This episode features an interview with Volker Ossendoth, Head of Data and Analytics at Douglas. Volker has spent the last 20 years leading the world of Business Intelligence in Europe, previously working at Unitymedia and EY In this episode, Volker shows us how data supports the beauty industry. He talks about the intersection of business and technology, e-commerce data strategies during the pandemic, re-educating your workforce, and much more.
This episode features an interview with Volker Ossendoth, Head of Data and Analytics at Douglas. Volker has spent the last 20 years leading the world of Business Intelligence in Europe, previously working at Unitymedia and EY
In this episode, Volker shows us how data supports the beauty industry. He talks about the intersection of business and technology, e-commerce data strategies during the pandemic, re-educating your workforce, and much more.
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Steve Hamm: Yeah, we'll do this. It'll be fine. [00:00:00] So it's nice to talk to you again, Volker.
Volker Ossendoth: good morning, Steve. Thanks for having me.
Steve Hamm: Yeah, so I think most of our podcast listeners in the United States probably aren't very familiar with Douglas cosmetics. So it would be great if you could start off by describing a bit about the company's history and the scope of it.
Volker Ossendoth: Yeah, absolutely. I'm happy to do so, Steve , so, uh, doulas seems to be Europe's first beauty destination, where sales over 3 billion euros. And this is a fact despite COVID-19 , uh, and that is also due to the strong fact that we've grown immensely in e-commerce over the last years. Um, uh, Douglas, we are looking back at a long and successful history and we started even in 1821 as a soap factory in Hamburg.
So warehouse districts. So it's a long way since then. And in 1910, we opened our first perfumery Douglas , uh, in Hamburg , uh, as it was followed by , uh, five other stores over the coming next years. And so we really accelerated our [00:01:00] growth and the 1980s where we started our international business in many European countries , like, uh, like France, Spain as an excellence, and even in the U S markets.
Um, so,
Steve Hamm: What was your own career path before you joined the company and what took you to the company?
Volker Ossendoth: And what took me to the company. Uh, so I was always very much interested in the topic of data and insights. So, uh, right after my MBA, I started by career was a drum on teleco in 2005. Um, because I felt that it's really my thing to. Do the translation between business requirements and , uh, data that a company is using.
So I started in the , uh, business intelligence department , uh, and was heavily involved in , uh, yeah, doing reporting stuff since like that, and really digging deep into the data. And then it. Uh, I came to the point where I wanted to see more of the data world. So, um, I , uh, changed jobs and started to be BI consultant and that's opportunity to give advice [00:02:00] on the strategy on the architecture on governance topics to , uh, companies coming from different sectors , like, like the teleco sector, like banking and retail.
Uh, but after about 10 years into consulting, I returned to my roots and joint, again, a teleco as director of demand and project management in 2017. Um, and that was a responsible. To , uh, assure the consistency of requirements of data. Um, so to make really sure that it's the most relevant information is presented to the management and being made available in the processes coming from this background.
Um, I joined as a perfumery Douglas in 2019. And I'm not responsible to provide actionable data and also as a right insights to our group and local functions. And what made me come and decide to join Douglas was especially , um, our move , um, that was driven by Tina Miller. Strengthening , um, the econ business.
So, um, we had this initiative , uh, hashtag fava beauty, [00:03:00] and that was putting e-commerce , um, it's a core of new business as a new business strategy. Um, so we successfully strengthened our e-com and we transformed the whole company from a stationary point of sales into a point of experience. And this was the foundations that we late in 2019, where we come from as a retailer with online shops and we evolved into a digital enterprise with stationary business.
Um, and just , uh, two years ago we started , uh, our online marketplace and drove money. So today we are looking at a 1 billion euros of revenue. That's just originating from our online sales. And so this is a well strong driver for our business, and that's the same time for me as a data guy. It's a very interesting , uh, field of affection.
Steve Hamm: Yeah. Yeah. It's interesting. You've kind of bridged between business and technology. Most of your career. A lot of it in [00:04:00] telecom is, is the cosmetics business very different than telecom in terms of the kinds of data you use and how you use it.
Volker Ossendoth: It depends. You could say. Um, so teleco is in that far different as a, you've got a very good understanding of your customer because you can identify each and every customer and you can attract your experience , uh, with your products where we re maybe rail. So in retail often you don't have this possibility to , uh, really get that close to your customer.
It's a little bit different with tubeless as we've got , um, one also strongest , uh, single brands , um, customer programs. So we've got , uh, more than 12 million , uh, beauty carts. Uh, so customers holding our cart and we can identify those customers and by doing so , um, we can provide some. With a very unique experience and , um, really , uh, focused on each and every customer.
Very well.
Steve Hamm: so you said, I think you said you've [00:05:00] joined the company a couple of years ago. When you initially joined, what was your, you know, your, your job kind of your role in the company and your, the strategy that you were supposed to focus on?
Volker Ossendoth: Yeah. Um, I think the first briefing , uh, that was given that was , uh, make data integration happen. So, um, one of the , uh, first initiatives. For me , um, was to integrate , uh, or to give an integrated view on the newly acquired businesses. Um, so we grew heavily , uh, even in stationary sales over the last years , uh, integrated , uh, new core countries , uh, France and Spain, for example.
Um, and , uh, we had , uh, the needs. To , uh, integrate not only , uh, as a pro process-wise, but also to have an integrated view on our business. So we had to integrate data coming from those countries to give , uh, the ability to top management for a holistic steering of the enterprise. Um, so, uh, this is coming from the strategic development of the company asking for integrated data [00:06:00] and being one officer.
Um, yeah. Highest priority issues in my first months and years was two colors. And , um, on the other hand side , um, we had a, let's say fragmented data landscape that was hampering our ability to quickly gain insights and therefore , uh, as a second task, which is a very much similar. Is as integration of silos of , um, on the one hand side, small, but also big data in one central data hub.
Steve Hamm: Yeah. Now you talked before about how the company really began a major e-commerce push. I think it was. Two years ago and under the leadership of , um, Tina Mueller , um, how did that shift over the last couple of years affect the overall it strategy and in particular, the data strategy.
Volker Ossendoth: Yeah. So, um, looking back, where do we come from? Um, we've got the very unique , um, enterprise data warehouse that is , uh, at least , uh, 14 years old. So, uh, it's , uh, quite rich [00:07:00] in data you could say, but it was developed as a classic data warehouse and that is covering , uh, most needs. Uh, stemming from our central functions , um, on the, of this infrastructure.
Well, we set up a enterprise reporting and that is originally focusing on stationary business. And now we've got some change , uh, in the business , uh, really pushing e-commerce forward. So, um, we have to evolve our infrastructure and , uh, we started that by integrating. At first, the online data into the existing data warehouse structures.
But soon we came to this appliance where big data came into play a couple of years ago, obviously. So, um, what we did was setting up a Hadoop cluster , um, and this was originally set up to satisfy analytical requirements stemming from the online business. But here's a challenge. Um, because now we've got two information silos and one is providing aggregated data for classic stationary business.
One is providing fine granular data for online business [00:08:00] purposes. And , uh, as long as those worlds are separate sinks are working quite well. You could say. But , uh, what about on the channel business, for example , uh, with this legacy infrastructure we cannot compete , uh, with the velocity of changes, especially in is across an omni-channel business.
Steve Hamm: Okay. So Volker, what is your data analytics strategy today?
Volker Ossendoth: Yeah. As though we had to change our minds and go forward with the new strategy. Um, what we do believe is that , uh, in order to put digital first. We need to establish one integrated physical data hub with all data that is relevant to our functions, it should be easy to access via standard tools. So not only be available to , uh, some experts, some highly skilled data scientists, for example, and , um, the data hub obviously has to be trust Versie regarding data quality.
Um, we have to have. Good governance to make sure that we are compliant with [00:09:00] regulations in place. And also , um, we need to allow for central development. So not putting all the workloads on one central team that might not be aware of a very specific business needs and requirements that are , uh, in some local markets.
Um, Existing. So, um, I would like to highlight , uh, here on two characteristics. So it's a crucial to the success of the platform. So one is a decentralized development, cetera. Uh, so data assets are , um, defined within what we call the data domains, where we can assign responsibility for data integration, to functions that are the closest to the data.
For example, online micro data is being integrated by digital analytics, core data assets, relevant to many functions is integrated by central data and entity it's team, which is my team. Um, And to ensure transparency and consistency of the data sets. Uh, we did set up early tech governance function that is responsible for the alignment of [00:10:00] all data definitions.
And the second point is about data access. So there is a rich variety of online data , um, that we , um, moves from our Hadoop cluster because that was never easy to access. And our goal is now to ease the use of our data by offering standard SQL interface , uh, that can be used by power users. And also we want to open all data to stand up BI tools that are coming to use by our business users.
Steve Hamm: now the company talks a lot about its beauty platform. I think it would be really helpful if you'd describe what that means. And then tell us how your data strategy is supporting that beauty platform strategy.
Volker Ossendoth: Hmm. Yeah. Um, so beauty platform is in a way two-folds you could say , uh, So when you look at technology , um, it's one platform on that. We run our various shops that [00:11:00] are set up individually for the different countries or customized to the different countries, but running on. One integrated technical platform.
Um, but , uh, what we not only do is have our own shops here on this platform, but we opened our platform to partners. Uh, so to make it a true marketplace so that we can offer to our customers, not only our assortment, but enrich it , um, with , um, as a products of , uh, as a companies and everything focusing, obviously on beauty.
Um, so what we want to offer our clients is then one unique experience. Uh, and this should be no matter what channels they get in touch with us. So this means , um, we have to be consistent. And so we have to Excel in all our communication channels. Um, so for example, When you look at our omni-channel , um, we offer a variety of possibilities , um, on how to , uh, get our product products, big , uh, doing , uh, online , uh, uh, purchase, going to our stores.
Uh, but we also offer a click and collect, or you can do an order from the store. You can [00:12:00] have a delivery from the store and so forth and just a variety of processes. And the data sources is the challenge , uh, that we aim to overcome with our integrated data platform. So by bringing together the data from the secondary stores, from the mobile app, our online shops and marketplace, we derive deep insights about category performance, customer behavior, and operational efficiency.
And in the end, all this information fits together in a consistent way.
Steve Hamm: Yeah, like your, your, your beauty platform and the technology underlying, it really put the company in a great spot to deal when COVID came along with when the pandemic came along, how has that been useful and, and how have you, how have you fared during this crisis time?
Volker Ossendoth: no, that's , uh, we did , uh, quite well. You could say. And , uh, Our colleagues both online and offline. Uh, they did a brilliant job. Um, so we [00:13:00] introduced , uh, all required measures in our stores, obviously to keep some open as long as possible. But at the same time, we set up processes to enable , uh, as a sales, for example, off store inventories, like as the online channel.
So hereby, we mitigated the threats of delivery, bottlenecks of our suppliers, for example, and this was. Data-driven we also examined carefully our , um, econ business processes and prepared them for potential threats. So, um, we enabled, for example, our local e-commerce houses to serve cross countries, so to mitigate, so lock down of warehouses in one single country and , uh, In the back, obviously again , um, you had to enforce all the data exchange to make sure that , uh, it was clear and transparent from which warehouse , uh, in case of a lockdown or a close down , um, you alternatively can , um, serve your customers.
Steve Hamm: Yeah, it's kind of amazing when you, you think about it because your company made these moves because they were good for business [00:14:00] over these last couple of years to TJ CLI, but it turned out that those very same moves. We had the company more resilient in the midst of a crisis that it could not have predicted.
So it's kind of, I guess there's kind of a lesson here on, on being, you know, having a flexible and, and, and non-siloed , uh, business and also tech technology and data landscape. So that's really cool. That's good to know. Hey, now I want to get back to you a little bit here too. Now you're a leader and you know, you've led you.
You've been there for a couple of years and it seems like they've been. There's been a lot of change in the business then of course there was the crisis. What have been the biggest challenges that you've faced in your role and how have you overcome them?
Volker Ossendoth: Well, um, this is the biggest challenge is the ongoing projects that we are running now. Uh, so is that is , um, to really , um, Modernize our analytical landscape because we are , uh, not even in the midst of the roadmap, you could say. So, uh, in a way we've just started, you could say , uh, so, um, our. Uh, aim is in the end to replace , uh, our current [00:15:00] platform , uh, that is catering for more than 14 countries.
For example, we provide some about 5,000 free reports to our Mazdas and 2,500 end users. And , uh, the challenge is , uh, to come up with a plan that is a shoring. Real benefits for the business because business doesn't want justice, a new technology. They want to see , um, benefits. So, uh, it never was a real option to come up with something, a big bang or with a lift and shift approach, because those didn't seem to be the right answer regarding what is the benefit that I get from this initiative.
Uh, so. We already set up and edit USCIS of the most important data use cases as we call it some strategic data use cases , um, that should bring with some added value to the business and not just replace existing use cases. And on the back of those use cases, we define a value based roadmap for a setup of a new analytical platform.
So we put [00:16:00] priority. Uh, especially to support our e-comm business early on with advanced insights and also a priority we put was on , um, the data assets, the tests , uh, biggest effect you could say on many other business functions. So, um, when we do our. Our , um, what accusation or our move into the new world.
We always look at the benefits and each step should really have some benefits to the management, to the processes that you really can showcase , uh, so that everybody understands why this is a core initiative to the whole country, a company.
Steve Hamm: Yeah. And when, and why did Douglas begin using the data cloud technology?
Volker Ossendoth: Hmm, we reviewed our existing analytical landscape right after my start with Douglas in 2019 and soon we realized. That we need to look for an architecture that allows [00:17:00] us to integrate the legacy data warehouse world, as well as the big data world. And after an intense technology review, we signed contracts with snowflake in 2020, and what made us choose snowflake were the ease of use and administration, and also the true dynamics credibility as well as a ability to very semi.
It's fractured input from our online businesses.
Steve Hamm: so please talk about one or two of the most. Kind of interesting or significant pilot projects that you've undertaken with snowflakes data cloud, what kind of business or technology problems were you trying to solve and what results did you achieve?
Volker Ossendoth: No. Uh, so we are running , uh, two tracks. You could say , um, one looking for standards and one looking , uh, for early benefits. So, uh, right now we've , uh, established with the first project, our standards and frameworks that are necessary , um, to [00:18:00] have sustainable. Um, application development in the future. So this is all about governance, data, quality management, and so forth.
So this is more a technical background stuff. And , uh, on the other track, we've worked on some , uh, MVPs to showcase , uh, the new possibilities and , uh, also to bring benefits to support our management and also our power users with insights. So. We've already migrated our big data from the head cluster to snowflake, and now our , uh, how our users have easy and controlled access to , uh, all this big data assets.
Right now, we are looking at use cases , uh, that have critical performance issues. Uh, so we are looking at , uh, um, functions and possibilities of snow flake that is offering , um, performance increases. And , um, here we've got , uh, some , uh, that's stemming from the legacy systems, especially with regard to , uh, peak rock loads.
Uh, you could think of , um, early Monday [00:19:00] morning, everybody in the store is looking for performance KPIs falls the last week. And , uh, if you imagined you've got more than 2000 stores and everybody is looking at the performance numbers, then there is one major peak , uh, every morning, Monday morning, eight o'clock.
So what you need here is a dynamic scanning function that , um, provides , uh, as a compute power, et cetera, specific point in time and for the rest of the week, but you don't need it any more. So, uh, here, the cloud really shows its potential.
Steve Hamm: Yeah. Yeah. Now the cloud marketplace is technology. Marketplaces is changing very rapidly. At the same time, businesses are evolving very rapidly. You know, a lot of people expect. Some pretty significant growth after COVID fades. So there's a lot going to be going on over the next year or so. I wondered if you could talk about what, what are some of the big data cloud trends and the new capabilities that you expect to see this year and maybe into next.
Volker Ossendoth: Um, [00:20:00] I think it's quite interesting to see how marketplaces , uh, gained enormous market shares , uh, in the last couple of years , uh, looking at Amazon for example, and their success lies in the. Range of assortment. That's offered to the customers and also in the, in the ease of use. So how easy you can , uh, get , uh, products , uh, take a look, try them out.
And , uh, Choose , uh, if you want to keep them or not. And , um, I can imagine something quite similar. So in the information age, the information itself could be traded over marketplaces , uh, in a much more , uh, Easy way you could say. Um, because today it's quite burdensome are unreliable. Uh, when you try to analyze your first party data and bring it together with a third party data from the outside world.
Uh, so it's often something to do with, okay. You have to integrate some Xcel coming from some source. You , well, Don't have so much trust in and I see big potential for a marketplace for [00:21:00] data , uh, likes the one proposed by snowflake, for example , um, where you could , uh, and this could drive digitization mean immensely , um, because it's not only a large variety of data of data that could be made available , uh, but at the same time , uh, to access to the state or.
Looking at it snowflake again , um, is , uh, really easy. So, uh, it's coming with near to zero efforts to integrate , uh, data stemming from the Stoweflake marketplace into , um, analysis that we are running on snowflake. So this is something for the next 12 months I expect , uh, to , uh, gain velocity. Um, And I expected the marketplaces to come up with something like plug and play capabilities.
Um, yeah.
Steve Hamm: Yeah, it's interesting. You know, um, snowflake has the data marketplace. It has a, it has a public one, and also you can do private exchanges and things like that. And they've been out there for a couple of years in the United States, gradually developing and with more and more. Uh, companies , uh, coming on board and more data, data types coming on board.
But you and I talked [00:22:00] before, one of the issues with you is that so much of the data that's on the marketplace right now is us market data. And what you're looking for, the kind of the gating factor is you need, you know, European and German data. So how do you see that developing , um, you know, this year?
Volker Ossendoth: Now let's say we hope for the best. Uh, because , uh, what we see , uh, with regard to the kind of data is it's quite interesting to us , um, be it a footfall data when you consider , um, your , um, store locations or maybe potential store locations , uh, out. Okay. Where's that information is a coming information , uh, that you might want to have , um, also , um, granular.
Demographic data , uh, is of high interest to us. Uh, and we see how easy , uh, it could be it to be integrated when looking at a data set is available for the us markets. Um, so, uh, I think this is something we want to see for the European specialties , uh, for the European markets. Uh, so that would be a great advantage for us.
Steve Hamm: Yeah, it's interesting to think about it because [00:23:00] these, this is about network effects and data network effects. Once you start to get more and more data sources. And, you know, customers and suppliers of different types from different regions, it can kind of have a snowball effect. It just gets more and more valuable.
The more people join and, and , uh, the more useful the data is. So it'll be interesting to see how that does develop over the next year. So yeah. Now we've talked about like, you know, looking out a year , Uh, I'm going to ask you to put on your visionary cap for a minute and look out like five years or so.
What are the big changes that are coming in the data cloud and analytics that will have a big impact on organizations and also in society?
Volker Ossendoth: Sure. Um, yeah, I mean, Think about the democratization of data and I'm really looking forward to this happen. Uh, so what we pursue internally is approach of an integrate that, and curate that data Lake house. So this should provide insights and data to all [00:24:00] functions by , uh, as I said, we want to ensure there's an ease of access and so trustworthiness , uh, and you need obviously some kind of.
Data governance to ensure this , uh, but this can then be an enormous driver for pervasive and other ticks , uh, because by introducing you technologies, big data together with classic data can be easily accessed by power users. So, um, analytical tools say support standard analytics done by power users, not only by data scientists and those data scientists, then , uh, freedom much more from those simple.
Day-to-day analytics and they can really focus on the more complex assignments. Um, so, well, let's see, it's a wide spread application of analytics from top management down to operations, much more than today. And as a result, it's the companies driven by data and reports nowadays will become really insights driven.
Steve Hamm: I mean, I just wanted to drill down just a little further into what are the [00:25:00] things that are needed for democratization. And it sounds like, you know, Accessibility of a wide range of data is table stakes, but really a user interface that's really useful for non-technical people. It seems like that's a piece of it. Um, but are there also different, you know, kind of like querying styles or techniques or something like that, that's gonna, that's going to bring, you know, The ability for, for regular people to access and get very sophisticated insights.
Volker Ossendoth: Yeah, I think here's the thing. If you want to really democratize data, if you really want to make sure that , uh, people in your company say take decisions. Based on data. Uh, you have to make the access to data as easy as possible. Uh, and it's the same time. You have to make sure that , uh, there is no room for misinterpretations, so you really have to work hard on your semantics.
To make some clear , uh, also the documentation of your [00:26:00] KPIs , uh, and also to make transparent what kind of data quality , uh, you can provides to your business. So if you've got, if you achieved that point, then people should be aware of what they can do with the data and maybe. They also, also should know what they can't do so you could call it a data literacy.
Um, and then , uh, the last step is to take the right decisions coming from this data.
Steve Hamm: Yeah, it's interesting. It's almost like one element of this is really almost to have a, you know, data science, one Oh one class for a whole array of people so that you're not just handing them a tool. You're actually giving them a framework. You're explaining, you know, how it's going to work. Those kinds of things.
It's almost like a reeducation of the workforce, right?
Volker Ossendoth: Yep. That's absolutely right. And I think with a , uh, modern technologies, it's not only that it's about [00:27:00] new technologies, but , uh, it's very much about , uh, technology is already in place. I mean, the I tools are not that , uh, That you, you could say. Um, but if you can use the tools that you already have in place, like Excel, for example , uh, but.
Apply this technology , uh, to the new data sources, because it's the backend, you've got a much more capable infrastructure. Then the user, he needs to have a better business understanding and you have to educate him with regards to what the data looks like and what it means. Um, but with regard to tooling, I think , uh, a lot of things , uh, are now.
Oh, it was a competitor. Complexity is hidden by as a tools , uh, sink of analytics tools , uh, that by themself say, choose. Um, so right, uh, methodology , um, to evaluate , uh, some statistical function. So as a data scientist, obviously , um, you can , um, develop models and , uh, you can , uh, evaluate what model is the right one, but , uh, nowadays , uh, the tools themselves to do evaluations , um, and , uh, come up with , um, some advice on what's [00:28:00] a statistical model you should choose and work with.
Um, so here , uh, from my point of view, you need somebody in the company. Who is doing the grant works education , um, like, uh, uh, I call them profits for , um, uh, analytics and it gives some sound advice and , uh, give best practice advice. Um, But in the end , um, there is a real user, small, like a classic power user who did a self service BI in recent years.
And nowadays , um, can easily be educated and trained , um, to also do , uh, more advanced analytics.
Steve Hamm: That's interesting. So, um, I know that like a lot of other knowledge workers around the globe that you have been working at home it for the past year, I have two young children, one, three, and very rambunctious. I understand. So you're doing a lot of your work. On video. Oh, zoom and others. And you've got kind of like the kids one corner of your eye and you got the, you got the video screen out the other corner.
So how, how have you been coping with [00:29:00] that? And also how has working at home kind of changed the culture of working at Douglas
Volker Ossendoth: Yeah, I think it's a change for the Atlas. Uh, as in , uh, Zupat Tom office was, let's say more the exceptions and the rule. Uh, but I think that's , uh, the case was many. Companies that I know. Um, so when COVID it's a world , um, we all had to improvise and we had , uh, from one day to another cope with home office and then , uh, homeschooling, I mean, I remember on Monday they said, yeah, maybe , uh, in two weeks you should prepare yourself for working from home.
And then on Tuesday they said , well, uh, maybe next week. Uh, so please take your , um, maybe your monitor or whatever with you, your screen with you. And then on Wednesday they said, okay , uh, now it's over go home and you work from home. So I'm in a very small span of, I think it was two or three days we came from.
Yeah, maybe you do some , um, home office to a full halt. Okay. Now you go home. Um, and , uh, this is the first visible change , uh, with [00:30:00] regards to culture was , um, The kids increasingly often joined our refugee conferences. Um, and you got a glimpse of the colleagues, private life , uh, what you never had in this , uh, privacy before.
Um, so in a way , um, looking back as a strange year of COVID , um, you could say formality has smelled it to a way and , uh, especially diversity is strengthened because it's not that. Uniform to go to work and everybody is working hard , uh, leaving his private life behind. And then yeah, you go home and you have your private life, but instead , um, we are part of a much more part of the private life of aura conics and.
I think this is kind of interesting because you could say, yeah, everybody is at home, nobody's coming to the office. So you're alienating from your colleagues, but , uh, in a way it's , um, as the contrary, so today we regularly invite our colleagues and even strangers new business partners to our home. Uh, and we let people into our lives and , uh, yeah.
[00:31:00] Literally into our kitchen , uh, peoples that we never saw before. So here is something , um, that I would like to take , uh, with me or with us into our new normal life , uh, too. Yeah. Be more free about your private life to be more open, open with , uh, colleagues and , uh, yeah. That's should be something , uh, that we keep when we start meeting again in person.
Steve Hamm: Yeah. Yeah. That's interesting. I wonder has it had the effect of. Perhaps people who weren't comfortable speaking at meetings being more comfortable, just because everybody's more familiar now, is that, is it kind of democratizing conversation, a business conversation or is that, is that happening or no?
Volker Ossendoth: Yeah, I think so. Um, when you're in a room , um, let's say , uh, timid , uh, persons , uh, that are not , um, uh, taking part in the conversations that much you could say. Um, and there is a. Uh, and that's a standard in , um, working online, having , um, visual , uh, virtual conversations. Uh, you have to , uh, take care much [00:32:00] more about , um, when people try to jump in because in a virtual session, only one is talking and all the others have to be quiet and.
Very different when you've got everybody in a boardroom. Uh, so I think people gets their voice that are normally, maybe overseen a bit in a normal conversation. So it's more democratic, more basic style of conversation.
Steve Hamm: No, that sounds great. That would be a good change for a lot of companies, I would say. So Volcker, thanks somewhat for your time today, you know, your stories and insights about what you're doing with data and how you do it has been fascinating, such a tremendously , uh, you know, big transformation of the, of the company, both.
The business side and also the it side at the same time. And it's also, I got to tell you, you know, we're so used to that, these stories about retailing where, you know, Amazon is hammering yet another competitor. Uh, but, but Douglas cosmetics seems like it is really. Kind of written to the top here and is really doing a [00:33:00] great job with, with omni-channel is doing, you know, uh, is being very flexible.
It's being it's it's knowing its customers and things like that. So it really seems like it's a, it's a model for how retail can respond and thrive into the future. So, so that's been great. That's been very edifying. Thank you.