hey there this is a video that's really important for anyone who works in data or wants to we've got Madison shot talking about how to effectively work with your stakeholders she's going to share her best tips and tricks as well as some of the pitfalls that you should avoid this is going to make you a better partner for your business and make you more effective at actually driving change for the company which in the end that's what the data analyst and data scientist job is all about so it's a great video check it out and let us know what you think in the comments one of the things you do very often in as part of your role and it's so important is working with stakeholders uh yet that's something that even though it's important in like every data role it's not really something that a ton of people are talking about in the data Community can you talk a little bit about why that's such an important component of your job um and data jobs in general and and also maybe if you have any thoughts on like why people aren't talking about this as much as a few people have noted in the comments this is kind of an underrated topic yeah so stakeholders are the ones that are actually using the data to make the business decision so it's super important that you get them what they need in order to make these informed decisions or else like the work that we do as analytics Engineers as data analysts isn't really important at all like if business can't act on it then you know we wouldn't even really have a job um and it's like so important you can see the Direct Value in that obviously um and I think people don't talk about it as much because it's something that is really hard to teach um like technical skills you know there's kind of like a clear learning path but when it comes to communication skills it's something that is really important to learn on the job to get feedback from your stakeholders from your manager and I like to use other people as like a good example of how to properly communicate and it's something that is uh not a concrete skill that is easy to teach you know in a video in a course in a newsletter um it's something that you really have to learn on the job yeah it's it's such a good point um yeah so maybe it's just that it's like PE people who want to be teaching and and helping other analysts it's too hard for them to teach so that's maybe that's why it doesn't get as as much love and yeah I I love your point that you're doing all this technical work you're finding these insights but if the person who is operating the business doesn't listen to you and you're not working with them it's all for nothing there's literally it's pointless it would just be cost for the business so um yeah I mean I think that's that's the clearest pitch for it could we kind of make this concrete a little bit I'd love to hear about some examples of like stakeholders that you work with we don't need their names but more just like the the Prototype stakeholder that um you might work with in analytics engineering role yeah so as an analytics engineer your main stakeholder is technically the data analyst so like for example right now I'm working on modeling Revenue data um so I'm meeting with our growth data analyst every week to talk about like different concepts and things that need to be added to the model um and then he's the one that essentially is working with like marketing growth teams every single week um so he's like understanding what they're what they need what they're asking for and can communicate that back to me to translate that into our data models but then I also work really closely with like email marketers um our sales team too because they are the ones that are using the metrics that I'm sending via reverse ETL directly in their tools so whenever they need a new metric that comes from the data warehouse or from a bunch of different data sources they'll come to me and ask me if I can code that and then send that to like HubSpot or intercom so I work very closely with them and then like depending on what other data models that I'm building um different people have different business contexts so depending on the logic you're trying to encode you want to reach out to the person that's essentially the expert in that yeah that's awesome um so let's take kind of that like the data analyst that your maybe one of your primary stakeholders how does it usually work are they usually coming to you with a need are you sort of coming up with hey hey I've got these things that I think I want to add in here and and how does the back and forth usually work yeah I think it's a little bit of both um typically like whatever the business stakeholders are asking for in those like weekly meetings that they have that kind of then leads the data team in one direction for okay what data models should we build because we need something that can support these questions that keep coming up again and again um and that's really the main thing um and then like in the individual conversations once we kind of decide on the data model that's going to be built um it's usually a lot of like okay I've looked at this piece of the model um for example and what do you see stakeholders asking about specifically with this um so like with Revenue I guess if you're like looking at churn for example okay well what different aspects of churn are they asking questions about that I need to consider when building my data model yeah so it's it's so interesting it's like you have this analyst is the stakeholder and they're they're also sort of like translating from another business stakeholder and you you really need to get your head around what that person really wants so you can figure out the nuances and how it needs to be delivered and all that yeah that's that's really really interesting um do you find yourself dealing with different stakeholders in different ways like if somebody's more technical or less technical is there like a different level that you have to talk with them about like how do you sort of think about that I mean maybe like a an executive versus like another like a data engineer or or an analyst like how do you think through that I think the biggest thing is obviously like translating problems that you're facing in to in a way that business stakeholders can actually understand you kind of just have to assume that they're not going to understand the more technical things and they don't really care about like what's happening in the weeds with the code or like this weird little Edge case that you're finding in the data it's really about translating value to them like why do they care about the problems that you're facing whether that means it's because you're going to take three weeks longer building this or it's because they have to change the way that they're doing something um I think the most important thing is to like always consider what they care about and how to like reframe it in a way that they'll find the most value um and understand the trade-offs that you're making yeah I I mean I think that's that's perfect advice and it that's something I struggled personally with when I was early in my career I would be like way too technical with business people and I'm showing them so much information and I'm sort of you know young guy I'm trying to flex my technical skills a little bit I'm like excited that I can do this thing and I'm like hey here's what we did and they're just like no interest whatsoever in the methodology they just want to know you know where do I spend the money to to make more money or like where how do I make the customer happier like just tell me what to do with the business like stop talking about this technical nonsense that like I don't even really care to hear about at all um and it took me a while to get that I think I think that's something a lot of data people struggle with because we're so invested in like building those technical skills and then to find out that there's important people in the company that could care less is um yeah can be kind of a roote Awakening right but but really it's you know I think it just points out that there is that other layer of skill that's so important which is knowing what they want and how to actually impact the business and then just make that digestible for them so um that's awesome what do you think uh maybe other than that like what do you think are some things that people tend to get wrong when working with stakeholders like what are some pitfalls folks should avoid I think people tend to say yes way too often I've seen this with previous managers where we were like really small data teams and they would just say yes to every stakeholder requests coming in and as you can imagine that did not end well that led to very quick burnout um and just overwhelm for everyone on the team but most importantly for them because they were the ones you know promising to deliver all of these things so I think more people need to say no and realize that it's okay as long as you're explaining tradeoffs and the things that you're going to deliver instead of maybe the thing that they're asking for or asking the business to be the one to make the tradeoffs saying okay well if you want this thing then I'm not going to be able to work on this other thing that you asked for two weeks ago because they're both very big projects um and just being like empathetic in your approach like everyone's just trying to get their work done they want to succeed you want to succeed um and a lot of times stakeholders don't know the complexity that goes into Data work so it's really important to just like understand where they're coming from have them understand where you're coming from and then kind of settle somewhere in the middle yeah I I think that man that's such a good one and um even like as an individual contributor I personally really struggled with that uh early on like at when you're an analyst or a data scientist you're you're using you're going to have many Masters right you you're gonna have your obviously your direct manager but then there's all these other people that are like hey Madison I need this data hey I I want can you build this pipeline for me um it's never just one person right and they none of them know exactly what you're working on and they all their thing is the most important yeah and it can be brutal and I think when when I was younger I solved it just with hours and I would throw more hours at it and um I can't now I'm tired and I've got little kids and like somebody needs to feed them and wash them and stuff so I don't have the same hours um for me it's now it's really about prioritization being really important and you mentioned communicating back and forth and saying like yeah I can work on this but this is the trade-off this other thing's going to take longer because of it um one of the things that I found really useful I don't know if you do anything like this but if you can somehow like publish your um your cue of of projects like on a Trello board or like a jira board or something and that's that's something that um engineering teams they do really well because engineering teams have historically been overlooked voted for decades and so they their que and it's really important to prioritize it but for some reason a lot of analytics teams haven't done that as much but I found when I do that it's much easier for me to say hey yeah I could do this I'm gonna put your thing it's Fourth on the list um and it's it's gonna you know so that's why it's going to come later and then the stakeholders can kind of argue about hey I think it should be higher um versus just saying yes to everybody and burning yourself out so so I'd love to say no and and also i' just add prioritize if you can a little bit um yeah that's great advice something that's helped us a lot is just having stakeholders like creating their own J tickets and like we'll have a template that they can fill out and a lot of times that's just like a little bit of friction that they need to realize like oh I can either do this myself or it's really not that important um and adding that little bit of friction can be really helpful for the workload of any data team I love that that's almost like the advice of like if you know somebody is asking you for a little thing just ignore it for like three hours and they'll probably do it themselves yeah you know um that's that's I really like that um what are some other kind of tips and tricks you think people should uh should be aware of and use to to kind of maximize their effectiveness with stakeholders I think not taking asks at face value is really important this is something that I learned um in the very beginning of my career and I'm still trying to work on because it's hard to get right but whenever like a stakeholder has an urgent ask and they're asking you to do something it can be really easy to like quickly do that rather than taking the time to like pause and like okay what problem are they actually trying to solve here is what they're asking for even the right way of solving it like the easier thing to do is to just do it um the harder thing is to like really get to the bottom of it and make sure that it's actually the right solution um like focusing on or acting on urgent requests like that has gotten me usually in some like pretty bad places like I'll act on something and then I'm like oh my God why did I do this this is all wrong and then um it just creates way more work rather than just like actually clarifying anything um and just like either jumping on a meeting or just continually asking questions is really important um the more questions you can ask the better because uh chances are the thing that they're asking for is not what they really need yeah yeah that's I I love that and that's again I'd say that's something that has changed over my career at the beginning of the career it was just okay I've got this request I'm going to do exactly what it is now it's always like a usually it's a little more questions like okay I think this is what you're going for and I might have other suggestions and um I think the sooner you can get to that the better it's hard at the beginning at the beginning you're just like fish out of water trying to survive and then when you get more familiar with the business and the technical parts of the role um you get to that but I such such good advice like make sure you really understand what they need not what they're asking for um often often close but not the same thing so yeah really really good advice how about I'm curious um are there any types of stakeholders that are usually like really easy to work with or any that are like usually really hard to work with and that could be like personel it wise it could be um just like the Prototype of what the person's skill set is like does anybody is there anybody that you're like I'd love to work with more other people that you are are tougher I think it's probably the ones that are the most stressed out that are the hardest to work with just because you know and you're stressed like you just you want what you want so you can do your job well and like if somebody is standing in the way of that they're frustrated and then like you can send sense that frustration so like that's when it's really important to show that empathetic side of yourself because like we get it we've all been there like we need to complete a project or there's an important deadline coming up um and I think it's just like a matter of really trying to understand them and where they're coming from and just yeah like showing them empathy is really big um and then like really determining a solution for them whether it involves the data team or not or like you can quick find like a quick workaround of something that might already exist that might kind of get them get them the same answer that they're looking for uh that's really important yeah I I think that's such good advice because especially like in a high stress environment you know people aren't always acting on their best behavior and I think the like leading with empathy and saying you know he's he's probably not a jerk he's probably like completely slammed and he's he's kind of freaking out about his job um such a good point and and not responding to stress with more stress uh is is really smart sometimes easier said than done right because you might also be under a heavy load but um but really really good advice um awesome yeah any um any aha moments like in your career like wow I just realized this thing and like it helped me out a lot or any advice anybody gave you maybe around stakeholder management that would be worth sharing with folks I think just asking questions and not being reactive is the biggest thing and I think that's something that I've learned slowly over time um and even now it's like still hard for me and still a skill that I'm developing is just like really getting to the bottom of things um like even yesterday like a question came up and it was someone that had more technical experience than me so I kind of like again just like assumed oh they their answer for what they want is probably right and um that's not always the case and that's like something that again like you important to push back and question things just so you can like get to the best solution and the simplest solution sometimes the thing someone asks for is more complicated than it needs to be um so just like working that muscle and um asking for feedback and having like people point out oh how could I have handled this situ better I think that's like the best way to learn and improve with uh stakeholder communication yeah that's awesome and and I think one thing that's interesting you know we we've talked about that more like the are you just an order taker is there more of a back and forth where you ask questions and I think one of the things that's really interesting we haven't mentioned yet is the way people perceive you is different if you do those two things right like if as a junior analyst I would just be an order taker and then as you as you start to sort of massage those questions and say hey I know you're asking for this but I actually think there's a better solution I can give you like that's somebody that they perceive as more valuable to the business so sometimes especially when we're early on like we get nervous we're not really confident and we can think like pushing back or asking these questions questioning what a more senior person wants is is a bad idea but you realize later on like that's actually exactly what they want like you're getting them to a better solution because you're able to answer those questions so um again easier said than done at the beginning we're nervous we're like but but like once you get there you start to be perceived as a more valuable partner in the business and not just like a set of hands that can do data stuff so um yeah yeah fantastic so Madison we've got a ton of questions here already are you good to go in the hot seat and we'll do we'll do we'll do some Q&A so hey there hope you're enjoying the video sorry to interrupt we'll get you right back to it right now Maven analytics is offering a deal that was just too big not to share it with you we've got an early Black Friday sale and you can save up to 50% off of Maven analytics paid plans so if you've been considering learning Excel SQL powerbi python Tableau and everything else that you need to become a data analyst or accelerate your career now is the perfect time check it out at Maven analytics. you can see the Black Friday offer in the banner at the top of the screen you can't miss it again if you're on the fence there's never been a better time we don't do a lot of these sales so I hope that some people will take advantage now enjoy the video I'll let you get back to it thanks how about this one this one's from Muhammad Muhammad has some so many good questions in this um the past couple of days uh how do you handle situations where stakeholders provide unclear or changing requirements and what methods can help you to clarify and maybe document their needs as well yeah this is a good one uh something that I always ask myself too I think some stakeholders are definitely better than at this than others um like we have one stakeholder who is amazing like she has a lot of data literacy She'll always create these like long document documents about why she needs a metric um like the different considerations that she went through when she needs it all this kind of stuff and that has been super helpful so like I've kind of used her as an example of like hey when someone else has another request hey can you maybe fill out all of this and I think in the process of them asking themselves these questions they kind of G gain more clarity in what they need um and also if requirements are changing then it's really important to set expectations and be like hey I'm not going to be able to get to this because like this is taking up too much much mental load like I have other things that I need to prioritize um I'll come back to this when like you're set in stone on what you need because right now it doesn't make sense for me to prioritize this compared to other things yep that's that's awesome um that's it's a it's a great answer here's another one this one's from David uh David says how do you maintain transparency and trust with stakeholders um especially when delivering difficult or unexpected results yeah this is definitely hard because obviously when you mess up it's hard to admit that and it's embarrassing um but I think at the end of the day like if you don't admit those things you're going to lose trust more and people are going to be they're not going to be able to depend on you and everyone makes mistakes at the end of the day so if you do make a mistake just own up to it and make sure that you're doing everything you can to be the one to fix it and you also have an action plan for how you're going to prevent things like that happening in the future oh I I love it so much because like probably the the worst person that I never want to work with is the person that would hide a mistake and you know try to cover it up and then at the top of the list is somebody who somebody who says I don't know I'm not sure I'll try to figure it out or like I sort of know but I'm not confident like being okay with your uncertainty or saying like hey look I presented this data to you yesterday and I realized I I made a miscalculation in it or or like if it was a data engineering task guys I screwed up this Pipeline and a bunch of the data's wrong um we should fix it right that the person that says that I love that person right because like you said that actually amplifies trust because then if I know you're that person I know okay cool Madison is giving she's telling me it's good I know it's good because when it's not good she's told me that too right so exactly versus like if you get caught um forget about it you're you're cooked in data you you have absolutely nothing if you don't have your integrity so um I I love I love your answer so much there uh great questions Guys these this is awesome um how about this one's from Chenille how about dealing with difficult stakeholders that don't provide much context and they're not willing to engage with you as much so they're like just throwing a request over the fence and maybe not as available for back and forth yeah this is definitely something that's fr frustrating especially when someone says something is urgent and you respond with your questions that need to be asked and then you don't hear from them for like 3 to five days and you keep following up and you still don't hear from them in those situations I just assume that it's not as important as they originally said because if it was they would be responding to me um and I think it's just important to keep like sending those messages and be like hey I didn't hear back from you from this amount of days is this still a priority for you and then depending on how they respond to that like um luckily within kit we have um open communication so like all of our threads are all public which is really nice so you can kind of see like when someone drops the ball or like someone makes a request like that and then you're the one following up um and they're not really responding much um so it's just like about being transparent and um just making sure you're still providing them what they need and if they're not responsive then that's on them that's yeah that's uh that's fantastic I love the that you're not if if you think the request is wrong and you have clarifying questions that you're not just saying okay they're they're ignoring me I'll just give them what they want um but you're you know you're saying like hey this these clarifying questions are a critical part of this process and I love that you know maybe this isn't as important as you thought it was if you literally can't just respond to my note and answer this right it's um and it yeah and I think that's again that's like the more confident we get in the role in ourselves the more we can do that and that's really the most valuable thing for the business and again I'm just picturing like 22y old me would be like oh my gosh they didn't they didn't get back to me I guess I'll just give them this thing that I know is wrong and I'll like waste my day doing it right but that's not that's not the right thing to do so no um yeah that's that's awesome um how about this one this one's from David uh what advice do you you have for not turning into a data dashboard help desk and instead being that trusted advisor that um that we've talked about being so important yeah again like asking lots of questions like okay you want this metric or you want to add this Dimension to this dashboard what question is this helping you answer um like what kind of solutions are you looking for and asking them instead by starting with the question of what they're trying to see in the data rather than like them just asking oh can you add this um is more helpful that way you're able to see either if that question can already be asked in the dashboard that you already have or if there's a better solution than just like continually adding different visualizations um on top of what you already have because too much data can also be a bad thing yeah totally true totally true um I I've got a question of my own and then we'll get back to uh to the audience questions um if people want to hear more from you Madison where where can they find more definitely subscribe to my newsletter learn analytics engineering and follow me on LinkedIn I'm posting on there uh every day and then I post a newsletter every Thursday awesome so I just dropped links in the chat to um Madison's LinkedIn and the substack learn learn analytics engineering um awesome stuff there so definitely uh highly recommend if especially if you're interested in Analytics engineering which you should be it's uh very much an in demand skill set um even if you just want to be a data analyst it's having some of those skills is super super super valuable it has been to me I'm I'm not as good as Madison but it lets me um it lets me do some things on my own especially if you want to work with earlier stage companies that might not have a big data team like you might be the data team at some companies and and you might be an analytics engineer and data analyst or data scientists at the same time so it makes you really versatile um and if you wanted to go full analytics engineer data engineer extremely um safe and lucrative path as well so um definitely check that stuff out and yeah let's get back to uh some of these questions so how about if you have um different stakeholders and there there might be some conflicting expectations or priorities among them I think maybe um maybe this the the idea is like there's different people and they both think their thing is the most urgent in the world and they they both like won't back down from that what would you do in that situation I guess trying to reframe it um of what's going to provide the most business value like most people can think both people can think something is really important but at the end of the day like what's benefiting the business more um I think that's kind of when you have to step in and say like okay this other thing is going to take us a little bit further in the short run so we need to work on this um and going about it that way yeah that's awesome um I think that's and that can be one of the hardest things too U again like especially if you were say you're dealing with like a couple of VPS in different departments or something like that but I think bringing it back to the business value um to make it trying to make it a little more objective than there objective opinion is is definitely the right thing at the end of the day you all have the same goal which is to have the business be successful that's what you have to remember yep exactly it's all I love it it's all about the business um let's see ah this is a really interesting one so um it's we're getting kind of in the weeds this is from aanaya uh what are some best practices for defining and locking project scope to avoid scope creep especially when stakeholders push for extra features after you deliver an MVP um how can you clearly communicate the MVP's purpose and uh keep the focus on core functionalities instead of just like a never-ending list of nice to haves like does that happen to you a lot in in your work yeah I think of um like requests for reverse ETL um a few months ago we had like a huge document of like all these fields that were like must haves nice to haves and like at the end of the day the nice to haves probably aren't going to be able to be gotten to because the ones that are mus haves are a lot more complicated than meets the eyes so um something that we recently started doing with our J tickets is defining definitions of done um so like that would kind of just State everything that the MVP has and that would be consider closed out once that is reached so I think that's really important to have that and then um you can always create like nice toab as different tickets in whatever project management system that you're using that way like once you have the core project like you've delivered on that and then you can uh prioritize the other little nice to haves compared to like some of your bigger projects or other work yeah that's really it's really smart even within the priority have or within the project have those things prioritized I think is really smart and that's that's like regardless of this if it's an analytics engineering role or if it's a data analyst role or even something else like one of the biggest things that we can do in businesses is have the correct priority stack because if you're if you're working on the bottom of your list and not tacking tackling what should be at the top you're doing it wrong and you're being less effective than you can and that that theme comes up o over and over again in everything that we do uh let's see uh let's see so uh this one's from Monique um how do you convince key stakeholders to take up action supported by data without um without them if they maybe had a conflicting point of view and this maybe a little less relevant to your work in analytics engineering but just kind of working in with data in general H um how to convince key stakeholders to take up action plans supported by data I think at the end of the day like stakeholders want to think that they're data driven but sometimes they're not and it's like you can only convince people so much but like if you have the data to support a certain way of doing things like that should be the driving force but it is going to take time for companies to become more data driven so it's kind of like about reframing how you work as a company and a team um and like kind of how you mentioned before like we're not just order takers like okay you as a data team needs to be the one to provide the data that's saying like oh this is what we should be going after as a business for example yeah yeah and I think I think you said something that that was in my head is that like different stakeholders are more receptive to a good datadriven argument than others some of them can be really stubborn and like they'll have a point of view and they'll have they think they know what the answer is and they they just want you to give them data that supports it and then others are like really um radically open-minded and like I I think I am like that as as a business person I at least try to be um like I love somebody saying John your idea is actually terrible and here is the reason because at the end of the day I come out with better ability to do something good for the business because they they made a good argument um and Chris my Chris dut and my business partners the same way um I think that's one of the things we get really right is we we're we're willing we're happy we we're happy to be wrong and and we'll smile about it but there's I've worked for people that are not like that and as a data person it can be so frustrating um the I guess the only other advice I would give for that person if you're if you're dealing with that person that's like pretty stubborn um one is if you can avoid that person that's probably better you know like work for somebody not like that but uh in all seriousness um if you can think about like what is it that they really care about right so is it a marketing person that's trying to drive Revenue um is it like a customer support person that's trying to drive customer satisfaction and those types of ratings and if you can kind of you know translate the data work you're doing into what they care about that's you know it can it's a little harder for them to ignore um when you can sort of quantify things there and say hey if you do this you'll make more money or your customers will be happier here um but even so sometimes you know PE there's some stubborn people out there it can be tricky so um amazing yeah there's so many good questions in here um huh this is a interesting one so this is from Daria uh how long in minutes do you believe you should make for a stakeholder presentation what's the balance of being resly short and not overload them with too much information um comparing with the story that you have to tell like how do you think about that uh that balance I actually don't have to present to stakeholders very much um I don't really work with bi too much but I think it's important to maybe like not take over the whole meeting just with like you talking but really let the questions lead um I feel like maybe a quarter or a fifth of the meeting um is probably enough to like present your ideas um and then just have like questions lead the rest of the conversation yeah I I think that's great you know if you've got good people in the room they're Curious by nature and give them give them what they need and then you know have have that conversation here's um here's one from Candace I think is a great one so uh you mentioned having a non-technical background what would you recommend for those coming from non-technical backgrounds if they want to uh connect with employers I think really play on your strengths and whatever you were doing before like you need to think that you have a unique perspective because you've done other things outside of data and outside of engineering and figure out how you can use that to provide value for what you're doing now I think one of the great Parts about working in data is that there's so many different kinds of people from different kinds of backgrounds and it's really important to have all different viewpoints on things because data like can be such a I don't know what the right word is um but it can be easy to kind of like blackbox yourself and like only I guess guess tunnel vision that's what I'm trying to say it can be easy to get tunnel vision and like for every data person to think the same but like when you have all these different perspectives it's really just going to make uh the outcomes so much more uh reliable and just like really comprehensive and um yeah so just like take what you've learned before data and figure out how you can apply it to the role that you're seeking yeah it's it's such good advice like no matter what you've been doing before you have strengths people get so hung up on like I wasn't in data and they get obsessed with that part of it but like you were doing something else and you have that advantage over people that were just in data too so like uh see the full picture of yourself your your strengths as well as your weaknesses I love that answer um here's another one this one's from uh ABA and what strategies do you find most effective for uh transparent and productive communication with stakeholders through a data Project Life Cycle so maybe like a project that is going to take you a long time like a few weeks how often are you communicating back with people on progress is there like a set playbook for that is it sort of AD Haw like how do you think about that this is definitely a hard one especially with data modeling work this has been something that my team has been struggling with trying to like find the right balance of because like with really long-term projects that take months like you're really in the weeds with like certain like edge cases that you might not want to like explain back to the business or they might not really care about so finding like a fine balance between okay like this is what we completed this is what's still left so I think like whenever you reach an important Milestone like just sharing that out whether you use like linear or J they have like projects and you can share project updates or even like a LOM Loom update um something that my manager was recently talking about that I think we're going to implement soon is like whenever you merge a PR like think about okay well now what questions can business stakeholders solve from the work that you just merged in this PR and I think that can be really powerful because it's kind of like reframing your mind to think of the business value in every single piece of code that you're merging to production yeah that's awesome and I mean also kind of a good way for your group to toot your own horn a little bit and like show the show the enablement that you're making possible so yeah I love that um let me do one more question Madison that is not stakeholder related but um I you're you're the best person in the world to answer uh this is from Tamil what are the key skills needed to land in the data engineering or analytics engineering role so one SQL that's any data person needs to no SQL um and then also data warehousing is really important um just like understanding cost management architecture um also data modeling whether you want to use a tool like DVT or not I think just knowing like basic skills with like dimensional models um Keys like kimbal um all of that is like really important to just gain like a basic Foundation um and then it really depends on like what specific companies are looking for um like data engineering and analytics engineering can be so broad that it really depends on like what specific area you want to focus on So like um if you want to focus on devops for example being familiar with like AWS products and how to use those and also python is really important um but if you want to be more of an analytics engineer like maybe you take time to really focus on that stakeholder communication or something like data quality um I actually have like a series on my newsletter that I've been doing where I take different data roles um like Netflix and Airbnb and I break down the requirements um and those have been really helpful for people to kind of just see like how much data positions vary and like how it helps to kind of have like a specific Focus within um to learn skills awesome I love it um great that's a a really good overview and intro and uh I just posted a link again to the newsletter in the chat and and again guys if you are interested in analytics engineering data engineering uh Madison has a one hour mavens of data I think the video is actually on the substack too I think I saw it there um it's a really really good uh like if if you've liked her style of talking about this stuff and you want to go more into um those roles she does such a good job of the overview and she even gets into some of the nitty-gritty details of some of those tools we talked about so highly highly recommend it thank you Madison thanks everybody and for having see you the next one hey there I hope you enjoyed this video this was part of our open campus event where we ran 24 sessions in front of a live audience over the course of 2 weeks it was a ton of fun we did all these live sessions and we opened up our entire learning platform for free you did not need a paid account to take advantage of all the courses so folks could learn Excel SQL powerbi tablet python Etc all the stuff that you need to learn data analysis skills and take your career to the next level if you missed it this year keep an eye out we'll definitely be doing this again next year probably again in October we would love to see you there and the last thing that I'll leave you with if you're looking to take your skills to the next level right now and you've been on the fence about a may an analytics paid plan we are currently running our early Black Friday sale this is the absolute best time to pay for Maven analytics if you want to so don't miss out you'll have the opportunity to save up to 50% on paid plans which is a pretty great opportunity you can check out all the details at Maven analytics. just look for the early Black Friday Banner at the top of the site you can't miss it hope that we'll see some of you there as well thanks for watching [Music]