Why Treating Data as a Product Means Alignment and Growth for Stanley Black & Decker

Transcript

This has been generated by AI and optimized by a human.

Show ID [00:00:04]:
The power of data is undeniable. And unharnessed, it's nothing but chaos.

The amount of data was crazy.

Can I trust it?

You will waste money. Held together with duct tape. Doomed to failure.
This season, we're solving problems in real-time to reveal the art of the possible.

Making data your ally, using it to lead with confidence and clarity, helping communities and people thrive.

This is Data-Driven Leadership, a show by Resultant.

Jess Carter [00:00:35]:
Stanley Black & Decker is a name that is synonymous with innovation, craftsmanship and resilience. With a history that stretches back over 170 years, the company has evolved from a small hand tool manufacturer into a global leader in tools, hardware, and security solutions. Today, Stanley Black & Decker operates in over 60 countries serving industries ranging from construction to automotive, and is committed to shaping the future of its industry through cutting-edge technology and data-driven strategies. But in today's rapidly changing world, even the most established brands must evolve to maintain their competitive edge. This is where data comes in. For Stanley Black & Decker, data isn't just a byproduct of business operations.

Jess Carter [00:01:20]:
It's a strategic asset, a product fueling smarter decisions, driving innovation, and enhancing operational efficiency across every corner of the organization. What I'm trying to say is, it's really important.
Enter Jason Winterbottom, a leader at the forefront of this transformation. Jason's journey through Stanley Black & Decker has taken him from finance to data governance and most recently into a groundbreaking role as product owner for the company's data as a product team within the sales analytics domain. His work in driving data integration, governance, and productization is helping to shape how the company uses data to empower its teams, inform key decisions, and ultimately drive greater value to the customer and stakeholders.
In this episode, we'll explore Jason's unique perspective on the evolving role of data in the global corporation and how companies like Stanley Black & Decker can leverage data to stay ahead in an increasingly competitive marketplace. From the complexities of global data governance to the practical applications of treating data as a product, Jason's insights offer a behind-the-scenes look at the strategic thinking that powers one of the world's most trusted brands. Stay tuned as we dive deep into the critical themes and explore how Jason and his team are paving the way for Stanley Black & Decker's continued success in the digital age.

Jess Carter [00:02:43]:
Welcome back to Data-Driven Leadership. I'm your host, Jess Carter. Today we have Jason Winterbottom, director of data product development at Stanley Black & Decker, Inc. Let's get into it. Jason, welcome.

Jason Winterbottom [00:02:55]:
Thank you for having me.

Jess Carter [00:02:56]:
Yeah, absolutely. Stanley Black & Decker. So this is a bigger name than I've ever had the chance to talk to on the podcast. This is an international company, right?

Jason Winterbottom [00:03:05]:
Yes.

Jess Carter [00:03:05]:
Okay. In what a whole bunch of countries do you have it memorized?

Jason Winterbottom [00:03:09]:
I do not. I do not. We are global and it seems the regions are constantly changing and in flux, but always, always an adventure.

Jess Carter [00:03:17]:
Very cool. Well, and your background is larger companies. You were at Johnson & Johnson too?

Jason Winterbottom [00:03:22]:
Correct. Yes, I have only two companies, both Fortune 500, but yes, Johnson and Stanley Black & Decker.

Jess Carter [00:03:27]:
Very cool. So we were chatting about, I think, how Stanley Black & Decker really integrated data governance into the business model. Is that a fair summary of the story?

Jason Winterbottom [00:03:39]:
Since the beginning we've had a thought that data governance obviously is a vital part of everything. And we've been on a long journey from a kind of centralized enterprise data platform that we've been building within our ranks. And governance is not always something top of mind or what the users are wanting to always hear talk about, but it's something that's essential in order to ensure that the longevity of what you're building, the accuracy and the true value overall, it's part of it. So.

Jess Carter [00:04:15]:
Yeah, well, and you're. So this is. You're like jumping right in and talking about the value proposition of data governance. But I'm going to make you do something that should be easier, but somehow is usually harder. How would you explain data governance to like a five-year-old, unpack it at the highest level. What is this? What are we talking about?

Jason Winterbottom [00:04:31]:
We've always defined it is, it's a combination of the people, process, and technology. So the aspect of it is the people have to be engaged. The process of it is ensuring that you have those controls because the people expect things to be accurate, to be up to date, to be in control. Right ?That environment has to be that way. And then obviously leveraging the technology where you can to ensure that you have the data quality checks, the monitoring, the process controls. This is very important information, very vital information for the company. But not only that.

Jason Winterbottom [00:05:04]:
But even from a cybersecurity perspective. So it's a combination of all those things. Ultimately it's. It should be seamless and not necessarily forefront for the user in truly because they should be getting the value out of it upfront without necessarily having the hurdles of data governance. It's that concept.

Jess Carter [00:05:20]:
Well said. Everyone has source systems. They're going to have a CRM for a sales system or they're going to have a core tool that they use for their business or workflow or project management. So governing that data, a lot of that is around, okay. So we have the source system, we have data, we know who our clients are or who our prospects are governing around. That is when we pull out reports, when we look at dashboards that we want to see the total number of clients we have today. And we want that number to be right every time.

Jess Carter [00:05:50]:
And we don't want you to pull one report and it says we have 82. And I pull another report and it says I have 115. That would be problematic to the business, right?

Jason Winterbottom [00:05:58]:
Correct.

Jess Carter [00:05:58]:
So when this all started, when this initiative took place in Stanley Black & Decker, obviously there's already some amount of governance sort of everywhere. It doesn't mean it's great or bad, but it kind of exists because people have reports. I'm curious about when this started and I'm, I'm assuming you were around like somebody walked you into an office and said, we need this initiative. Is that real?

Jason Winterbottom [00:06:18]:
It is real.

Jess Carter [00:06:19]:
Okay.

Jason Winterbottom [00:06:20]:
We've been on this journey for about a little over five years. Started as a concept of, we call it the, the one source of truth, to your point of multiple systems, multiple ERPs. And yes, each one of them have their own controls and their own governance. But when you're trying to build a global platform and enterprise data, we envision this platform that’s multiple different technologies, cloud-based computing, and storage. You have to wrap it in some sort of overall strategy of data governance. So yes, actually when I moved into the governance role originally, before moving to now more data product development, but it was just that, it was here we have this initiative, we're building this platform of enterprise data and we need to be able to govern it. So that was my task actually was standing up a lot of things around role-based access management, working with those people in the, that had the responsibility of governance on the input side of systems. And it's definitely, it's evolved over time, but I've actually handed off and it's making even more strides recently of adding a new technology around data quality monitoring and giving us that kind of trigger point of oh, you know, you have new values or it's, you're skewing in the wrong direction of how many null values or things of that nature.

Jason Winterbottom [00:07:36]:
And it's an ecosystem of from start to finish. And when I came in, nothing was there. So it's been a, it's been a jersey. Yep.

Jess Carter [00:07:45]:
Yeah. Well, so. And I'm curious about when they asked you to step in and handle some of this. Was that thrilling? Was that scary? Like literally, how did that make you feel?

Jason Winterbottom [00:07:53]:
Honestly, it was scary. I'm technically an accountant by trade. I've been in finance and more or less have moved into the enterprise data just simply because of my experiences around data integration. So this was a new adventure both for me as well as the company and walking into it. We had some, you know, some smart folks that had been around for data governance aspect and obviously we brought in some smart consultants to support us in that first endeavor. But yes it was. You just kind of had to foundationally, you know, role-based access governance, data governance of when we're ingesting new information or new data into the environment, what are those checks and balances that have to occur? And then the last, most latest development is again that monitoring piece of we've got this foundational data and now we need to be able to monitor it to ensure that it's not. We've put all the effort in the upstream side of things to try and control things.

Jason Winterbottom [00:08:46]:
But you want to have those indicators that something might be going awry so you can address it quicker rather than later.

Jess Carter [00:08:52]:
Yeah, well, so Stanley Black & Decker is not a small company. You're being asked to do something like pretty substantial and broad. How did you approach that? Like I'm assuming that they probably expected some value early or they wanted you to start like did you break it down into segments of the business? How did you approach something like this?

Jason Winterbottom [00:09:10]:
We broke it down into—and it's actually a lot of it was is consulting with stakeholders around what areas of the business, what data domains are your most valuable or the most pressing at the moment? Which is kind of a shift from where we first started. With the first started we were doing a lot of things of just data science-driven and pushing to try and get things predictive modeling and things of that nature. When we took a step back and truly asked the business okay, what's keeping you up at night right now? And what areas of the business and if we were to able to get you that next level from a data perspective would make things easier, allow you to make another step forward. So we took it kind of more in that data domain-by-domain perspective and we over time obviously and we still monitor it today of we have kind of a honeycomb grid of data maturity by domain across different businesses and we continue to take retrospective looks at you know, we got advanced data in sales and inventory because those have been big things for us lately where we might be a little bit more immature in things like procurement data and things like that. I think you really just have to break it down and use the business to lead in which is going to drive the most value faster.

Jess Carter [00:10:22]:
Yeah. And then they're going to stay bought in because you're actually serving the business. It's not just. I think that that's a pit that a lot of people fall into is we forget that we're getting...You know, we love these technical problems. They're like a big puzzle for a lot of us. Finance or technology or both in your case. And then we kind of start working on them and people will forget they're working on them for the sake of the business.

Jess Carter [00:10:44]:
And so we’ve got to add value quickly. We got to understand what are the problems keeping them up at night. I love that you asked that question. So I'm curious about a few moments. One is if there was a day or a moment where you were like, oh, this train is making hay, like we're doing it. And what that was like. And equally, was there a day where you were like, is this ever, are you ever going to get there?

Jason Winterbottom [00:11:04]:
Some of the tribulations that we had early on were we put some initial things out there and we weren't getting traction with the business of them leveraging some of the technology tools. We were monitoring usage statistics and we get blips of usage of some of the applications that we were trying to put in there to allow people to identify data. But we already have data. And as you said before, a global business, we're very fragmented and siloed in some cases. And my colleagues are always. They're very resourceful in that if they can't find it easily, they'll try and brute force their way and find the data themselves. And that's not always efficient. So a lot of it was just around once we started putting things out there. It was.

Jason Winterbottom [00:11:45]:
It's the change management piece of things that we had some difficulties. It just took some reflection and kind of change of strategy and communication plans, then we've got the right footing. So once you get some of those champions within the business of saying, hey, this is really great, and that word of mouth starts to spread. So I would say that try not to get discouraged early on and maybe take a chance to pivot your strategy or something along those lines. So that was probably one of the biggest things that I had to overcome because we spent all this time trying to put things in place and your usage statistics and things when they're not climbing as fast as you had originally hoped.

Jess Carter [00:12:23]:
It's insightful too. I like that you, the whole time you were the team's working on this. And I'm going to say. I'm going to say you in this episode. And I realized we cannot talk about this without I'm using royal 'you'.

Jess Carter [00:12:33]:
I realized that there was a whole team working on this. Whenever we talk about it, you remind me like, this is not just Jason. I get like, you are a wonderful leader in that way. And I know that it's true that there's a great team that worked on this. I like that when you talk about this process, though, it was like you never forgot who it was for. And that usage statistics, the willingness to not get so obsessed with what you built that you're then you're defensive or on your heels when no one's using it. And it's like, well, hang on, maybe just scrap some parts and build this a different, different way or in a different way that they can consume it more easily or give us feedback on what about it don't you like? Is it valuable but it's just not done where you want to do the work and we need to tuck it in or make it less clicks to get to access the product?

Jess Carter [00:13:15]:
It sounds like you kind of get to be like a detective, right? Where it's like, what problem am I diagnosing? And I don't want to throw the baby out with the bath water. So what is sticky here and what isn't and how do I leave that stuff on the drawing room floor, right?

Jason Winterbottom [00:13:29]:
Yeah, and actually I can give you kind of an example. So about a year ago, we actually did a survey of… I think actually we sent it over a couple thousand folks that would be potentially touching our environment from a data perspective. I think we got six or seven hundred responses. And within that we had questions that were, you know, kind of like a Gallup poll or something like that of what are we doing right? What are we doing wrong? Have you engaged with this system? What other systems or things do you currently use? How frequently? But then towards the end, we started asking more subjective questions around do you trust our data and have them rank it and scale and what are the reasons why you don't trust it? Or then we had data catalogs and some quality things and some governance things that coming out of that, we changed a few things that from a strategy communication perspective. But ultimately what ended up happening is when I mentioned before that three legs of the people, process, and technology, we realized that one of the technologies we're using was probably at the time we implemented it was great in the three years since. So we actually made a decision to switch technologies from a governance and cataloging data quality perspective. And so we took the last four months actually and it just went live about 30 days ago.

Jason Winterbottom [00:14:47]:
But we totally took that pivot and said that there's another competitor that had a thing that would kind of help solve some of the feedback we got from the users of it was too complex and our environment is so large within the catalog and things of understanding it. It was the technology we had to help document the context of what the data was for. It was too cumbersome. So we went with a new technology that had a much more streamlined user interface. And again now we're back into that same kind of launch mode of getting people engaged. But it took that survey to kind of really reflect and say, hey, the people want the data, the people understand we have the processes in place. It was truly that technology that kind of was holding some things back.

Jason Winterbottom [00:15:31]:
And even today we were trying to do the monitoring on those key trust measures. And we're actually going to be doing another survey here in the next few months of our users to get a barometer to say out of five stars, how are we doing type of thing? And we're monitoring and measuring those on an annual basis now. That's what it takes, I think.

Jess Carter [00:15:49]:
Yeah, I'm blown away. I mean even just the idea of a survey response that's 700 results is like a dream. So much content, even that alone is like okay, where do we begin?

Jason Winterbottom [00:16:03]:
As a data nerd myself, it was fascinating because we had some qualifying questions on there of like, we have user profiles. Are you a data consumer? Are you a data analyst? Are you a data scientist? Are you a leader decision maker that may not get into the data but need summarized views? So and they all classify themselves and then answer the questions. So it was really interesting to go through and see in those different pockets of users, what are they trying to get at? And obviously it was different and we've put different strategies in place of on that leadership mantra. I don't want to spoil things, but we're starting to get into generative AI and being able to for more leaderships to interact with gen AI so they don't have to go into the data weeds and say ask it general questions, what was my top selling SKU this month or which customer business am I losing type of things. That's a roadmap long into the future. But those are how you can segment what you're doing but still get results.

Jess Carter [00:17:02]:
Yeah, that is so cool. Well, because that was going to be one of my questions is for someone who's not lived and breathed data governance. I'm still trying to make sure even this story makes sense. When you have six to seven, eight hundred users that are giving you feedback, that's not even just all the users. Could you walk me through a use case? Like is someone logging in literally to the catalog to look at data? And like what's a user look like I guess is what I'm asking.

Jason Winterbottom [00:17:27]:
The users are truly those that are trying to access our platform, our ecosystem of data that the enterprise data, they're consumers. Right. So they'll. The data analytics people are those that are building analytics calculations for decision-making. Right?

Jess Carter [00:17:42]:
Right.

Jason Winterbottom [00:17:43]:
General users or your consumers are those that typically use those reports and those analytics. And, and we're big on democratization so we have a team that's building the central repository. But within the businesses there's advanced teams that within commercial space that are always building price volume mix predictors and cash flow predictions and order-to-cash reporting suites. But it's all based on the central the repository. Right. So our environment, our main users. But at the same time from a governance perspective, you can't lose sight of the people that are actually in the ERPs to your point, those that are creating new customer accounts and they have to follow the process of the input of when a new customer is created. And being a global company that's, you know, over the last two decades has acquired multiple companies that comes with, it creates more complexity.

Jason Winterbottom [00:18:34]:
Right. You inherit systems and that's why we had to build this central repository, this central environment which we call Caspian by the way, which is the largest lake in the world is what we call it. That's where it started, that's where the name came from.

Jess Carter [00:18:46]:
That's great.

Jason Winterbottom [00:18:47]:
But those are the people that actually can influence your governance environment more than the consumers. Right. The consumers expect it to be there a certain level and that's why we monitor the trust score in there. But on the actual day-to-day transactions, that's where we then start working with them to say and using PIM technology of okay, here's the process when you want to create a new product. That then becomes your golden record for then loads into SAPs and JD Edwards systems and division systems and warehouse management systems. It all starts at that one. So those are the people that actually, as a governance person and on our side we have to continue to talk to them, but the users won't. Right?

Jason Winterbottom [00:19:29]:
They just know that, okay, the new products in the system or the new customer’s up and running or a new vendor's in the system. But really the true champions of that governance space can is really those that are involved in the creation of that record, which they are definitely some cases the unsung heroes in these where they have to then, again, the business is always asking them we need to move faster. Where you're balancing, you have to balance then those controls to ensure that everything's accurate. Which is hard.

Jess Carter [00:19:54]:
Yeah, I love the unsung heroes concept too, that it's hey, people entering data and using your source systems are actually your, it's the first stop at data quality is the people in the process piece. And then you actually, you mentioned a phrase I don't think I've ever explained on the podcast before. I think it would be interesting to hear you or me riff on this. But if you were going to explain what a golden record is to like a C-level executive who's like, I need this report and I don't think is this really the number of saws we sold? And I'm totally riffing it a terrible way about guessing something that you sell at Stanley Black & Decker. But like, how would you explain what a golden record is?

Jason Winterbottom [00:20:29]:
The way we define the golden record is obviously for a particular component, right? Or it could be a product, it could be customer, it could be a vendor, because those are the three kind of trifecta data domains that we typically refer to as golden records. But it's any and every attribute associated with that item. So if it's a product. So from a commercial perspective, right, what's the brand, what's the name, what's the SKU, what's the description, what are the performance stats? But also within that same thing on the maybe supply chain side, okay, what's the unit of measure, how many come in a box, how many fit on a pallet? What is the size, what are the dimensions? Is it a 20-volt battery? Is it a 40-volt battery? It services multiple different areas, but it's all one record. So it's all related to one SKU, one product. Right, you may have…

Jason Winterbottom [00:21:18]:
And actually we've started evaluating these things because we've been doing a lot of SKU rationalizations to really simplify things for us. But when we actually sat down and looked at it, we still had, I think it was over 200 attributes that we had to monitor for this one SKU.

Jess Carter [00:21:32]:
Wow.

Jason Winterbottom [00:21:33]:
And yeah, that's, and that's how complex it can be. But it depends on who's you're going to service. Commercial obviously has these that they have to be able to see. Supply chain has theirs that they have to see. Procurement. You know, what components go into that SKU and how many. You know, there's four screws, there's a circuit board, a battery, a trigger, plastic shells, Those are all part of that golden record. So it's really just meaning to be. It's the official piece.

Jason Winterbottom [00:21:59]:
And from a customer, same thing: addresses what, tax jurisdiction, what country. Those are all part of the individual, you know, golden records. And where it's coming into play for us is that to your point, we have multiple ERPs around the globe, multiple SAP instances, multiple QADs, JD Edwards, Navision. All of those ERPs we transact out of globally and they all have to have similar information. So we're trying to create those golden records to ensure that then when you have a skew in a Europe ERP or an Asian ERP or North America, they all say the same thing. Which has been a journey. And it's still not, we're still not there. And we still have a thing, a way to go in ensuring that those golden records are maintained across the entire environment.

Jess Carter [00:22:48]:
Okay, I'm so glad I asked you to answer that question. I didn't take a stab first because it's so much more interesting. Your answer is more interesting. So for me, working in public sector government forever, the golden record was always: you have different agencies all over the state of Indiana where I live, and they each have a Jess Carter. They have the Department of Workforce Development has my wages and Department of Revenue has my taxes. My health is at Department of Health. And there's no real golden record for Jess at the state.

Jess Carter [00:23:13]:
But the concept for me, I've always thought about it as like a citizen, but man alive at a company that's international in so many countries with parts that are, you know, there's parts you sell and there's parts put together that you sell. And the concept of what is a unit or what is a golden record to you guys is fascinating. You could ask a hundred people in different departments at Stanley Black & Decker and they'd probably have a different answer. And you've had to figure out how do you accommodate that as a customer? Holy smokes. Sounds breezy.

Jason Winterbottom [00:23:41]:
It does. The concept of it is not difficult, but to your point, the complexity of your own environment is what truly makes it either difficult or easy. Obviously, we've acquired companies that have a single ERP, so it's. Their process is very easy to manage. Their vendor list, their customer list, their product list. Right. Because it's all in one place already. But when you put all that together on a global scale, it can seem daunting.

Jason Winterbottom [00:24:06]:
But fundamentally it's the same activity. You want to have a golden record, a way to control it because you're obviously creating new customers every day, you're creating new products every year, you're creating new vendors. So circling back to that people, process, and technology, the process is when those are created, that's where you have to start ensuring that all of that governance is put on that process of creation. Otherwise you're going to just keep filling a leaky boat. Right. So that's where you start. And then technology to continue to monitor it longer term and then from a people perspective is just once they get engaged and they start to trust that the data you have is controlled, it doesn't change much or it changes when it should. And then obviously then that trust comes and that's where you truly start to drive value in to your data-driven leadership as people start to then come back to your data as vital for them to make business decisions.

Jess Carter [00:25:02]:
Yeah, this is just so neat. Well, and, and I still want to ask you two more questions or cover two different topics. So the summary that I do want to actually get to is you have made momentum and you have succeeded to some extent. Right? Like data governance exists, you guys are using it, you're still iterating on it. So it was hard. You had to throw some things out, try some new things, you had to get a new, implement a new system. But you are seeing consumption usage trust increase.

Jess Carter [00:25:27]:
Is that right?

Jason Winterbottom [00:25:28]:
Yep. Yes.

Jess Carter [00:25:29]:
Awesome.

Jason Winterbottom [00:25:30]:
And again, I think one of the other barometers that we typically use is, you know, we're a central IT organization of enterprise data and we have within that governance, we have development and we have where I'm in my new space around data as a product, where we're building things for the business. The businesses interest in investing in us building out new capabilities is we're constantly seeing our pipeline of projects is not getting smaller. So that's kind of an indication of, okay, the business sees the value, you know, outside of the user. But we're in conversations now of all of our 2025 plans and the roadmap of things we want to do next year. One of the things we're doing is competitive SKU matching. So when we have a SKU, what are all the competitive SKUs and being able to track. Once we've got the foundational stuff.

Jess Carter [00:26:17]:
Yeah.

Jason Winterbottom [00:26:17]:
Then you can start to really explore things like being able to arm a sales rep to say you've got a competitive SKU X or 1, 2, 3. Well, we also have this and this is why it's better than that. But if you can arm that sales rep with that cheat sheet of the one-to-one matching and say hey, this is what we can do. And oh by the way, the competitor doesn't have a product that we have. And so that's some of the new adventure, the white space that we're getting into. But you really can't do it unless you have a solid foundation. The investment from the business into our space is not drying up. So that's a good indication that they see the value.

Jess Carter [00:26:55]:
Yeah, for sure. Well, and let's go here for a second. So you've mentioned the data as a product that is new for a lot of people. What do you mean by that?

Jason Winterbottom [00:27:06]:
What we mean by that is obviously as a central organization, as an enterprise data, our job is to maintain the environment of central data. Data as a product is, it's an organizational model that we've adopted more recently around taking kind of ownership in a capability. And I'm leading a team more in the commercial space. So we have other teams within our enterprise team that is organized around supply chain. So they're constantly working with the supply chain stakeholders and on that team they have business relationship managers is what we call. So that's the liaison between our team and the business and the data requirements, what are the use cases, what are the things. But that kind of vertical structured team owns those supply chain analytics. So if we're delivering any analytic, whether it be just a data mart or a suite of reporting or complex analytics metric calculations, they own that end-to-end capability.

Jason Winterbottom [00:28:05]:
So it's a product team within our organization, but we're segmented to where I own my roadmap from a commercial space. So I know what I have now. I'm working with the business to see where we're going if I need to enhance some of the older stuff to get us up to speed. So treating those assets within our environment as a product that we're providing to our users. If I've got 50 certified reports from a commercial perspective, I have a roadmap now that says I'm going to retire a few of them, I'm going to add 20 more because those are what the business needs right now. That's the dynamic that we're starting to get into now is treating it as a product of that's my product. Our team owns those, that ecosystem. But obviously, the barometer is, or the guiding principle is the business.

Jason Winterbottom [00:28:51]:
What are our stakeholders? Just like any other business, right? What are they going to buy? What are they going to use? That's truly kind of the area that we're moving into as data as a product, is those assets that I'm creating for the users is my portfolio of products.

Jess Carter [00:29:04]:
That's awesome. It's a little heady for people who don't live in this space often, right? That it's like, okay, and I kind of like that we're explaining this while you work at Stanley Black & Decker. It's like people can picture a product that you guys sell on a store in a shelf. We are just saying. And I'm going to butcher this. Jason, I invite you to correct me, but the pseudo analogy I would draw is some people are going to go to their data team and ask for a report and they're going to get that report, they're going to decide it's valuable and they're going to want a dashboard. So you're going to build a dashboard so they can refresh it at the right frequency. At some point, the data around this domain, this thing I need a report and then a dashboard about, is maintained in a way where we treat it like it is a product.

Jess Carter [00:29:47]:
It is a product itself that we maintain, just like we would maintain the products we're selling to clients. And to your point, it's because it has clients. There are leaders who are making decisions based off of these things all the time. It's a little meta, but it's a product of its own. And you then are investing in the quality of that product, the enhancement of that product to help the business make the best decisions they can make. Is that fair?

Jason Winterbottom [00:30:10]:
Yes. And we've used a couple different analogies of. I actually had a business partner that. There's two that I'm thinking of right now. One's laundry related and one's more of cooking related. Which one do you want first?

Jess Carter [00:30:23]:
I want both. Yeah, give me laundry first. Give me laundry.

Jason Winterbottom [00:30:25]:
Laundry. So the way we try to pitch it is laundry. In the beginning, it's a basket of mixed clothes, right? Varying quality, varying dirty, you know, and our process for us is how do you take that unorganized, mixed data? And okay, so maybe you've now washed it or maybe you sort it by color. Okay. So you've got a little bit more organization. Right? And in some cases, some users will engage with it.

Jason Winterbottom [00:30:53]:
Now, what if we've put it in by size or by type? You know, you folded it, you've put it into nice neat little stacks and now it's really easy for the user to say, yep, I need shorts, I need socks, I want this color, that color. And you've put that out there for them to then engage with. And that's that end product, right? And that's the progression of the way we treat data within our environment of raw, unencumbered mass amounts of data. No one wants to deal with it. And if they have to, you know, that's where you then get into people doing it differently and organizing it differently. And then you get different answers. We're trying to give them that menu of items, but ultimately the cleanest at the end, the most mature data at the end is very organized, very clean, very clear for them to engage with. Same thing with the kind of the food or the cooking analogy of you have a bunch of ingredients in your pantry, it's all over the place, there's nothing.

Jason Winterbottom [00:31:47]:
You don't have the vision of what to make. Okay, so you grab a few things and you might have a recipe, right? So now you've organized, you put things out there and then you start using the recipe to get everything in order. And then by the end product, right, you've got this beautiful plate of food that everyone can consume and it looks great and it looks organized and they can then choose which what they want to eat. So it's a similar analogy around data as a product. Everyone thinks of that end game, but as an enterprise organization and governance is obviously weaved in at every level. But in order to get there, you have to do all those steps in between. And also then depending on your user profile, like I mentioned before, they may want to engage at different levels. So your data scientists trying to do predictive models, things like that, raw data, millions upon millions of records, they love it.

Jason Winterbottom [00:32:39]:
Data analytics, okay, maybe you've got it all organized and it's in like data. So all your sales transactions, whether it came from SAP or another three different ERPs, you create a common data structure, you put it all there, they can then engage with that. Or you just want your data consumers to a point, they want the report, they want the organized things that it can toggle, flip, refresh, we all treat them, those are all products, right? But that's kind of that mantra that then I own or our team owns those in silos based on the data domain. So we've got product teams, customer teams, commercial teams, supply chain teams. But we're focused and we have accountability for creating those roadmaps of everything within our own space. For those users.

Jess Carter [00:33:24]:
The best part of those analogies is that most people just want dinner or their laundry clean. They don't want everything in the middle. And that's what we do.

Jason Winterbottom [00:33:33]:
Right. But to the famous question of what do you want to eat? A lot of times our stakeholders are like, I have a problem, but I don't know how to do it or I don't know how to solve it. So that's where we value the product ownership structure is, okay I know I have a commercial problem. Right. One that actually just had some conversations around was evaluating promotions and rebate. So you know, are they actually driving value for our customers? But our business doesn't know exactly how to approach that.

Jason Winterbottom [00:34:02]:
So that's where then we come in and it's multiple conversations of here's what we have, here's the data we have into always a back and forth play of here's what we have, let's start playing with that. And you do iterations, you keep agile. And for some of these complex problems, it may take three or four iterations to say being able to do this analytic and be able to monitor volume and price uptick and all these things on a day-to-day basis for certain customers is really going to be able to evaluate our performance on these promotion plans and things like that. So that's that journey that you then have to take. But again it's. That's your R and D more or less, right? In a product sense. And I'm doing that.

Jason Winterbottom [00:34:46]:
But once I publish that, that report or that suite of reports or dashboards, then they can use that and that's the end product.

Jess Carter [00:34:54]:
Yeah. Well, and hey, I know we need to wrap up soon, but I'm gonna put you on the spot. Before we hit record, you said something that I loved and I said, you got to say that again. When we're recording, do you remember what it was?

Jason Winterbottom [00:35:05]:
I do. Okay. Our very wise CIO, she put things in perspective for us as an IT leadership. The analogy she gave was we're a three-legged stool and we always have to manage the governance side, the security side. So that's one leg, the other side is the speed and value you drive for the business. And then obviously the third leg is cost control. Right. They can all get out of balance, you know, throw costs to the wind and you can do a whole bunch of stuff, but they all have to three be in balance.

Jason Winterbottom [00:35:36]:
And then her analogy ended with the seat or the stool. Top of the stool is obviously your talent Your organization, the people within your organization. That's kind of how we have to make sure that we maintain that balance of doing the right thing within the cost controls that we have, all within the governance and security that we must maintain as in this day and age, from a cyber perspective. But yeah, she summed it up, it's a really good, great visual and understanding that it has to maintain balance.

Jess Carter [00:36:02]:
Yeah, I reserve the right, since we only focused on one story today, to beg you to come back and talk more about the data product stuff and maybe generative AI stuff at some point. Is that okay?

Jason Winterbottom [00:36:13]:
Sure.

Jess Carter [00:36:14]:
Okay. All right. If I were to ask you one more question and I were to pivot to generative AI, if you had advice at a high level for companies who are interested in figuring out what do they do or think about that, what is one piece of advice you would give an organization?

Jason Winterbottom [00:36:30]:
So one, take your time and objective approach. One of the things that one of my leaders that I on my team, he's take a very methodical approach to the generative AI and not just jumping right into the deep end, working with a partner, Microsoft in our case, but working with them and understanding what we can do, ensuring that we have safeguards, that it's not going to leak proprietary information to the world, but also having specific use cases. So what's been successful for us is we've had some really—they seem small in the concept of generative AI—but it really snowballs as you learn, because that's one of the biggest things I think in the marketplace is it's so new, it's changing so quickly that there's no one expert and our people are constantly learning on the job of as you teach an AI model your data, your things, you think about things differently. And prompt engineering is an art form that I'm just scratching the surface, but we've taken small examples of customer service having an AI agent that our own customer service reps can engage with to say, what's our warranty protocols, what's the documentation on a product? And being able to have a side conversation while talking to an actual end customer on the phone and just typing it in. And our AR agent says, well, on this page of this document, here's the paragraph on how they can check to make sure that it's under warranty and it cites it and it will tell you all this stuff. But that's a very small but a very impactful piece of things that's not so widely or wild in the space of things.

Jason Winterbottom [00:38:13]:
But I mentioned earlier that having the AI agent kind of pool information around on. Okay, if we have a DeWalt drill, our largest SKUs, we sell millions of them. What's our competitor SKUs for that? What's the like, for like, example for other brands? And having the agent go and collect that information, it's small little things to start. And once you start to get that institutional knowledge of how you can use it, then it opens the door and you have that, you know, that knowledge base to go. So I think taking a methodical approach and taking it slow and then it ramps so fast because yeah, it's endless possibilities.

Jess Carter [00:38:53]:
Awesome. So, Jason, this has been amazing. Like I said, I reserve the right to bring you back and talk about generative AI more and also data products because I think these are new concepts for a lot of people and it sounds like you guys are just further ahead in the roadmap of maturity around, the, these things. So thank you for joining us today.

Jason Winterbottom [00:39:10]:
No, it was great. I'm glad we got together. I know it's just a random conversation kind of spurred this, so…

Jess Carter [00:39:17]:
It's so cool. So thanks for being open and willing to share your experience with us. And if people want to follow you and keep up with stories and thoughts that you have about this, where would they find you?

Jason Winterbottom [00:39:26]:
I think LinkedIn would probably be the best. I do try to read and post and share some things that I see within the environment.

Jess Carter [00:39:32]:
Awesome. Good. We will put your LinkedIn in the show notes so people know where to find you. Okay. Awesome. All right, thank you for listening. I'm your host, Jess Carter. Don't forget to follow the Data-Driven Leadership wherever you get your podcasts and rate and review letting us know how these data topics are transforming your business.

Jess Carter [00:39:50]:
We can't wait for you to join us on the next episode.