Dora Cortinas & Stibo System's Neda Nia - Master Data Management

Sri (00:00)
Hello Austin and welcome live to the main stage of Stibo Systems Connect 2026. I'm of course your host, Sri, also known in the industry as Papa Raj, co-founder of ThinkBlue Consulting, co-founder of the CPG Guys, and father to pop stars Lara Raj from the band Katseye, who just performed on Saturday Night Live this past weekend. And now a Mac girl Toyota and Kiss Nails Girl Rhea Raj, a solo artist herself signed to Republic Records as of last Monday.

That just happened. And now, if you're wondering where my industry co-host, my best friend for life, Mr. Peter V. S. Bond is today, Peter couldn't be here. Because he's actually down in San Francisco. What is he doing? He's actually live at Amazon unBoxed doing a bunch of podcasts there for Amazon, with Amazon leadership right there. So I'm gonna need your help to crank up the volume here to eleven.

We don't wanna be a ten, we don't wanna be a seven, so let's hear it for these two ladies on stage who've been brave enough to be with me.

What?

All right, before we crank it up today, make sure you hit that subscribe button on the CPG guys on Apple Podcasts, Spotify, YouTube, wherever you listen, and of course, smash that five star rating because five guys is the best French fries. And of course, five is everybody's favorite number. Follow us on LinkedIn so you never miss our daily industry insights. As I said, we're six hundred and fifty episodes old. We talk to CPG retail service providers like Stibo, the C suite, up and down the chain of corporate America, cross functional.

And what are we gonna do today? Today, like I mentioned, we're gonna take a deep dive into the absolute backbone of modern CPG and e-commerce, master data management. So multi-domain connectivity and the bridge between enterprise transformation and probably one of the most important terminologies in industry in corporate today, agentic AI. We're not talking about data as an isolated IT back office project. We're talking about data as a high velocity business engine.

To bring this story to life, we invited these two lovely ladies to join me and two phenomenal leaders in our industry. First, she's driving global growth, market expansion, platform innovation for Stibo Systems. Please welcome Neda Nia, Chief Growth Officer at Stibo Systems. And joining her is a visionary leader from one of North America's most dynamic CPG beverage giants, oversees the enterprise data analytics, decision intelligence strategy at Keurig, Dr Pepper. So please join me in also welcoming

Dora Cortinas, Vice President of Enterprise Data Analytics at Keurig, Dr. Bever. Neda and Dora, welcome to the CPG guys. How are you doing day three of the conference? Awesome.

Neda (02:51)
Thank for having us.

Sri (02:53)
Awesome.

Thank you for joining me as well on the CPG guys. So I'm gonna jump right in and let's get started with the discussion. And Dora, I'm gonna go to you first. So your role at Keurig Dr Pepper, allows you this unique visibility to keep your finger on the pulse of your own business challenges at KDP. while also evaluating the broader market today, because you gotta look at share. That's how you grow.

What do you feel is the number one data friction point or legacy bottleneck that most CPGC suites are looking to solve right now?

Dora (03:24)
So Sri, the reality is, is that a lot of companies have jumped right into building AI use cases, models, AI agents, without really looking at the underlying data that's running, that's making allowing those agents to run. I'm sure a lot of the folks in this audience own Ferraris, Maseratis, right? Really sleek, cool cars. Great ride, right?

If you put that car on an unpaved road with potholes, that experience, driving experience, is completely different. It's the same thing with these agents and these AI use cases. Product, customer, supplier data was folk has been focused on project by project, silos across the organization. And historically haven't been asked to agree with each other.

Today you ask a cross-domain question like what customer should be getting these products and when, the AI has to guess. And it's going to make potentially an average answer or potentially guess on the wrong record. And when that agent is building a board deck for Monday morning, you hope it guesses right. So

This is something that has been a challenge for CPGs. It's known that PepsiCo's gone and and harmonized across 200 countries, 200,000 products. We're doing the same thing across a single source of truth. McKinsey actually reported that nine out of ten companies, CPG companies, are not going to be able to scale AI agents because of this challenge.

Sri (05:04)
Yeah.

Dora (05:05)
nine out of ten, McKinsey quoted. and

It's a sequencing problem. The agents are being deployed before the data's ready.

Sri (05:14)
I know you have an opinion. Go for it.

Neda (05:17)
just with the whole thing about that, yes, absolutely. because the we have heard the term garbage in and out in the past for the last decade or so, but now it's garbage in and garbage out at scale at a speed that we have never seen. So I fullheartedly agree

Sri (05:32)
Nine

nine out of ten is a big number. We're gonna be at CAGNY in January. This is where the CEOs of the world's largest publicly traded brands present to Wall Street. I know this question is gonna come up again and again. So let me just ask by show of hands, how many of you have prioritized getting the data right before agent agents were deployed?

How many of you after this conference are gonna flip that equation and focus on getting the data right? you don't wanna annihilate the human species by 2030 and that rests in your shoulders and hands now, remember. I'm not joking guys, like for real. For real, real. All right, Neda, let's go to you.

Chief Growth Officer Stibo, you said, as I mentioned, you sit at the intersection of tech, tech, platform innovation, enterprise needs, including global CPGs like KDP. Historically, brands manage product data in one tool, you know, customer data in a CRM, supplier data in an ERP. Do you see the shift towards unified multi-domain foundations coming together in our industry, connecting product, customer, supplier domains, and

I feel it should be like a non negotiable substrate for CPG enterprise transformation. Let's hear your thoughts.

Neda (06:50)
this month is actually my twentieth anniversary of working in corporate world and from day one companies Thank you so much. I feel very wise. from day one that I for whatever company I've been working for, companies wanted to get the product data right because that's the backbone of their business.

So for me, exactly like you mentioned, Sri, it was a non-negotiable. Then I entered AdTech, and in AdTech, it was a little bit more advanced when machine learning was not even a big topic. 10 years ago, 15 years ago, they were thinking about linking product and customer data for better contextualization, personalization, and recommendation. And I'm just so surprised, we are 2026, we did a sip survey among 400 businesses.

And only six out of ten businesses were able to say that they can connect customer and products in a reliable way. And reliable is a keyword here because you may have some loose connections between different databases and all that, and maybe it's the side effect of different swim lanes, different functions and silos, right? but that number is still very low. And then we asked the same group of people, the 400 businesses, that what about

product and non-customer data, like product and supplier or product and location, are you able to link them together? Only 40% ish they said that they're able to connect. So there's a lot of opportunity for us. And I think the window of opportunity is rather short because AIs won't care who owns data. They want data for better decisions.

Sri (08:29)
Yeah, I kinda get it that six percent is a problem, first of all. but I kinda get it. Agents are like cool and pretty and sexy. So I can see CEOs in the C suite wanting to chase it. It's the hot word in the industry, but again, I'll just repeat it. We're gonna say this in all our media coverage four million times if we have to. You screw up the data foundations. This is gonna be the most ugliest agent reporting the wrong set of information possible. Now imagine if you're in the medical field what that could do.

Dora (08:58)
Wanted to add a comment to what Ada was saying. For us, it's been a real challenge around we have a very sophisticated route to market, and we'll have a business partner that has multiple roles. So it's not just the combining of customer and product, but it's being able to look at that business partner and understand that it could be a distributor as well as a vendor for us. And being able to have that ship to, remit to, and pay.

pay to representation is is really a great opportunity for a product like Stevel to help with. It's been amazing to help us have that multi-role capability for a single business partner.

Sri (09:42)
Absolutely no debate. So I'm gonna move that next question back at you. So Dora, let's get into KDP's transformation story. Let's kind of bring it to life a little bit. Looking back at your journey at Keurig Dr Pepper, by the way, I still remember when I worked for PepsiCo, we used to call it DPSU. It was down the road from Plain in Plano, Texas.

What was the tipping point operationally, or let's say the business bottleneck that convinced leadership, God bless them, for knowing that they should invest in master data management, that it needs to be a enterprise priority led by the business and just not an IT endeavor?

Dora (10:23)
We started our digital journey a few years ago and there was an awareness that data was an integral component of making that digital strategy a reality.

We had been funding our data initiatives project by project, as I alluded to. And so we didn't have a consolidated view or one single 360-degree view of our customer or our products, not as one hierarchy at the enterprise level. And we had a lot of institutional knowledge, what we call tribal knowledge sitting in people's heads, that we needed to pull out and make sure that it's reusable and searchable for everyone. So knowing we

actually partnered with McKinsey they they did a in 2019 and it's still true today a global data survey that 30% of the work at the enterprise level is focused on non-value added work

Which is focused on data quality and bringing data together. That's a lot of dollars. And since our CFO was a big sponsor of this, it was great. And we impl sponsored a multi-million dollar multi-year MDM initiative. And basically focusing on the plumbing and the rails for our digital strategy has been invaluable.

Sri (11:44)
The next one kind of feeding off of that, I'll ask you both to chime in on this one, but Dora, maybe you can go first. For a big CPG portfolio like y'all have in beverages, coffee, and also in the carbonated beverages space, as well as beverages in general, which is a big CPG portfolio. I gotta imagine getting the route to market, you have to have trusted data to deliver measurable ROI.

Faster time to shelf, operational efficiency, and of course the most important one for you in the manufacturing business, retailer satisfaction. Where is KDP seeing the most tangible business value from your data transformation?

Dora (12:25)
For me, I can if I think about one story that I think we are very proud of as a team is we had an opportunity in Latin America to fill some white space due to a competitor having

Sri (12:38)
business.

Dora (12:39)
New business being able to put some some product on the shelf that was empty. what used to take us weeks to do, which was to set up a product and get it out to be able to so think about make, move, sell, that would take weeks. We were able to do that in four days because we were able we had built a workflow that allowed us visibility from when that product got manufactured all the way to when it hit the shelves and

gave us visibility through SLAs and the right stewardship and data ownership across that process. So that is real value and speed to market is invaluable for us.

Sri (13:19)
If

I heard you right, your enterprise data transformation is now touching being a DST company, all parts of make move sell. That is absolutely fabulous. Neda, you're out in the industry a lot and you s work with multiple global CPGs. What are some examples of this? Do you see the same type of ROI being quantified?

Neda (13:40)
Yeah, time to value is definitely one of them we hear, and we have a lot of success stories out there on our website talking about from weeks to days and sometimes hours. And that that matters because speed to shelf, and then it also impacts speed to transaction. So there are two types of KPIs that we work with. One is commercial, that transaction or loyalty that you drive from having the right data in front of your customers, and then the operational costs, and it's shocking to

Hear the numbers of non-value adding tasks that we have to do just around data when we don't have the right data. The stats that we are hearing, and this is based on the research we have done, is that mature orgs report maybe strong positive ROI on AI at 2% higher than the ones that are not mature in data. And you know, we are really early in terms of

measuring outcomes from AI, but I we know that this is the right thing to do and we always encourage our customers to do that. Sixty four s sixty-five percent of leaders are also saying that their business users always have to check the outcome of AI maybe because they are not too confident in the data that they put in. That's an over operational overhead. So you are adopting AI but at the cost of operations. So the ROI gets a l again

messed up. You don't get the ROI on AI. So long-winded answer, but commercial and operational metrics that we try to measure with our customers.

Sri (15:14)
Kinda makes my think of a my mind think of a hot topic next to get this ROI out, which is institutional people knowledge. But before I get there, show of hands please, how many of you are from CPG brands?

How many of you are industry service providers in data management?

Retail?

What do the rest of you do? Like for real. Service provider, retail, CPG. Okay, so I want to get to the people institutional knowledge space, because it's a very guarded kind of space. It also getting that knowledge from people into these LNMs is no joke, but it needs to be there.

So a common theme of the future is how do we break away from that institutional knowledge? Critical business rules live in someone's head, kind of they've been passed down generationally within a certain department of function. Being a service provider in the MDM space, how do you help large organizations get past this that people can trust and adopt?

Neda (16:25)
Yeah, tribal knowledge and knowledge of people in this age of AI is a sensitive topic to be honest. And maybe these mega brands like Anthropic and all that, they are not doing a good job because what was it? Anthropic CEO last year said that fifty percent of white collar jobs will go away. And here we are we actually need more people. so it's a sensitive topic. I want to always put a disclaimer that people's subject matter expertise that they have learned through many years of doing the work still is

Very important. experience and judgment, you know, that's what makes the enterprise work really valuable.

So when we talk to customers, there are multiple ways. There's no one playbook. One that I'm hearing and seeing more is the talent density and how they are treating their junior employees and they pair them up with more subject matter experts. We have some of our Japanese customers and partners in the room, so I hope I'm not butchering the name. But there's this concept of Shoshin, the new

you know the the beginner's mind. So you put the beginner's mind next to someone who has subject matter expertise. The beginner's mind will ask questions, will say why? Why should I comply with status quo? So they are challenging the subject matter experts to change the way they are doing things and in the process they're also knowledge transferring and then updating the transfer. So one is from a talent perspective and then the other one is redesigning your workflows.

Because some of these subject matter experts become a bottleneck in your workflows, and you won't be able to see your bottlenecks in the workflow unless you have enough data and context about your business to say, okay, maybe this last mile of syndication is taking this many days. We sh let's see. Maybe someone is on vacation, maybe that let's transfer whatever that person knows into our tools. So it's definitely a combination of tool set, mindset, skill set.

Sri (18:26)
You know, I'm in the middle through some of my other ventures of working with the world's largest beverage company on the impact of AI on shopping. We've done primary research, shopper secondary research. It comes back to the same thing when I talk to whether it's a CIO, a CEO, or CCO, the conversation still revolves around the lack of understanding of the importance of the data data foundation. So one thing that I do want to take a second and reflect on again is the importance of the role.

of everybody here and that tribal knowledge that we kind of just talked to, passing it on, it's very important you used a word and that's what I want to get to before the next question, which is workflows.

The master data management and getting the data foundation right is step one without which the AI collapses. But the second piece is operationally in the everyday job, if the workflows aren't transformed to support AI, we still live in status quo land. So this one is for both of you, but Dora, I'd love for you to kick it off. CPGs are obviously built on constant dynamic portfolios, MA activity all the time.

A giant like KDP acquires or launches new brands, speed to market is everything. How does a standardized MDM architecture drastically accelerate brand onboarding and catalog expansion, especially when you have to distribute to retail?

Dora (19:50)
Well, we are part of a very dynamic organization. It couldn't be more exciting to be part of of the KDP family. We're constantly merging and acquiring new

Brought products for our portfolio. A few recent ones is we brought GhostIn for close to $2 billion into our portfolio. Dila, we had a minority stake, we converted that. And we've recently announced the purchase of JDE Pete's in terms of one organization, and then we're going to create two fantastic new companies, a BEVCO and a Coffee Co. So you can imagine all the data, right, that's coming in and being moved around.

And you put all your stuff in a box, right? If you don't label those boxes, you're gonna end up with pots in your bedroom and maybe a lamp in the kitchen, right? When you start unpacking. And it could take months to figure out where everything is, depending on how large of a move that is. Well, we were able through the steeple step system be able to harmonize and create consistent definitions, consistent business rules, and and have a reliable inventory so that.

that when we have an acquisition we now map that into our hub and it really makes it much more effective and consistent and accurate and allows us to be much more nimble and and do this much much faster.

Sri (21:22)
Neda, how are you all helping in the M<unk>A space when this happens? Because speed to market, as I mentioned, is the story here.

Neda (21:28)
Kudos to Dora and her team because we know that Harvard Business Review talks about 70 to 90% of mergers or MA activities in general fail. And that's across all the industries, software and S E PG. But I think it comes down to having the strategy and designing how deep are we going to integrate? Are we completely merging hierarchies and mixing the brand, or do we keep them separately? The good news is that step our

product has the architecture that you can go either way. You can deeply integrate multiple brands or you can keep them separate, which is important because maybe your leader decides that's this may be a three year thing and then they will decide to demerge or do something else. And we don't introduce any technical limitations. I think that's one of the most powerful things that I've seen within STEP. And then you had a good point about once you have the strategy, then what? We have to

Do the tactical steps and the governance becomes very important. How do we govern it? Because there's a PMI activity, but also you're running your business and you have to hit your targets. So step, our product provides the flexibility so you don't have to worry about the rest of the things, difficult things that come with MA.

Sri (22:47)
No doubt about that whatsoever for our listening audience. I am speaking live today with Nerania from Stibo Systems and Dora Cortinhas from Curic Dr. Pepper. Dora, I'm gonna give you an easy one. So obviously the industry is talking about AI and being AI ready. Knowledge, there's still a knowledge gap on what that actually means, some early starts with agents, et cetera, but

Here on the CPG guys, we're very clear, I've said it enough times, garbage data in, garbage AI at the end of the day. So what is that one thing you'd hold on to as clutch for saying, yeah, my company, my set of brands is enterprise AI ready?

Dora (23:30)
We talked about garbage in, garbage out. and it's not because team members are careless, it's that the data is inconsistent. and so if you look at in one system, so and an entry was made for potentially one of our partners, it might have been set up as polar P O L A R in one system, and then it was set up as Polar Enterprises in another. separate IDs and and records across those. and they

were done because it was done in separate initiatives like I alluded to. Well we talked about AI can't doesn't know the difference between the two, doesn't know what's wrong. So what we've done is we are actually working with our partners across our data analytics leaders across the BUs to come up with a framework of what it means to have AI data that's ready. And we've just recently started talking about three stages. It'll if it's land it it'll be landed.

If it's consistent data, it's going to be connected. That means that we can connect it at a domain level and we can trust those connections. If it's

complete and then ultimately it's AI ready, which is the third gate. If it's accurate and it's governed. Neda talked about governance and that's absolutely critical. If that's there's no data ownership or data steward that's identified for that data, then it's not AI ready. And we have that as a very critical toll gate of our process. And that starts at the data domain level before we get to the data products, which is where the AI models will sit on.

depending on the use cases.

Sri (25:13)
In a large company

like Curic, Dr. Pepper, how much time are you spending on education on AI so that this can be right?

Dora (25:22)
We actually are partnering with our HR team on building a very a very specific curriculum around what it means to leverage AI as an accelerator. we want to remove the fear from that from the organization. also looking at being careful around identifying biases and ensuring that the data is ethical as well, which is really critical. and so yeah, there there is a focused approach where we're doing that. We do have

hackathons right throughout the organization, a lot of great stuff that because you have to it's not just building it, you want people to apply that and and and leverage all those things.

Sri (26:00)
Let's ask the audience, show of hands, how many of you are prioritizing or thinking about the education element of AI, especially a hard topic for business executives to understand like master data management?

You know, this is a live podcast, so I'm not gonna be able to run around a mic and ask you, but I'd love to learn from you post this at the break, you know, what you're doing on the education piece, because that education is what is gonna drive the investments at the end of the day. Neda AI is obviously, I'm gonna tell you something that's obvious. AI has evolved from simple chat prompts, descriptive analytics into agentic AI that's actually executing against commands and actions. How is Stibo Systems master data geared up for that?

Neda (26:45)
Yeah, that's a good point. The chatbots when we were talking to them earlier, like two, three years ago, they give you information. And then you can act on it, you can ignore it, you can ask follow-up questions. When we move to actions done by agents, which is t some sort of autonomy, you're essentially delegating authority. And that authority, you need to have some confidence when you give someone the authority to make a decision. the whole promise of master data is that you will provide some.

Trust the data to these agents to make actions, and then you provide the governance so that if they don't make the right action, you collect context and you enrich your information so that the next time the action is going to be better. In our research, we saw that about only 12, 10 to 12% of businesses are comfortable to talk about autonomy because they don't know if they can trust these agents or not. And we believe

Sri (27:40)
The

tribal institutional knowledge people don't want to let go.

Neda (27:43)
Exactly. And the context that they may the agents may not have. And if we have learned anything from autonomous cars, it's not a switch that you just turn on and off. It's it's a journey. You have to train, retrain, provide more context. So I think we are taking the steps today towards that autonomy. We were AI enabled, then AI assisted, and then we will go to autonomy. And master data will play a role in all of those steps.

Sri (28:12)
You know, great example of context is what I said this morning. I asked you if you're from CPG retail or a service provider. The world is 50,000 times bigger than that. You could be in commodities, you could be in manufacturing, you could be in shipping. All of you need master data. What a just a great example of how my context was in one space.

This this is for both of you. So obviously when CPG brands connect product master data with customer and channel data, it impacts retail relationships. From annual operating plans, what we in the industry call joint business planning, all the way into delivering product into a customer retailer's warehouse.

Dora, how does having a trusted data foundation elevate KDP's execution with your major retail partners all the way from Walmart, Kroger, all the way down to the local ones?

Dora (29:05)
Once we harmonized the product hierarchy and we built an end to end workflow from when that product gets created in RD all the way through when it gets set up for distribution or manufacturing, that workflow allowed us to have visibility to those relationships by location and down to our consuming systems. We have multiple SAPs in our environment. So that visibility ensures that the right

product it that's authorized for that customer is mapped correctly according to the right rules. And that allows when we have our commercial team in front of a joint planning session and they're defining the planograms, it ensures, right, that that the

That data set up according to those agreements and those planograms. Now, I'll be honest, we're in it's work in process. There's multiple functions that we're still mapping our master data to. We're actually now reaching out and working with our marketing team. And we want to start engaging and connecting to our digital shelf content as well to drive that. They have some additional initiatives around precision marketing, really getting that last media.

dollar out, right, and getting the most value out of that. A lot of the rails that we put in place are going to now enable that connectivity as well there. So it is cross-functional across the organization where you find value.

Sri (30:31)
For those of you supporting CPG brands in any capacity, no matter what function, what company you represent, digital shelf content is the number one space where the CPG brand is actually gonna need help, because that's what faces the customer, the consumer, the shopper, which all of us are at the end of the day. Neda, did you want to add anything to that? How do you support KDP in this journey of getting it done from make, move, sell?

Neda (30:58)
You talked about digital shelf. We definitely need to provide the backbone, which is right product data. And then we also help with the last mile syndication. But Sri, we are also looking ahead in a world that PDP, you know, we design PDP for consumers, for end users, right? Product

Sri (31:15)
Let's make sure we tell the audience what a PDP is.

Neda (31:17)
detailed page is where you are interacting with certain product if you're a on a web shop. Let's say you want to buy a can of soda.

Or a pack of soda. The way that you will know about the ingredients is on PDP. But those PDPs were designed for end users, a human being. Now, if I hire an agent to be my shopping assistant, that agent doesn't have all the context that I have. So we have to provide machine readable all that data. So not only are we helping hopefully our CPG customers to have a better relationship with their retailers and drive that digital shelf shelf performance.

We're also looking way ahead about the agent to agent world.

Sri (32:01)
So I'm going stick to the M<unk>A theme. Let's say a C-suite leader in their quest for doing an acquisition is getting prepped up. What's the very first thing in your minds they should audit in their data foundation to welcome that new brand or whatever they choose to buy?

Dora (32:19)
I think the instinct is typically when you have a merger or a new spin offer is to look at or at where you might have some synergies across platforms and look at where there's redundancy, what you can retire and save. I think that really the way to look at it would be is look at the value use cases that are in that investment deck and trace those value, those

use cases down to and map the master data that's gonna make those investment synergies a reality map that to the master data so for example you wanna find savings in procurement look at your supplier master can you roll up without you know s easily enough all your suppliers bend across both companies to get a full view if you're looking at supply chain synergies

Can you

Get your product and location master data, is that harmonized and is it clean? You want to improve your cross-selling? Look at your customer master data. Can you easily find where you have overlap in your customers without weeks and weeks of reconciliation? I think that is where if there's focus on the mapping the master data to where the value is in a merger or a reorg, then the synergies will be, the roadmap for that synergy will be much more achievable.

by companies.

Sri (33:48)
What you said, you referred to functions, is at the end of the day, context matters, and when when I ask you questions such as auditing a data foundation, it's gonna be different by context, but they're all equally important. How do you feel about that?

Neda (34:02)
I think our task as a good partner for any CPG or brand that is going through MA is to provide some more context around decision traceability and also explainability. Like we do audit logs, but now in the agentic world, we have to also give more insights why an agent arrived to a decision. What's the evidence they used and when did they use what evidence? traceability and explainability, and I think the term is XAI, that's going to be

big and centralized governance is something that we've been doing for two decades and that also matters because you will be asking qua asked questions why did we make a decision and maybe it was the right decision but now we have to revisit and we have to change our governance rules for next round.

Dora (34:47)
it's interesting you mentioned that, Nidek, because when a lot of these deals are happening, there's an audit around getting the data clean, and it's a one-time snapshot. What you want to do is make sure you have a system in place, a governance process that ensures that that data stays clean because there is shift in the data as over time, and you want those agents to stay accurate and give you, and that you can continue to trust those.

Sri (35:13)
I'm gonna ask us to look fast forward, which I know is hard to do since there's rhetoric now that the human race is gonna be annihilated by 2030. But walking out of here, we're all walking away with the good people in this room, the three of us included, are walking away with knowledge that we can prevent it. And let's assume we've done that. So let's fast forward a decade. Do you guys see this?

Data foundation still sitting in silos, whether it's product, customer, supplier, the separate functional departments, how have we achieved Nirvana one data foundation?

Dora (35:47)
I think that if you think about it in terms of what it's going to look like in the future, that contextual layer that's going to sit on top of the enterprise, and you've alluded to that, Sri, that's where your supply, customer, location data has to all fit together. And that's going to break those silos, right? Because that contextual layer has to have ownership to ensure that it's maintained and it's up, it's real time. The functions matter, but that's going to bring and break some of those silos in the organization, which will then I think cascade down to that data.

foundation.

Sri (36:19)
Neda,

you're in the hot seat. You're in an MDM company in the space of AI. You better have executed this. What's your response to that? Ten years out.

Neda (36:30)
We won't exist if we don't

Sri (36:31)
There we go. And then what's the big action you're gonna take walking away from this conference to really champion knowledge? Bad data, bad AI, bad LLM.

Neda (36:47)
action item for me and my team in product management is to walk the walk with our their customers as they go through this journey to fully autonomy and realizing the most ROI out of their investment in data, their people and AI.

Sri (37:04)
Yeah, the one thing I'll re emphasize to everybody in the room, myself included, y'all included, we are privileged that we get to talk about data. We're in the middle of it. Do not assume under any circumstance that the people you pitch to actually get the budgets, understand what you do, be very patient, provide education, explain yourself away if you have to. Because again, annihilation now resul is rests in all of your collective hands. No pressure, Neda. So let's close this out by sending

people here, our audience here in Austin and those who are listening on the show with a closing thought. Five years from now, what will fundamentally separate the market-winning truly data-driven companies from those trapped in the legacy world and legacy operating models of silos? Neda first.

Neda (37:54)
I think we need to, all of us, regardless of what industry we are in, we need to dare to look at our workflows with a fresh mind and say, before AI, we designed this workflow, now there's AI. Do we have to have the same workflow? Do we have to have the same functions? Do we have to solve the same problems, or are we ready to graduate and take on the new problems that AI is bringing on? Can we have new metrics and KPIs? And how do we make sure that we put whatever we

have already in good use to compound the value of whatever we have. This is the time for all the work that we have done to get the ROI on that. And data sits in the middle of all that. Whatever we have done somehow, it's if we have the workflow, it generated data. If we sold the count a cost product, it generated data. So now it's the time to put that data to work really hard for our future.

Or we won't exist, any of us. we are all at risk if we don't embrace this moment.

Sri (38:54)
Dora?

Dora (38:55)
Think of in a few years, we're not gonna be asking, do you have an AI strategy? That's gonna be like asking, do you have email? Right? It's gonna have to it's gonna have to be a combinator. The differentiator is do you have trusted data and do you trust your data enough to have it, your agents and your AI models run autonomously on that data? I think that's gonna be the competitor differentiator. And it sounds boring and it's not glamorous.

But it's absolutely critical as the backbone of what I think the future is headed. And by investing in the master data and in those rails today, there's compounding value in that. Because every time that an agent is created, it's going to be created cheaper and faster on top of those rails. And so that compounding effect will, that gap between those that do and don't have that trusted data will widen over time due to that speed.

Sri (39:55)
Awesome. We were lucky to get an absolute master class in enterprise data found transformation, multi-domain MDMs, and AI execution, of course. Dora Cortinas from Keurig Dr Pepper and Neda Nia from Stibo Systems. Thank you both for joining me live on the CPG guys main stage and for trusting we can actually do this live.

Dora (40:16)
Thank you.

Thank you again.

Sri (40:23)
To all of you, I mean it when I say you're data stewards in some capacity data captains. You own the future of how successful AI will be no matter what domain you come from. So do take a chance to connect with Neda and Dora and the Stibo team.

Get that curiosity out there. Ask the questions that are on your mind to our listeners. Check out the digital show notes of this episode for links to Stibo Systems, Curid Dr. Pepper, and both these lovely ladies who gave me 45 minutes of their life here today. Don't forget to hit subscribe on Apple, Spotify, YouTube, Smash that five-star rating. Do follow us on LinkedIn. To all our listeners, partners, and of course our sponsors, a big thank you from Peter and me. Without you, there is no CPG guys.

Let me wrap this up by saying I'm Sri, signing off live from Austin at the Stibo Systems Connect conference. For the CPG guys, keep pushing the conversations forward. We'll see you in the next episode. And as my younger daughter, Lara Raj from Katseye stopped me to be fiercely unapologetic about my culture. Yalarkum Bonakum in Tamil, that means thank you so much. That's a wrap, guys.