 What's going on everybody? Welcome back to another video. Today we're going to be talking about the five biggest mistakes that beginner data analysts make. Now I'm just going to go ahead and say it. I've made all of these mistakes and more but these are the top five ones that I see kind of across the board that a lot of people make when they're first starting out as a data analyst. Let's jump right into it with number one. The first mistake that a lot of beginner data analysts make is not asking questions. Now I'll give you an example from the very first job that I had as a data analyst. And my boss came to me and he said, hey, could you build this? And I said, yeah, of course, no problem. So I went back and I spent like two days building everything out in Excel with all these pivot tables and calculations. And I bring it back to him like, hey, before I keep going, you know, I just want to make sure, you know, I'm on the right track. I'm doing what I should. And he's like, he's like, this is not what I wanted at all. He's like, what have you, is this what you've been doing the whole time? I was like, yeah, that's what I thought. And he's like, no, no, no, no, no. He's like, here's what we actually need. And then when we really spelled it out for me, I was like, if I had asked some follow up questions, I would have been smarter and not made this like really simple mistake. So not asking questions can actually hurt you and slow you down because you may not have all the facts and you go and try to do something and it's not what they want. So by not asking questions and just kind of plowing through and doing what I thought I was supposed to do, it actually cost me a lot more time. I also think I just didn't want to sound stupid, right? I didn't want to sound like I didn't know what he was talking about. If I really think back and really think about like that moment, I had just started that job. I wanted to like hit the ground running and really know what I was doing and really prove to him that he made a good hire. And so I think I just rushed into it. I didn't take the time to ask the right questions or do the right thing. And for that example, it just would have clarified what to do. But sometimes asking these clarifying questions can actually stop you from doing work you shouldn't do at all. Sometimes stakeholders are going to ask you to do things and you can just say, okay, then you go try to build it and after a few days, you realize this isn't actually what they need or want or this wouldn't be beneficial. Whereas if you just ask clarifying questions upfront, they could kind of get into some more details and you can have a lot better understanding of what they want and if you actually need to build it or not. The second biggest mistake that I see is not understanding business context. This was a big one for me because I came from healthcare. I was working at a healthcare analytics company when I first started and I was like, okay, you know, this is my domain. I know it. But I didn't really understand that well, the use case and the business context around the healthcare that we were working with. So I'd work with these doctors and these companies. I'm like, I understand this data. I know this data. But I didn't really understand the context of why they needed this data and why they needed to build this product. This kind of goes back to the first problem, which is just asking questions like, why do we need to build this for you? What are you going to use this for? How many people are going to use it? Asking those follow up questions is really helpful to understanding the business context of why you're going to be working with that person. This one is definitely not super straightforward when you're first starting out. You don't really understand how the business operates, all their products and how they all fit together and what the customer really wants. So when you're first starting out, that's your job. Your job is to figure it out and talk to your manager and talk to other employees and talk to the customer about exactly what they want and why they want it. Mistake number three that I know I've made 100 times, which is rushing to build. This one doesn't really coincide with the first two, at least it's not supposed to, although I'm sure it could. But this one really refers to the actual process of after you have the data, you know what you're going to do, just rushing to build instead of doing all the things you should do, like cleaning your data and making sure your data has good quality and then setting up all the relationships in the database and then building. So there's a lot of things that go before it and oftentimes you don't really want to do that stuff. You just want to build a product, hand it off, but that's where really bad dashboards come from is bad data. I've always said this, but the actual building of reports and dashboards is usually only 10 to 20% of your job. The other 50% is you actually data cleaning and working with other teams to make sure that data is good and where it needs to be in order to actually build that end product. This happened for me with one of our biggest clients at my first company. They sent us a bunch of data and I went straight to work on building something. I sent it back like a day or two later and they just torn apart. I mean, they really looked at the data, they looked at everything I sent back, all the numbers and they're like, this doesn't match up, this doesn't match up, that doesn't match up. Why are you using this? And I was like, it's because I didn't do a good job. I just rushed into building it. But if I had done all the data cleaning, all the prep, all the stuff I should have done at the beginning, I could have sent them a really good end product. Now I eventually got there, thank goodness, but it was really embarrassing. Like I just remember very, very vividly, my boss talking to me and I'm like, hey, I'm super sorry, I should not have done that. And he's like, it's okay. He's like, you know, they're really good customers. They check their data and they do all this stuff. He's like, just go back, do it right. I'll check off before and then we'll send it over. Honestly, mistakes are how I've learned the most. So I'm sure a lot of these mistakes that I'm talking about are just things I've learned from and grown from. The fourth biggest mistake is not documenting your work. Now this one can be super, super simple to mess up. I know I didn't do almost any documentation for the first like years of data analyst. And it came back to bite me in the end, but you should be doing that from day one. It's just something most data analysts overlook because it's not a high priority. You're just trying to learn the databases and, you know, learn all the different products that you have and talk to the different customers and talk to the different employees. That's mostly what you're focused on. But once you actually start doing the work, you start building code and you start actually creating files and real work, you need to document this stuff because if you don't, it can really cause issues later on. I cannot count on my hands and toes how many times this has happened where someone will ask for something. Six months later, I'm like, I have no idea where that is. I have no idea how I did that. And it's embarrassing. And then you have to like track it down and go through old emails and old messages and find it. And it's not good. So keeping things organized, not only in your file explorer, but in like your code. So if you write code in SQL or in Python, commenting your code, documenting what you're actually doing and why you're doing it and then saving it in your file explorer online somewhere, saving that information in a really easily understood way that you can document and find easily. Super important. Something that I wish I had been doing my entire career. The fifth and final one is isolating yourself. And this one was something I struggled with at the beginning. I don't anymore because I learned from my mistakes, but when I first got into data analytics, I kind of just wanted to work by myself. I wanted to do it myself, show that I could do it myself and I don't need other people. I just kind of isolated myself and I would do the work that I go and I had handed off and that was it. But as I've grown as a data analyst, as I've worked in the industry longer, I've realized that building those relationships and kind of leaning on other people's strengths will actually help me get my work done so much faster. That was another reason I kind of isolated myself. I didn't want all the distractions of other people. I just want to kind of focus in and get it done and do it well. And I found that that's kind of the opposite. I usually would make mistakes or I didn't have all the information or I didn't, you know, get somebody else's perspective who's just better at it than I was. And I made mistakes that would cost me more time. And so building those relationships and understanding people's strengths and kind of building that community within your workplace is really, really, really important and extremely helpful. Not only will it help you get your work done faster, but you'll also build relationships and connections that will help you get promotions later on. So if you want to keep working at that company or if you ever want to come back to that company, building relationships is actually super important. So don't be me. Don't isolate yourself. Try to make those connections and build those relationships within the company. So those are my top five mistakes, but I promise you that's the tip of the iceberg. I've made a lot of other mistakes, but those are the ones that have continually come up in conversation with other people. And just me thinking about, you know, my career as a whole, I've made those mistakes several times. And so hopefully you can learn from my mistakes. You can kind of identify it before it gets out of hand and get ahead of the curve on a lot of those mistakes and be a lot better data analyst than I ever was. So I hope that that was helpful. If you liked this video, be sure to like and subscribe, and I will see you in the next video.