are, where we are, and where we're going. As a school leader, I always understood that data has an expiration date, right? So, as quickly as we can turn around data, it's really what is informing our practice, which is allowing us for students to be successful.”

In this episode, he emphasized the need for accurate and timely data to predict and influence student success in his district and beyond. Of course, there are many challenges with data in the education field. Dr. Rico highlighted an important one: “Every community is different.” This leads to challenges in procuring data affordably and setting up agile systems. 

On the non-profit side, Chris Williams joined us on the podcast to discuss his work with Alteryx for Good. He has helped multiple food banks implement much-needed analytics: 

“We are enabling them to not just show them how to organize their data; we're also teaching them how to obtain the data, how to clean this data, and how to make the data work for [them] because this data is going to be used to request grants… The margin of error to do that data entry and data ingestion… has to be really on point from the start because they may not have the staff, and they definitely may not have the budget to go into a significant effort to help them with their data needs.”

Deanna Sanchez explained in her episode on building a culture of analytics in non-profits how Alteryx spreads across the organizations she works with because of their reporting needs:

“It's really tremendous the way when you start working with nonprofits… you find that Alteryx might start in one part of the organization, but then it spreads, you know, like wildfire, really. The other departments ask, ‘How are you doing this? How are you making your reports in two minutes and updating this automatically?’”

In these fields, where it is expected to do more with fewer resources, analytics and automation are key to securing grants, predicting outcomes, streamlining processes, and understanding where improvements can be made.

Stay Tuned

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