Speaker Key:
DD Damien Deighan
PD Philipp Diesinger
CS Christoph Sporleder
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DD: This is the Data Science Conversations Podcast with Damien Deighan and Dr. Philipp Diesinger. We feature cutting edge data science and AI research from the world’s leading academic minds and industry practitioners so you can expand your knowledge and grow your career. This podcast is sponsored by Data Science Talent, the data science recruitment experts. Welcome to the Data Science Conversations Podcast. My name’s Damien Deighan, and I’m here with my co-host Philipp Diesinger. How are you doing, Philipp?
PD: Good. Thanks Damien. Pleasure to be here.
DD: Excellent. So, today we are talking to Chris Sporleder about the data and AI consulting industry. Large companies in particular spend a lot of money each year on projects with external consultancies, and Chris has some unique insights on how to make that work for all parties involved and ensure that real business outcomes are delivered. We will also talk about how the consulting industry in general works. So, if you’re a practitioner
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who wants to get an insider perspective on what it’s like to work in the consulting industry, this will be extremely valuable for you.
A quick word on Chris’s background from as far back as the mid-90s, Chris worked at SaaS who are of course the original data analytics powerhouse. He also spent seven and a half years at McKinsey where he worked as a partner. Currently, Chris is a managing partner at Rewire who are a data and AI consulting firm with offices in Amsterdam, Heidelberg, and Tel Aviv. Chris is responsible for the Germany and DACH operation and developing new client relationships in Germany, Austria, and Switzerland. Chris, thank you so much for joining us today and how’s it going with you?
CS: It’s wonderful. Thanks for having me, Damien and Philipp. It’s great. It’s a bit warm here where I’m, but summer is finally here, so that’s great.
DD: Excellent. So, if we start at the beginning with your journey, Chris, how and why did you get into consulting?
CS: Well, first of all, I got into data and analytics and then it was a bit of a natural journey. So, from my first job that I ever had in an energy company in a utility, actually before unbundling, I focused on analyzing mainframe data. I helped calculating the first wind park that we’ve been building and I got really attracted by data analytics. My next step was when joining
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SaaS, and SaaS was a combination on one end, as you said, it was a data and analytics powerhouse. So, there was the software part. I had the opportunity in a fast-growing organization to build out the professional services division and that’s what got me into consulting actually. First all around one product, then later, much broader around auto help organizations with data and analytics. And as you said, it started as early as the mid-90s. Those were the times when you needed to explain everybody why analytics is important.
And then there was this big jump when cloud computing came, we all of a sudden could do things that we knew about but we didn’t have the resources to do, which would cost a massive, massive jump in attention and focus on data and AI then at the points or allowing machine learning, deep learning, et cetera. And that made just a huge kind of change in terms of before I had to explain why is it important and then it switched to how do we actually do it, how do we get impact from that? And that was super, super exciting actually. And it still is.
DD: And I mean how did you actually explain or articulate the value of data back in a timeframe which was actually possibly pre-internet or right at the very start of the internet?
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CS: Unfortunately, it was already at the start of the internet, I’m not that old. But look, organizations, many organizations of different industries have been using data for ages. If you just think about insurance actuarials, we are always like statisticians, they always work with data. And for them it was like modernizing getting into new techniques, broadening from a simple regression analysis to more machine-based learning algorithms, et cetera. So, those guys were always more about how do you get your data under control and how do you use step-by-step more modern algorithms. In other sectors, that was obviously different and still today by the way is there’s still sectors that are rather a bit nascent when it comes to the use of data analytics.
But with the entire Gen AI revolution that started a few years ago now everybody woke up and is making plans and started lighthouses, et cetera. So, it’s no longer about explaining why it is important and how you create impact more. How the heck do I get it done and how do I get actually the cash in because there’s a lot of activity but also very often without a lot of direct outcomes. So, that’s something still to work on.
PD: And Chris, how did you experience the move from local computing to cloud computing?
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CS: Again, that was a quantum leap for data analytics, right? A, in terms of capturing and storing more data from different sources. I mean this went along also with the IoT revolution, so we’re not only talking customer data, we’re now also talking machine data at a much larger scale. Although I have to say now comes the old war story. Even in the 90s, if you think about the old mainframes, there was tremendous amount of data that has been stored, right, data about the machine. Pretty unreadable, pretty hard to decode, but if you managed to decode, you could do performance predictions, load predictions, capacity predictions with pretty basic statistics. Now with a move to code, all of a sudden millions of devices being connected with a rise of more e-commerce, more online services, more customer data got connected, more behavioral data got connected, and that actually allowed to build a much broader set of algorithms.
I mean think about the days in classical industries like banking when you went to the counter, not much data has been captured other than somebody wants to transmit some money from A to B, now you’re using an online application, you log in, you move somewhere, you check your balance, whatever. That’s all like direction points and storing that, making that available, gaining more intelligence, personalizing, that’s the customer side. And on the machine side it’s all about understanding failures earlier but also understanding machine behavior to an extent in complex
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production processes. So, it allowed just a whole new world of A, more data and B, a much broader set of algorithms that again, we knew from probably the 60s, but we couldn’t execute on that.
DD: You talked there about a lot of things that have changed in the last 20 years. Are there any things perhaps haven’t changed that much in terms of how companies utilize or attempt to utilize data?
CS: Yeah, there’s a few things that still are bothering to an extent. The data topic is something I feel like we went also through several evolutions. If you think about the data management evolution, it started with a data warehouse. I always explain data warehouse as low-tech high governance, so a lot of rules on top of the data warehouse, but the technical foundation was like relational databases or something. Then you moved into the data lake area because everybody felt the data warehouse is too restricted and not getting out what I want, it’s too complex. And the data lake times where super low governance and super high tech, it ended up being a total and utter data chaos based on a lot of great technology. And I think just now things start to come together with the data mesh and kind of data product thinking which tries to strike the right balance between technology and governance.
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You need both components, and the governance components has always been the weak part in it. Whereas, if you think about data and you want to make somebody in the business responsible for data, this is always on top responsibility. And if the person doesn’t have it and is personal kind of objectives and is compliant as an objective, it’s the first thing that gets dropped off the table, and it is still today like this. So, I think the real breakthrough in having the business engaged in managing data and providing data and ensuring quality, and accessibility, and fairness of data, I think that’s still many organizations, many, many organizations, not the case.
PD: Chris, you managed a lot of problems that many organizations struggle with and typically problems where external specialized companies can help organizations. How do you see the role of consulting services in the space of data and AI?
CS: I think you have to differentiate different types of different consulting services. There’s not the consulting service, there’s many different flavors and shapes of consulting service. I think on the one end and it’s a spectrum, you have kind of the body lasers, you order three kilos of data scientists and you get three kilos of data scientists and then you have to tell them what to do. And on the other end of the spectrum, you have the strategy consulting firms like the peak of the mountain, the high
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performing and super-fast consulting firms and everything in between. There’re system integrators that are more a little bit to the left and there’s the boutiques that tend a little bit more in the middle or to the right. You have to find what’s the right thing in terms of consulting for the state that I am in and arrange with the right type of consulting firm and it’s not one probably to end, you would probably need different types of services at different points of your maturity.
PD: So, you mentioned already strategy consulting. Do you see strategy as the starting point of a data and AI project?
CS: Yes, I do. I do. You need to be aware that really doing data and AI can originate in organizations from different points. That can be the super excited board member that says, I believe in it no matter what. That a brilliant starting point and then your strategy that you need to put in is a bit more operational because you don’t need to convince anybody on the board level anymore. You can really go in and basically say, okay, how do we get started and how do we get to impact? If you don’t have that convinced board member, the first thing you probably need to do is to analyze what are my value pools, what’s the commercial impact that I can gain? What’s the cost for such a transformation and that requires a different consulting service for you.
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It’s probably also a shorter engagement, but it’s in many organizations still some work to be done to get to this level of commitment on the board level. And I’m totally convinced that you will not implement data and AI at scale if you don’t have senior leadership commitment. It’s always then going to be in some silo, or some isolation, or some department, but it’s not going to scale it and to say, well, everybody says AI is going to change the world and it’s going to change every process. That’s only going to happen if you have your most senior leadership not only behind it, but if there is a conviction that this is a new enterprise capability and that’s fundamentally different.
PD: That makes a lot of sense. Yeah. Do you also see differences in different sectors in different industries?
CS: Yeah. I mean except before that where industries for ages that used to work with data because it was core of the business model, especially in the risk management area, was always around data, right? I’m an insurer of, I’m a bank. It’s always been a lot of a data game to be able to really understand and analyze. Other industries were a little bit more behind the kind of manufacturing energy utilities. They only come in a bit stronger with the rise of IoT and connected devices. Now infrastructure players, it takes a lot of time to smarten up an infrastructure. If you think about energy grids, we’re not talking about smart grids, but most of the
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infrastructure that’s out there in the field is decades old and just now with the energy transition being modernized, so those players naturally come in a little bit later.
Again, this is a very generic view, this is not to offend any organization that in some specific area had built capabilities around data and AI, but the kind of is that a strategic capability for us that we kind of the retailers, the insurers, the banks were much earlier in the game, the life sciences companies for whom data and AI and R&D is absolutely essential. They were earlier the game, the infrastructure, the industrial players, et cetera. They came in all a little bit later.
PD: We’ve talked about the challenges of data and AI from an organizational perspective. If we switch to a consultancy’s perspective, what are typical challenges that they are facing?
CS: One challenge with the consulting is you need to pick, again, the right consultant for the right phase. You start with a strategy. This is intense. You probably get a strategy consultant for you to do that and they basically put in a murderous pace for you. So, actually, you need to think about backing up your consultant, your strategy consultant, at least one-on-one with your own resources because the throughput of these teams is amazing and you need to be able to consume what they produce. You can
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probably last that speed for 2, 3, 4 months and then it gets a bit exhausting, right? This is good. This is excellent work, typically. And again, I would always say strategy consultant is great if you want to identify new business models, new areas to enter into and create the business around it or identify the market opportunity, what have you. Again, the challenge is you need to make sure that you’re not outpaced by your consultant and manage that accordingly.
If you go to the other end, you know exactly what you want to implement. Very often you’re going to the board and you say that’s a typical system integrator job. And system integrators are also cool in what they’re doing. Some large protagonists in the market that do the implementations for you, they typically grew. We’re talking data and AI here, but the system integrators typically grew from large infrastructure, ERP and what have you engagement. Now, system integrators are used to very, very detailed statements of work, so you need to describe upfront what you want and then the system integrator does exactly what is written down in the statement of work and anything that’s not there is a change request. Which means be prepared depending on the kind of engagement that you’re having that you build in some flexibility because you’re not going to end up where you started.
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That’s quite natural I think, and the data and AI even more than in anything else. Then you have the body lasers, right? I mean this is just an extended work patch. You have total control. It’s like you need to look at it as this is two, three more employees I get on a temporary basis. The challenge here is I saw many organizations building a pretty strong dependency on these extended workbenches and on highly depending on individuals actually at the end of the day. And if that one individual says, well, look, I just thought that it might be great to go on a world tour, you have a problem, right? There’s no company you can talk to because it’s typically the body leaders are kind of building freelancers for you, and you need to make sure that you have a reliability with these freemans and body lease consultants to make sure that you get stability. And you need to make sure that you also have your backup plans because it’s like an employee is not there and you don’t have a backup plan, you have a problem.
PD: Have you ever faced situations where there’s a misalignment between the data and AI solution being developed and the client’s business goals?
CS: Actually, less than in other engagements, but yes, of course that happens. It’s not that much that you say the objectives are falling apart, but what often happens is that the slicing falls apart. So, what is it where you say, what’s your starting point? If you have a complex thing right now
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discussing for example, these copilot things built on Gen AI, what a copilot for a certain function. And this is typically huge, this is not one-use case. This is multiple use cases under the hood of a copilot. And slicing that means you need to make a very conscious choice. Do I go for maximum impact first? Do I go for speed of deployment?
Do I go for breadth in terms of how many functions, how many processes, how many assets do I want to address with this copilot? What is it that I actually want to do? And some actually go for strategic scale. I built my first use case, so it helps me to develop the infrastructure and the data products, which again helped me with the next use case to be faster. So, there’s different design choices you can make, and these as to your question, can’t fall apart, right? Sometimes you develop something that’s actually not aligned to your strategic direction, and that’s super important to get that fixed very, very early.
PD: When organizations are at the beginning of the data and AI transformation, do you see common challenges that they face?
CS: Yeah. Very common is the kind of expectation versus reality gap. So, one of the things to deal with that is capability building, and the first step in capability building is knowing the art of the possible. So, there’s a lot of expectations, especially now with Gen AI about what it can and what it
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can’t do, and I think here, spending some time on the art of the possible is super helpful, otherwise you have a totally mis set expectation. Second is the amount of infrastructure it requires and the amount of data management it requires to produce an AI use case is most often underestimated. So, there’s different directions how transformations fail, some overshoot on investing first in technology and data. I’ve just seen a program was like three years working on data and not a single use case has been deployed. That’s of course not a good choice. On the other end, on the other axis, we’ve seen programs that just deployed three use cases without thinking about how do I build out a common data foundation or a common infrastructure.
Both extremes need to be avoided. The right approach would be really to think about all dimensions and develop your infrastructure, and your data foundation, and your organizational capabilities and processes along the use cases that you want to build that create impact along the way. And again, they’re most often transformations fall apart. I’ve recently posted on the end of the lighthouses for example. We’ve seen many organizations building up three exciting lighthouses by three different groups in the organization, no common foundation, no connection between those. The lighthouse itself may be reasonably stable, but it does nothing for your
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organization to scale data and AI, so there’s no scaling plan behind it. That’s also very common challenge.
PD: Yeah. We’ve already talked about how crucial it’s for organizations to choose the right partner, when the partner is chosen are there things that can be done to make sure that the consulting company and the organization work very well together? What can be done to set the collaboration up in the first place?
CS: I think there’s common things like A, if you choose your partner, think about what are your common objectives. More often I see performance-based coupling between the organization and the consulting partner, and we’re working towards common objective. Your remuneration is also based on reaching these objectives. Second thing is how do you set up your engagement governance? Do you have regular feedback loops between the client organization and the consulting organizations? Third is a very common thing if you’re not talking system integrators and body users, but anything else in the consulting spectrum, you have to understand consulting organizations are very interested and it’s one of the value propositions of consulting organizations to develop their people, which means on longer term engagements there’s going to be some rotation of people and that needs to be carefully planned.
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That’s always a point where friction can start that the consulting company says, look, this guy has been four months, five months, six months on your project, now this person needs to do something else, otherwise the consultant is not going to learn anything anymore. It starts to repeat the same things, which isn’t good. So, that’s something that needs to be discussed, that needs to be planned for, that needs to be an open conversation.
PD: Are there any things that can be improved with regards to procurement, setup of RFPs of legal structures in the first place?
CS: Yeah. Again, this is not a generic answer for everything in procurement, this is very related to data in the AI. Data and AI projects have a lot of dynamics in different dimensions, and it’s probably one of the things as in the early days of online marketing, you don’t end up with a project where you initially thought you would end up. That doesn’t mean that you don’t create the impact, right? Just along the way you figure out you may take a slightly different path and you still create the impact.
So, for procurement, that means don’t engage data and AI consultancies in the classically way you would engage with the system integrator, this kind of very strict, very tight statements of work or rather hindering your success, right? You’re not creating the impact that you want this way. I
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would rather start from the other end and saying, what’s the objective that we’re trying to accomplish? What’s the impact that we’re trying to create with that and make that the objective for the engagement. And I think that’s going to be much more successful.
PD: How important do you think is leadership involvement when it comes to AI or even Gen AI project?
CS: Crucial. Absolutely crucial. If you think about AI projects you want as an organization, not just another application, you want AI to be embedded in the process and not to sit on top of the process. That actually means you’re not only doing a technology development at the same time, you’re changing business processes, you’re building new capabilities, you’re potentially changing job descriptions and scopes of work for your employees because you’re now having a solution integrated in your work process in your workflow like the copilots we discussed and want to say, you have to work with it. And that requires change management. That requires capability building and that’s all the things with appropriate leadership attention. You also sometimes need to push through decisions.
We talked about data in the beginning and data, again, is still a super crucial point. If you want somebody in the business to become a data product owner or just a data steward, make sure that you think about this
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is an activity that consumes time. That means a business leader would need to go across the departments and say, look, you need to book like 5%, 10%, 20% of this person’s time to ensure that we have quality data. You need to ensure X percent of capacity with that person that is now the product owner for a certain AI solution. And those things require strong, strong senior leadership engagements because it goes across the organization. So, it’s not just the CDO, or the CDIO, or the chief data officer requires really senior leadership attention.
PD: If we stay on this topic about executing a healthy kind of engagement between the consultancy and the client organization, what kind of dimensions do you think about that are crucial for that to have a healthy kind of collaboration with an impactful outcome at the end.
CS: You actually need to combine three domains, right? You need to combine your business domain, with your leadership domain, with your technology domain. So, there’s a tech team that’s not only kind of your delivery arm, it’s your partner to develop new capabilities in the business, new AI and new insights, new whatever smarter processes. You have the leadership that we just discussed in terms of making things happen, making the process change happen, making capacity locations happen, helping to change your organization. And you have the business involved as such because this is where the ideas need to come from at the end of the day.
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You can spark a couple of ideas outside in because others have done it and it’s probably an arms race type of use case, but a lot of the ideas that are really, really value driving you’ll find in the core business functions, not in the support functions. This means you need the business to be aware and to understand what the abilities of AI are. And those three together, business, leadership and tech, make the right triangle for data and AI engagements.
DD: I would like to take a brief moment to tell you about our quarterly industry magazine called “The Data Scientist” and how you can get a complimentary subscription. My co-host on the podcast, Philipp Diesinger is a regular contributor and the magazine is packed full of features from some of the industry’s leading data and AI practitioners. We have articles spanning deep technical topics from across the data science and machine learning spectrum, plus there’s careers advice and industry case studies from many of the world’s leading companies. So, go to datasciencetalent.co.uk/media to get your complimentary magazine subscription. And now we head back to the conversation.
PD: So, we talked a lot about the current situation already. How do you see the future of the data in AI space? Are there any trends that you see emerging already or what is important for organizations to set up now to be productive in the future?
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CS: I think there’s a couple of trends. One is on the infrastructure side, so anything that has to do with the technology I use to build data and AI use cases. There’s now of course the hyperscalers that are dominating the infrastructure space. But I see that architectures are getting more and more componentized. There’s so much investments into data and AI technology that if you decide for whatever data cataloging tool today, in three months, there’s most likely going to be a much better one, a much smarter one, a much faster one. And that means it’s just a component of your architecture and you need to componentize your architectures in a way that you can just exchange one thing with another rather seamlessly. That’s on the tech side. If you think about the process side, again, we’re talking more and more about AI within the process as opposed to AI on top of the process. But do I have just the next tool on my workplace or do I think about embedding AI as a natural process step?
Also taking certain simple operational decisions away or driving it as far that only the human needs to say, yeah, I agree with that decision and then check it off, which is much more seamless integrated into the process and just putting the next tool on the side. The third one is probably on the trend of Gen AI. I think there’s going to be a lot of discussion. Of course, now with all the Gen AI language models, image generation, video generation, what have you, in the next month we’re going to discuss much
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more how do I integrate conventional AI and Gen AI through, for example, agents. Create end-to-end processes where a conventional AI model does whatever the risk scoring and an agent hands it over to a Gen AI model. That’s then basically integrating it, for example, into a conversational process or something like that. Integration of different types of AI is probably the next level that we’re looking at.
Lastly, on the data side, we already talked data mesh. If you really want to build a data mesh, it’s super complex. It can take you ages. What we currently see as a trend is a bit of a pick and choose from the data mesh theory, take the things that you can implement right now and start the journey. So, kind of data product centered thinking is something that you can do right now without a problem. There are other components in the data mesh that will take much longer to implement. But I think that’s a very smart way, a pragmatic way and heading in that direction
PD: Makes a lot of sense. Maybe to turn the question around, what are big mistakes that organizations should avoid at this stage?
CS: There is always, again, especially when you come from the business, still this habit of I want to see something on the front end and disrespecting that it needs a lot of backend to create that little fancy thing on the front end. It’s like there is infrastructure, there is data management involved to
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do something, to create an AI use case, and I think avoid the mistake of expecting. It’s only to create an additional model on top. There’s a lot of foundational work very often to be done. That’s hopefully getting better, by the way, with building better data products, building scalable infrastructures, et cetera. So, my expectation would be this mistake can be avoided in the future, but right now I think everybody needs to be clear. There’s still infrastructure work to be done. The infrastructures aren’t perfect.
DD: You mentioned earlier, Chris, that consultants after four to six months potentially get swapped out of projects. Let’s say there’s a consulting engagement, a 12-to-18-month project, it’s likely to go in length and the team consultants is maybe five to seven on a team. How many of those would get swapped out? Would they all get swapped out over 12 to 18 months or are there certain consultants that stay fixed on the project? How does that work?
CS: So, what you typically see is that your leadership and your engagement management stay on, right? So, you’ve got the partners of course, or the senior managers of your consulting organization that lead the client relation, that lead the overall program. That’s a constant. Typically, your project manager also stays on for long, we were talking data and AI projects. You also have the data engineers, the data scientists, you have
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the infrastructure guys. That’s something if there’s no learning anymore, and that’s the key element, then there’s typically an ask to be either swapped from one work stream to another or swapped to another engagement.
And that’s again, there’s nothing bad with that. You want consultants who actually have a broad background, who’ve seen different situations, who’ve worked in different environments, who’ve looked at different business processes, dealt with different personalities, also work with different technological settings. That’s what you want, and this is why the consultants can’t just body lease somebody for an 18 months program for you. If it’s a data scientist that’s like two, three years into the job. That person needs to see more. And there’s typically, it works actually, well if you announce it at the right point in time, if you set that expectation in the beginning, look, this is a long-term program. Your leadership stays stable, but the data scientist and data engineers may change from face-to-face.
If there’s a certain product completed and you go to the next one or you go from MVP one to whatever scaling into a different region, it might make sense. You’re not ever as a consulting company would want to exchange a full work stream at once. But to develop your people and to give them the opportunity to learn and grow, it is important not to keep them for a super long time and we’re talking like more than nine to 12 months on a
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single topic. That’s not good for their development. And that’s important as a consulting company to make sure that you create the learning opportunity and growth opportunity, and on the other end to provide your client with the right people at the right point in time based on the expectations that you’ve been setting.
DD: And in addition to making sure you don’t change many people at the same time. Are there any other tips for managing those changeovers in a way that avoids friction or slowing down the project?
CS: Well, if I’m advising consulting companies here, the only advice I can give is announce that early, right at the beginning. Basically, explain your structure, explain why you are a good consulting company because you’re developing good people, and set that expectation on each of the work streams. Make a long-term plan. So, this is nothing that should ever come out of the blue. That I’ve seen a couple of times and that never works well, and that never goes along well. Then of course there’s situations where clients really fall in love with a certain consultant because you totally understood the business and connect super well to the people, and you always need to make sure that you say, well, the next one that’s coming can do it as well and can grow into this role, right? And you’re going to have a partner that’s as good as the one that you previously had.
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DD: Great. And you also mentioned the change management piece, which I think is usually undervalued and underrated, but has a huge impact on the eventual outcome. Can you talk about both from the client and the consultancy side, what needs to happen and what they can do to make sure that that’s handled correctly?
CS: There’s a couple of interesting things from a client perspective that I’ve seen. Some clients basically feel like, okay, I’m changing a business process, whatever, in production, and my HR department is doing the change management for me. That’s not going to work very often. It’s nothing you can delegate away as a business leader. Just because you have a HR department with talented people that can moderate this doesn’t mean they do it for you. So, the key thing that then applies both to the organizations as well to the consultants, don’t believe that anybody else can actually do the change other than the business people that are directly affected. The only thing you can do as an internal function or as a consultant is to effectively moderate this process and give the right tools, give the right nudges, give the right insights to actually accelerate this process. That’s the best you can do.
But taking the responsibility for change is always in the business function that is affected. It requires the business leader to go marching first. It needs a couple of kind of early movers or front runners, whatever you
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want to call it, that are more open to this kind of change that is happening. Those who need to identify, this is where your consultant can start helping you may be saying, what are the characteristics? What are you looking for? What kind of persona do you want looking at the process to be a front runner? And then providing them with materials, providing them with formats. How do you basically do kind of this change? How do you run workshops, et cetera. The techniques, and the facilitation, and the orchestration, that’s what an external provider can do for you. But again, as a business leader, nobody’s going to take the responsibility away from you to own the chain.
DD: So, changing things up maybe can you talk a bit about your current position and your current company, Rewire?
CS: Yeah, more than happy to. You’re not the position in the beginning. So, I’m responsible for Rewire, the DACH region, that means Germany, Austria, Switzerland. Rewire has originally been created in the Netherlands quite a while ago. I think it was 2008 something, and Rewire was born and invented. And for a long time, the company was actually named My Company coming from MI, Market Intelligence. And then just very recently has been renamed to Rewire because we a bit out of these shoes and are now basically a data and AI transformation company. The thing that
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describes us best is we’re sitting in the middle of the spectrum that I described.
So, we’re not a body leaser, we’re not a strat-com, we’re a data and AI focused organization. That’s the one thing we do, but this we do end to end. So, from designing the transformation together with our clients, looking at the impact areas, implementing data and AI products, helping to basically shape very often it’s rather to right size the infrastructure, then we actually help the clients with capability building and what I described previously, the orchestration of change of the business processes. That’s the end-to-end thing that we do and that’s where we feel most comfortable with.
DD: And is there a difference in terms of philosophy or approach to the market as you’ve previously described it?
CS: Well, we’re trying to define of course, our own category in the things that we do, and there are similarities with maybe other types of consultancies. There’s, if you look at our leadership, including myself, many of us have a background in strategy consulting from the leadership part. So, we bring in this kind of affiliation to leadership where we’re able to be in the boardroom and discuss with the most senior client’s what data and AI would do for them and how they practically can implement that. But on the
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other end, we have the practitioners that actually have the deepest possible knowledge in terms of how to implement data AI and data AI infrastructures.
We have that vast experience in terms of capability building with clients where we’ve educated and trained and upskilled thousands of people in an organization. So, I think it’s a bit differentiating for us that we’re saying we’re covering end-to-end, but in a very dedicated space. The other thing is probably the engagement models. So, we’re not body leaders, we’re always working impact oriented. We’re not strat-cons. We’re not just telling people what to do, we’re also doing it together with them. So, there’s a couple of differentiating mechanics that we have that we’re all very proud of.
DD: And how would you compare the pace and the way that you work to, for example, the strategy consulting, which you mentioned earlier was high octane with high burnout probably on both sides of the equation.
CS: A, don’t get me wrong. What strategy consultants do is right for the thing they do. Probably you don’t want a strategy consultant for two years or three years in a row because that’s indeed going to probably outpace you as a client organization. What you probably need is more flexibility from your partner that helps you through the transformation in terms of scaling
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teams up and down. You want a partner that on one and challenges you but doesn’t outpace you, right? And finding this balance is something that we find is super important.
And that also means it requires a lot of flexibility in the engagement model, which we want to provide. So, our message to our clients is that with you for a transformation may take 1, 2, 3 years, but we’re very flexible in the way that we support you. That may be phases where it’s more about change, more about capabilities. You may want to open up a domain where it needs a strong push in a very short timeframe to create the initial impact before you then scale it, whatever, globally across all your skews, or products, or assets, or what have you. But this flexibility and engagement model is something that I think is super important and that’s also differentiating the criteria.
DD: And are there specific sectors that you guys are focusing on at the moment, or is it broad?
CS: It is actually pretty broad. Historically, there was a lot of involvement in telecommunications, banking, insurance. Lately, chemicals, semiconductors, energy, life sciences, retail. Probably, there’s only one thing that we don’t do so much and that’s the public sector. But anything else you can see, that’s where we are active.
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DD: So, looking at the practitioner’s perspective, Chris, so a data scientist, data engineer, someone who’s doing the technical hands-on work in industry, you’ve been in consulting such a long time. Why is consulting, in your view, a career that practitioners should at least consider maybe doing a few years at least in?
CS: It’s a very individual choice, of course. And many times, in my career when I was changing roles and changing positions, I was thinking like, do I switch this side of the table and do I go to the other side? And I always decided for the consulting. My personal motivation is I’m doing that for a very long time now, and I’m still learning, learning, learning. Every new situation, every client is different. And it’s, for me, the biggest learning opportunity. You meet a broad and diverse set of people and characters. You work on a broad set of different business challenges. You can work in different geographies and what have you. And that’s of course something that I like. One thing I’ve been once missing is sometimes if you just do the strategy, you miss a little bit the reward when the impact comes in.
That’s now something when we’re thinking about Rewire again, which is the reward that we are working for, and we want to stay there until you capture the impact as a client, that was one of the motivations for me to change to another consulting firm. And again, we’re not even considering ourselves as consultants, we’re considering ourselves as partners of our
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client and we’re in there with you knee deep and we’re doing the things together with you. Still at a point in time, we handed over to you and then you run on your own, right? Again, for me, the part why considering a career in consulting is the learning angle. It’s the experience angle, it’s the personal growth angle if you want. Being in so many different conversations with so many interesting people all over the world and different companies, for me, always has been spectacular and rewarding.
DD: And are there any opportunities at Rewire currently for people who might be interested in a career in consulting and data and AI?
CS: Yeah, absolutely. I mean, we’re growing. We we’re in the fastest growing field probably in everything considered digital right now. And we’re looking for data scientists, data engineers, but also project leads and program managers who are excited to go on a journey with our clients to help them transform and scale the impact of data and AI.
DD: And so, that concludes today’s episode. Before we leave, you just want to quickly mention our magazine, “The Data Scientist.” It’s packed full of insight into what the world’s leading companies are doing in enterprise data and ai. And the next issue of our magazine out in September, will feature part of this conversation, and you can subscribe for that free
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@datasciencetalent.co.uk/media. So, Chris, thank you so much for joining us today. It was an absolute pleasure talking to you.
CS: Thanks for having me, Damien.
DD: Great. And also thank you, Philipp, for you and your great questions as always. And thank you to you for listening. Do check out our other episodes @datascienceconversations.com, and we look forward to having you with us on the next show.
