Tips for Advancing in a Data Analyst Career

Navigating New Data Analyst Challenges... As a new data analyst you will be faced with common challenges.Think of each hurdle is an opportunity for growth, and by mastering these, you'll set yourself up for success. 💎 Excel's Limitations💎 As much as I love Excel... Diversify your arsenal with advanced tools like Python, SQL or Powerbi for a more robust analytical experience and visual experience. Excel is just one piece of the puzzle, not the entire picture. 💎Art of Data Curation💎 Choose data wisely! The temptation to accumulate mountains of data is real, but finesse lies in cherry-picking only the most relevant. Avoid analysis paralysis; be a discerning curator of information. 💎Elegant Visualization💎 Craft compelling visualizations that captivate, not confuse. Avoid creating chaotic graphs that resemble spaghetti. Simplify your visuals... remember, less complexity often equals more impact. 💎Causal Does not = Correlation💎 Distinguish correlation from causation. Mere coincidence of data trends doesn't imply causality. Employ sound statistical techniques to establish causation, avoiding hasty assumptions 💎Honesty in Analysis💎 Maintain data integrity and transparency. Refrain from molding data to fit preconceived narratives. Trust in your insights is paramount... let the data speak its truth. 💎Plain Language💎 Effective communication is vital. Translate your findings into plain language for universal understanding. Avoid cryptic jargon that alienates non-analysts. 💎Collaboration💎 Foster collaboration...data analysis is a team effort. Seek insights from experts, engage colleagues in discussions, and learn from your peers. Together, you can orchestrate success. As a data analyst with over a decade of experience I still struggle with some of these... A chart that I think tells an amazing story but is too complicated... An analysis that is sound but so technical my end user has no idea the impact of the program... Remember working the in world of data is a journey where you are constantly learning... #dataanalytics #dailylearning #dataanalyst #careeradvancement

Harsh truth for data pros: You won’t move up just by getting better at tools. You have to change how you think. Here are 6 mindset shifts That quietly transform analyst careers: ⸻ 1/ Overthinking kills clarity → You don’t need the perfect chart. You need a useful one. The best insight is the one they actually use. ⸻ 2/ Vagueness kills accuracy → If the request is unclear, the output will be too. Don’t just accept ambiguity — push for clarity. ⸻ 3/ Over-design kills insight → Flashy visuals don’t fix fuzzy thinking. Good analysts make data make sense — not just look good. ⸻ 4/ Overpolishing kills delivery → You tweak, revise, reword… Meanwhile, the moment passes. Done > perfect. Impact needs a deadline. ⸻ 5/ Assumptions kill collaboration → “They probably meant X.” “They won’t get it.” Ask. Align. Communicate. Don’t build in a silo. ⸻ 6/ Inaction kills trust → That follow-up you didn’t send? The insight you never shared? The teammate you left waiting? Follow-through builds reputations. Delay erodes them. ⸻ These don’t show up in your resume. But they shape how you’re seen - and how far you go. Credit to César Solís who inspired this post.

Best way to stand out early in your data career? Think like a business owner 💡 👉 Talk to stakeholders to understand their motivations 👉 Build domain knowledge to learn the nuances of the business 👉 Clearly articulate how your analysis ties to specific goals or KPIs 👉 Draft a measurement plan before you even touch the data Early in my career all I wanted to do was build fancy reports and dashboards, but as soon as I started thinking this way everything changed. Not only did I start earning respect and recognition from management, but I began to actually see (and measure) the impact of my work. This was probably the single biggest catalyst in my career growth and development as an analyst. So to all the seasoned pros out there, what other advice would you give to help an analyst accelerate their career?

The most challenging transition from "breaking into" a data career to "growing" your data career is your relationship with technical skills. Getting into data requires much investment in growing your technical skills and showing proficiency. The harsh truth is that these technical skills are just the bare minimum. While it's essential to upskill and improve your technical understanding, this alone won't get you promoted. What gets you promoted is applying your technical skills to business problems and getting buy-in to implement them. The key phrase here is "buy-in to implement," and this is where you NEED to become proficient in soft skills and selling internally to your peers and leadership. It's why I spend so much time talking to stakeholders across the business to understand the pains they experience and how data can support their respective business goals. It's why I spend so much time scoping problems and their impact. It's why I spend so much time bringing my stakeholder along the building process so they feel it's their project as well. Stop focusing on data itself, and instead focus on what data can do for your stakeholders and watch your career trajectory accelerate. #data #ai

I started working in data analytics 8 years ago. I’ve learned a lot in that time. Here’s one of the BIGGEST mistakes to avoid to be more effective in your role and advance your career. In the world of data analytics, getting bogged down with ad hoc, reactive requests is a common challenge. The key to transcending this reactive loop lies in probing the WHY behind each request. When a co-worker comes to you with a request, don’t just immediately get started working on it and then send them the solution once you finish. It’s important that you discover the real need. Often times business users come with specific data requests based on their limited understanding of what data can do. As an analyst, when someone asks for a particular data set, it's crucial to ask why they need it. Understanding their underlying motivation can reveal more about what they're trying to solve or understand. If you don’t do this you’ll end up wasting A LOT of your time and you won’t even provide them the best solution. Once you grasp the real question or problem, you're in a position to offer a more effective solution. For example, if someone asks for a specific data pull, by understanding their ultimate goal (e.g., understanding customer behavior, improving operational efficiency), you might suggest a better, more comprehensive way to look at the problem using data. Business users aren't typically data experts, and allowing them to dictate data solutions can lead to suboptimal outcomes. Instead, train them to approach you with problems, not preconceived solutions. This approach not only leads to better data-driven decisions but also educates users about the potential and limitations of data analysis. By understanding the true motivation behind data requests, you position yourself not just as a data analyst, but as a strategic partner in problem-solving. This approach allows you to leverage your expertise to provide more insightful, impactful data analysis, ultimately enhancing the decision-making process within the organization. Remember this next time you get a request! 🤝 Every Thursday I send out a free newsletter to 9,000+ data crunchers like you. The content varies each week but includes SQL tips, open data jobs, freelance gigs, datasets for portfolio projects, data memes to keep it fun, and any other useful info we find. Click the link in my profile page to sign up or you can go to thequery.jobs! #data #dataanalyst

Are you a data professional who is sad with having a low-impact project at work? Don't be too disheartened; there's many paths to growing your responsibilities! Here's three: 👇 1️⃣ Get buy-in to scale your project up 📈 Is your data science model creating a 100% lift in the click-through rate of a small button? Time to present a pitch about applying that technique to bigger, more prominent entry-points. Small button yesterday, front-page widget tomorrow! 2️⃣ Get "promoted" to a bigger project ⬆️ Proving you can execute on a smaller project is one of the main ways people get increased responsibilities. If you are a data analyst who owns metrics for a small project but go above and beyond the median data analyst, then you'll be the natural choice to own a larger project when names are being pulled. Be the position you want to be tomorrow, today! 3️⃣ Ask for a lateral move or change companies 🏃♀️👋 It's possible that the team you're working on is nothing but an entry-level farm. In that case, see if you can move internally if you feel unsatisfied with your growth. And if your company doesn't recognize your execution or have any opportunities for growth, then get the heck out of there!

Hey LinkedIn Family ! As we venture through an era rich with AI breakthroughs and the rise of large language models, I've noticed a lot of concern among friends and colleagues about the future. As a Python developer, data scientist, and MLOps specialist, I've seen firsthand how the tech landscape is shifting. One key lesson that I learned through my journey : being technically adept is crucial, but it’s not the complete picture. And the more you only rely on your hard skills, the more vulnerable you become! Here's the brighter side: tech is as much about understanding the impact of our work as it is about executing tasks. It’s about seeing the bigger picture. Those who broaden their horizons beyond just code and data often find themselves in a stronger position. 🌟 My advice is simple but powerful: Lean into the career development opportunities your workplace offers. Think beyond the code! Expand your horizons to include management skills, communication, leadership, and technical writing. For those starting out as junior software engineers or data analysts, try your hand at agile management. Document your achievements and your workflows, make sure to to be vocal about your accomplishments, and make sure you’re seen—don’t just wait for tasks to come your way, actively ask for new tasks, and if you are in benches for sometimes, ask to help your colleagues in a new endeavor so that you can show your accomplishments to managers. Being visible matters. If you’re not seen by your manager, you might be overlooked when it comes to recognizing the company’s successes. Collaborate, share your successes, and ensure your contributions are acknowledged. The secret to securing your place in today’s job market? Be proactive, embrace a spirit of professionalism, and steadily ascend the leadership ladder. 🔑 Be more than unfirable. Be invaluable. #CareerDevelopment #Leadership #TechIndustry #MLOps #DataScience #ProfessionalGrowth

Data communication is one of the leading skills that will separate you from the rest of the pack in the data space. ✨ Here are 𝐚 𝐟𝐞𝐰 𝐤𝐞𝐲 𝐥𝐞𝐬𝐬𝐨𝐧𝐬 from presenting & iterating on the most advanced data analytics project of my career thus far... 1. Familiarize yourself with the KPIs you will be presenting This one may seem obvious, but in a world of so many demands it is worth noting that it is easy to miss or forget the source & details of specific KPIs mid-presentation. Spend some time going over your dashboards and reports to gain an idea for how you will present them and in what order. Better yet, 𝐡𝐚𝐯𝐞 𝐭𝐡𝐞 𝐝𝐨𝐜𝐮𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐫𝐞𝐚𝐝𝐲 𝐚𝐧𝐝 𝐢𝐧 𝐚 𝐟𝐨𝐫𝐦𝐚𝐭 𝐩𝐫𝐞𝐬𝐞𝐧𝐭𝐚𝐛𝐥𝐞 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐦𝐞𝐞𝐭𝐢𝐧𝐠! 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐍𝐨𝐭𝐞: Work with stakeholders to prioritize the KPIs of focus if there is an exhaustive number of them for delivery! 2. Plan out how you are going to start the presentation. Kicking off a data storytelling presentation is always the hardest part. Break the ice! To do it with confidence, have an idea for how you will kick off the conversation and lead into some valuable insights and pointers for the business that is contained within your data product. 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐍𝐨𝐭𝐞: I like to give an overview visually first. Run through the reports or dashboards you've created to give the project and code base you've been developing some identity! Then dive into the nitty gritty details... 3. For more advanced reporting metrics, frame the question for what the business is trying to answer and how this solution accomplishes it. A great data mentor in the space told me the importance of framing the question to contextualize someone brand new to a report or data problem. This same piece of advice follows when storytelling data! I’ve found that stakeholders ask for metrics but then, when presented with them, don’t always wrap their head around the visualization or need to ask clarifying questions. Knock this out up front by contextualizing the problem the metric solves with a business lens and what the output number(s) represent. Assuming your dashboards follow a logical order, these steps paired with an organized report will help guide you in delivering a clean, concise and useful data storytelling presentation. If successful, this will spark iterative questions and feedback for enhancements. Remember, in data visualization perfection is overrated. 📊 Lean on iteration and getting the product in front of the stakeholders efficiently to maximize your effectiveness as a data professional! #datastorytelling #datacommunication #datavisualization

You got your first data job, congratulations! 🥳 ...but after starting and getting comfortable in your role, what comes next? How do you keep growing your skills, even after you've gotten your first job? Here are a few ways: 📌 Ask why. ↳Why is the stakeholder asking for this? Why does this data matter? Why is your analysis important to business goals? 📌 Find mentors. ↳Look to people on your team or in your department who have been in your shoes and learn from them! 📌 Keep learning. ↳Data analytics is ever-changing so keeping up with new tools and technology is a must. You can even pick up a new tool and grow your skill set. 📌 Build relationships. ↳Connect with colleagues outside of your immediate team and participate in collaborative projects. Grow your professional network. 📌 Learn the business. ↳Business and domain knowledge can not only help you gain a better understanding of your data but can make you even more valuable as a team member. Getting your first job is a huge accomplishment and you should be proud of that! But don't be afraid to continue growing and learning - it can only have a positive impact on you and your career. #dataanalytics #data #firstjob #personaldevelopment #professionaldevelopment