In this episode, Dana Gardner, Principal Analyst at Interarbor Solutions, is joined by Kevin King, Senior Director of Business Intelligence and Analytics at Coca-Cola Consolidated. They explore the evolution of data strategy at Coca-Cola Consolidated, the shift towards greater data availability, and empowering business analysts with dynamic data access. The discussion also covers the critical need for continuous refinement and innovation in data practices to drive business results.
In this episode, Dana Gardner, Principal Analyst at Interarbor Solutions, is joined by Kevin King, Senior Director of Business Intelligence and Analytics at Coca-Cola Consolidated. They explore the evolution of data strategy at Coca-Cola Consolidated, the shift towards greater data availability, and empowering business analysts with dynamic data access. The discussion also covers the critical need for continuous refinement and innovation in data practices to drive business results.
[00:00:00] Producer: Hello and welcome to the Data Cloud Podcast. Today's episode features an interview with Kevin King, Senior Director of Business Intelligence and Analytics at Coca-Cola Consolidated. Hosted by Dana Gardner, Principal Analyst at Interarbor Solutions. Together, they explore the evolution of data strategy at Coca-Cola Consolidated, the shift towards greater data availability, and empowering business analysts with dynamic data access.
[00:00:28] Producer: The discussion also covers the critical need for continuous refinement and innovation in data practices to drive business results. So please enjoy this interview between Kevin King and your host, Dana Gardner.
[00:00:39] Dana Gardner: Welcome to the Data Cloud podcast. Kevin, we're delight to have you with us.
[00:00:44] Kevin King: I'm glad to be here.
[00:00:45] Dana Gardner: Hey, tell us a little bit about your role at Coca-Cola. Consolidated, maybe a bit about your IT background and what excites you about data strategy and its return to the business over the next few years.
[00:00:58] Kevin King: Yeah, I've been with Coca-Cola Consolidated for 20 years. I lead our business intelligence and analytics department.
[00:01:05] Kevin King: As of recently, I also picked up our data quality and architecture organization as well. You know, what I really enjoy about data strategy is really understanding the foundation of what we do as a data organization and making sure that we have good data, the right data, good quality data in order for our end users to really get the best experience out of what they're trying to do to drive better results for the business.
[00:01:35] Dana Gardner: And what have you sensed has changed about what the businesses are expecting from organizations like yours. How are you enabling organizations to be better, faster, and perhaps more accurate in their decision making?
[00:01:49] Kevin King: Yeah, I would say the thing that's changed the most is the availability of the data. You know, I think our organization is looking to have, you know, data in the hands of our analysts throughout the organization, really empowering them to be able to be very self-sufficient.
[00:02:06] Kevin King: The bottling industry changes day by day and, you know, what we look at today may be very different the next day. And so giving them the ability to be more dynamic with data and ability to drill, drill down without necessarily relying on a traditional business intelligence team is probably one of the biggest opportunities and, and one of the biggest pushes that we have as a data organization.
[00:02:31] Dana Gardner: And for our listeners and viewers, tell us a bit about Coca-Cola Consolidated. What do you do? And that way we can drill down a little bit more about how analytics is helping you and how you're developing it better.
[00:02:42] Kevin King: Absolutely. So we're the largest Coca-Cola bottler in the United States. We manufacture, sell and deliver Coca-Cola products.
[00:02:50] Kevin King: So not only from our digital footprint with online, but also from the brick and mortar, delivering that product to the stores and actually taking orders from those stores. That's really, you know, what we do best.
[00:03:03] Dana Gardner: And how long have you been in business doing this?
[00:03:06] Kevin King: I've been doing it for 20 years, so this is the only industry that I've been in.
[00:03:10] Kevin King: And it's all been in some form of analytics or data perspective. And now having the ability to lead the data organization has been quite an honor.
[00:03:22] Dana Gardner: And how about Coca-Cola Consolidated itself? How long has it been bottling?
[00:03:27] Kevin King: Well over a hundred years. So we've been here a really long time.
[00:03:30] Kevin King: It's a family owned business, a third generation family. So, you know, we know how to do it pretty well.
[00:03:37] Dana Gardner: And you're regional, you're distributing around what area of the United States?
[00:03:42] Kevin King: Yeah, so our footprint is on the East coast, so from the Carolinas up to Tennessee, Arkansas, Ohio, Indiana, also in the Washington DC area.
[00:03:53] Kevin King: So pretty much the I-95 corridor is really where we operate.
[00:03:58] Dana Gardner: So I have to imagine that the data that you're looking to improve includes all the things around transportation and logistics, all sorts of things around procurement, and of course manufacturing and you know, factory floor type of efficiencies.
[00:04:16] Dana Gardner: And then there's always the back office typical for any business. Is it fair to say that you have a wide variety of data needs and therefore you have to pursue a lot of data resources?
[00:04:27] Kevin King: Yeah, you have it absolutely right. We have a large amount of data, different types of data where we have to, you know, figure out how to get all that in one ecosystem, which obviously Snowflake has been the data cloud that we've been able to do that with.
[00:04:42] Kevin King: But not only that, also external data as well, right? So we have our day-to-day internal data, but as we move towards the future, there's even more need for external data to be smarter and be more relevant to how to serve our consumers and our employees.
[00:04:57] Dana Gardner: And so as a hundred year old plus business, there's a maturity there, but you can always do things better, right?
[00:05:03] Dana Gardner: And so you're not looking to perhaps re-engineer your business, but to refine and improve and automate. Is that fair to say that this is an exercise in refinement rather than wholesale change? Or maybe have that wrong?
[00:05:17] Kevin King: No, I think you have it right. I think everything we're trying to do from a data organization is adding incremental new value, unlocking new potentials.
[00:05:25] Kevin King: You know, because we've been an organization that have done things a certain way for a really long time, that creates a lot of manual processes, so types of inefficiencies with what our processes are and what we do with data. So I think it's definitely an incremental approach with the capability to unlock new potential.
[00:05:45] Dana Gardner: So all of us are very familiar with Coca-Cola, the brand and the drink. I have to imagine, is there more that you're distributing than just the beverage or is it strictly the Coca-Cola beverage that you're bottling and distributing?
[00:05:59] Kevin King: Yeah, it's strictly the Coca-Cola beverage. We do have some trademark rights for Dr.Pepper and a few other brands, but you know, majority of what we deliver is gonna be within that Coca-Cola trademark.
[00:06:10] Dana Gardner: And so we all take it for granted. We go to the convenience store, the supermarket, maybe just a machine on the corner, and there's the beverage. What is it about getting that there that people perhaps don't appreciate?
[00:06:23] Dana Gardner: Is there some part of being a distributor and a bottler that maybe we take for granted or don't understand?
[00:06:29] Kevin King: Yeah. I would tell you when I started, you know, I never realized that when you go into a, you know, a retailer such as a Walmart or a Harris Teeter that most of the product that you see on the shelves is actually put up by a Coca-Cola employee.
[00:06:44] Kevin King: So it's a very high touch from the warehouse floor to the transportation to get it to the outlet, and then someone that's putting it on the shelves. And you know, I think that the unique thing is the amount of detail that goes into it, right? The brand order, the number of products that's on the shelf, the flow, the look and feel, making sure everything is rotated.
[00:07:06] Kevin King: So there's a lot of science behind the scenes of making sure that our retailers and customers have the best experience, which obviously drives more velocity for our consumers.
[00:07:19] Dana Gardner: Sure. And then I can't think of too many better use cases for people, process and technology, having to work tightly together and looking for the refinements across those domains.
[00:07:30] Dana Gardner: So tell us a little bit about how data science and AI now are starting to drive these improvements. What does it bring to the table that you couldn't have done before, for example?
[00:07:40] Kevin King: Well, I think first, you know, understand the business in a different way. You know, I mentioned how fast paced the business is and having the ability to provide quicker insights, the ability for a salesperson to walk in the store.
[00:07:56] Kevin King: And we've already synthesized all of the information for how they're supposed to set the store, how the store's supposed to look, how we price the store, synthesizing all of that data to allow them to really focus on selling to the customer and focusing on, making sure we have everything set correctly, is it's just a new place that we're at versus, you know, the amount of time that they will historically spend trying to get all that data together and then to process all that data, right?
[00:08:25] Kevin King: It's just real time insights to allow them to, you know, be the best that they can be without worrying about all the other stuff.
[00:08:34] Dana Gardner: Sure. Imagine that the more automation you can bring to your drivers, distributors, with all that touch along the way, as you say, needs to bring the digital to the tactical, you know, the tactile, even, the hands.
[00:08:46] Dana Gardner: How are you bridging better, getting more people in that process able to be, you know, take action and work with the data? Is there some threshold that we're crossing in terms of perhaps making it conversational or using bots so that your employees can take advantage of the data to the best of their ability?
[00:09:06] Kevin King: Yeah, I think that's the, I'm gonna call it the North Star. Which we're not there yet, but you know, I think the more information we can put in the hands of the sales team, the warehouse team, the more focus that they gain, the more time they understand their KPIs, their key performance indicators, and I think that's critical.
[00:09:28] Kevin King: You know, I think minimizing the disruptions with the data outputs that we have, you know. I think in the future, the days of standard reports and push reports that they get every morning, you know, I imagine I walk into an outlet. It tells me all the key things about outlet. Hey, I had a customer give me a call and they submitted a ticket because they had an issue.
[00:09:49] Kevin King: Hey, I'm missing these key promotional activities. Hey, I have these targeted innovation things that I wanna sell in. I don't have them sold in yet. I think it's just synthesizing all that information in a very, very real time manner, which will really unlock the set of potential for the outlet.
[00:10:06] Dana Gardner: Yeah, that's that conversational question and an answer query, and then pursue more information and knowledge. That I think is, as you say, a North star for a lot of what, you know, chatbots and other types of large language models are providing. How deep are you into that? How much of the large language model side of AI do you use to try to bridge the gap between the data and the people?
[00:10:32] Kevin King: Yeah. I would say we're at the very beginning of that journey. You know, I think what I'm excited about is, you know, things like Snowflake Intelligence, right? Which will allow for those LMS and those bigger data models to really be put in the hands of our employees so they can talk and converse back and forth to have a dialogue.
[00:10:56] Kevin King: I think obviously we're concerned with things like hallucinations and, you know, I think the technology is getting better day by day, but I think that's where we really think, you know, that's the sweet spot, if I had to call it, where, you know, we're a really able to take advantage, and then you can move away from our traditional CRMs as well, right?
[00:11:14] Kevin King: To have a lot of information and notes and really be able to, you know, as you said, have a dialogue, have a conversation with the data. I think that is just a key unlock that is our true North star that we're approaching.
[00:11:27] Dana Gardner: Sure. How about the way you’re using Snowflake services and technologies now, and how you see that changing so that you can progress in this direction.
[00:11:37] Dana Gardner: What is it about Snowflake in particular that's paving the path that you want to take?
[00:11:43] Kevin King: I would tell you, you know, we've taken a very organic approach to analytics and AI and machine learning. And what I mean by that is, you know, we didn't come out the gate trying to do major investments, you know, going out, getting a lot of third parties.
[00:11:59] Kevin King: What Snowflake has allowed us to do is to be very natural. As they create more innovation, right, understanding their innovation, being locked together as a key partner, and really allowing us to take our time as we're learning things like cortex analysts or document AI, for example, to be able to scrub PDFs and then not even to think about all the structured data that we have, pictures and PDFs, and how can we scrub the data and then put that in data warehouse and then allow those individuals that deal with the data.
[00:12:32] Kevin King: To be able to have those conversations that we're talking about. So it's been instrumental just to, you know, they're doing a great job meeting us where we are in our journey and, you know, the more they innovate, the more we're somewhat falling behind. But then, another great thing is they also allow us to give feedback, right?
[00:12:48] Kevin King: What works? Where are opportunities, you know, where are we dreaming at and how can they, you know, meet us there as well?
[00:12:54] Dana Gardner: You know, Kevin, thinking more about your industry, I have to imagine there's a great variety of different people that you're distributing the beverages to something very sophisticated, like a Walmart, where they have end-to-end insight into their supply chains and real-time data and analytics that they can rely on.
[00:13:12] Dana Gardner: But there's probably also a couple of mom and pop shops along the way and everything else in between. So that means you've got all sorts of different interfaces and processes that could be, hey, you know, couple of decades old and tried and true, but old. So that to me means you get a lot of unstructured and different types of data interfaces that you're dealing with.
[00:13:31] Dana Gardner: Is there anything about the way that Snowflake helps you with unstructured data that allows you to still bring this all into the digital domain that you need in order to do those analytics?
[00:13:42] Kevin King: Yeah. You know, I think that's our biggest opportunity, right? That data strategy ability to really understand all the traditional systems that we have internally and, you know, how do we get all that data out of those systems and how do we manage traditional processes and future processes to make sure that, you know, data is a priority.
[00:14:06] Kevin King: Many times we have a lot of gaps because, you know, unfortunately as valuable as data is, sometimes leaders forget about, you know, the structure of the data and how important the downstream impacts are with the data, and then adding the ability to data share, right? So, you know, that's a new outlet that Snowflake enables us with, right?
[00:14:28] Kevin King: The ability to, you know, minimize these manual emails or PDFs and invoices and just the ability to do a data share where I can just work with that company's IT team. And you know, within days we have a data share, we're sharing data back and forth. And so I think that's an example of a capability that Snowflake is unlocking for us.
[00:14:49] Kevin King: It's really making the world smaller, right, as we converse with different retailers and customers. And you know, I would say size used to matter. The scale of the customer would matter around how we can do data share. I think, you know, Snowflake is allowing us to shrink that disconnect that we historically had, no matter what size and scale the customer is. Which is pretty cool.
[00:15:13] Dana Gardner: Yeah, and you know, we talk so much about AI these days, but you know, the cloud is still important and we don't talk about it as much, but having the data both centralized and decentralized, but in a common platform environment that's cloud accessible is a pretty strong and powerful technology and capability.
[00:15:32] Dana Gardner: How is that a benefit to you all? I should think that you've got people out, up and down the I-95 quarter in the Southeast of the United States, a very large geographic area, and you've got drivers and trucks and machines. Is the cloud model itself also powerful for you to keep all eyes on the same page, so to speak, when it comes to data and process?
[00:15:57] Kevin King: I would say absolutely. You know, I think that's the key unlock, right? From a traditional data warehouse to a cloud data warehouse, the ability to one, condense all our data in a structured way, including unstructured data, the ability to, you know, minimize costs in how we do that as well. Right. You know, we historically were on SAP HANA and it was extremely expensive to one, store the data and then two, to be very performant.
[00:16:24] Kevin King: And I think the cloud-based approach that Snowflake has has really done a great job of, you know, optimizing those capabilities within our data warehouse. I think secondly, you know, not only have they optimized it, but they're always thinking about how to make us more efficient with costs, which is quite unique, right?
[00:16:44] Kevin King: That's how Snowflake makes money. But they're always bringing innovation, attempting to, you know, make us much better when it comes to our compute and our data storage, which is why I think it's just such a great partnership.
[00:16:57] Dana Gardner: Yeah. And so there's probably no better person than you to go to say, you know, what's the metrics?
[00:17:03] Dana Gardner: How are those KPIs now returning on your investment? So do you have any, you know, quantitative ways of measuring how your approach to data and analytics and your data strategy is benefiting Coca-Cola Consolidated?
[00:17:19] Kevin King: You know, I would say, sadly, the answer would be no. And part of the data strategy principle we have, and we're actually about to do this, we have this term called data dot.
[00:17:29] Kevin King: So, you know, we are, you know, post three, four years since our Snowflake migration, and we've done a lot of great things and now we're in a place where we want to take a pause and go back and evaluate, you know, do we have the right queries? Do we have the right setup? You know, do we have junk out there that we just have lost attention to?
[00:17:51] Kevin King: So we're gonna take a data dive and really dive in and really partner with Snowflake and understand our queries and our costs and what does that really mean? Because I think we just have not, you know, kept that, you know, as a top focus. And I think, you know, where we're going now and looking at the data strategy that's important, especially as we
[00:18:10] Kevin King: begin to build our analytical capabilities and we wanna invest, you know, making sure that our house is in order is probably, you know, my top priority as a data leader right now.
[00:18:21] Dana Gardner: Yeah, no, that's not uncommon at all. For very mature businesses that are very large and complex. The priorities are always in getting the job done and keeping the customers happy.
[00:18:30] Dana Gardner: And it's only when you become very sophisticated and mature in your IT that you can then take that step back and not be fighting fires, but actually getting to know yourself really well. So I think that's a very, you know, intuitive and fortuitous approach. And it's probably the first time in the hundred plus years that Coca-Cola Consolidated has been around that you're actually gonna get that real solid view of what's going on with, you know, within the covers of your company.
[00:18:58] Dana Gardner: Any other thoughts about bringing those people, process, and technologies together? We've talked a lot about product and process, but not so much the people. Are you getting a sense that you're gonna have to adjust culture or educate people or get 'em thinking differently about these tools as they become available.
[00:19:17] Dana Gardner: So once you get to know yourself, how do you then take action on that? Because it's the people that ultimately will be a part of that payback.
[00:19:26] Kevin King: Yeah. I think as an organization we're, you know, kinda in a paradigm shift. You know, the business wants to go really fast. They want a lot of information.
[00:19:37] Kevin King: We're bringing all different types of data into the data warehouses I spoke about earlier. I think we are in a period to really educate and create a culture around data. I think that's why that data strategy is so important. I also think we're at a place where we want to increase the empowerment of our organization with accessibility to data.
[00:19:57] Kevin King: Obviously there's things you have to be concerned about, cost, obviously being first, security, the safety of the data, the accuracy of the data, the quality of the data. You know, I think part of this journey is to begin to educate and sometimes explaining data is difficult to do because everybody just wants it.
[00:20:18] Kevin King: They don't really know or want to care about the downstream impacts to their decisions. And the reality is, most of the time when we have issues with data or inaccuracies of data, it's typically driven by a business process. And you know, they may not want to accept that, but I think that's just a reality.
[00:20:38] Kevin King: And so I think we have to build a broad perspective that we have to educate. And then also for the people that we're empowering, to access data. You know, one of the parts of our strategy is to create a data citizen policy. So how do we teach you how to use data? How do we teach you how to leverage data in Snowflake?
[00:20:59] Kevin King: How do we teach you how to use the appropriate tool? How do we as an organization choose the appropriate tool for you to be able to do the things that you want to do for data? So there's a lot that we're trying to figure out around this data strategy, which is, you know, most of it's just really educating and creating literacy around data and what we do and how we use it.
[00:21:20] Dana Gardner: Cool. Alright. Before we close out, I wonder if you have any advice for other organizations, perhaps mature businesses like yours that are trying to get that data-first culture and benefits going. You know, looking back with 2020 hindsight, what would you advise others to do now that you've been through the part of the journey you have?
[00:21:43] Kevin King: You know, I think the thing that we jeopardized along the way was that data strategy. You know, being very thoughtful in what we're attempting to do with data, not only in current state, but in future state. Where do we see it going? Because, you know, when I talk to peers and other folks in industries, I think, you know, the thing that the data scientists says all the time is that this is really bad day and I'm spending more time scrubbing the data than I am actually, you know, writing logic and doing statistics and analytics.
[00:22:18] Kevin King: And so I think you have to have self-reflection, right? What are we doing well? What do we really suck at? And then what are the processes and procedures and strategies that allow us to kind of come to the center? And, you know, I would tell you, I don't regret however, you know, we kind of do a lot of guerrilla warfare as we centralized BI.
[00:22:38] Kevin King: And as we, you know, began to take over responsibility for more of the data assets for the departments, I think that was the right muscle that we needed to build on in order for us to be as impactful as we expect to be in the future. But, you know, the last thing I would say is it starts at any time, right?
[00:22:59] Kevin King: So no matter if you did it right in the beginning, if you're reactive to it. The biggest thing is you have to start and define what your data strategy is, no matter where you are, and then take it very seriously and socialize it. You know, we can't do it alone as a data organization. We have to do it with the support of our leadership team and support of the employees that we're trying to give data assets to.
[00:23:26] Dana Gardner: Well, great. I think that's an excellent place for us to end. Thank you so much to our latest Data Cloud Podcast guest, Kevin King, Senior Director of BI and Analytics at Coca-Cola Consolidated in Charlotte, North Carolina. We so much appreciate your sharing your thoughts, experience, and expertise with us, Kevin.
[00:23:44] Kevin King: Thank you, Dana. It was my pleasure.
[00:23:47] Producer: Calling all developers, business leaders, IT execs, and data scientists. Snowflake World Tour is your chance to learn and network. Discover how Snowflake's AI Data Cloud can transform your career and company. Experience the future. Join us on tour. Join the Snowflake World Tour to experience the future of the AI Data Cloud with Snowflake.
[00:24:05] Producer: Hear from experts, engage in breakout sessions, and network with peers. Transform your business and career with Snowflake. Register today. For one of our 23 stops worldwide at snowflake.com/world-tour.