You're NOT Ready for The Next Phase of Data Analytics

Most people calling themselves data analysts right now, they're not going to make it through this next phase of analytics. And no, this isn't some prediction for 2030. This shift is happening today. If you're treating AI like an option, if you think being a tool operator is enough, you're already falling behind and you don't even realize it yet. Here's the part that no one is saying out loud. The data analyst role is evolving faster than the industry can keep up with. Companies don't want basic analysts anymore. They want analysts who can think, automate, strategize, and drive decisions without being told what to do all the time, and if you don't evolve with this shift, you will be replaced. Now, if you're new here, I'm Kedisha, and just 5 years ago I was delivering pizzas and Amazon packages for $8 an hour. I didn't break into data analytics because I was the smartest or the most technical. I failed Calculus 3 and discrete math in college. I didn't even graduate with a 3.0 GPA. I broke in because I understood where the industry was headed and I adapted before everyone else. And today I'm gonna teach you 5 critical skills so you can do the same thing and be top 1% data analyst in this new era of tech. Now, first I need you to hear this. Most analysts today are tool operators, not problem solvers. They react instead of anticipate. They wait for instructions instead of influencing decisions, and they let AI think for them instead of using it to amplify their thinking. And I know this sounds harsh, but it's because I've been there myself. I used to be the analyst who thought knowing all the tools in the world was enough. I've been that analyst who would build all the pretty dashboards that look great but didn't move the needle. I've been the analyst who wondered why I wasn't getting promoted while doing all the right things. So I'm not saying this to judge you. I am saying this because I don't want you to stay stuck the way I was. You can't survive in this next phase of analytics if you're stuck in the old one. And for years, the playbook was simple. Learn Excel, pick up some SQL, build dashboards in Tableau and Power BI, and maybe sprinkle in some Python. That was a formula, and for a while it worked, but that era is over. AI tools now can write basic queries and generate code. They can automate some reports and take some analysis that used to take hours to build. So if your entire value proposition is, I know SQL and I know how to code, you're competing with robots and that's not a fight you're going to win. But here's what's exciting. While the basic analyst roles are getting squeezed out, there's a massive opportunity opening up for people who understand what's coming next. Companies are throwing money at analysts who can bridge the gap between traditional analysis and strategic decision-making. And the future doesn't belong to people who just master all the tools. It belongs to those who can master thinking, influence, and leverage. That's exactly what we're gonna break down together. All right, so skill number 1, the skill that separates good analysts from the great ones, influence and data storytelling. And I wanna be real with you about something. This is the skill I underestimated the most when I started. I thought if my analysis was solid, the work would speak for itself, but I was wrong. Many analysts fail here. They communicate information instead of insight, and they present data and not direction. Peter Drucker said it best, the most important thing in communication is hearing what isn't said. So let me paint a picture for you. You can write the most elegant SQL query in the world, you can build the most beautiful dashboard, but if you walk into a meeting and you can't explain why revenue dropped 23% in the Northeast region and what business should do about it, you failed. Not because your analysis was wrong, but because you couldn't translate it into an action. And here's what took me years to understand: technical skills do get you in the door, but influence is what gets you promoted. Influence is what gets you that 6-figure salary. Influence is what gets you a seat at the table where real decisions are made. So think about it. Who do you think will get promoted faster? Analyst who quietly builds perfect dashboards in the corner, or the analyst who can walk into a meeting, present insights, and guide a decision that can save a company millions of dollars? It's really not close. Now here's something else that also shifted my perspective. Influence isn't manipulation, it's clarity. It's helping people see what they couldn't see before, and it's guiding them to better decisions with confidence and evidence. When you frame it that way, influence becomes a service and not a tactic. And when you master this, you stop being the person who just answers questions. You become the person who shapes strategy, you become the person leadership can't afford to lose. Influence is the multiplier on every technical skill you have. You could be average at SQL but exceptional at storytelling and still outearn analysts who are technical wizards but can't communicate. Now, someone commented on one of my previous videos asking me about recommended books on my bookshelf. So let me give you a few favorites for this skill set. And I'm not just listing these because they're popular. These are books I've personally read, some multiple times, and still reference today. First up, Data Storytelling with Data. This book changed everything for me. It's not about making pretty charts, it's about understanding that every visualization should drive a decision. Cole breaks down exactly how to remove clutter, direct attention, and build a narrative that moves people into action. Don't read it once, read it twice. This is what I always tell my students. This is actually the very first book that I read when I first became a data analyst. It's a short read, but it'll completely rewire how you think about presenting data. Second, Information Dashboard Design by Stephen Few. This is a reference book I used to keep on my desk at my very first data analyst job. Every time I had to build a dashboard, I would always flip through. There's even a section on the 13 common mistakes that's a must-read. It'll show you all the ways dashboards fail and how to avoid them. Most people make these mistakes without even realizing it. So this is a book that is a must-read. Third, Better Data Visualizations. This one goes deep on the principles behind why certain visualizations work and why others don't. It's not just about what chart you use, it's about understanding how people actually process visual information. And when you understand that, you stop guessing and start designing with intention, and you start making choices that guide your audience exactly where you want them to go. These 3 books will take your data storytelling and influence from amateur to professional. And honestly, there's a lot of analysts that have not read some of these books, so this can be your advantage. So while everyone else is focusing on mastering every single tool in the book and all this machine learning and advanced stuff that the market really isn't asking for, you'll be mastering the skill that actually gets you paid. Okay, so skill number 2. AI. AI is where I see people making massive mistakes every single day, and I want to help you avoid them. Most analysts treat AI as a shortcut. They ask ChatGPT to write all their code, well, all their analysis, and make all their decisions, and then they blindly trust whatever comes out. That's not leveraging AI. That is outsourcing your thinking, and that is dangerous. I came across a Reddit post the other day that perfectly captures what I'm talking about. This is what vibe coding gone wrong exactly looks like, and this is what happens when you let AI think for you instead of thinking with you. People People are copying and pasting code that they don't understand, running it into production and wondering why everything breaks. They're building entire analysis on outputs they never even validated, and then they're shocked when their insights fall apart under scrutiny. Here's what happens when you let AI fly the plane. It hallucinates and it makes up data that doesn't even exist, and it'll write code that looks perfect but pull the wrong information, and it confidently gives you answers that are completely wrong. I've seen people present AI-generated insights only to find out later that the entire analysis was based up on made-up statistics.

The report by consultancy firm Deloitte was handed to the government a month earlier. And it came at a hefty price tag of $440,000. But it turns out Deloitte used AI to produce the report, causing the mistakes. Among the errors, Chris found the report wrongly referred to a key federal court case and misquoted the judge.

That's not just embarrassing, that can be a career-ending mistake. So let me be clear, you want to be the pilot always. AI is your co-pilot, your assistant, your accelerator. You're the one with the hands on the controls, and you're the one who understands the business context. You're the one who catches when something doesn't make sense. Elite analysts treat AI as a second brain, a force multiplier. They use it to speed up cleaning and exploration. They use it to generate first charts of code, and they build small automation agents, but they still own the decision-making and strategic framing, and they check everything and they validate everything. Now, the analysts who resist AI will still get left behind, and the analysts who blindly trust it will crash and burn. The ones who win are the ones who can learn how to work with AI while staying in control. That's the balance, and that's a skill worth Okay, so next, something that gives you an unfair advantage that most people overlook: domain expertise. Here's what's happening in the market right now. Tools are becoming commodities. Everyone knows SQL, everyone can build a dashboard, everyone has access to the same tutorials and courses. But domain understanding, that is becoming a currency. AI can handle surface-level analysis. It can probably crunch numbers and spot patterns, but what it can't do is apply deep domain experience and business context. It doesn't understand why a 2% drop in customer retention is a 5-alarm fire in SaaS, but maybe a small blip in retail. It doesn't matter that certain metrics matter even more in Q4 in your specific industry, and it doesn't understand the politics, the priorities, and the unspoken rules of how decisions actually get made in your company. So this is where you win. Healthcare, finance, retail, marketing, supply chain— analysts who understand KPIs in these industries become indispensable. They're not just running queries, they're translating data into decisions that make sense for that specific business context. And here's something I want you to really hear, especially if you are a career changer. This is why many career changers actually have an advantage in today's market. You bring domain domain knowledge from your prior careers that people who went straight into data don't have. If you're a teacher, you understand education metrics, student outcomes, and how schools actually operate. If you worked in retail, you understand customer behavior, inventory cycles, and seasonal patterns. If you are in logistics, you understand supply chain optimization, delivery windows, and operational bottlenecks. You don't need to start from zero. You need to combine what you already know with the data skills, and that combination is incredibly powerful, and it's something you can't just learn from a course or college. So pick a lane, get specific, and study your chosen domain. Learn the metrics that matter, learn the business language people actually use in the field, and understand what keeps leaders up at night in that industry. Because the next wave of hiring, companies don't want to ask, can you analyze data? They'll ask, do you understand our data? Do you understand our business? And when you can say yes with confidence, you become an obvious choice. Okay, now look, I know this sounds like a lot, and if you're sitting there thinking, okay, Kedeisha, I get it, but where do I start? In what exact order? How do I know if I'm even wasting time on the wrong things? I got you. I created a free training that walks you through the exact roadmap that my students and community of over 80,000 use to land data roles in 6 months or less without going back to school and wasting money on bootcamps and courses that just don't work. Click the link in the description to access that training. If you're ever ready to work with me directly, we can talk about how to fast-track your transition with real mentorship, real projects, and real results. Okay, so the next skill that took me from making dashboards to building systems is automations and analytics engineering. Before you panic and go thinking, I'm not technical enough for this, let me stop you right there. This isn't about writing production-level code for building complex applications. This is just about becoming a person who can take a simple business problem and solve it completely end to end. So let me tell you a story about what that looks like. For my very first project on my very first data analyst job, I was tasked to build an HR dashboard. The data sources were Excel sheets sitting in a folder produced from a legacy system. So I did what I knew how to do at the time. I manually cleaned the data, unioned them in Tableau, and created a nice dashboard that I was pretty proud of. When I presented it, the manager was so excited and he loved how it looked, but then he asked a question that stopped me cold. How do we automate this every month? I didn't know how. I had no idea, but I learned Python. It took me about 45 days of focused effort, but within 3 months I had built an automated data pipeline that cleaned the data, removed entries that weren't supposed to be there, and automatically updated to the Tableau server. No more manual work, no more monthly scramble. This project taught me something I'll never forget. Data analysts of today should have a full-stack mindset to really be highly valuable. It's not enough to just analyze. You need to think about the entire system. This is what's gonna separate analysts making $60K from those making $120K. It's not just about being smarter, it's about being more valuable. And value comes from solving problems completely, not just partially. So again, you don't need to become a data engineer or a software engineer. That's not what I'm saying. But you need to know how to scale your impact. You need to think beyond the one-time analysis and ask yourself, how do I make this repeatable? How do I make this sustainable? How do I build something that keeps delivering value even when I'm not touching it? This is a mindset shift, and once you make it, you'll never go back. Okay, next up, positioning. Positioning is what most people completely miss the mark on, and honestly, this might be the most important thing that we talk about today. I'm not talking about LinkedIn tips or personal branding hacks. I'm talking about strategic positioning, the same kind of positioning that businesses spend millions of dollars figuring out. Here's what I see all the time: most analysts position themselves as just a data analyst, and I get it, that's what the role is called. But when you position yourself that way, you become a commodity. You're competing with thousands of other people who have the same title, the same skills, skills and the same projects on their portfolio. And when everyone looks the same, the only way to compete is on price. It's a race to the bottom, and that's not where you want to be. Here's what you need to understand: your career is like a business, and you need to treat it as seriously as any entrepreneur treats their company. That means thinking about positioning the same way businesses think about positioning their products. When a company launches a new product, they don't just say, here's another product, it's pretty good, please buy it. They figure out what makes them different, they carve out a space in the market, and they position themselves so they're not competing head to head with everyone else. You need to do the same with your career. There's a concept called blue ocean versus red ocean. So let me break this down. The red ocean is where most people operate. It's crowded, a bloody marketplace where everyone is fighting over the same opportunities, the same job postings, the same keyword resumes, generic portfolios. Everybody looks the same, sounds the same, competes for the same scraps of jobs. The blue ocean, on the other hand, is completely different. It's an uncontested market space. Instead of competing with the masses, you create your own category as a data analyst. You position yourself so uniquely that competition becomes irrelevant. The water is blue because it's calm, it's open, and room to swim. So instead of data analyst, you need to become the analyst who reduces customer churn for e-commerce companies, or the analyst who automates financial reporting for healthcare organization, or the analyst who builds predictive models for supply chain optimization. You see the shift? You're not competing anymore. You created a category of one. And this goes way beyond LinkedIn and posting content. This is about how you position yourself on the job and in your career, how you show up to meetings, how you frame your projects, and how you communicate your value to leadership when you want a promotion. On the job, you position yourself as a person who solves problems that no one else wants to touch, a person who just doesn't answer questions but shapes strategy, the person who builds systems and not just dashboards. That's thought leadership 101. Not posting motivational quotes online or thirst traps on LinkedIn, actually leading your thinking in your day-to-day work having a point of view, and backing it up with evidence. Now, if you want to go deeper into this, I got 2 books that genuinely changed how I think about my career, and I don't recommend these lightly. First, Positioning. This is a classic that started it all, and it was written for marketers and business leaders. But when I read it, I realized everything in this book applies to how we position ourselves as professionals. It teaches you that the battle isn't about being the best, it's about being different. It's about owning a space in the marketplace. When a recruiter or hiring manager thinks about a specific problem, you want them to think of you. That is positioning, and this book gives you the framework to make that happen. Second book, Play Bigger. This one is all about category design. The idea that instead of competing in an existing category where you're fighting against everyone else, you create your own category and dominate it. Companies that win biggest don't just build better products, they define entire new categories. So think about how Uber didn't position themselves as a better taxi company. They created a category of ridesharing, and you can do the same thing with your career. You don't have to be a better data analyst than everyone else per se. You can be the only analyst who does what you do the way you do it for the people that you serve. Now, these aren't necessarily light reads like the earlier recommendations, but if you're really serious about building a career that compounds over time, these are the books that will give you a massive edge over everyone else who's just focused on learning more tools. Okay, so there you have it, the 5 skills that will define the next phase of analytics. The market is shifting right now, and the only question is whether or not you're gonna ride the wave or really get crushed by it. Now, if you're ready to make this happen, you want the exact blueprint that my students use to land these roles, book your free data career strategy call. The link is in the description. Don't let someone else with less talent and less experience take your spot. Make your move, and I'll see you in the next one.