In the age of data and analytics, businesses can enhance and make well-informed decisions by leveraging data and technology in their favor. In this episode of the Decisions Now podcast, we talked to Erica Brigance, Vice President of Data Science at Arcbest on how to realize value from analytics and artificial intelligence (AI) projects.
Co-hosts, Rigvinath Chevala, Evalueserve’s chief technology officer, and Vice President of Marketing Erin Pearson chatted with Brigance on the future of using technology well, ROI (return on investment), demand and value realization, and of course, the hype of ChatGPT. Subscribe to the Decisions Now podcast today, you can find us on Spotify, Apple Podcasts, and Amazon Music among other platforms.
Future of Using Technology
“How are you seeing technology changing and, um, in order to help meet customer challenges?” Pearson asked Brigance.
The biggest change she has seen in the 19 years she’s been with Arcbest is connectivity, Brigance said.
“We are all connected, and it’s exponentially growing the number of devices and, and people that are part of our connections. So, with that, we’re always evaluating technology and, and new methodologies to deliver solutions to our customers,” she added.
“The new technology, of course, keeps evolving, but from a business-centric standpoint, typically you want to not always take the shiniest stuff you want to test and try and make sure it stands to the enterprise-grade needs,” Chevala said.
Instead of focusing on new trending technologies, companies need to identify use cases and understand how these technologies can drive business value or customer value for them.
Companies must listen to their customers’ challenges, without assuming what they need or how they need to solve those challenges, Brigance said.
“So, really what I’ll call deep listening to even the things that they’re not explicitly saying, but what are the challenges that they’re up against? And once you identify those challenges, it’s about taking the full suite of technological solutions and understanding what can solve it and where it plays, and then what’s the cost and benefit of exploring that technology,” she added.
The trio also mentioned its essential for teams to develop their soft skills often and understand emerging technologies to then tie them to relevant challenges when possible.
ROI
When it comes to analytics-based decision-making, before predicting or making a recommendation to a client, teams must understand what’s causing the current challenge and know the ROI thesis for the project.
Here are some questions teams must regarding ROI:
1. Have you identified the right metrics that you anticipate moving?
2. Do you have the right data to impact a decision to change that metric?
3. What metrics matter to the business and customers that we need to make an impact on?
Chevala asked Brigance what kind of data prep goes in before optimizing use cases and if she could provide an example of it.
“So, I have to optimize not only for what I know at the beginning of the day, but what may come in throughout the day. Um, how good is the data that I’m getting? You know, if, if I think I’m getting one pallet, but I show up and now I have three pallets, do I have space on the truck for that?” she said. “It gets complex just because there’s that in and out of our process through that city route optimization.”
Demand and Value Realization
“The first thing about demand is everyone understands the value that we can bring and what challenges we can solve, whether it be just a diagnostic project where we’re trying to dig in and figure out what’s going on all the way up to a predictive or prescriptive,” Brigance said. “So, some of the demand realization is about building awareness for what we can do. And that comes from socializing some of the projects that we’ve already done.”
“You may have heard of the term the ladder of inference. And you know, people appreciate once they understand the inner working of something and then realize what the value of that is, and if you make that transparent, that goes a long way in kind of getting that trust and credibility within the organization,” Chevala said.
When delivering insight, some people find more value in it than others and it’s about delivering it when they can make an impact and having a feedback loop, Brigance said.
“We tend to not focus so much on is the person going to trust the insight. It’s about building trust with them,” she added.
Two tips the team discussed when launching their analytics projects:
1. Start small and don’t assume that you have the data you need to predict or prescribe something.
2. Know that just because you can predict an outcome, doesn’t always mean it can drive business value.
The ChatGPT Hype
As a bonus discussion, the team debated the hype of Open AI’s ChatGPT.
Certainly not a fad, it’s impressive what it can do but it goes back to the original question of how it can solve a business need, Brigance said.
“I have challenged them to come up with a couple of use cases to really understand the value there so that we can think about where it might fit in. But we are not just going to use ChatGPT because it is the new thing out there,” she added. “The question is how it can help solve a business challenge or drive business value or drive customer, you know, needs in their supply chain.”
Learn more about realizing value with analytics and listen to this episode of the Decisions Now podcast featuring Erica Brigance for more expert advice. Subscribe today!
