#2 - Why you should adopt a modern, operational and accessible data strategy for your distribution network.

Transcript:

00:00:09

Gaëlle Helsmoortel: Welcome to Dgenious Let's Talk Retail, the podcast that shares tips and best practices from key players in the retail and franchise industries. Each episode is a conversation with an inspiring person or an expert in one of the key areas of modern retail. My name is Gaëlle Helsmoortel, I'm the CEO of Dgenious and I work every day with my team to enable retailers to boost their business through easy access to their data. If you're not afraid of new ideas and want to have a good time, this podcast is for you.

00:00:38

Gaëlle Helsmoortel: Hello and welcome to another episode of the Dgenious podcast, Let's Talk Retail. Today's guest is a man who has been immersed in data and business intelligence for over 20 years, Yves Collinet. Hello. How are you doing?

00:01:00

Yves Collinet: Hello Gaëlle. Listen, I'm doing, I'm doing very well. In fact, I have to say that we are living in exciting times, so everything is going well for us.

00:01:08

Gaëlle Helsmoortel: Good. You've been the Managing Director of Micropole Belgium for a few years now. So, Micropole is a very large French group, a very large French IT group, 1300 employees, 800 customers in Europe and Asia. I hope I'm not mistaken. But, Yves, what really interests me today, and the reason why I wanted to have you with me today on this podcast, is that from your beginnings at Business Objects, in the 90's, to Micropole today, you are really someone who has experienced all the evolutions, the revolutions, even in the way companies can manage what I call playing with their data. So, as you know, our auditors are retailers, franchise managers, franchisors. And so, my first question is, today for our listeners, what is the strategy of modern data implementation?

00:02:15

Yves Collinet: This was obviously a particularly important question. In these times of global digital transformation, I would say that it has obviously accelerated in the last few months with the events we are all experiencing. I would say that today, we now dare to talk about it and say loud and clear in all businesses, in all sectors of activity, that the companies of the future are the companies that we will call data driven, that are driven through data, and that the ongoing transformation of all the interaction processes of the company in digital mode only generates more and more data. To come to your question "what is a modern data strategy?", for me, first of all, it is a company, but I would say a modern company, is a company that considers that data is at the heart of its assets or its patrimony, the most strategic and therefore, by definition, uses the word strategic. It is a company whose data is managed at board level, and even by the shareholders in some cases. It is a company whose data strategy feeds the different populations of the company, i.e. the populations. What is this? Obviously, we'll talk about this in more detail later, but the operational people, who until now have essentially been consumers, but their consumption was not the same as the production they were doing (I'll come back to this later), the famous business and data analysts that we have been putting in place for years now. And I arrived at the time when analysts were taking over data at the end of the 90s. And then, obviously, the much more strategic stakeholders, which are the C-Levels, the board, the external partners (to whom we now dare to consider giving access or having them contribute to the data) and the customers (the customers being potentially the B2B of a company, or even the B2B2C), we can go as far as the consumer today. A modern data strategy is also a strategy that works at the speed of the business and no longer at the speed of the information systems' capabilities, and therefore delivers a very iterative mode, a very sprint mode, as we say in our jargon, based on the use cases that are necessary to support the company's strategy and operations. So, what does that mean? It means that data, as soon as we talk about operations; I think that, Dgenious, you are a remarkable case in this sense; it is that, in fact, today, we have all the technological environments from a software and infrastructure point of view, at costs that are really... that we have never had in the history of information systems and IT, where you can even pay per use, you only pay for what you really use, and if you stop using it, you don't pay for, it exactly like a tap, you turn it on and off. It has exactly become IT as a service globally. And this means that we are now able to capture data as close as possible to the event where it is generated. So, an event, if we're talking about the sector that concerns you, for us at Dgenious, is a purchase by a customer in the retail sector. An event is a truck arriving at a store and delivering to the inventory. Another event will be a customer who came, bought or came and didn't buy. Different types of events and at the moment when it happens, we are able today, since we are in a digital world; I start from that principle, obviously; to capture the data at that moment, to store it and to make it directly available to the operational person on the spot, in a mode that is completely, I would say, user-friendly for him or her in the usual systems, what we call the devices of the laptop, it can be a PC, it can even potentially be the screen of each room a little bit everywhere. We now know how to display the data to him or her at that moment. So, in real-time, I call it more ... well, we call it in our jargon, more monitoring, so we constantly monitor the activity and based on this monitoring, we can make decisions at the right time. There he is in the store, he hasn't come in for two days, although he comes in every day. What do I tell him? What am I talking about? And I'm notified by my system. That in fact, he hasn't come in for two days or that he's buying something other than what he bought the other times. So, typically, here, we are potentially integrating artificial intelligence, machine learning and scoring technologies in real-time. That's on operational data. I cut you off, maybe you had a question?

00:07:02

Gaëlle Helsmoortel: No. Not at all. I just wanted to make sure that I understood all the information that you're giving me. And so, can we say that one of the big differences is that maybe before everything was captured, everything was done at the HQ level, maybe.

00:07:24

Yves Collinet: Absolutely.

00:07:25

Gaëlle Helsmoortel: And that today, a modern data strategy means capturing this data where it is actually done, i.e. in stores, online. Is that it? Is that what you're telling us?

00:07:43

Yves Collinet: Yes, absolutely. Yes, yes, but that's not all. Yes, obviously, that's the heart. For me, today, it's really becoming the heart and it's a historic opportunity that we have now. All the elements are converging to allow us to do this. We always needed it, but we couldn't do it. So, we had put in information systems to consolidate all that at the level of the ... because it was expensive. It requires very important technical skills, very important latency and implementation times. And so, we used to do ... we could only do this at the level of the headquarters, of the companies, in systems, in specialized departments, that we called and that we call IT directors who remain today obviously, the heart of the activation of the technological means of the company. But today, we can connect them with the field and feed these central systems which remain the truth of the company and remain in these central systems, obviously. On the other hand, we can add today a very very operational dimension and is much more efficient to support the business with these data capture systems on the spot, if I can call it that.

00:08:51

Gaëlle Helsmoortel: Well, obviously Yves, I really like what you are saying because I think that today, our listeners need a data strategy that is modern, that is operational and that is accessible. So, in fact, this is something that I think is very positive. You've already highlighted some of the benefits of implementing such a strategy, that is, a data strategy that is based on this operational data. Let's call it what it is, but maybe you can highlight the key benefits of such a strategy.

00:09:29

Yves Collinet: Of course, there are some. I've listed a few that come to mind here, there are certainly many others but I'm not being exhaustive here. But the ones that come to mind quite naturally are one of the recurring problems that we have always had in decision-making systems, and that is the quality of the data that is made available to the entire decision-making and monitoring chain of the operation. Why is that? And here I come back to what was said a while ago. Because in fact, the producer of the data, the one who is at the point of sale in the retail sector, is going to be the first consumer. In fact, it is he or she who will consume it directly in his or her application to see. So, what is going to be less, what is not going to be well done at the checkout, obviously, is going to be reflected in his or her ability to manage the operation. And so, somewhere along the line, we put the management of data quality back into the same person at the same time, in the same event. And this is one of the fundamental problems that we have never managed to solve until now. The companies that manage to understand this and to integrate this notion of data quality into the production of basic data will make the entire chain, from consolidation to the HQ level, much more interesting in terms of the content of the data that is retrieved. That, for me, is one of the most interesting things, one of the most interesting subjects of what we see here. Then, there are other arguments such as a reduction in latency for the people who are in operations, between the moment when the data is produced and the moment when they can use it. Typically, between the capture of the data and the action that can be taken, so it's from the data to action, we completely shorten the activation cycle, I would say, to finally make it almost in real-time. This is really essential. This means that we are really on the concept of data driven enterprise that we were considering a few minutes ago. We're at the heart of it. In other words, we have operations that are driven by data. This allows the data to be more detailed and more qualitative. And finally, as we were saying, it is more detailed and therefore, at the level of company management, when we consolidate all of this at the HQ level, we obviously have information that is more detailed, that is richer, I would say, available to us for reporting, dashboarding, and analysis, as we have been doing up until now, and which we will always continue to do in budget forecasting mode. So, another great interest is that, as we can see, data is at the heart of the company's life, but it can also be implemented. Applications can be implemented at the speed of the business, simply because it is the business that will do it. And IT or the specialists or the technical departments will serve, will finally be at the service of the business. And then, another point that we have suffered a lot from over the last 15-20 years, I would say, is really this effort to change and train people. That is to say, since people are the ones who take the initiative, the operational staff are the ones who take the initiative for data systems. Well, they need to be trained more because in fact, they are the ones who asked for it in the first place, so those who asked for it, if we make it available to them by definition, they should not be trained because they find there what they asked for by definition and they have piloted the whole creation chain. And so, in fact, the result is that we have a real business intelligence system with the words business and intelligence. The ones that make sense at that point are focused on business strategies and operations.

00:13:10

Gaëlle Helsmoortel: I hope that, like me, the listeners will appreciate his speech because you can really feel the passion behind it. I love passionate people and it's really nice to hear. You can feel that you are living this data. I also hope that they will hear that I am passionate too. Anyway, thanks for sharing. I would just like to add the word monitoring to the benefits because you did use that a little earlier in the interview. And I think it's really important. You explained it in another way in the advantages, it's not having this latency, to have the operational people directly on the ground, so that they can monitor the things that are happening, so I think that's important. When you present this new trend to your clients, are they receptive? I guess so, but how do they react? Are they surprised?

00:14:11

Yves Collinet: First of all, I tell them that I'm a data addict and that's how we call ourselves from our tag lines. In the Micropole group, we call ourselves data addicts. It's our passion. In fact, we are very enthusiastic and we are convinced that this is the generation, as I said, that is going to ... winning, in fact, there are some very, very strong cases today in the market. First and foremost, I use the word opportunity with lots of exclamation points. These are opportunities first and foremost. There are real, real opportunities to exploit thanks to the data that already exists in the company or that is available to it. Either it's there, or it's available, and this data is relatively inexpensive compared to what we've spent on automation processes up to now. Then I tell them opportunity. It's essentially, for me, the opportunity, it's to get the benefits of their digital transformation first. Because in fact, there is one thing that is generated and that brings, I would say, quality to the company. It is this digital transformation that makes the essence or the fuel or the result of the digital transmission, it is the data. And this is recurrent. So, it's both its fuel and its result to re-fuel other processes. So, for me, it's an opportunity to concretize in a very strategic way, the digital transformation. Afterwards, I talk to them ... that it's a need. They really need to see it as a need. Because in fact, every day, they have new players coming into their industry who are coming in without knowing anything about their industry. We see people who manage to launch rockets without ever having made a rocket before or to build cars without having made a car before, because they integrate digital and data to understand a sector and disrupt it. And that, for me, is the subject that must attract the attention of decision-makers today, whoever they may be. In fact, we should no longer look at our own sector, but thanks to data, we can say that we can go into other sectors or that others can come into our own sector without knowing anything about it at first, because they can model it thanks to digital and data. So, it's both an opportunity, but ultimately an opportunity for everyone. So, you have to be aware of it. Afterwards, it also allows you to align your budgets and investments with your operations, with your strategy. In other words, we've seen quite a few cases in which Business Intelligence decision support systems have completely gone off into their own world and have become relatively disconnected from the life of the company. And then, we saw more, very well and we found in certain cases ...

I have known cases of a large telecom operator where there were huge information systems that aggregated the data, that processed it with dozens, even hundreds of people, working on it. And finally, when the CEO had a decision to make, he would go see a person he had identified in his organization, who had a big server right in his office and who finally gave him the information he needed to make his strategic decisions. So, there were total alignments between the technology that had taken over business intelligence and the reality of the company's decision-making system. This is also an opportunity for innovation. Innovation, why? Because in fact, in companies, Dgenious is a remarkable example, that we call start-ups, you offer yourself solutions, really ... because you arrive in sectors of activity, as I said, without necessarily being players. At the beginning, you don't have a history and you come with ideas. You come with innovation, with ambitions, with the world you want to create. And if we are open as an existing company with a large historical base, I would say that we can welcome these innovators that you are on relatively standardized and open data platforms. It gives the company a huge leverage opportunity on its own data. With these innovations, there is only a modern BI, a modern data environment, which I call a Data Platform, which allows to welcome this innovation and to accelerate it. After that, it also allows ... it's quite paradoxical, but well, we think it's paradoxical, but it's not, it's a result: it allows the security of the data today in these platforms that are being set up everywhere today, and in the cloud in particular. It is much greater today and we can see that the security problems that are appearing today are on the old platforms. More and more, on old data platforms, we have the big security problems and not on modern ones. So, I have an image that I heard recently that really helped me see this; it's the world of currency, actually. Historically, before banks existed, you had your money under your mattress, and everybody managed their money at home. And then the banks came along. You put your money in the bank because it was safer to have it in the bank than under your mattress. This is exactly what is happening with our data today. So, let's think of the cloud, or solutions like the Dgenious platform, as the bank of data if you will. And then, that in fact, it will be much more secure there than if I keep it at home. And I will be able to confront it with innovators. On the one hand, an ecosystem of partners and customers who will be able to access it and to whom I will be able to give more possibilities and who will enrich it in a totally professionally managed way. Then there is the aspect of talent and also the attractiveness of talent. So, data management, data analytics and artificial intelligence talents are obviously very rare in the market today. We train them, but this generation in addition... well, they also know... so they also get paid. And so, this makes the company much more attractive. It makes it more attractive to attract these people to its teams. And then, it also helps prepare the company for what I call the AI tsunami, which is coming in the position of AI, machine learning, as the wave of the new industry that is emerging. And it really allows the company to be ready to innovate with their talent, ready to integrate AI into their processes and into their new offerings.

00:20:56

Gaëlle Helsmoortel: Thank you very much for this beautiful argumentation. In any case, I'm buying. If I am one of your customers, I understand what you are saying. Well, obviously, the word opportunity, innovation, these are words that particularly speak to me. Perhaps now, could you, to finish this podcast, give .... what would be your one or two very concrete pieces of advice so that at the end of this podcast, they can act? Or already, maybe ask themselves the right questions? What would you concretely advise them to do?

00:21:32

Yves Collinet: Very concretely? I wanted to talk about 3 axes, actually. Sorry, there will be 3.

00:21:39

Gaëlle Helsmoortel: No problem.

00:21:43

Yves Collinet: The first one, the one on the data axis itself, on the people axis, I would say, and then on the business axis. On data, if I can be very, very concrete, very pragmatic: look, as a manager or a leader, make a concrete and written inventory. I would say the data that you have in your company or that you know is available in your company. So, what are we talking about? We're talking about the machines that are in your new environments, the buildings, the information systems, the cars, the trucks, the people that you have, the digital applications, the websites. Take an inventory of everything you have there. What's the data there? And what is it about? Simply a list that we will call a mapping. Look at the interactions between them. There, there are already techniques to do that, etc. And not just ... I would say don't start with the information systems. They're important, but it's not the existing information systems that you're going to get most out of but it's the environment, the business environment of your company.

00:22:45

Yves Collinet: On the axis of people. Try to see who understands, who masters and who has the best understanding of your business and who, in relation to this business understanding, is capable of expressing the strategy of your company in relation to data. And therefore, who today manages the business on a daily basis. The pilot who makes the decisions and do these people use data? And if so, which one? This allows you to see, in relation to what you've done with data, I would say, in the first step, to see who the people are in relation to this, whether they use it or not, and where it's happening in relation to your business. Then, see what those people are doing during the technical type activities. We talk a lot about data crunching, data preparation, data analysis, technical development. Try to identify a little bit the volumes of time, hours, TP people that are in there. And who is responsible for that? In which department? Often, we see the CFO, we see the operational people, we see people all over the place, but it's not sufficiently understood. I would say this people aspect, in order to eventually come up with a roadmap for training or recruitment, which you would need in order to go further between the data assets that you identified at the beginning and the people that you have on board, which will allow you to optimize your organization in the time that follows. 

00:24:19

Yves Collinet: On the Business axis. I love to ask the question: What are your competitors doing and what are they doing with their data? Have you ever had a case where you lost or you don't feel in control of your position because you have a competitor who has done something that you don't understand how they were able to identify this opportunity, how they implemented it. After that, in your business, who's doing what with the data from your appointment? We're coming at people a little bit. But then after that, a particularly hot topic is seeing a little bit in the people who are handling the data at your place; all of what I called, the people who are doing the crunching, the preparation, the analysis; what are their frustrations? What would they like to do that they can't do today? See the tensions that can exist between technical departments and business apartments. And then also, most importantly, look at what success you already have and where it's happening. To try to not only promote them, but maybe accelerate them. And between the investment you're making in people and the frustrations, the tensions you have, you can identify a gap that is actually your roadmap for improvement. And that's where it's going to be, actually. And that's where you're really going to find, relative to your historical heritage, what you have as skills on board. What people are doing and what frustrations there are with your business or what your competitors are putting in your face. You found spaces to exploit in iterative mode very sprint based like that, really in small iteration to evolve your understanding and integration of data in the life of your business. And at the end, I would say a very simple example of two things that I love to ask as well, is: in the last few weeks and months, do you have three strategic or operational decisions that you have made? Yes or no? If yes, which one? I mean, you don't have to tell me, but hey, think about it. What data allowed you to make these decisions? And who was in the magnifying glass with you to make those decisions? And did they bring you their understanding of the data to make those decisions? Just ask yourself these questions to start with. And here, we can already see a little bit of what's going on. And there are things to try to improve.

00:26:33

Gaëlle Helsmoortel: Listen, Yves, thank you very much because what I find very interesting in what you are telling us is to put the business at the center. Because all our listeners, that's what we were saying at the beginning: what is my business, how do I make it evolve and to make sure that the platforms are an aid to business decisions and that these platforms are not constraints in relation to business decisions. Like me, I unfortunately still hear too much. And if you agree, I would add that we need to find the right partners to accompany this decision making and not to put the business and the operational at the center. Listen Yves, thank you very much for the passion that you have transmitted to us about data, which is not necessarily the sexiest area that we want to tackle. Really, thank you for this great exchange. Well, very soon.

00:27:37

Yves Collinet: Thank you very much Gaëlle and I wish you a lot of success in your initiative. It's very good what you are doing.

00:27:42

Gaëlle Helsmoortel: Thank you very much, goodbye.

00:27:46

Gaëlle Helsmoortel: Thank you all for watching this new episode of Dgenious, Let's Talk Retail. The full transcript of this interview is now available on our website Dgenious.com in French, but also in English. And I'm also putting the direct link to this transcript in the bio of this episode. I'm looking forward to seeing you in two weeks for a new Dgenious tome and a new guest on Let's Talk Retail and until then, have a good business, ciao!