About the Guest Rowdy is the Director of Data Science and Analytics for Corporate Marketing at Lumen. He has been with Lumen for 22 years with a central focus on Business Intelligence, Data Engineering, and Analytics across Finance and Marketing during that time. The current scope of his role is to be a central hub for several organizations (Marketing, Product, and Business Units) supporting Digital & Customer Experience, Data Stewardship & Governance, Campaign Performance, E-Commerce, and AI/ML. Connect with Rowdy Clagg Key Takeaways Data analytics is necessary for digital transformation because there is so much going on in the world today with technology and customers. There is still a lot to explore in the analytics space and there are many ways to use data to drive customer experiences. Data analytics is necessary for any company looking to move into the digital age. Millennials are becoming the key decision-makers and cloud-based data analytics can be a challenge for companies of a certain size. The way to succeed in the e-commerce space is by implementing customer experience metrics and aligning business goals with those metrics. Quote “The more data you have, that’s where the challenge is. You’ve got to be willing to innovate, you’ve got to be willing to move into those new areas that help you get there.” – Rowdy Clagg Highlights from the Episode Why do you believe there’s a need to heavily evolve data analytics for digital transformation? We’re in a data-rich environment today, a lot of interactions are now more digital than ever. There are so many tools, capabilities, and access. This journey is it’s not new. It is, I think, harder for large enterprise companies to make that pivot quickly. Luckily, I think we [Lumen] have done a fairly good job of making that shift much sooner. We started our journey and we’ve gone through plenty of successes and plenty of failures along the way. There’s so much that has changed even over that three-year timeframe. Even in today’s today’s world, there’s still a lot more to explore. My question would be, why wouldn’t you evolve and try to use everything at your fingertips? How are you leveraging cloud-based models for data analytics right now? That’s a new area for us. You would think that with all the cloud computing that’s out there, we will be utilizing that framework a lot more. We’ve been slower to adopt cloud computing, quite frankly. But my team has it sort of on the frontier, that there are IT organizations who are now very supportive and open to allowing us access to cutting-edge tools. I feel like if you’re on-prem with traditional data warehousing tools, they’re limiting. And with data growing so rapidly, it’s exponential. You’ve got to be able to use higher processing capabilities, to analyze it, understand what you’re looking at, and then provide those insights. The more data you have, that’s where the challenge is. You’ve got to be willing to innovate, you’ve got to be willing to move into those new areas that help you get there. We’re going to keep moving forward, we’re going to keep innovating, even if it’s clunky. What are some of the challenges you face while trying to implement this cloud-based model? You got to have a really good alignment with your other organizations. So obviously, we’re heavily dependent on a lot of our data infrastructure today. Going into this, we knew that there was only one other organization that had successfully stood up to this and was using an off-prem solution pretty frequently. As we started to go down this path, we thought, from our perspective, we have a bunch of different options that we wanted to explore. We probably had three or four different vendors that we were thinking about and we quickly ran into some roadblocks. And we quickly realized that we can’t swim against those challenges, we got to work with them. So we met in the middle, and we chose the vendor that was preferred from an IT perspective. Beyond that, to get our cloud platform up and running, it could be minutes or less in there for us to go through all the security checks and work with IT teams who don’t have a lot of familiarity with standing up these offering solutions, even though we sell offering solutions. We ran into some challenges there. And it ended up being about two weeks before successfully getting our platform up and running. And even then I think we’re still going through some configuration things to dial it all in. How did you decide which solutions would be ideal for the e-commerce strategy? So we have a lot of products. Some of them are complex and require a salesperson to kind of work through what the solution looks like for the customer. Others we know are much simpler. It’s simply internet access, or it’s like a DDOS solution. And we only have a handful that meets the criteria to be able to sell online, where we can provide the content, we know we’ve got enough information to guide the customer to be able to make a decision. And so once we get to that point, then it becomes about the price, other aspects that need configuration, and seller experience. To be able to deliver the service, even in a timely fashion, was a critical piece. So we’ve done a lot of listening circles to gather feedback from the customers that helped guide us as to what they expect from an overall digital journey. And one of the things we ran into was that they expect products to be installed within a certain timeframe. Well, right out the gate, we couldn’t meet some of that. Those are the challenges and so we’re still working through how we can make that more transparent. If you can’t deliver within a certain timeframe, then you just don’t offer it as an e-commerce solution. What are some of the tips you wish someone or maybe even your future self would have told you before you went on this journey of e-commerce? Don’t assume you know everything. I can’t tell you how many assumptions I’ve put out there thinking “Oh, yeah, you know, this is simply a data problem, right?” oh no, this is more than a data problem. There’s a reason why you have data problems. There’s often underlying reasoning, whether it’s a process gap, how we do business, or what is creating your data problem. I’ve made a lot of assumptions early on. I know better.