**The Challenges and Opportunities of Data Consultancy**

In today's fast-paced world, juggling with multiple technologies on an ongoing basis is a common reality for data consultants. With so many different technologies to manage, it can be overwhelming to determine which ones to use and how to approach each new project. According to our guest, the key thing is to understand what the client needs and what technology sets they have available. "You need to take stock of the situation on where the client's located do they have access to these skill sets in their talent pool," he emphasizes.

**Codifying Solutions for Reusability**

One way to overcome this challenge is to codify solutions at a higher and more abstracted level, making it easier to apply them to different industries and projects. Our guest shares an example from his previous work, where he was able to take the concepts of continuous production and apply them to a new client's problem in a different industry. "That's like having a starting point of understanding," he explains. "You can then explore the technology sets separately that are going to be more relevant for this new client."

**The Importance of Curiosity**

Continuous learning and curiosity are essential skills for data consultants, as they need to stay up-to-date with new technologies and techniques. Our guest notes that researching what's available is crucial to getting off the ground quickly. "Looking through that data, like how do you handle it? How do you sift through it?" he asks. In one example, a team member was able to use a Unix environment to open up files and decipher their contents. "That or being able to write a python script that would do on a line by line basis output the data cleaned up with a delimiter," our guest recalls.

**Detective Work and Problem-Solving**

In cases where there is little context and no prior knowledge of the technology, detective work and problem-solving skills are essential. Our guest notes that having an enthusiasm for going in and seeing what can be gotten out of the situation is crucial. "It's like having a Swiss army knife for a data analyst," he says about regular expressions (regex). Regex is a skill that comes in handy in many different projects, and learning it can make a big difference.

**Advice for Aspiring Data Consultants**

For those interested in pursuing a career in data consultancy, our guest offers some advice. "The future is really bright, and there is high demand for anyone who's looking to join the data journey itself," he says. "Just head on over – we're always looking for amazing people." He emphasizes that the world is getting started on the Consulting side, and it's an exciting time to be a part of it.

**Conclusion**

In conclusion, data consultancy is a field that requires a unique combination of technical skills, problem-solving abilities, and curiosity. By understanding what the client needs and what technology sets are available, and by codifying solutions at a higher level, data consultants can overcome many challenges. Continuous learning and curiosity are essential, as well as detective work and problem-solving skills. For those interested in pursuing a career in data consultancy, now is an exciting time to be joining the industry.

"WEBVTTKind: captionsLanguage: enyou're listening to data framed a podcast by datacamp in this show you'll hear all the latest trends and insights in data science whether you're just getting started in your data career or you're a data leader looking to scale data-driven decisions in your organization join us for in-depth discussions with data and analytics leaders at the Forefront of the data Revolution Let's Dive Right In foreign a frequent theme of this podcast is to try and encourage you all to gain some data skills so you can solve data problems yourself without having to rely on others today we're talking about the opposite thing using data science at consultancies where the whole point is to solve other people's data problems in a previous stage of my career I briefly worked as a data science consultant and I found that there are a lot of interesting challenges that apply to this field like having to build trust with new clients and juggling multiple projects under tight deadlines since successful consultancies necessarily have to become greater data communication and data project management there are lots of lessons to be learned for other Industries too my guest today is pratik agrawal a partner at Kearney analytics he's a software engineer turned data scientist and has led teams of data science Consultants at Boston Consulting Group and now Kearney he's an expert in digital transformation programs and digital strategy so I'm hoping to pick his brains on how data science Works in consultancies hi there pratik thank you for joining us just to begin with can you tell me a little bit about what Kearney does Kearney is a global management consulting firm we are over 5000 folks in more than 40 countries sizable presence in quite a few geographies brilliant so can you tell me a little bit about what you do as a partner at Kearney absolutely Richie I as a partner at Kearney and specifically in the Carney analytics practice lead our automotive and Industrials Transportation sector and what that means is understanding our clients needs in those sectors as it pertains to a wide variety of topics be it supply chain be it manufacturing as well as understanding the customer and the growth side of things as well it requires us to understand where our clients are headed or even push sort of the thinking on where the clients can actually start exploring both new markets as well as game you know efficiencies or gain efficiencies and in what they already do can you maybe give us some details on examples of things that your customers are trying to solve in the automotive transportation and Industrial sector so if you think about let's let's take manufacturing and as an example we're undergoing the fourth Industrial Revolution or in I 4.0 and a lot of different marketing terms I'm sure either you've heard or the general audience has also heard but what that entails is how are we helping optimize processes within manufacturing as AI is coming of age how do we bring it to typically areas where analytics didn't really play a part it wasn't necessarily infused digital was not a part of the discussion how do you use things such as computer vision to understand safety violations or safety issues labor productivity how do you understand bottlenecks in a plant and can you do early detection can you build early Warning Systems how do you organize your inventory within the plant to be able to say I I don't have to shut down my plant because I didn't have the right parts or the right labor available it's fans a bunch of different topics but each of our clients typically tends to go through that Journey starting with one topic and so on so forth there are aspects of it that can be built into a program such as Factory of the future program which basically entails analytics continuous Improvement Automation and this is all on the manufacturing side so it sounds like there are a lot of optimization tasks there and just really preventing or have having some warning about if something disastrous is going to happen you want to know up front before that disaster does occur absolutely I mean it's predictive it's also getting ahead of the curve is assimilation can we simulate what our world will look like tomorrow or in a month's time and if we can simulate we can get ahead of planning you know get ahead in our planning discussions and that is a frontier that we're pushing with with our clients brilliant I'm really curious to know how analytics from the consultancy side differs from when you're doing analytics for your own business so perhaps can you talk me through at a high level what does a consultancy data project involve in the industry what I saw is working in analytics teams machine learning teams or AI teams was essentially that you had a singular Focus you weren't necessarily looking to go span across the Enterprise there were very few opportunities to essentially help optimize what one would do as a firm and I think as a consulting firm we come in with a different perspective is let us look at the business overall and help you optimize that portion of it and instead of going with just the Strategic tools let us bring in analytics as well and that spans across the Enterprise versus let's say having a more of a focused view on where analytics could apply that's really interesting I guess because you've seen lots of different businesses you have this high level vision of what it can look like is that correct yeah absolutely and I feel that it's definitely been the unlock for my own personal Journey having worked both in industry and then switching to Consulting where I saw that I could would understand the value that I was bringing very quickly in these shorter projects shorter programs and versus I think working towards a project that would be typically a year multiple years if I were to be in Industry at least that was what my experience was and maybe just to make this more concrete for our listeners can you give some examples of projects you've worked on maybe something you've enjoyed or had a big success with a ton of projects that were all interesting but chiefly among them one experience that I always recall is my experience working in South America this was for a large forestry client and an amazing opportunity to spend time on the ground working with the teams that were very diverse coming from different countries different backgrounds different educational profiles and what we were doing was helping this client go through the digital Journey that I was mentioning before helping their plants enter the digital age with is essentially things like can we help you reduce production waste can we help you improve your quality so essentially yield and all of that using real-time AI using the cloud-based systems developing our AI applications but then pushing them and having them in Cloud systems to do essentially round-trip calculations in less than a second that was eye-opening both for the client and for us to see that this was definitely possible and it was driving value at the end it's it's we were able to achieve a few percentage points in reduction and raw material required really interesting project love the culture love understanding all the people are and what the work culture is somewhat different than what I was used to as working in the US that's what opened my eyes is like your different geographies have different ways of approaching problems as well as how to come up with a solution and Implement solutions that does sound like a fascinating project I guess you're mixing the very high-tech stuff with the AI and then you got I guess grow growing trees is a by default a sort of very low-tech thing it's trees so mixing the two does sound like a very interesting project I'd love to get into more detail about how you do that perhaps later but before we get to that I'd like to talk about who your team is and who gets involved in these analytics projects so perhaps can you tell us a little bit about your team who's working for you so usually it is start out with as one would think Consulting engagements would be accelerated fast-paced versions of what a typical sort of due diligence might look like or when you're assessing whether a project actually has or a problem has legs and has the right solution can have the right impact so starts off with a pretty small team of I'd say like a manager plus one consultant or so and they'll be looking through both the business value of what we're trying to solve for actually even prior to that what the problem is defining the problem putting sort of the bounding box on that then going ahead with understanding what the value would be starting trying to help prioritize what do we actually work on do we go ahead and work on the first problem that we encounter or do we go ahead and start prioritizing based on what's going to be the overall impact for our clients and from that prioritization Matrix essentially helping the client through on building those Solutions with a somewhat larger team I'd say still Limited in terms of the number of folks that would work on an engagement but somewhat larger team with which would constitute of data scientists data Engineers typically if you're in a full-blown scaling and implementation mode it requires software Engineers UI experts usually you know myself as an engineer and as a data scientist I'm not really familiar with what works for users and in terms of interfaces and so bringing a UI expert is amazing because they can interact through interviews and start building out what is actually required for that particular problem and so like bring in the UI folks the full stack developers it's a massively joined team in terms of like the skill sets that one needs to bring to bear so it's not just the data science and the AI aspect of it but it is all these different pieces that need to move together so this is actually really fascinating that you have this Dynamic team that starts off with a few people and then grows to a much larger team and I guess working in a company that feels slightly unusual to me because I'm part of a team and that's like it's the same people all the time so I'm curious as do people work with each other on Modern one project then do you find that people tend to Cluster and repeatedly work together or is it different teams every time I think that's an excellent question I mean the way the Consulting model is set up it is that if you're early on in your career you get to experience a ton of different projects across multiple Industries and that's actually encouraged you don't start aligning yourself that quickly do a said sort of Industry practice or industry itself early on in your career if you're just getting out of college if you're just getting out of grad school you're encouraged to spend more time across different engagements at a variety of them across functional different functional topics what that does is it helps you build a much broader base of understanding of who uh what you what you can accomplish and what you can do as well as you start thinking about what you would want to specialize in given sort of what all you've seen so it's only with tenure that you start specializing and you start seeing folks coming together on certain topics so as an example our Automotives segment again if I take that there are Partners who are already working in the automotive sector have said clients they decided this as either as an industry for them to focus on or this was something that they had experienced prior experience in as they stepped into Consulting but we continuously do work in automotive as partners but then if you think about let's say principles and managers Etc you start slowly specializing into a specific industry it just sounds like there's a sort of fairly well defined career path straight from being a graduate out of your University to being someone who's a lot more senior so that kind of career progression is very cool I'm curious as to what the mix of people with data skills is and the other maybe less technical consultants one thing that I've found that has changed over these last many years in Consulting is we've become more accustomed to working with data and this is just broadly speaking so it's not just the analytics practices or the AI practitioners but it's the broader sort of Consulting landscape and that's to do with we think about where we can drive decisions from data-driven decision making is is I think is baked in now in in almost all parts of Consulting having said that there are specialized skills that are required by analytics practitioners who are coming in because you're now the tip of the spear in terms of how you're both understanding the problem as well as defining what the solution might look like and with that thinking through one can you handle the larger data sets it's taking things Beyond Excel I think we were at Excel a few years or many years ago I think it's taking it beyond that and if you are somebody who who loves doing that I think there's definitely a huge huge place for you to be in a Consulting team it sounds like the difference between data analysts and the rest of the Consultants is blurred a bit so it isn't just people with data in their job titles that need data skills actually everyone needs some level of data literacy so can you tell me a bit more about what are the common data skills that people need absolutely Richie I think you know the the fact that we're approaching or we have already approached point where their Advanced skills in data literacy that are needed or or just Advanced Data munging skills I'd say and on that front having somebody having skills in Python somebody having skills in handling larger data sets that span more than just a million rows or so are par for course now as an interesting example you know we've had clients who've asked us can you handle a terabyte of data can you handle a petabyte of data or rather the requests that you've made response this much volume of data and trying to find that skill set to be able to handle that kind of volume is always always in demand so I I'd say is somebody who has skills on handling big data has skills and good level of Python Programming that's the kind of data literacy that I typically look for who isn't phased by the volume of data who isn't phased by the challenges that come by with data that's that's what I typically look for in folks that's really interesting that python skills and big data can just the entry level that you need in order to start working in this field because that's quite a high requirement compared to some other fields so related to this are there any domain specific skills that are needed so for example do you need experience in automotive and Manufacturing in order to work with you that's a great question I think domain-wise somebody coming fresh out of college or some fresh out of University as a grad you may not have the exposure to the domains that we typically have right like the different domains but you know maybe you've worked in in a specific domain as an intern but what happens is that as you're progressing if you're coming in as an experienced hire we're looking at you as somebody who has the experience in a set domain so the domain knowledge can actually differ on on two aspects of one is more sort of genetic domain knowledge is like what is the value chain for a let's take as an example automotive automotive has a very defined value chain on who the different players are what oems are what the different year one suppliers are how does the ecosystem work with dealerships Etc somebody coming in with that kind of knowledge how does sales work that would be fantastic right or you could develop that knowledge while you start your journey with Kearney the second and more technical aspect of it is there are different systems that are very specific to each of these domains so having exposure to them is definitely a huge plus point but not to say that you cannot gain that expertise again while becoming a consultant or being a consultant I'll take as an example working in manufacturing it's very common to be working on systems such as historians and historians or specific systems around recording sensor data sensor signals at a very minute scale so like at a very at almost like a second level or even even lower frequency than that so it sounds like this traditional flow then is that you learn the technical skills and data skills first and then you learn the domain skills after that and I do find it interesting that particularly manufacturing you've got this sensor data is incredibly important so it strikes me as being a little bit like The Internet of Things data this becoming very popular the moment oh that absolutely is Right data is the iot or in some references it's also industrial internet of things so iiot and we're quickly getting into now the metaverse and how digital Twins play a huge role in going Beyond just utilizing sensor data I don't know anything at all about digital twin so perhaps you can elaborate on that I can talk about digital twins in more sort of the manufacturing and the supply chain aspects of of the world and what that entails is additional representation of how the world around you Works let's take a manufacturing plant as an instant understanding how each operation in your plant Works understanding what's the cycle time for every possible job or task within the plant and being able to codify it into a model which is essentially a clock driven or a system clock driven let's say procedure you are now able to quickly step through the machinations of what a day might look like in the plant if you know what you need to produce if who you have in terms of producing so what's the supply as well as the folks and you know enough about your assets so things like the Machinery that are present in the plant and you know how they perform you're able to then quickly go ahead and say the next shift or the next day is going to look like this this is where I'll have bottlenecks this is where I'll have an asset that's going to be done and that is incredibly powerful because you can now on the Fly decide what you need to do in order for for you to preemptively deal with the situation or de-risk the situation okay so yeah there is it's like a sort of a simulated version of your existing Factory or perhaps part of it it sounds like it might be quite useful for scenario planning absolutely so to use it for trying to understand different use cases like okay if our orders go up by 20 then we can figure out how to deal with that is that the idea absolutely so if you've got what kind of mix of products you're going to be creating or manufacturing and you know it was going to be coming into the plant you're able to go ahead and say I can't sufficiently do this or I'm going to be under and my throughput's going to be lower and if you know that then you're able to actively do scenario planning on like let me think about Shifting the demand to a different plant that might be able to meet the production demand and it won't suffer on throughput loss now that's that's one use case but there are multiple use cases that are you can think about for the digital Twain it's what are the opportunities to do Improvement within the plant what are the opportunities is about if I were to add another area another sort of production facility or in the packaging facility where should I add that across my network so now you're you're taking it Beyond just the four walls of the plant but you're thinking more on a network basis so a ton of questions that can be answered by by digital Twain things like what should my new facility look like so Green Field applications if you've studied your existing setup how do you optimize that that sounds like a really fascinating field I'd like to Pivot the conversation back to where we're talking about skills we talked a little bit about the data skills you need so things like Python and working with large data sets we talked a bit about domain knowledge and how that tends to be acquired over time I'd like to talk a little bit about the soft skills so are there any soft skills that you find to be particularly important as a consultant that's a that's a great question and a great point that you're hitting on Richie soft skills are Paramount to I think a whatever we do being successful a large part of Consulting is change management is to help our clients embed whatever has been developed and ensure that there is sustainability of those Solutions so as far as that goes soft skills being able to communicate clearly crisply articulate what is required as well as I'd say communicating progress and really being there for the client and having having an active year for them is is really important a lot can be quickly accomplished if you've got the right sort of communication skills okay so it seems like communication is perhaps the number one soft skill that people need is there a difference between communicating within your company between team members and communicating to a client I I'd say that within the team itself communication is everything communication being able to collaborate effectively being able to communicate as quickly as possible and not hesitating from raising your hand if there is something that seems that you might not have familiarity with it's really important to be able to communicate as quickly as possible and one shouldn't shy away from that because what that does is it immediately gets the team involved it brings out a lot of discussion and you have your answers very quickly as opposed to trying to solve things by yourself which can tend to be a little bit longer since you're trying to research topics which you don't have familiarity it's just basically crowdsourcing I'd say like crowdsourcing if that's a pain point for you you've not been able to be able to come up with the right solution as quickly as possible crowdsource understand where where pockets of expertise might be and and leverage them and with clients I think it is it's all also one important aspect is let's co-design with clients so it's again do not develop something in a silo because ultimately the client has the right domain expertise as the expertise right they've been in the farm for a while they know exactly how things work involving the client themselves in this design process making them a part of that journey is also Paramount to making these projects successful all right brilliant and just to wrap up the section on skills for anyone who's interested in working as a data consultant what do they need to do to get hired by you well I think three three key things one be genuine and genuinely interested in the problems that you're trying to solve so be there for the client be interested in what the problem is and helping them the second is be curious and by that I mean curious enough to explore the art of the possible don't no need to shy away from Innovation or pushing the envelope in terms of what can be done frequently we do get inspiration from other sectors and try to bring it to our own clients so being curious is really important in trying to and the third I'd say is is collaboration so being open to collaborating that is that will get you the most learnings as quickly as possible in your journey as a consultant now in terms of data itself I'd say have the technical skills but these three points I think these three points are really Paramount to you becoming successful and it's getting hired as well so moving on to talking about how consultancy differs from other businesses it does seem to be a fairly unique Beast so I'd like to know if there are any challenges to working with data that are specific to consultancies well I think the time pressure Richie there's always a timeline that one is working against and so working with data in a consulting firm is usually dependent on whether we actually have the data so timelines are based off of how quickly can we get the data from the client to start working on at least an initial hypothesis a lot of time gets spent also on on just the cleaning and really distilling and building sort of a single source of Truth which which one can go off of and the team can go off of as he mentioned uh data cleaning there and I imagine that if the client knew how to get the most out of the data themselves then they'd probably send you the clean data set but that's the reason they're hiring you right is because they need help working with their data so I I can imagine that you do have some fairly gnarly data sets to deal with just related to challenges with data how do data privacy and security issues affect you so if your clients have to share data with you do you run into any roadblocks around privacy and security well that that is always the concern and that is always something that we keep an eye out for in terms of how we work and the kind of system so that we're not violating any any policy any any regulations I think gdpr is is one huge regulation that we work with so like let's say working in Europe is working in or anywhere actually as a matter of fact it's Paramount that we don't work with bii data and and with that also understand that the analytics that we build is ethical so there is an ethical component to it it's being responsible and it's to ensure that we were not introducing any any bias in any systems that are getting built so we'll work with legal thing to work with HR teams both of the client ourselves our our own teams to ensure that we're working and in compliance okay that's really interesting that you make an effort towards having these ethical systems and do your data analysts and your machine learning scientists do they have any training or expertise in how to make ethical models we've definitely been going through that over the last few years I that that change has come through in terms of both as we recognize how the data that we already start with might already be affected how do we actually help our clients in dealing both with what's already been recorded versus also ensuring that there isn't a bias that comes through the system and in order for us to do that we we work with our data analysts our data scientists in wetting those models and we're involved in the quality aspect of it on on an ongoing basis and I'm curious as to what your customers and clients want out of the projects so my experience of consultancy is that most of the time this will know oh how are you going to make them more money but occasionally you sometimes you get someone who's Technical and they really want to learn like all the details of like how your methodology worked and things like that so I'm curious as to your experience of like what your customers actually care about as the sort of results of the project encountered this quite a few times where clients want to really deep dive into the modeling aspect of how does this work and let me bring the expert from from my team and and frequently that's an amazing experience because you can quickly nerd out on on models as well as model specifics that helps us in socializing what's been developed so it's not just that we've developed something only for the sake of the client getting an upside but it's also there's a technical component and helping them establish the longevity of such said Solutions with their teams because ultimately the client teams will own such Solutions if they've not been co-developed with the client and and having said that I think the more Curious I think we have folks from clients I think the better it is because it also helps us understand that there is a team that can catch the solution itself or can be a part of that solution building process okay so it seems like that involves some sort of level of skill transfer to the client and I'm curious as to how that happens in general so for example after your project is finished how do you make sure that you don't just sort of leave the client to deal with whatever you've given them on their own is there some sort of Maintenance that goes on after a Project's been completed we work on helping the client either stand up the capability so like personally I've helped clients stand up in entire digital organization help them stand up their data science organizations so it's it's both capability build but then also the training of teams so we'll we'll do let's say two in a box a solution where we co-design the solution with the client and along the way we clean up the clients team members as an example on the digital twin front well like I've personally done that where we've trained client team members while developing the solution so that the client actually had an entire team to continue working both with the solution that was developed but then develop new Solutions new digital twin Solutions and so that was capability building both from standing up the team but also training up the team that's frequently the way that we'll go about but those are definitely larger projects larger programs on on the shorter term on the sort of smaller scale projects we document everything we hand over materials to clients we organize training sessions so that it that can ensure sustainability for the client and of course we're around for our clients to be able to answer questions while they go off on the next chapter of that Journey and just circling back to something you said earlier about you often have quite strong time pressures in terms of delivering projects so it seems like you must have a few tricks up your sleeve in order to be able to run projects efficiently so do you have anything you can share with us so I'd say like on on that front I think we know for a specific industry what to be looking out for what are usually the areas where clients might be suffering then of course our own relationships with the client we were able to get to it much quicker I think on the project management aspect of it it does become that we know what the typical risks are and where there might be slip up and that's already a part of the training curriculum so anyone who's becoming a new manager will will go through the basis of getting that kind of training so as as you're an associate and going on to becoming a manager you're receiving those trainings along the way as well and it's important you as an associate or as an analyst in the Forum you're managing your own work but as a manager you're managing not only your work but you're managing your associate and analysts work and quickly those trainings start becoming very useful because you're dot in how to structure your work and how to structure your project okay so having that sort of structure in the template it sounds like it helps you reuse work from one project to the next that's something I'd like to get into in a bit more detail so how do you go about ensuring that you're not having to reinvent things from scratch every time you start a new project so in order for us to do that we'll capture the stories right we capture our our stories of what we've done in previous projects and are they similar in nature to what we might be working on currently or we might be working on in the future we use that to build our base of how the project went and it's it's building off of those stories to be able to go ahead and say this is our base of understanding on how that went usually these are common pitfalls for this kind of an engagement so like as in if we take predictive maintenance as an example we know what systems one will be working with what are common pitfalls of predictive maintenance and the forestry industry or in the airline sector or in the automotive sector and how do you help a client through that so building a base of understanding going off of what we've already seen before is usually helpful speaking with those teams so opening up like having a much more collaborative channel on what works and what doesn't work and if you're running multiple projects at once how do you juggle the context switching from one project to another that's always good there's always a challenge in that but there's also the thrill in being able to juggle of your projects and why I say that there's a thrill is is the fact that you can see the movement on all these different projects at the same time which which is interesting I think the context switching can be a problem especially if you're new and if you've gained the experience I think you know you've got some errors under your belt you start becoming better at it but there's still always going to be a slight challenge around context switching and we we try to limit like on how many things that one can pick up as an individual because there's only so much time in the day there's only so much time in the week that you can devote to topics so how many topics one can pick up and make a meaningful progress you decide based on like where you're not as an associate coming into the formula or as an analyst you're focused 100 on one project as a manager you're focused on 100 on a project and thereafter it starts becoming a question of like how much time one needs to put where and back near the start of this you mentioned your projects in South America about working with forestry and having these sort of AI Solutions which sounded quite high tech so maybe can you talk a bit about what the technology in that involved so this one was more around helping the client understand and build a uniform product and the uniform product being pulp production so this is called production from their own sustainable Farms how do you understand the biochemistry of the wood that comes in to be then able to say what's the product quality going to be you could predict that you can then preemptively adjust levers to essentially ensure that you're getting consistent quality or you can ensure what mix of work goes into the creation of Pulp to be able to ensure the the uniformity of quality why that was interesting was because we had to study what it meant in terms of the internal cell structure of trees and what sort of is the moisture content to be able to then say how much of it is going to translate into a bad quality product versus a good quality product so it's fascinating because one wouldn't ever think or we we didn't really think that that's the key aspect but again working with the client and the client's data science team we were able to gain that understanding because the data science team there had already been working on something like this to try and figure out what's the internal cell structure and moisture content and we were able to leverage that to take it further on on the product quality side so that's fascinating I realized that question was a bit of a jump from talking about project management but what that sounds like is you have a very specific solution to that problem I'm curious as to what happens when you have to build technology for one specific project because it's it's quite a niche specific thing but then you have all these other projects I'm wondering how do you juggle with the different Technologies in different projects I think that's part of why one is I think as a data scientist as a data engineer as an engineer I think in working in Consulting that's the part of being curious is we need to juggle with frankly many different Technologies on an ongoing basis I think the the one key thing always is okay the high client what are the different Technologies do you use today is there room for us to push either the envelope in terms of bringing new technologies to the client but we also need to take stock of the situation on where the client's located do they have access to these skill sets in their talent pool the talent pool that they're likely going to hire from and so we have to take stock of that situation before we go ahead and arrive at technology specifically but outside of that I think it's codifying what we can at a higher and an abstracted level from each of these solutions that get built so as an example the forestry example I was sharing we we were able to take it to an abstraction level where we could codify saying this is what happens when you are trying to solve a product quality improvement problem in so and so specific industry and that's like continuous production now I could take that and apply the same Concepts in a different industry which is still continuous production and now just use that as as a starting point of the understanding but explore the technology sets separately which are going to be more relevant for this new client and you've mentioned curiosity a few times throughout this it just seemed like continuously learning new skills is an important part of being a data consultant so can you perhaps tell me a bit more about how you go about or how your team goes about learning these new skills I think the what what the research really is like if you encounter it researching what might actually help get us off the ground quickly enough so as an example let's say are you working with a a data set that was sent over to a by a client to us now this who came back with the consultant on a secured drive and we weren't able to travel this was a different geography this was a sensor-based data so it wasn't pii but looking through that data like how do you handle that that was we didn't have any context on what those files look like we had absolutely no connection with the client given the remoteness of where the client was located we didn't have access to them and so how do we look through and sift through the data and even understand what those files contained was an interesting challenge but one of the Consultants on the team was able to quickly open up the file in in a Unix environment peek through on a couple of lines and then be able to decipher that well this is this looks like a certain data type and then research it and be able to download essentially the software to be able to go ahead and start reading those files and it was that or or being able to I think in another example it was okay this file actually has delimited values which the delimiter is unknown to us and uh quickly being able to write a a python script that would do on a line by line basis output the data cleaned up with a delimiter that the whole team could work with okay so this sounds like a lot of detective work and problem solving in cases where you're not really sure you don't have much context and you don't really know exactly what you're doing but just having that enthusiasm for going in and having a look and seeing what you can get out of it that seems like a really important skill the more tools you have on your fingertips I think the better that Journey looks like my own personal journey be having the familiarity with Unix based systems or the Unix toolkit I think was fantastic just being able to open up quickly something being able to code up using regexes and quickly clean up files I think that was an amazing skill to have learned at one of my previous employers how regex works right and and that was that seemed to me like a almost like a Swiss army knife for a data analyst absolutely regular Expressions regex is they're purposely one of the most underrated skills is like it comes in handy in so many different projects absolutely well worth learning I agree do you have any final advice for people who are interested in data consultancy I'd say the the future is really bright and there is high demand for anyone who's looking to join the data Journey itself especially in on the Consulting front I think we're just getting started I think the whole world is getting started on the Consulting side so just head on over we're always looking for amazing people all right wonderful that's a great note to end on thank you very much for your time it's been really fascinating stuff I'm getting excited about consultancy now but thank you thank you Richie thank you for having me and thank you everyone for tuning in you've been listening to data framed a podcast by datacamp keep connected with us by subscribing to the show in your favorite podcast player please give us a rating leave a comment and share episodes you love that helps us keep delivering insights into all things data thanks for listening until next timeyou're listening to data framed a podcast by datacamp in this show you'll hear all the latest trends and insights in data science whether you're just getting started in your data career or you're a data leader looking to scale data-driven decisions in your organization join us for in-depth discussions with data and analytics leaders at the Forefront of the data Revolution Let's Dive Right In foreign a frequent theme of this podcast is to try and encourage you all to gain some data skills so you can solve data problems yourself without having to rely on others today we're talking about the opposite thing using data science at consultancies where the whole point is to solve other people's data problems in a previous stage of my career I briefly worked as a data science consultant and I found that there are a lot of interesting challenges that apply to this field like having to build trust with new clients and juggling multiple projects under tight deadlines since successful consultancies necessarily have to become greater data communication and data project management there are lots of lessons to be learned for other Industries too my guest today is pratik agrawal a partner at Kearney analytics he's a software engineer turned data scientist and has led teams of data science Consultants at Boston Consulting Group and now Kearney he's an expert in digital transformation programs and digital strategy so I'm hoping to pick his brains on how data science Works in consultancies hi there pratik thank you for joining us just to begin with can you tell me a little bit about what Kearney does Kearney is a global management consulting firm we are over 5000 folks in more than 40 countries sizable presence in quite a few geographies brilliant so can you tell me a little bit about what you do as a partner at Kearney absolutely Richie I as a partner at Kearney and specifically in the Carney analytics practice lead our automotive and Industrials Transportation sector and what that means is understanding our clients needs in those sectors as it pertains to a wide variety of topics be it supply chain be it manufacturing as well as understanding the customer and the growth side of things as well it requires us to understand where our clients are headed or even push sort of the thinking on where the clients can actually start exploring both new markets as well as game you know efficiencies or gain efficiencies and in what they already do can you maybe give us some details on examples of things that your customers are trying to solve in the automotive transportation and Industrial sector so if you think about let's let's take manufacturing and as an example we're undergoing the fourth Industrial Revolution or in I 4.0 and a lot of different marketing terms I'm sure either you've heard or the general audience has also heard but what that entails is how are we helping optimize processes within manufacturing as AI is coming of age how do we bring it to typically areas where analytics didn't really play a part it wasn't necessarily infused digital was not a part of the discussion how do you use things such as computer vision to understand safety violations or safety issues labor productivity how do you understand bottlenecks in a plant and can you do early detection can you build early Warning Systems how do you organize your inventory within the plant to be able to say I I don't have to shut down my plant because I didn't have the right parts or the right labor available it's fans a bunch of different topics but each of our clients typically tends to go through that Journey starting with one topic and so on so forth there are aspects of it that can be built into a program such as Factory of the future program which basically entails analytics continuous Improvement Automation and this is all on the manufacturing side so it sounds like there are a lot of optimization tasks there and just really preventing or have having some warning about if something disastrous is going to happen you want to know up front before that disaster does occur absolutely I mean it's predictive it's also getting ahead of the curve is assimilation can we simulate what our world will look like tomorrow or in a month's time and if we can simulate we can get ahead of planning you know get ahead in our planning discussions and that is a frontier that we're pushing with with our clients brilliant I'm really curious to know how analytics from the consultancy side differs from when you're doing analytics for your own business so perhaps can you talk me through at a high level what does a consultancy data project involve in the industry what I saw is working in analytics teams machine learning teams or AI teams was essentially that you had a singular Focus you weren't necessarily looking to go span across the Enterprise there were very few opportunities to essentially help optimize what one would do as a firm and I think as a consulting firm we come in with a different perspective is let us look at the business overall and help you optimize that portion of it and instead of going with just the Strategic tools let us bring in analytics as well and that spans across the Enterprise versus let's say having a more of a focused view on where analytics could apply that's really interesting I guess because you've seen lots of different businesses you have this high level vision of what it can look like is that correct yeah absolutely and I feel that it's definitely been the unlock for my own personal Journey having worked both in industry and then switching to Consulting where I saw that I could would understand the value that I was bringing very quickly in these shorter projects shorter programs and versus I think working towards a project that would be typically a year multiple years if I were to be in Industry at least that was what my experience was and maybe just to make this more concrete for our listeners can you give some examples of projects you've worked on maybe something you've enjoyed or had a big success with a ton of projects that were all interesting but chiefly among them one experience that I always recall is my experience working in South America this was for a large forestry client and an amazing opportunity to spend time on the ground working with the teams that were very diverse coming from different countries different backgrounds different educational profiles and what we were doing was helping this client go through the digital Journey that I was mentioning before helping their plants enter the digital age with is essentially things like can we help you reduce production waste can we help you improve your quality so essentially yield and all of that using real-time AI using the cloud-based systems developing our AI applications but then pushing them and having them in Cloud systems to do essentially round-trip calculations in less than a second that was eye-opening both for the client and for us to see that this was definitely possible and it was driving value at the end it's it's we were able to achieve a few percentage points in reduction and raw material required really interesting project love the culture love understanding all the people are and what the work culture is somewhat different than what I was used to as working in the US that's what opened my eyes is like your different geographies have different ways of approaching problems as well as how to come up with a solution and Implement solutions that does sound like a fascinating project I guess you're mixing the very high-tech stuff with the AI and then you got I guess grow growing trees is a by default a sort of very low-tech thing it's trees so mixing the two does sound like a very interesting project I'd love to get into more detail about how you do that perhaps later but before we get to that I'd like to talk about who your team is and who gets involved in these analytics projects so perhaps can you tell us a little bit about your team who's working for you so usually it is start out with as one would think Consulting engagements would be accelerated fast-paced versions of what a typical sort of due diligence might look like or when you're assessing whether a project actually has or a problem has legs and has the right solution can have the right impact so starts off with a pretty small team of I'd say like a manager plus one consultant or so and they'll be looking through both the business value of what we're trying to solve for actually even prior to that what the problem is defining the problem putting sort of the bounding box on that then going ahead with understanding what the value would be starting trying to help prioritize what do we actually work on do we go ahead and work on the first problem that we encounter or do we go ahead and start prioritizing based on what's going to be the overall impact for our clients and from that prioritization Matrix essentially helping the client through on building those Solutions with a somewhat larger team I'd say still Limited in terms of the number of folks that would work on an engagement but somewhat larger team with which would constitute of data scientists data Engineers typically if you're in a full-blown scaling and implementation mode it requires software Engineers UI experts usually you know myself as an engineer and as a data scientist I'm not really familiar with what works for users and in terms of interfaces and so bringing a UI expert is amazing because they can interact through interviews and start building out what is actually required for that particular problem and so like bring in the UI folks the full stack developers it's a massively joined team in terms of like the skill sets that one needs to bring to bear so it's not just the data science and the AI aspect of it but it is all these different pieces that need to move together so this is actually really fascinating that you have this Dynamic team that starts off with a few people and then grows to a much larger team and I guess working in a company that feels slightly unusual to me because I'm part of a team and that's like it's the same people all the time so I'm curious as do people work with each other on Modern one project then do you find that people tend to Cluster and repeatedly work together or is it different teams every time I think that's an excellent question I mean the way the Consulting model is set up it is that if you're early on in your career you get to experience a ton of different projects across multiple Industries and that's actually encouraged you don't start aligning yourself that quickly do a said sort of Industry practice or industry itself early on in your career if you're just getting out of college if you're just getting out of grad school you're encouraged to spend more time across different engagements at a variety of them across functional different functional topics what that does is it helps you build a much broader base of understanding of who uh what you what you can accomplish and what you can do as well as you start thinking about what you would want to specialize in given sort of what all you've seen so it's only with tenure that you start specializing and you start seeing folks coming together on certain topics so as an example our Automotives segment again if I take that there are Partners who are already working in the automotive sector have said clients they decided this as either as an industry for them to focus on or this was something that they had experienced prior experience in as they stepped into Consulting but we continuously do work in automotive as partners but then if you think about let's say principles and managers Etc you start slowly specializing into a specific industry it just sounds like there's a sort of fairly well defined career path straight from being a graduate out of your University to being someone who's a lot more senior so that kind of career progression is very cool I'm curious as to what the mix of people with data skills is and the other maybe less technical consultants one thing that I've found that has changed over these last many years in Consulting is we've become more accustomed to working with data and this is just broadly speaking so it's not just the analytics practices or the AI practitioners but it's the broader sort of Consulting landscape and that's to do with we think about where we can drive decisions from data-driven decision making is is I think is baked in now in in almost all parts of Consulting having said that there are specialized skills that are required by analytics practitioners who are coming in because you're now the tip of the spear in terms of how you're both understanding the problem as well as defining what the solution might look like and with that thinking through one can you handle the larger data sets it's taking things Beyond Excel I think we were at Excel a few years or many years ago I think it's taking it beyond that and if you are somebody who who loves doing that I think there's definitely a huge huge place for you to be in a Consulting team it sounds like the difference between data analysts and the rest of the Consultants is blurred a bit so it isn't just people with data in their job titles that need data skills actually everyone needs some level of data literacy so can you tell me a bit more about what are the common data skills that people need absolutely Richie I think you know the the fact that we're approaching or we have already approached point where their Advanced skills in data literacy that are needed or or just Advanced Data munging skills I'd say and on that front having somebody having skills in Python somebody having skills in handling larger data sets that span more than just a million rows or so are par for course now as an interesting example you know we've had clients who've asked us can you handle a terabyte of data can you handle a petabyte of data or rather the requests that you've made response this much volume of data and trying to find that skill set to be able to handle that kind of volume is always always in demand so I I'd say is somebody who has skills on handling big data has skills and good level of Python Programming that's the kind of data literacy that I typically look for who isn't phased by the volume of data who isn't phased by the challenges that come by with data that's that's what I typically look for in folks that's really interesting that python skills and big data can just the entry level that you need in order to start working in this field because that's quite a high requirement compared to some other fields so related to this are there any domain specific skills that are needed so for example do you need experience in automotive and Manufacturing in order to work with you that's a great question I think domain-wise somebody coming fresh out of college or some fresh out of University as a grad you may not have the exposure to the domains that we typically have right like the different domains but you know maybe you've worked in in a specific domain as an intern but what happens is that as you're progressing if you're coming in as an experienced hire we're looking at you as somebody who has the experience in a set domain so the domain knowledge can actually differ on on two aspects of one is more sort of genetic domain knowledge is like what is the value chain for a let's take as an example automotive automotive has a very defined value chain on who the different players are what oems are what the different year one suppliers are how does the ecosystem work with dealerships Etc somebody coming in with that kind of knowledge how does sales work that would be fantastic right or you could develop that knowledge while you start your journey with Kearney the second and more technical aspect of it is there are different systems that are very specific to each of these domains so having exposure to them is definitely a huge plus point but not to say that you cannot gain that expertise again while becoming a consultant or being a consultant I'll take as an example working in manufacturing it's very common to be working on systems such as historians and historians or specific systems around recording sensor data sensor signals at a very minute scale so like at a very at almost like a second level or even even lower frequency than that so it sounds like this traditional flow then is that you learn the technical skills and data skills first and then you learn the domain skills after that and I do find it interesting that particularly manufacturing you've got this sensor data is incredibly important so it strikes me as being a little bit like The Internet of Things data this becoming very popular the moment oh that absolutely is Right data is the iot or in some references it's also industrial internet of things so iiot and we're quickly getting into now the metaverse and how digital Twins play a huge role in going Beyond just utilizing sensor data I don't know anything at all about digital twin so perhaps you can elaborate on that I can talk about digital twins in more sort of the manufacturing and the supply chain aspects of of the world and what that entails is additional representation of how the world around you Works let's take a manufacturing plant as an instant understanding how each operation in your plant Works understanding what's the cycle time for every possible job or task within the plant and being able to codify it into a model which is essentially a clock driven or a system clock driven let's say procedure you are now able to quickly step through the machinations of what a day might look like in the plant if you know what you need to produce if who you have in terms of producing so what's the supply as well as the folks and you know enough about your assets so things like the Machinery that are present in the plant and you know how they perform you're able to then quickly go ahead and say the next shift or the next day is going to look like this this is where I'll have bottlenecks this is where I'll have an asset that's going to be done and that is incredibly powerful because you can now on the Fly decide what you need to do in order for for you to preemptively deal with the situation or de-risk the situation okay so yeah there is it's like a sort of a simulated version of your existing Factory or perhaps part of it it sounds like it might be quite useful for scenario planning absolutely so to use it for trying to understand different use cases like okay if our orders go up by 20 then we can figure out how to deal with that is that the idea absolutely so if you've got what kind of mix of products you're going to be creating or manufacturing and you know it was going to be coming into the plant you're able to go ahead and say I can't sufficiently do this or I'm going to be under and my throughput's going to be lower and if you know that then you're able to actively do scenario planning on like let me think about Shifting the demand to a different plant that might be able to meet the production demand and it won't suffer on throughput loss now that's that's one use case but there are multiple use cases that are you can think about for the digital Twain it's what are the opportunities to do Improvement within the plant what are the opportunities is about if I were to add another area another sort of production facility or in the packaging facility where should I add that across my network so now you're you're taking it Beyond just the four walls of the plant but you're thinking more on a network basis so a ton of questions that can be answered by by digital Twain things like what should my new facility look like so Green Field applications if you've studied your existing setup how do you optimize that that sounds like a really fascinating field I'd like to Pivot the conversation back to where we're talking about skills we talked a little bit about the data skills you need so things like Python and working with large data sets we talked a bit about domain knowledge and how that tends to be acquired over time I'd like to talk a little bit about the soft skills so are there any soft skills that you find to be particularly important as a consultant that's a that's a great question and a great point that you're hitting on Richie soft skills are Paramount to I think a whatever we do being successful a large part of Consulting is change management is to help our clients embed whatever has been developed and ensure that there is sustainability of those Solutions so as far as that goes soft skills being able to communicate clearly crisply articulate what is required as well as I'd say communicating progress and really being there for the client and having having an active year for them is is really important a lot can be quickly accomplished if you've got the right sort of communication skills okay so it seems like communication is perhaps the number one soft skill that people need is there a difference between communicating within your company between team members and communicating to a client I I'd say that within the team itself communication is everything communication being able to collaborate effectively being able to communicate as quickly as possible and not hesitating from raising your hand if there is something that seems that you might not have familiarity with it's really important to be able to communicate as quickly as possible and one shouldn't shy away from that because what that does is it immediately gets the team involved it brings out a lot of discussion and you have your answers very quickly as opposed to trying to solve things by yourself which can tend to be a little bit longer since you're trying to research topics which you don't have familiarity it's just basically crowdsourcing I'd say like crowdsourcing if that's a pain point for you you've not been able to be able to come up with the right solution as quickly as possible crowdsource understand where where pockets of expertise might be and and leverage them and with clients I think it is it's all also one important aspect is let's co-design with clients so it's again do not develop something in a silo because ultimately the client has the right domain expertise as the expertise right they've been in the farm for a while they know exactly how things work involving the client themselves in this design process making them a part of that journey is also Paramount to making these projects successful all right brilliant and just to wrap up the section on skills for anyone who's interested in working as a data consultant what do they need to do to get hired by you well I think three three key things one be genuine and genuinely interested in the problems that you're trying to solve so be there for the client be interested in what the problem is and helping them the second is be curious and by that I mean curious enough to explore the art of the possible don't no need to shy away from Innovation or pushing the envelope in terms of what can be done frequently we do get inspiration from other sectors and try to bring it to our own clients so being curious is really important in trying to and the third I'd say is is collaboration so being open to collaborating that is that will get you the most learnings as quickly as possible in your journey as a consultant now in terms of data itself I'd say have the technical skills but these three points I think these three points are really Paramount to you becoming successful and it's getting hired as well so moving on to talking about how consultancy differs from other businesses it does seem to be a fairly unique Beast so I'd like to know if there are any challenges to working with data that are specific to consultancies well I think the time pressure Richie there's always a timeline that one is working against and so working with data in a consulting firm is usually dependent on whether we actually have the data so timelines are based off of how quickly can we get the data from the client to start working on at least an initial hypothesis a lot of time gets spent also on on just the cleaning and really distilling and building sort of a single source of Truth which which one can go off of and the team can go off of as he mentioned uh data cleaning there and I imagine that if the client knew how to get the most out of the data themselves then they'd probably send you the clean data set but that's the reason they're hiring you right is because they need help working with their data so I I can imagine that you do have some fairly gnarly data sets to deal with just related to challenges with data how do data privacy and security issues affect you so if your clients have to share data with you do you run into any roadblocks around privacy and security well that that is always the concern and that is always something that we keep an eye out for in terms of how we work and the kind of system so that we're not violating any any policy any any regulations I think gdpr is is one huge regulation that we work with so like let's say working in Europe is working in or anywhere actually as a matter of fact it's Paramount that we don't work with bii data and and with that also understand that the analytics that we build is ethical so there is an ethical component to it it's being responsible and it's to ensure that we were not introducing any any bias in any systems that are getting built so we'll work with legal thing to work with HR teams both of the client ourselves our our own teams to ensure that we're working and in compliance okay that's really interesting that you make an effort towards having these ethical systems and do your data analysts and your machine learning scientists do they have any training or expertise in how to make ethical models we've definitely been going through that over the last few years I that that change has come through in terms of both as we recognize how the data that we already start with might already be affected how do we actually help our clients in dealing both with what's already been recorded versus also ensuring that there isn't a bias that comes through the system and in order for us to do that we we work with our data analysts our data scientists in wetting those models and we're involved in the quality aspect of it on on an ongoing basis and I'm curious as to what your customers and clients want out of the projects so my experience of consultancy is that most of the time this will know oh how are you going to make them more money but occasionally you sometimes you get someone who's Technical and they really want to learn like all the details of like how your methodology worked and things like that so I'm curious as to your experience of like what your customers actually care about as the sort of results of the project encountered this quite a few times where clients want to really deep dive into the modeling aspect of how does this work and let me bring the expert from from my team and and frequently that's an amazing experience because you can quickly nerd out on on models as well as model specifics that helps us in socializing what's been developed so it's not just that we've developed something only for the sake of the client getting an upside but it's also there's a technical component and helping them establish the longevity of such said Solutions with their teams because ultimately the client teams will own such Solutions if they've not been co-developed with the client and and having said that I think the more Curious I think we have folks from clients I think the better it is because it also helps us understand that there is a team that can catch the solution itself or can be a part of that solution building process okay so it seems like that involves some sort of level of skill transfer to the client and I'm curious as to how that happens in general so for example after your project is finished how do you make sure that you don't just sort of leave the client to deal with whatever you've given them on their own is there some sort of Maintenance that goes on after a Project's been completed we work on helping the client either stand up the capability so like personally I've helped clients stand up in entire digital organization help them stand up their data science organizations so it's it's both capability build but then also the training of teams so we'll we'll do let's say two in a box a solution where we co-design the solution with the client and along the way we clean up the clients team members as an example on the digital twin front well like I've personally done that where we've trained client team members while developing the solution so that the client actually had an entire team to continue working both with the solution that was developed but then develop new Solutions new digital twin Solutions and so that was capability building both from standing up the team but also training up the team that's frequently the way that we'll go about but those are definitely larger projects larger programs on on the shorter term on the sort of smaller scale projects we document everything we hand over materials to clients we organize training sessions so that it that can ensure sustainability for the client and of course we're around for our clients to be able to answer questions while they go off on the next chapter of that Journey and just circling back to something you said earlier about you often have quite strong time pressures in terms of delivering projects so it seems like you must have a few tricks up your sleeve in order to be able to run projects efficiently so do you have anything you can share with us so I'd say like on on that front I think we know for a specific industry what to be looking out for what are usually the areas where clients might be suffering then of course our own relationships with the client we were able to get to it much quicker I think on the project management aspect of it it does become that we know what the typical risks are and where there might be slip up and that's already a part of the training curriculum so anyone who's becoming a new manager will will go through the basis of getting that kind of training so as as you're an associate and going on to becoming a manager you're receiving those trainings along the way as well and it's important you as an associate or as an analyst in the Forum you're managing your own work but as a manager you're managing not only your work but you're managing your associate and analysts work and quickly those trainings start becoming very useful because you're dot in how to structure your work and how to structure your project okay so having that sort of structure in the template it sounds like it helps you reuse work from one project to the next that's something I'd like to get into in a bit more detail so how do you go about ensuring that you're not having to reinvent things from scratch every time you start a new project so in order for us to do that we'll capture the stories right we capture our our stories of what we've done in previous projects and are they similar in nature to what we might be working on currently or we might be working on in the future we use that to build our base of how the project went and it's it's building off of those stories to be able to go ahead and say this is our base of understanding on how that went usually these are common pitfalls for this kind of an engagement so like as in if we take predictive maintenance as an example we know what systems one will be working with what are common pitfalls of predictive maintenance and the forestry industry or in the airline sector or in the automotive sector and how do you help a client through that so building a base of understanding going off of what we've already seen before is usually helpful speaking with those teams so opening up like having a much more collaborative channel on what works and what doesn't work and if you're running multiple projects at once how do you juggle the context switching from one project to another that's always good there's always a challenge in that but there's also the thrill in being able to juggle of your projects and why I say that there's a thrill is is the fact that you can see the movement on all these different projects at the same time which which is interesting I think the context switching can be a problem especially if you're new and if you've gained the experience I think you know you've got some errors under your belt you start becoming better at it but there's still always going to be a slight challenge around context switching and we we try to limit like on how many things that one can pick up as an individual because there's only so much time in the day there's only so much time in the week that you can devote to topics so how many topics one can pick up and make a meaningful progress you decide based on like where you're not as an associate coming into the formula or as an analyst you're focused 100 on one project as a manager you're focused on 100 on a project and thereafter it starts becoming a question of like how much time one needs to put where and back near the start of this you mentioned your projects in South America about working with forestry and having these sort of AI Solutions which sounded quite high tech so maybe can you talk a bit about what the technology in that involved so this one was more around helping the client understand and build a uniform product and the uniform product being pulp production so this is called production from their own sustainable Farms how do you understand the biochemistry of the wood that comes in to be then able to say what's the product quality going to be you could predict that you can then preemptively adjust levers to essentially ensure that you're getting consistent quality or you can ensure what mix of work goes into the creation of Pulp to be able to ensure the the uniformity of quality why that was interesting was because we had to study what it meant in terms of the internal cell structure of trees and what sort of is the moisture content to be able to then say how much of it is going to translate into a bad quality product versus a good quality product so it's fascinating because one wouldn't ever think or we we didn't really think that that's the key aspect but again working with the client and the client's data science team we were able to gain that understanding because the data science team there had already been working on something like this to try and figure out what's the internal cell structure and moisture content and we were able to leverage that to take it further on on the product quality side so that's fascinating I realized that question was a bit of a jump from talking about project management but what that sounds like is you have a very specific solution to that problem I'm curious as to what happens when you have to build technology for one specific project because it's it's quite a niche specific thing but then you have all these other projects I'm wondering how do you juggle with the different Technologies in different projects I think that's part of why one is I think as a data scientist as a data engineer as an engineer I think in working in Consulting that's the part of being curious is we need to juggle with frankly many different Technologies on an ongoing basis I think the the one key thing always is okay the high client what are the different Technologies do you use today is there room for us to push either the envelope in terms of bringing new technologies to the client but we also need to take stock of the situation on where the client's located do they have access to these skill sets in their talent pool the talent pool that they're likely going to hire from and so we have to take stock of that situation before we go ahead and arrive at technology specifically but outside of that I think it's codifying what we can at a higher and an abstracted level from each of these solutions that get built so as an example the forestry example I was sharing we we were able to take it to an abstraction level where we could codify saying this is what happens when you are trying to solve a product quality improvement problem in so and so specific industry and that's like continuous production now I could take that and apply the same Concepts in a different industry which is still continuous production and now just use that as as a starting point of the understanding but explore the technology sets separately which are going to be more relevant for this new client and you've mentioned curiosity a few times throughout this it just seemed like continuously learning new skills is an important part of being a data consultant so can you perhaps tell me a bit more about how you go about or how your team goes about learning these new skills I think the what what the research really is like if you encounter it researching what might actually help get us off the ground quickly enough so as an example let's say are you working with a a data set that was sent over to a by a client to us now this who came back with the consultant on a secured drive and we weren't able to travel this was a different geography this was a sensor-based data so it wasn't pii but looking through that data like how do you handle that that was we didn't have any context on what those files look like we had absolutely no connection with the client given the remoteness of where the client was located we didn't have access to them and so how do we look through and sift through the data and even understand what those files contained was an interesting challenge but one of the Consultants on the team was able to quickly open up the file in in a Unix environment peek through on a couple of lines and then be able to decipher that well this is this looks like a certain data type and then research it and be able to download essentially the software to be able to go ahead and start reading those files and it was that or or being able to I think in another example it was okay this file actually has delimited values which the delimiter is unknown to us and uh quickly being able to write a a python script that would do on a line by line basis output the data cleaned up with a delimiter that the whole team could work with okay so this sounds like a lot of detective work and problem solving in cases where you're not really sure you don't have much context and you don't really know exactly what you're doing but just having that enthusiasm for going in and having a look and seeing what you can get out of it that seems like a really important skill the more tools you have on your fingertips I think the better that Journey looks like my own personal journey be having the familiarity with Unix based systems or the Unix toolkit I think was fantastic just being able to open up quickly something being able to code up using regexes and quickly clean up files I think that was an amazing skill to have learned at one of my previous employers how regex works right and and that was that seemed to me like a almost like a Swiss army knife for a data analyst absolutely regular Expressions regex is they're purposely one of the most underrated skills is like it comes in handy in so many different projects absolutely well worth learning I agree do you have any final advice for people who are interested in data consultancy I'd say the the future is really bright and there is high demand for anyone who's looking to join the data Journey itself especially in on the Consulting front I think we're just getting started I think the whole world is getting started on the Consulting side so just head on over we're always looking for amazing people all right wonderful that's a great note to end on thank you very much for your time it's been really fascinating stuff I'm getting excited about consultancy now but thank you thank you Richie thank you for having me and thank you everyone for tuning in you've been listening to data framed a podcast by datacamp keep connected with us by subscribing to the show in your favorite podcast player please give us a rating leave a comment and share episodes you love that helps us keep delivering insights into all things data thanks for listening until next time\n"