Analytical Skills Improvement

You can't get promoted in FP&A by just working longer hours FP&A people spend too much time on routine tasks during their standard work week. But the most successful ones find ways to level up their analytical skills and impact. It's about building up the value you deliver. How? Here are 5 key areas to focus on: 1. Data visualization: Start using charts like waterfalls to better visualize and explain your analysis. Move beyond just reporting the numbers to provide clear data storytelling. 2. Driver-based analysis: Don't just report revenues and costs. Dig deeper to identify and track the key drivers behind them, like units, prices, industry factors, etc. 3. Statistical analysis: Pull basic statistics like averages, distributions, etc. to summarize and interpret your data using Excel analysis tools. Compare across segments and time periods. 4. Forecasting techniques: For major forecasting items, apply data science methods like regression and time series analysis. Review historical forecasting errors to identify improvement opportunities. 5. Optimization modeling: Work with data scientists to build optimization models using linear programming for areas like marketing spend allocation that management needs decision support on. The bottom line? Actively upskilling in these areas shows you're going beyond reporting. Set weekly goals, find online courses & resources, and get hands-on practice. In that way, you can create more value than most FP&A professionals do in years. Greater impact leads to career growth.

  Please Stop memorizing Python and SQL syntax. It’s not helping you grow. Let me tell you why: A junior analyst once asked me: "How do you remember all those SQL functions?" I don't. - I replied him. Just Last Tuesday, I spent 10 minutes Googling how to properly use CASE WHEN statements for a client report. And you know what? That's completely fine. Here's what 5 years in data analytics taught me: Memorizing syntax is like memorizing phone numbers in 2025 - pointless and outdated. The real skill? Knowing WHAT question to ask and HOW to break down problems. When stakeholders dump a messy Excel file on my desk asking for "insights," I'm not thinking about perfect SQL syntax. I'm thinking: What story is this data trying to tell? Where are the gaps? What patterns matter to the business? The dirty truth about real projects: Data is never clean (despite what tutorials show) Requirements change mid-analysis That simple dashboard becomes a 3-week project My actual workflow: Understanding the business problem Sketching out the approach Google/ChatGPT the syntax I forgot Test, iterate, break things Fix what I broke (usually with more Googling) Even senior analysts in my team have Stack Overflow bookmarked. We share code snippets on Slack. We debug together. The skill that actually matters? Problem-solving and critical thinking. Anyone can memorize GROUP BY. Not everyone can spot why conversion rates dropped 15% last quarter or explain complex findings to non-technical stakeholders. Please Stop cramming syntax. Start solving real problems. Your brain will thank you. Drop in the comments the weirdest thing you've had to Google as a data professional... #ikechukwueluwa

Many people aspire to become great data analysts, but actually doing so requires grinding out small gains daily for years, and most "shortcuts" are B.S. But there is one way to rapidly speed progress that does work (no, not steroids...) Find yourself a great training partner - someone with your same drive to improve, at roughly your same level, but ideally with complementary strengths. Having a great training partner provides motivation, consistentency, accountability, feedback, encouragement, learning, and allows you to perform exercises you can't effectively do by yourself. It doesn't surprise me that many outstanding analysts I know are also committed weightlifters - Owen Price, Kelly Adams, Gustaw Dudek, Wilson Man, Martin Raffeiner, and Raphael Schagerl just to name a few. Weight training is a highly data-driven, lifelong pursuit that has much in common with the daily effort to improve your data skills. Here are just a few specific suggestions for how you can effectively leverage the benefits of a data training partner: 🔸Participate in challenges together - pick an area you both want to improve your skills in and begin participating in the same challenges - Excel, SQL, Power Query/M, DAX, Python, R, etc. Meet up briefly on-line and take 5 minutes for each of you to explain to the other how you attacked the problem and how your solution works. You will double your learning while improving your communication skills at the same time. 🔸 Create dirty data for the other to clean - recently I showed how in under 2 minutes you can create realistic dirty datasets (link below). However, learning is greatly enhanced if someone else is feeding AI the algorithm used to "dirty up" the data. Did you find all the "mines" your training partner planted in your dataset? Did they find all of yours? https://lnkd.in/eQer8dHH 🔸 Role play to prepare for interviews - also recently, I posted a comprehensive workflow for preparing for technical interviews, and a very similar approach can be used to prepare for behavioral interviews. While AI plays an important supporting role in this workflow, it is more effective when used in conjunction with a human partner, who can play the role of the interviewer for you, and vice versa. https://lnkd.in/enZgzaYe 🔸 Build full reports together - all of the best Power BI reports I have built, both in my portfolio and or in "real-life" have been done in conjunction with other developers, and I learned a ton more than I would have working alone. As a hiring manager, I always think it's a big plus when a candidate can specifically point to exceptional work that have produced as part of a team. Finally, having a great training partner makes the journey not only a lot more productive, but much more enjoyable. Arnold Schwarzenegger and Franco Columbu (below) became lifelong friends, and I ended up marrying my grad school lifting partner, but that's a story for another day... 😉 #career

Getting better at data analysis is simple but hard. It requires consistency. Here's how I go to work on my skills weekly: I take the time out to journal each week to build my critical thinking skills. I complete PowerPoint presentations to practice improving my delivery of insights. I complete weekly coding challenges by Data In Motion & Excel BI in Python, SQL, or DAX. When I have the time, I complete dashboard challenges by Onyx Data , fp20analytics, and Maven Analytics. Though I usually don't post all of my dashboards. I read tech books by Packt and O'reily on statistics, Power BI, Python, and Azure. I complete youTube follow-along videos to help me understand the flow of a new skill I'm trying to develop. I take the time out to read other people's code on #github to help improve my code structure. I practice wireframing dashboards in #figma. I study other analysts' data solutions on Tableau or novyPro. I participate in #workoutwednesday and #makeovermonday data viz challenges. Getting better is a daily habit for me. P.S. What do you do to keep your skills sharpened? #data #dataanalysis #dataanalytics #python #code #dax #powerbi #tableaupublic

Want to be a better Analyst? Here's an easy "hack" for any data person to seem (and be) more valuable... Before you share any analysis, pretend you are the business owner. Don't stop at insights. Ask: what should be done to improve the business? Make recommendations for ACTION. Do this every time you work on something. You have a huge advantage. You're seeing the data before anyone else. When you present your work, others will be seeing your findings for the first time. You've had plenty of time to formulate ideas. Use your extra time to figure out how to drive improvement in the business. This is how you go from "operating the scoreboard" to being on the field. Insights are always valuable. They are a great start. IMPACT is better. Make a habit of being the person pushing for improvement with specific recommendations. And then follow up and see those recommendations through to execution. Your career will thank you. #data #analytics #careers