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Jorge Lozano, Manager of Data Science and Innovation, and his team at Steelcase use this approach and the organizational impact has been tremendous. Jorge breaks down some of the innovative initiatives his team has been working on and the lessons they’ve learned along the way.
Listen to “How Data Innovation Drove Company Value to the Tune of $180 Million” on Spreaker.
VP of Advisory Services at Privitar
Manager of Data Science and Innovation, Steelcase
Jorge: What really makes a Data Science Initiative or just a data scientist successful, is often less associated with the rigor and complexity of the model. But with the experience that’s created around it. Welcome to InConfidence, the podcast for data ops leaders. In each episode, we asked thought leaders to break down the topics and trends concerning it and data professionals today, and to give us their take on what the data landscape will look like tomorrow. Let’s join the data conversation.
Nick: I’m Nick Curcuru and this is the InConfidence podcast sponsored by Privitar. InConfidence is a community of data practitioners encouraging conversations that will inspire, enlighten, educate and inform data leaders of today and tomorrow. Thank you for taking time to let InConfidence be a part of your day. Today joining our community as Jorge Lozano from Steelcase, who has been on an analytic journey that has taken him from Monterey, to Iowa, and now Grand Rapids, Michigan. Most people go from cold weather to warm weather. But Jorge is not most people. He is going to share with us some of his successes, and how one setback he looked at from a different perspective led to a $180 million value proposition for Steelcase. It is this type of inquisitive mindset that has helped him look at problems differently at work and experiment with recipes while grilling for his family. He’s always making a difference. Jorge, welcome to the InConfidence community. Thank you.
Jorge: Thank you for that introduction, Nick. Happy to be here.
Nick:It’s our pleasure to have you. You know, one of the things I think people would like to know a little bit about is that people really don’t think about Steelcase as being one of those analytics leaders or data driven companies. But you have been there from the beginning. So kind of tell us I mean, you studied economics, you focused on econometrics, but you joined Steelcase as a customer service representative. How did that happen? And how did you get to advanced analytics at Steelcase?
Jorge: Yeah, it’s been quite a journey. It started with me looking for a job that would allow me to prepare for graduate school because I did want to do a graduate degree in Science. And during my first interview with Steelcase, I just fell in love with the culture, it’s a great environment, it’s a company that cares about their people. You’re right, it was a customer representative role, I was basically customer support for our dealers who wanted to get a quote, and we’re having issues with getting the right products or the right discount, or whatever. Within the first six months of me being at Steelcase, there was a new role called pricing data analytics, and there was a big push around the organization. This was probably 2012, a big push on where we got to be more data driven, we got to start focusing on analytics, that’s the big thing. That’s what’s gonna get us where we need to be. But I think a lot of people didn’t really know what that meant. And we’re struggling to try to figure out what it even means? And I think one of those people was my former mentor, Michael Mira, who was the director for pricing at that time. And he said, Well, let me let me open this role in Monterey and just just see what comes out of it right out of the gate. It was a perfect match for both of us, because he was hungry to try new things. And I was eager to, you know, test out a couple of different things and just think outside of the box and push myself outside of my comfort zone. I think nobody expected that role to become what it turned out into. Because we made a very big impact in the organization. I think originally, people thought that this was going to be more like you’re just going to create a bunch of reports. But we managed to really transform the way the organization thinks about pricing. It started with us, kind of creating a model that would transform the conversation into something simpler. We created this term called pricing percentiles, which basically, rather than our team having to memorize how to price all these different products, we transform everything into percentiles, by, you know, modeling their distributions, and you immediately change the conversation. And so conversations shifted from, okay, what’s the price? What’s the discount into what’s the percentile, and right out of the gate, you did not have to know by memory, where each product line was priced or what their discounts were, all you needed to know is what that percentile was to really be able to come up with a point of view on where you wanted to price right. Well, that brings up a good point there because you’ve got hundreds if not 1000s. As a product at Steelcase, and trying to memorize all that makes life a little bit difficult. And this percentile, you guys made that conversation easier by bringing it with margin discount and a couple other really key variables into that percentile calculation. Right, exactly. We wanted to be able to factor what is that percentile based on a similar scenario, given the size of the project, what are some of the other things that are being sold, etc. And so it really, it made conversations much simpler, which sped up the process allowed for a dialogue that was more strategic in nature. And I think a lot of people appreciate it because you have senior executives, who could make decisions over text messages, rather than needing a phone call to get some context, because if you just send the number there, they need to remember what that number means in relation to that product and to other things. That was just a really fulfilling experience. And it was just the start of this awesome relationship. But I think it gave me confidence that I could add some value, even though I did not have a formal background in data science. But I think it also spoke to the nature that the organization was really thirsty for some of these just out of the box things and creative things. I think that’s actually, you know, when you talked about it, your thing was being inquisitive, and asking questions, and I think that’s been you said before, it’s because you really weren’t a data scientist, but you’re asking questions of why, but he took it from a business perspective, you know, bring that a little bit in how you brought the business side into the way you thought about creating the percentiles or these models that made a difference, Nick, I think that’s such an important point, what really makes a Data Science Initiative are just a data scientist successful is often less associated with the rigor and complexity of the model. But with the experience that’s created around it, it’s not about complex models. It’s about creative solutions. In that, the more you take the time to think about the user and think about the collective experience. How are people using that data right now? How am I expecting our analytics to lead to maybe a change in behavior or a change in process? Is that something that’s going to be absorbed? What is the output that’s required? Those things I’ve learned are just super important on data science initiatives. And don’t get me wrong, the first few classes of this initiative required iterations, the output wasn’t necessarily there. Maybe the math was right. But the approach or the deliverable kind of fell short. And we had to iterate and we had to pivot until we really understood a successful way to connect with whoever was going to consume this data and make it actionable.
Nick: Well, I think that’s actually really important, you found a way to connect to the person who was consuming what you were producing. And you bring up when I look at the projects you’ve done, we’re going to talk about a couple of them. It’s that connection that you strive to make and what he’s used to say, what how’s it consumed? And how’s it going to be used? And who’s going to use it? I think those three questions if you keep those in mind as, as you as you talk, you know, that was an you know, when we talked and had a conversation. That’s what really stood out to me. You keep that always at the top of your mind when you’re having those conversations with both the salespeople, the executives and even Michael, your mentor.
Jorge: Yeah, yeah. So it was a really good experience. In 2013, I left Steelcase and moved to Iowa to do my graduate degree in Actuarial Science. Although I formally left Steelcase I did stay in touch. I interned with them through that winter. And that summer, shortly after my internship, I was offered an opportunity to join the newly formed advanced analytics team. This was in fall 2014. And so upon graduation in spring 2015, I relocated to Grand Rapids, to be part of the advanced analytics team, now with a degree in Actuarial Science. And that’s where my journey here at Michigan started. Well, you said that even when you started in Michigan, there were a few hiccups as you joined the team. And I’d like you to talk about the one that led you to probably one of the breakthroughs not just for yourself, but even for Steelcase. So I think it was around you trying to predict churn. Yes. Well, your customer base, this is a great story. And it’s just, it’s something that has a very special place in my heart when we when the organization chose to invest on an advanced analytics team, you know that creating a team overnight is a very difficult endeavor. So I think the organization was thirsty for some of those wins. And the team wanted to go for those typical projects, those that every company should have. And I think one of them was customer churn, right? Let’s analyze our customer sales and figure out who is getting ready to leave us and whose customers are at risk. And I was part of some of the conversations involved in this project. But it’s one of those projects that it was doomed to fail before it even started. I think everyone had a lot of energy. And there was a lot of thirst to prove ourselves and prove the worth of the advanced analytics team. But I think we were after the problem in an incorrect way. And so I just remember being in a meeting, where we were sharing this initial pass, and it was pretty bad. I mean, one of our senior executives actually left the room and said, “This is a complete waste of my time. I don’t think you guys get it. And I mean, those feelings, they stay right. Over time. This was one of those projects that it was just there were always shower thoughts. I’m like, how would I? How would we have approached this differently? And eventually, we had a couple of wins and a stage of maturity. And I remember telling my manager at that time, Tim Merkel was a great leader. I said, Tim, I think I want to take another stab at this customer churn thing but I’ve been thinking like we can take a spin on it. Now. I remember him looking at me and saying like, are you? Are you sure you want to? You want to open that again? Because but you know what, what I felt is that our heart was in the right place. We did need something to help our sales field. But it’s just that the question that we were after was the incorrect question, because we were trying to solve for what customers are getting ready to leave us. And that might be a great problem for certain industries, maybe like Netflix, or any other type of service industry, like subscription based, but in the office furniture industry that approach didn’t really resonate. And so I thought of changing the question rather than trying to find out who’s getting ready to leave us, can we predict based on historical sales and customer activity, which of our customers, whether we’ve won or lost engagements with them in the past, do we believe are getting ready to redo their space, because furniture has a cycle and every so often companies upgrade their spaces, and changing the question led to a completely different perspective and value proposition for Steelcase. And that’s how we came up with the concept of predictive leads. Because what we were doing is we were analyzing historically, when was this customer? When was the last project that they had, and say, based on that, when are we expecting their next project to be. And using that we were able to score all of our list of customers and produce leads for our salespeople that say, these 1020 customers from your region or your portfolio have a high likelihood of having a project within the next 12 months. And in our industry, being there early is a game changer. And this, this was just an awesome, awesome experience. I gotta tell you, what I love about this, this particular story is most sales organizations. If they put it into the last column, no one goes and looks at it. No one’s like it’s lost. So we’re never gonna go and check out, you know, the How to get to use those losses and turn them into pluses. I mean, that’s just the way you looked at this as a completely, it’s a completely, you know, the term out of the box, and a different thought process. So I love that story because it just talks about, we looked at the problem from a different perspective. So continue, please tell me more. This project created a lot of excitement. And I think it was one of the first initiatives at Steelcase, where an entire organization came together to execute on an analytics initiative. And the reason why we why I say this is because it was It wasn’t just about creating the leads, we actually created a way to upload them directly into our CRM system, it would tag the appropriate account owner, because we’re talking about 1000s of leads, it would attack the appropriate account owner, it will tell them, This is what we pitched the last time whether we won or lost. And if we won, this is the furniture that they had, this is what they bought, then if they lost, this is what we went with and who we lost to. And we also created all this material that would enable the salesperson to break the ice, like a little script as we know you had this project back in 2013. We really want an opportunity to talk a little bit about some of the new things that we have in our portfolio, new ways of thinking about space. And so it was an entire experience. It wasn’t just that the model that we built was an entire sales strategy. And just being part of things like that is extremely exciting, extremely compelling. Well, the bottom line is you brought $180 million worth of extra value to the organization that, you know, that was there, but no one was mining it, you guys went and found it, you mined it, you brought it up. And all of a sudden, you’ve not just increased that customer experience relationship, you added to the bottom line, you know, if you can, could you explain a little bit how you got all the people involved and at the table and what it took to get people on board, this train that was leaving the station?
Nick: So can you explain a little bit how you got all the sales team, the marketing team, your team, you know, all those people together?
Jorge: Yeah, it’s really there’s an art and there’s an art to it. Practitioners of data analytics will know very well that if you work in a silo, chances of your initiatives to scale and to be able to penetrate and transform organizations are slim. At the same time, I think that’s still the case with data literacy. And there’s, there’s a lot of skepticism, and then there’s still pockets of the organization that are going through that evolution. One thing is that through small wins, we were able to build enough trust with some of our senior executives. And they, they believed in us, and I don’t want to say that they gambled a little bit, but they chose to put their trust in us. And so it was us saying, Hey, I think there’s something we could do here and then believing in it and doubling down on it. And just that senior level engagement is really what allowed just things to align, and everyone to rally around a common theme, a common objective. And just what created this sense of camaraderie, and now we had a mission and data science was a component of it. But it wasn’t the only component by any stretch.
Nick: I think that’s an interesting story is that, you know, you guys are taking, you know, experimentation and trials and taking a little bit of count, I would call them calculated risks. And your executive team was willing to do that man, I wouldn’t think of Steelcase as someone who has a set of executives who are willing to say, let’s try that.
Jorge: You know, you don’t think of that. And I think that goes to what you said before, which is the culture that still caves in is different. And I think that goes into how that plays throughout all levels, makes a difference when you’re being able to do projects or experiments, like you were doing with this lead generation. Yes. And that’s just something I love about steel cases, the willingness to embrace creativity, innovation, trying new things. And I think that’s what I want to lead me to next week, because we think about innovation, and you think about trying new things. I don’t know that, you know, we talk last I mean, I would never have thought of Steelcase thinking about the ability to gather and share data from its partners and bring those together so that everyone collectively learns, eventually, essentially, you created your own little, I want to say data exchange, where they brought it, you know, your designers brought data to you, you brought the information back to the designers and working together in that sharing data just started to, you know, make that snowball. So if you can, you know, describe a little bit about that need for extra data that you guys had, and then how you gathered it and how you return value back into, you know, your partner organizations. I think that’s a great story of how you can have a symbiotic relationship with partners around data and information. Yes, it’s a great story. And in, as you mentioned, it’s a partnership, there’s an ecosystem, having those wins. And building that trust in that relationship with some of the senior executives, you know, gave them a little more willingness to try things out of the box. And I think at some point, we were like, okay, yeah, you’ve done some really cool stuff. Here’s, here’s the century old question, right? We know how many Lego pieces we sell. But we don’t really know what LEGO sets our customers buy. It sounds like, what do you mean? It’s like, well, if you look at what we sell to a given customer, we’re going to be able to know how many brackets, how many chairs, how many work surfaces, if somebody had adjustables but we don’t really know exactly how they came together. It’s very hard for us to know that at scale. We, we believe we know but there’s really no way of us knowing because there’s a lot of gaps in the data now. We could really crack that nut if we could figure out at a scale, what the settings for our customers look like, Man that that could that could really transform how our insights are formed, how it informs some critical decisions around new product development around how do we better support our customers. And when we first tried to take a stab at this problem we were trying to figure out how to do that with the data that we had. But the way I can describe what we were trying to do is imagine that I went to the supermarket and then handed you the receipt with everything I bought. And I told you, Nick, I need you to predict or to classify everything I’m going to have for dinner throughout the week just by looking at this receipt. And it was just difficult, right? It always comes back with yes, always. So what we thought of is well, I mean, we have a pipeline that allows for some transmission of data from design, software’s and configurators all the way to our manufacturing facilities. And I have to bet that within those design software’s there’s some added pieces or pieces of information that could really help us understand product adjacencies. So how might we think of a way to give them a ride just far enough where we can make those connections ourselves and allow us to understand which products came together. So this work became known as applications mapping, what we refer to as an application is a collection of products that are from a setting, think of a meeting room, or a workstation or a pack of workstations. Obviously, this data that we were after, was not created by Steelcase It was created by our designers and our partners. And so we chose to create what we call a dealer data Co Op, right? They would sign in, they would volunteer to give their data and we would have, we would work towards that data on insights that would have something in it for them. And when we started when we turned on the pipeline, it was January 2020. So as you can imagine, within 90 days, like the world completely, completely flipped on its head. And we were starting to better understand this data. But at the same time, we were starting to face questions that we’d never faced before. And I remember getting a call from our chief of staff saying our CEO just got a call from the federal government. And they’re looking up to us to provide insights on the current state of the North American office, because obviously, we are the biggest office furniture manufacturer in the world. And so they thought they should give us a call, what can we say? And I just remember being able to make that connection to say, well, this data that we have is just so rich, that it allows us to understand product adjacencies to the degree that I can analyze a floor plan and say this is the average distance between seats. This is the average division and barrier between employees. So we started creating an entire repertoire of descriptive analytics of the current state of the North American office. What was the distribution of distance between workers? Were they face to face back to back side to side? How often did they have a division? Was that division big enough? Oh, you’re talking at the early onset of COVID, when people were leaving the workspaces, workplaces, you in your teams, were starting to think about how to return people back to the workplace safely, or using the data you have to do that. At that point. We were just thinking like, okay, let’s look at what all this data is able to tell us. Right? But then immediately, we started thinking okay, then, you know, based on the standard social distance guidelines, there are spaces that meet them and spaces that don’t meet them. So let’s try to understand a little bit more, those spaces that don’t meet the standard social distance measures, like how would we fix them? Like what what? What are things that we could do to help customers who have spaces that look like this and empower them to be able to bring their employees safe? At the same time that this was happening? Steelcase was working on an engagement where the MIT to study the transmission of pathogens in an office environment, which was going to be very relevant at that time. But we were trying to understand just what was the best way to scope the engagement so that we could maximize the applicability for Steelcase. And so what we did was we weren’t able to tie all this together and integrate, it just feels like serendipity. And like, everything came together, because we had this data, we understood how to analyze it to be able to understand settings that are at risk. And then what we said is, well, using our current product portfolio, this is how I would retrofit to fix it. And now we put those to the test with our engagement on MIT to say, Okay, do they really make a difference? Right? Do they really diminish, minimize or have an effect in the spread of pathogens? If I’m sitting next to somebody, if I’m sitting in front of somebody? And what are those small tweaks that we could do to the way we’re retrofitting the settings to play an even bigger effect. And this was, this was incredibly helpful, because now we had some tangible, scientifically based evidence of ways that customers could retrofit their spaces to improve safety in the office, especially for circumstances where, you know, people had to be back in the office not not ever, like, sometimes we forget that not everyone had the opportunity to be able to work from home. And that was a difficult decision for a lot of people. And some employers, like, being able to invest in the safety of our people could have eventually been a life or that situation in a few cases. But we weren’t done . It was heating up, but it’s just like everything was happening so fast. How are we going to scale this, right? Because right now we got these, like siloed pieces. And I remember saying, like we got to create some sort of experience, like, I think we have the pieces, we just need to figure out a way to connect them. And that connection was this experience that we created. That’s called Space Camp. And what I love about space, again, is that it’s another example of kind of analytics, by itself, is not what’s going to transform your organization, the organization is gonna be transformed by experiences. And so what was this experience? Well, we knew how to analyze floor plans from customers. And understand that throughout the floorplan, being able to call out, okay, these settings are high risk, because they don’t meet with the social standard social distance measures. And we also knew, Okay, if this is the type of setting that you have, these are ways that you could retrofit them to support your customers and still be able to meet some some requirements that you have of maybe like, compliance with density, square footage, number of people in and we created this experience where customers would give us their floor plan, we would run it through our analytics, identify the settings that were high risk, and then provide a proposal on things that they could do to retrofit their spaces, minimizing the amount of investment that they would have to do, because we, we knew exactly what they had. And so we knew exactly what worked. So they didn’t really have to redo their office, yet they were able to transform their office in a way that made it safer for people to come back. And that was just it. We were able to create a big return based on the leads and the engagements that we had. But I think the most important thing that this created is it gave us purpose in a time where it was just rough. It was rough for everyone. Now, that is great. I mean, what a great story, you did give yourselves purpose. I mean, I’ve seen some of the effects just as we’ve started to open up the way people have retrofitted offices. And little what I have thought about that back in April in May of last year, as chaos was happening, or as things were going on, that you guys were already thinking ahead of how to help your customer, and how to help the actual end person become safer. I mean, that is just I wouldn’t have thought of it. When to think about it. That is just a very, very good story on how data analytics, not just the math, but how it actually creates, you know something with a far greater value for everyone. That’s a great story.
Nick: Great story. And you guys are still doing that today, too, right?
Jorge: What this initiative has allowed us to do is to be better at understanding what people are designing and what those new spaces are looking like. And as you know, there’s just so many question marks about the future work. There’s companies that say that all of their employees are going to be remote, there’s either to say that all of them are going to be in person, or some of them say that they’re going to have flexibility. And it’s a big bet. It’s a big bet of what it’s going to be. And this data really allows us at scale, to be able to better understand much more intimately. How is the office being transformed? And what are those spaces that are becoming more effective, more desirable, I’ve been sharing just a small insight. But that says a lot in the fact that just by analyzing this data, we saw a dramatic change in the number of meeting spaces that had stool height, work surfaces and stools, as opposed to desk height. And what we began to understand is that this is a direct response to people wanting a little more flexibility in choosing how close they are to someone else. Because when you provide people the opportunity to either be sitting down or standing up yet keeping that eyesight at the same level, you can, you can have somebody that just maybe puts their laptop in their work surface and maybe takes a cup and steps back, maybe he moves a little bit, but they’re all at the same level. But that person can choose how close they want to be to the work surface, you may have another person that’s completely comfortable. He or she uses their stool and all kinds of work surfaces because he’s got his hands down, or maybe he’s taking some notes. But it’s about choice. And those choices and providing those choices. And the need for safety and wellbeing just is translating in different ways that you might not have thought of before. But that to us is super rich to understand that because at the end of the day, what we do is we want to build spaces that empower people to do their best, right. And so those tiny things are like I just feel much more comfortable coming into meaning because I have those choices.
Nick: That is that’s just, I can see why you like working for Steelcase and being in that culture, because you affect people every day. You know, when you think about it, you know, you’ve actually just changed roles from being an individual country contributor to now managing people. And if I look at what you just described, it’s like how you just empower the person who’s sitting at the table. But there’s different ways that you’re looking at how to empower your team. And what you need. I mean, every one of your examples you’ve brought in, there’s different skill sets at the table, through all the stories that you’ve you’ve shared with us. And now as a people manager, you know, I think if as people make that transition, a lot of them, it’s a hard transition. But you’ve you’re making it a good transition, how should they think about putting together a team and the different, you know, skill sets that should be on an analytical team to help the business transform itself? What are your thoughts as you’re moving through this next part of your journey?
Jorge: Now? I think that that’s a super, super important question. And I think it’s something that, again, data practitioners, and people who lead data teams might have faced are either facing right now or have faced before about two and a half, three years ago is where I took over my first people leading role. And at that same time, we were transitioning into a new data science environment. It was cloud based, open source, built for scalability, but completely different to what we had. And we were not like our problems, we were no longer just building a time series, doing a model and giving me the R square or the p value. Now we were talking about how I need this to automatically trigger this action behind the scenes. And so we were facing things that were beyond the realm of what just a statistician or a core data scientist would do. And when I took over a team, I was actually given the opportunity to grow the team and hire more people because we were in growth mode. And rather than bringing more data scientists, this was a great opportunity to just take a step back and say, Well, I don’t think we need a team of data scientists. I think we need a data science team. And that’s that’s different. Because there’s so many things that need to have been for data science initiatives to be successful. And you may try to find that unicorn, or somebody that just checks all the boxes. But it’s, it’s often going to give you a lot more bandwidth and flexibility and power to have a diverse team, a team that has people that know about DevOps and data engineering, people that know about machine learning engineering, people that know have that strong business acumen and can be that analytics translator for you. Because part of the magic is learning how to translate a business problem into a data problem. If you can’t do that, you can have really good business problems or really good data problems, but you’re not going to solve either of them. Right. And it was a good opportunity to rethink our go forward strategy in terms of what does it mean to be part of a data science team at Steelcase? So it’s a very, very engaging and motivating time for the company. Oh, they’re again, looking at something from a different perspective. And that, I think, is one of the things it’s one of my takeaways as I listen.
Nick: But you know, when you think about it, you’ve got this terrific team of diverse people, you know, how do you, you know, think about how do you keep them motivated? How do you keep them on track? How do you make sure that, you know, they’re, they’re doing what’s necessary? What are those things that you, as a people manager, are trying to do with this diverse team to keep them moving in the right direction?
Jorge: Yeah. And man, this is such a great question, because one of the things that still happened, started to really embrace agile. And the concept of building agile teams and being Scrum and following sprints. And this is very powerful and very effective for certain types of teams, where you have absolute clarity, you know, your stories, data science, it’s just that it’s not the principles that apply. The routine that is often successful for some teams, can lead to tension in the data science realm. But in spirit, what really helps us number one, you got to have clarity, you got to have clarity on what is the problem that we’re trying to solve. You have to have a multi like, like a diverse team team, a team that complements skill sets, and that each understands the other person’s role and strengths. And you have to be able to give them the clarity of why they’re part of the team, what is their value proposition on the team, and what are we all working towards. And that tends to lead to very successful teams. They collaborate with each other. As you can imagine, some of these roles have a little bit of overlap. So there’s, there’s definitely some knowledge transfer that happens. And so by creating these product teams, you slowly scale up your teams, and then they become more sustainable, more powerful, more adaptive. But the other critical thing is making sure we have that tight connection with our business partners, like we cannot work in isolation. And so more successful initiatives have been those where these product teams include key stakeholders, people from from, from other teams from the business, etc.
Nick: Okay, I liked that. I liked that opportunity you give your team sounds like you give your team enough autonomy, but within the boundaries of what that project needs to be done. I think that’s that, that’s excellent. That’s excellent. So again, we’ve got to start wrapping up this conversation. I don’t want to put we’re going to have to so as we look at this, you know, what do you say? Or what are you thinking is the next big thing for Data Innovation at Steelcase?
Jorge: Well, one of the things that happened at Steelcase recently as we have a new CEO, RC our new CEOs around Brewster, and we’re working on a new strategy, which is timely, not just because Sarah has a vision and a lot of passion and a lot of energy. And boy is she making us proud. But we also know that the value proposition for an office space is going to change and it’s going to mean different things to different people. And one of the key things about this new strategy that we’re coming up with, at least our winning proposition is we help people work better. But let me break this down for you a little bit. Because when we say we help people work better. We don’t just mean the person who ends up sitting on a Steelcase chair, like the automobile industry. There’s so many additional people that depend on our industry, we have a tremendous supply chain. We have our dealer network, we work with architecture and design firms with real estate firms. And in one way or another, they all have to interact with Steelcase to make things happen to manufacture furniture. And so what’s coming isn’t really geared towards a let’s do a sales forecast. Let’s do XY and Z. Let’s build compelling experiences that within the environment, or what still represents , helps people work better, helps dealers do their job more effectively, helps firms feel inspired by the work that they do and wants to work with Steelcase because it makes their job more fulfilling. How do we help our supply chain? Work through the hurdles that we’re all going through with all these disruptions? How do we help mid market customers that more often than not struggle and are ignored by the attention that these huge giants bring to the industry? But they’re like a very important part of our industry as well. What are those experiences that we can create that automates things for them? What guides them? Maybe it’s a data driven design assistant? Maybe it’s the automation of quotes? Maybe it’s a better recommendation engine? What is that Stitch Fix meets Netflix experience for them, but for the office furniture, so that they feel empowered? To be able to understand what their needs are? And what’s emerging? And how do they build a compelling space in the war for talent? Nice. So that’s a huge topic these days. You know, at the end, if you think about everything we just discussed, and your peers that are listening to you, what do you think should be their main takeaway from this discussion? Or if you had a chance, saying, hey, and it all comes down to this one or two things? What would those things be that you would tell your peers that are listening? I would tell everyone, the focus of a successful team is to build compelling experiences. Think not about the model, start not with the data, think, think about the experience, it used to be to think about the problem, and let that drive your data needs and your models. But I think the times have changed. And it’s not about solving a problem. It’s about creating a compelling experience. And when you do that, you’ll find that sometimes you don’t even eat, people didn’t even know that they had a problem. And that’s what transforms industries. Think about, you know, Uber, Amazon, Netflix, like, did I think I needed that and all of a sudden, I can’t imagine my life without it, right? So think not about solving problems, think about creating experiences, because that’s what’s really going to transform with the order. But when we talk about experiences, one of the experiences that you always do is how we always get back to food is you create experiences through experimentation, such as testing recipes with your wife.
Nick: So I’ve got to ask you the question in those experiences that you and your wife and to create, you know, what’s on the grill? Because I know you like to grill? What’s that experiment you’re going to do this weekend?
Jorge: That’s going to be fun. Yeah. So we’re, this weekend, we’re gonna try some braised short ribs, we’re gonna see how well we can slow cook them and just play with the timing and the temperature a little bit. There’s a little bit of science there. And so yeah, and it’s, it’s, it’s not just about the protein, it’s about the sides. They’re super important. So it’s another piece of advice from my peers out there. Don’t underestimate the thinking about the ecosystem again, right? That whole ecosystem, see experience, right? It’s everything that surrounds it.
Nick: Well, I gotta tell you, I always enjoy talking with you, because I learned something each and every time, when we have a discussion, you’re doing some unbelievable work at Steelcase. And I’m just so happy to have you on the show and sharing that with our listeners. So thank you for taking part, Mick. It’s an absolute pleasure. It’s an absolute pleasure. I’m honored to be here. And for the rest of you, folks. Again, it’s been a great discussion. I thank you for listening to InConfidence. It’s a podcast for the community of data leaders like yourself and or hey, we hope that you found your time with us well spent. I know this conversation has already been well spent for me.
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