Unlocking the Power of Data: A Guide to Digital Transformation for Manufacturers with Vinny Maurici

Lisa Ryan: Hey, it's Lisa Ryan. Welcome to the Manufacturer's Network Podcast. I'm excited to introduce our guest today, Vinny Maurici. Vinny heads up the data engineering practice at ObjectEdge, a digital consultancy based in the Bay Area of California. He brings over 15 years of experience launching data programs for Enterprise and Fortune 500 B2B manufacturers and distributors. Vinny, welcome to the show.

Vinny Maurici: Thanks for having me.

Lisa Ryan: Share your background and what led you to do what you do at ObjectEdge.

Vinny Maurici: As you just mentioned, I've been in the data world for about 15 years, and for me specifically, my brain has always worked in a way where I've seen data as more than just a means to an end. I like to build tangibility around how data can affect businesses. It's fallen into more of the consulting world, and it's been an excellent opportunity to work with our customers over the years to say, Hey, let's take this thing called fluffy data. You go to conferences all the time, and they say data are the new oil, and you need to turn data into an asset for an organization, and nobody knows what that means.

It's such a big, wide-open topic, and for my team, we pride ourselves in saying, Hey, let's turn the intangible into a tangible. Let's create steps, order of operation, and actual outcomes and benefits for why data is essential to an organization. And it's been such a great time trying to build all these models for our customers.

Lisa Ryan: With data just expanding at the rate it is. Before the show, we were talking about how AI is transforming everything, and data can be used for good and can also be used for evil, and it goes the whole spectrum in ways that we would've never thought about before.

So specifically, though, when it comes to manufacturers, what are some of the data challenges that manufacturers are facing today?

Vinny Maurici: There are some very unique challenges that manufacturers are seeing. If I take a step back, for example, and I look at even the distribution side of things like B2B distribution, they're all pushing to be more digital. MSC Industrial, for example, is a major MRO distributor that just came out last year in fiscal 2022 saying, Hey, We now do 2.28 billion in digital revenue alone, and they're growing at 17% year over year. So that's at a distribution level. So, what does that mean for manufacturers? It means that all their customers, whether a distributor or a retailer, are pushing to increase the channel presence. This means that the amount of data that these manufacturers, many of them are mom-and-pop, is more significant. They now need to support all these digital transformations that their distribution network their customer network is going through.

It can be difficult because I have to manage more than I used to. And so I, as a manufacturer with a lot of unstructured data or data that I'm just used to, have supply chain information. So I want to send my products efficiently, not necessarily for people to use a digital network to make a purchasing decision.

So how do I do that without hiring an army of employees or utilizing my current employees without burning them out? Because there's so much more now than there was. That's a difficult thing to solve as it creates a foundation of a problem even though they know that there is revenue and their success on the other side of the equation because all of their customers and digital partners are distribution partners and are doing great in that.

Lisa Ryan: Can you give some examples of what you're talking about when talking about digital data and monetizing it? I'm wrapping my head around what that looks like, especially in a mom-and-pop shop or somebody that didn't necessarily have the technology, but they have all of this data. Yeah, just putting it in simple terms, what does that mean?

Vinny Maurici: Yeah, that's a great question. So the way that it has been working for decades is that I, as a manufacturer, I'm going to go and I'm going to make something, some widget, and that thing will go through a lot of engineering and requirements and drawings that I'm going to have to build that thing. And then I'm going to go and sell it. And I usually sell it to distributors or wholesalers or whoever will take my product. In the old days, I would need an E R P, and the E R P would manage a couple of important facts about my product. For example, how much does it weigh? How many go into a box, how many boxes go onto a pallet, and what's the weight of everything so I can send it across?

It's only a couple of pieces of information, and then your distributor goes out and sells it to their customers. And if it's B2B, they're picking up the phone, talking with their sales rep, and then placing an order. Now everything has changed. If I go back to an example of a distributor, these are complex parts.

They're trying to make it so that their customers are no longer picking up the phone. They want to make it so that a buyer, an inventory manager, is going onto their site and finding the exact products they want, which leads to, Hey, I've got forty attributes across a job or drill bit, which might be a very simplistic product or an indexable cutting tool or welding equipment that I would typically have not needed to provide as a manufacturer, that type of information to my customers.

So now they must go back to their engineering documents. They have to go back to see how they're utilizing that information and figuring out how to structure it. That's not a simple task, especially when much of this lives in PDFs or unstructured databases. So that is a significant hurdle for these organizations pulling out their hair, saying, I want to support my distributors. Still, I need the wherewithal. I need to have the data organized. I don't have it in a place that's easy to. We call it syndicate, syndicate downstream to my network of customers.

Lisa Ryan: When looking at some of these, what are some of the other unique challenges you will encounter when you have this direct-to-consumer manufacturer?

Vinny Maurici: Direct-to-consumer is a fascinating topic because, for a while, direct-to-consumer was this significant initiative or forefront for a lot of organizations because when we look at the advancements of cookies and adaptability of searching and a customer going out and asking for what they want, you could then grab that search result or grab those cookies and quickly serve them ads. I, as a manufacturer, don't need to rely on Amazon, or I don't need to rely on distributors.

I can go and serve products directly to consumers and then not have to pay that in between the margin hit of getting it to a wholesaler and distributor. The problem is, last year, what did Apple do? Apple created this opt-in era where they said, " Hey, on default, we're going to turn off cookies, and you have to opt-in as a customer to serve those types of ads.

All of a sudden, and there are many case studies about this, direct-to-consumer manufacturers started nose-diving in terms of their revenue because they couldn't find their customers very well. The whole point of this process was it was a very cheap way of customer acquisition. So now they have to differentiate themselves through good product data better.

They can't rely on something like cookies through the customer data they now own. But, hey, somebody just searched for TVs. So I will serve them as a TV manufacturer, some options because that whole model has been cut off. And now, I need to build out those data profiles by myself because I now need to trust that consumers will do research.

I need to bring my organic searches up through Google or Bing. And I have to create the best possible experience model so that people trust my site and make that purchasing decision. It has completely turned the way that folks are purchasing things on its head because they served up particular advertisements may be a thing of the past unless there's something new that some advertising agencies are trying to figure out.

Lisa Ryan: That's so interesting because, for so many years, you get used to accepting cookies and everything. Yeah. And as a consumer, I would look at one ad on Macy's, and then for the next three months. I would just be getting this. And I never realized how that happened. It went to the extreme, and now it's being pulled back to protect the consumer from targeted advertising. So it's better for the consumer because we're not slapped upside down with 40,000 ads daily. But as you said, it makes it harder Yeah. For the manufacturers, because they don't own, you always want to own your list.

Vinny Maurici: Exactly. People who didn't own those cookies and didn't have access to that information anymore. And it has made a struggle for an organization that built up an employee base, revenue model, and supply driven off a specific customer acquisition dollar.

That customer acquisition dollar has been taken out of the equation, and they have to build a new equation now to figure it out. It's caused a lot of stress and anxiety with employees, with enterprises where we discuss things like the interest rates going up. These companies no longer have infinite cash, as they have free money from the banks.

So this anxiety has this direct to the consumer group. I call it an industry, even though it's filled with sub-industries trying to figure out their way. All because, in essence, Apple started the trend of Google's now talking about doing something similar, and you say it's for the sake of privacy. There are arguments on both sides of the equation. Now you almost have to funnel to significant distributors like Amazon, Wayfair, or Walmart, who have substantial inroads into being able to advertise cause they have the money to do that. These smaller organizations need more money to do it.

Lisa Ryan: This is new information for me. That was just the way that I saw it. Yeah. As far as all the trouble that Facebook got into for the privatization of data. That's the thing. Something good, we'll always find a way to use it for evil.

Vinny Maurici: Exactly. Unfortunately, that's always the case. I know.

Lisa Ryan: And it will be. Or else we'd never go to the movies because that's all the movies are about. So, when looking at good data and sound data practices, how would you define these when discussing an engineering lead for manufacturers?

Vinny Maurici: That's an excellent question because this is, in essence, the first aspect of recognizing and realizing there's a problem. When we talk about good data practices, the first thing I preach is, do you, as a manufacturer, have a center of excellence focused on data? I might have multiple business groups. For example, I might have various regions cause I'm a global company. And a lot of the time, what you find is everything is siloed. Oh, Europe works differently than the United States. That works differently than the Asia-Pacific region, which works differently than the Latin America region. in one regard, that might be fine.

Regulatory requirements will be different in various areas, but in the grand scheme of things, on how I manage data, I talked before about manufacturers. They have a lot of engineering documentation and tend to be in prints and in unstructured things, not in a way that's easily digestible or analytical to create business intelligence around.

The first step is to say, Hey, how do I have good data practices? I've built a center of excellence that goes above everything. It goes above the business unit. It goes above regionality and it. I want to ensure data is supported consistently and accurately as an organization.

I look at it like a library sciences methodology, right? If you look at the Dewey Decimal system, I know nobody uses the Dewey Decimal system anymore because we have computers in the library, but that is the fundamental of one of the very first taxonomies created or attributes created around how to find a book.

The same thing should be your mindset: how do I discover and find a product or widget I am building within my organization? How do I define my customers, and how do I determine my customers in a very efficient, concise, and consistent way? Globally. So that creates a way to build data stewardship that is first in your mind before you do anything else.

Remember, data should not be viewed as a means to an end. Oh, I have a new customer. I have to fill out pieces of information. I have to ship a product, fill out this information, and ship it. That's just a means to an end. I want to say, This customer is essential. They consistently buy X, Y, Z. Therefore, I can now upsell these other parts because they're related to these, and all of a sudden, I have all of this information at my fingertips to build a revenue pipeline and build out my customers in a much better and more meaningful way.

And we want to create win-win situations. It's not. Like, how can I make more money? It's how I can make my customers do a great thing for their end consumers and increase the data sets or SKUs that they will be selling to their customers.

Lisa Ryan: So how would for a large organization that's been focused on data for decades? Yeah, it's easier because they have a whole department. But if a smaller manufacturer, a mom-and-pop shop, who doesn't necessarily have the manpower and the knowledge to think about all the different things you just brought up. How do they even get started?

Vinny Maurici: Interestingly, you asked that because for a smaller or mid-sized organization, I always have this feeling that is building out good data practices, and I'll get to the way to get started in a second. They're more important than large enterprises. Large enterprise has enough money to live through the pain. They might be sending spreadsheets everywhere, massaging data, and trying to figure out business intelligence in every way to serve their customers better.

But they can do that because they're so large. So as a smaller, mid-size organization, the focus needs to be on, let's take a deep breath. We know this is essential to how we must go forward and serve our customers. And as I said before, we also can't just burn out our employees, and I don't have the money to hire many folks.

So how do I get our house in order? And so that's my first suggestion to any customer I'm working with asking me what the first step is. Let's get your house in order. Let's worry about analytics and trends and all these other areas. Second, let's first decide, hey, how do you build and structure your data today?

Because, more often than not, we'll hear an answer. Joe Schmo has this spreadsheet that they maintain, and there's this other spreadsheet, and we send emails back and forth, and the first step is just recognizing and writing down how data flows through your organization in a current state.

And then all of a sudden, eyes get wide because people don't realize how deep they are into it until you map out your data flow through an organization and then, You say, Hey, there's a lot of low-hanging fruit here. So, for example, if I were to build out an enterprise categorization structure or if I were to integrate attributes that are being held into a spreadsheet into a better purpose-built database or workflow engine, I'm going to save my team tons of time.

And then it's a win-win because the company will have all this better data to make more money. And then the employees who are in product management or who are in digital merchandising or who are in engineering or regulatory, they now have a much easier way of managing all this data as opposed to pulling out their head because they've probably been struggling with it just as long as anybody else.

Those are probably those who have been asking for change for a while. And now you get the enterprise to recognize how important this is.

Lisa Ryan: So you had mentioned employee burnout. On this show, we talk a lot about company culture. So how do you get your employees to buy into this and realize that all the little data points and all of the quote-unquote paperwork that they're doing that they're being forced to do now to collect this data at the beginning is going to pay off for them? What are the words to use or the things to put in place, or so we have a little bit of pain here to have much less pain in the future if we do this correctly?

Vinny Maurici: That's so important because change management is a part of anything we do for our customers. You can't just go in and say, Hey, you're doing this poorly, and I'm just going to plop down a new process, technology, and things that will change everybody's working life.

Because people are still people at the end of the day, right? They might be going through a cruddy process, but they might do it for 15 or 20 years. You don't know that. And so, any change they might view as a negative. And the critical aspect here is to ensure the business user is a part of the process from start to finish.

Suppose we will change technology or process to enable data to be an asset within an organization. In that case, the people managing that data must be at the forefront. So we cannot just say, Hey, C-Suite is mandating this exercise or C-suite is man mandating this initiative without having the folks who are going to be a part of that initiative or who are going to be the end consumers of managing and making sure that data is in there clean and consistent and accurate.

They need to be a part of this process from day one. They need to be giving their input into, in terms of what they do day to day and have knowledge into this is what we're going to do, and this is why we're going to do it, and this is why it's going to make your lives so much better. It's not going to be painless because change is always going to change.

You can go from a green screen to some UI-based system that will bring people to tighten up and say. I don't want this type of change. But if you flow with them through the process and allow them to be a part of any of these data initiatives. It becomes a lot easier, and it starts connecting and clicking, and then people start buying in.

And that's the key. It doesn't matter how much money you spend on technology; it doesn't matter how good a consulting firm is. We're the best consulting firm there is out there for data. If you don't get adoption by the people who will be doing it, it's all destined for failure.

People are important, and those working on data are essential to any data initiative and program.

Lisa Ryan: Just from the standpoint of showing your employees how valuable they are to the organization, and as you have your tenured employees that are, that have that whole history of the company and why you did everything, and then they start to feel that maybe they're going to be replaced by technology or by automation and just capturing what we're losing in the data that they naturally have in their mind to put into that process so that they feel they're valued and they also feel that they're contributing to a greater mission in the organization than maybe they felt before. Having that as part of the conversation