Michael Shoemaker, MBA

A few years ago, showing off your SQL skills or Tableau dashboards could get you noticed. But now, the analysts who stand out aren’t the “tool wizards.” They’re the ones who sound like business partners and analytical thinkers. They speak the company’s language. They talk in metrics, outcomes, and recommendations. Hiring managers don’t want someone who can just query data, they want someone who can translate it. That’s why I teach something called the KPIS Framework - four simple steps to make your work stand out: • K — Know your top 10 metrics. Start with the ones that drive your industry. • P — Practice why they matter. Tie each to a stakeholder and a business decision. • I — Imitate leaders. Borrow language from earnings reports or industry blogs. • S — Showcase them. Thread KPIs everywhere - your portfolio, your resume, your interviews. It’s not about looking more technical. It’s about sounding more business-ready. 📹 I walk through real, practical examples in this week’s video! → https://lnkd.in/emF3QWpz P.S. Applications to the Analytics Accelerator are now open. If you want help on your journey to becoming a data analyst, you can apply for mentorship here → https://lnkd.in/eyiVS56N

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Dear Data Leaders and CEOs, Your data team doesn’t need more headcount or another data platform to find value from data. They need to be able to focus! Right now, they’re drowning in: - Random asks and flights of fancy - Tool sprawl(Of course you should be using Snowflake, Bigquery And Databricks right?) - Broken data pipelines and schema changes You don’t need another dashboard for the business to ignore. You don’t need more people that go through the motions of building data pipelines and automated workflows. You need clarity(and honestly the willingness to say no). Clarity on: - What matters to the business - What matter to the industry as a whole - What success looks like Until you fix the upstream bias for reaction and align on priorities, you’re just scaling dysfunction. Value doesn’t come from doing more. If you are a data or a business leader who wants to better understand how to align your data team with the business, then the Data Leaders Playbook community is for you. If you're interested in joining, let me know below!

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Your data analysts aren't just "report people." They're strategic partners being underutilized. Want to get the most value from them? 1. Include them in planning meetings BEFORE decisions are made, not after 2. Share the full context behind data requests, not just the request itself 3. Ask them "what should we be measuring?" instead of just "can you measure this?" 4. Give them time to explore the data before demanding conclusions 5. Listen when they push back on a metric or approach Often, the difference between a team that uses data effectively and one that doesn't isn't the quality of their analysts. It's how they treat their analysts.

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I asked AI to write my SQL query. It hallucinated a stored procedure, joined 4 tables I didn’t need, and added a WHERE clause that filtered out all my data. Shipped it to production then said: "Hope this helps!" But really.. AI isn’t here to replace analysts. It's a super power to make you better and faster. If you're learning data right now, here’s your actual edge: - Learn enough AI to speed yourself up - Learn enough SQL/Excel/BI tools to catch when AI is wrong - Learn to think like an analyst The ones who understand the logic will win. -- Need a little help in the exact spot you are stuck in right now? Join Analyst Hive (the best data community in the world) P.S. It's free. Just sayin'

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How to sound like a data person: - Say "It depends" then stare into the middle distance like you're calculating the meaning of life - Ask "How do you define Tuesday?" (noun doesn't have to make sense) - Mention sample size is too small, even if you're discussing lunch plans - Promise to "pull the data" for literally everything, including the weather - Suggest A/B testing with a 6-month timeline to answer "Should we order pizza?"

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This is the fastest way to burn out as a data professional: (And it's so common that it scares me) Being an order taker. Someone asks for a dashboard... You say, "Sure." Someone asks for a report... You say, "Okay." Someone tells you, "I need this by Friday..." You say, "No problem." Eventually, you've built a backlog of dashboards that nobody uses. ... a ton of metrics that don't tie to any useful decisions. And worst of all, a reputation as a "BI help desk." Why is this such a big deal? Because you're setting the standard for how you want to be treated. Most data teams fall into this trap. And this is why most data teams are exhausted, undervalued, and lost. They’ve turned themselves into order processors instead of problem solvers. There's only one way to fix this problem: Learn to say, "No." Not a hard “no” that shuts the door... But a strategic no that redirects the conversation to what matters. A "No" that allows you to investigate: • What decision is stuck right now? • How will this be used once it’s built? • Is a dashboard even the right tool?    Here’s the takeaway: The best analytics teams are the slowest to say “yes” to ad-hoc requests. Why? Because everything must tie back to ROI. ♻️ Share this to help someone in your team avoid the order-taker trap. Follow me for more pragmatic takes on how to make analytics drive outcomes.

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Data professionals struggle to answer the following: "Where do you see yourself in 5 years?" One of the best things about working in Data is that your role sits at the intersection of technical expertise and business impact. But growing faster and making the right career moves is easier if you have a clear direction. There are 2 big paths you can take in Data: 1️⃣The Managerial Path Focuses on leading teams, driving strategy, and making data-driven decisions that impact the business. Roles you might find here: ↳ Data Lead ↳ Head Of Data ↳ Chief Data Officer ↳ Analytics Manager ↳ Data Engineering Manager Success comes from building strong 𝗹𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝘀𝗸𝗶𝗹𝗹𝘀, understanding 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗽𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝗲𝘀, and the ability to translate 𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝘄𝗼𝗿𝗸 𝗶𝗻𝘁𝗼 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀. 2️⃣The Technical Path This path focuses on deep technical expertise, designing systems and building scalable data solutions. Roles you might find here: ↳ Data Architect ↳ Solutions Architect ↳ Senior Data Engineer ↳ Data Platform Specialist ↳ Machine Learning Engineer Success comes from honing advanced 𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝘀𝗸𝗶𝗹𝗹𝘀 and becoming a 𝗴𝗼-𝘁𝗼 𝗲𝘅𝗽𝗲𝗿𝘁 for critical challenges. With a clear path, it becomes easier to grow, make strategic career choices and confidently answer the classic interview question. —— Where do you see yourself in 5 years? I'm José and in 5 years I would like to be a Data Lead.

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💡 STOP SAYING "I DON'T HAVE DATA EXPERIENCE" - You Have More Than You Think The biggest lie career switchers tell themselves: "I need to start from zero in data." Wrong. Dead wrong. After coaching 50+ professionals through data transitions, I've discovered something powerful: Your "non-technical" background isn't your weakness—it's your competitive advantage. Here's How to Translate Your Real Experience: 🏦 BANKING/FINANCE → Risk Analytics Expert ❗ What you think: "I just processed loans" ✅ What employers hear: "I analyzed financial patterns, assessed risk factors, and made data-driven approval decisions under regulatory constraints" 🛍️ RETAIL/SALES → Customer Behavior Analyst ❗ What you think: "I just sold products" ✅ What employers hear: "I identified purchasing patterns, segmented customers by behavior, and optimized conversion funnels through A/B testing" 🏥 HEALTHCARE → Healthcare Data Specialist ❗ What you think: "I just managed patient records" ✅ What employers hear: "I ensured data quality in life-critical systems, maintained HIPAA compliance, and tracked outcome metrics" 📚 EDUCATION → Learning Analytics Professional ❗ What you think: "I just taught classes" ✅ What employers hear: "I measured performance metrics, identified improvement opportunities, and used data to personalize learning outcomes" 🏭 OPERATIONS → Supply Chain Analytics Expert ❗ What you think: "I just managed workflows" ✅ What employers hear: "I optimized processes using KPIs, forecasted demand patterns, and reduced inefficiencies through data analysis" The Strategic Reframe: Every job you've had involved: • Pattern recognition (hello, machine learning!) • Problem-solving with constraints (data modeling!) • Stakeholder communication (business intelligence!) • Process optimization (analytics!) My Own Proof Point: I went from Banking Advisor to Senior Data Analyst in 12 months. Not because I learned to code overnight, but because I learned to articulate how my banking experience WAS data experience. The Million-Dollar Question: What problems were you already solving with data—you just didn't call it "analytics"? Your Action Plan: 1️⃣ List 3 decisions you made using numbers/patterns in your current role 2️⃣ Identify the business impact of those decisions 3️⃣ Rewrite them using data terminology 4️⃣ Build projects that demonstrate these skills The Truth: Companies don't just need people who can write SQL. They need people who understand business problems AND can solve them with data. That's you. --- Ready to stop undervaluing your experience and start positioning yourself as the data professional you already are? I'm opening 5 "Experience Translation" strategy sessions this week. We'll map your background to high-value data roles and build your transition roadmap. Comment "TRANSLATE" below and I'll send you my free "Experience-to-Data Skills Decoder" worksheet.

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I never expected to understand containers in Tableau—until I forced myself to sketch them out. Here’s the full story. I was stuck. My dashboards felt messy and hard to manage. This was a problem. If I couldn’t structure my layouts, my designs would continue to fall short. I needed to learn containers. But I was overcomplicating them. I thought they were just boxes inside boxes. Confusing. Frustrating. Something I avoided. Everything changed when I started drawing them by hand. Instead of dragging and dropping blindly, I mapped out how each container should work. Horizontal. Vertical. Nested. Suddenly the logic clicked. • I could split dashboards into clean sections. • I could align elements perfectly. • I could swap views in and out without breaking the design. • I finally felt in control. That one shift changed how I build every dashboard today. Here’s the thing: containers are the backbone of clean, flexible designs in Tableau. If you struggle with them, I promise you’re not alone. I put together a playlist to help you learn them step by step: https://lnkd.in/ePbbcWjJ P.S. For the first time ever, you can try out a quarterly subscription to Next-Level Tableau free for 14 days and decide if it’s right for you. DM me to learn more.

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Losers say you can’t teach yourself data analytics in a week and land a job in another. Here’s the 2-week plan to prove them wrong: Day 1: Become a SQL expert Day 2: Become a Tableau expert Day 3: Become a Microsoft Excel expert Day 4: Build a portfolio website with one project for each skill Day 5: Optimize your resume Day 6: Optimize your LinkedIn Day 7: Learn to answer non-technical interview questions (STAR Method) Day 8: Learn to ace technical interview questions (coding problems and practice) Day 9: Find out where your friends and family work, see if there are data analyst job openings there, and ask for referrals Day 10: Attend a bunch of data and networking events and apply for 150 jobs Day 11: Take a break and sit in an ice bath all day Day 12: You’re finally ready to learn Microsoft Paint Day 13: Interview for 5 jobs Day 14: Accept highest paid job offer and brag on LinkedIn It’s that easy.

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🚩 Dashboards aren’t supposed to look like Christmas trees 🎄 Have you ever open a dashboard and just sit there like, “Wait… what am I even looking at?” Charts everywhere. Colors shouting at you. 3 minutes in, and still no clue what’s important. That’s not insight. That’s noise. 📍 Dashboards aren’t built to impress. They’re built to guide decisions. → More visuals ≠ more value → One clear story > ten half-stories → If people feel lost, the dashboard failed 📍 Your dashboard isn’t for you. It’s for the person who needs to act on it. → If it looks like a Christmas tree, you built it for yourself. →If it looks like a compass, you built it for the business. 📌Drop one dashboard tip or advice so others can learn #dataliving #favouribude

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Ok there are legit applications for pie charts! There I said it. 🥧 Unfortunately, serious business dashboards are not on the list. Pie Charts, although incredibly popular, are usually among the worst possible options to visualize data. Apart from finge cases pie charts tend to be a waste of space and attention, insulting the recipients eyes. Especially when filled with 5 to 50 different slices and in 3D they get ugly rather quickly. So bad that Ralph Ginzinger and I dedicated a large part of the very first episode of UNF#CK YOUR DATA on how bad the are. But just hating is easy. We want to help everyone getting better: For serious business dashboards the boring but effective answer is: Most of the time you can't go wrong with a bar chart. Sabine Schwenninger also created a quick decision tree to pick the right visualization, if you send her a kind message she might share it again. (And there are similar ones out there like this: https://lnkd.in/e9SM5CY6 by Yan Holtz). If you desperately want to use a pie chart, ok I hear you, there are some legit applications: 1) You want to make a visualization on pie/cake 2) Your data resembles pac man (75% to 25%) and you color it yellow 3) You want to get a message across with some humor 4) Your name is Katja Berlin and you want to get an important message across (with some humor) If your use case is not on the list. Probably a pie chart is not the best option. So now some CTA: Please share the best/worst pie chart you have seen! What your take on those pesky visuals Bonus question: Which card game do you have to think when looking at the chart? (Told you humor is a legit application!) PS all of the above is also true for donut charts, the evil twin of pie charts.

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Debugging SQL for Analytics - The Old School Way that still delivers Problem: You have this annoying long legacy query. You need to understand why the output data looks like your dog's poop. The person who built it is no longer in the company... Maybe that's good, or you'd be tempted to commit a crime! Who the hell writes queries like this?! Potential approach to the problem: - Start from auto-formatting. At least it will make it a little bit easier to visually inspect - Before diving into the nitty-gritty of the code, try to build a generic idea of what it tries to do. What business logic does it try to represent? I found that when I don't know what it's really supposed to be and just start deconstructing pieces - I tend to focus too much on overcomplicated technicalities. - Ok, when you know what it is supposed to do, now it is time for a classic deconstruction and mapping things out. What do all those CTEs/subqueries do, and how/why do they tie together? Sometimes you may end up with a blank query and then re-constructing the monster step-by-step to understand the flow better. - If the query has many CTEs built on the top of each other - start from top to bottom as that’s probably how it flows. If gigantic multiple-nested subqueries - start inside out. Some additional comments: - One thing that bad long queries have in common is that they do things in a round and overcomplicated manner. So, if I know that e.g., it's supposed to calculate X, I can spot easier "stupid" parts of the code, clamp them together and discard. Instead of understanding every single (unnecessary) step my predecessor wrote. - Digging deeper into surprising bits. If there's an obvious way of doing something - why did the author fail? Was it because of a lack of skills or - are there some unknown reasons? Maybe in the past, it was not possible to do otherwise? Or I don't understand the issue fully, and there are some nuanced relationships, etc. - Prehistoric pen and paper may be a life saver! You can use digital flow diagram as well of course Feel free to share your debugging approach! As I write this, I realise this is entirely no-AI-support so I’d love to hear how you’d use LLMs to help with this too. It would be a waste not to use AI here as that’s one of the better tasks for it! —-— 👋🏼 I'm Slawomir. I guide companies at any stage to turn data into clear action and real business impact. ♻️ Repost to help your network

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Data jobs look cool from the outside, but they can be a tedious grind too. Many people always talk about the excitement of being part of interesting projects at work. Sure, you'll have moments of excitement like building dashboards and brainstorming solutions. This was my (now I'll admit NAIVE) expectation before my first major role back in December 2021. 3.5+ years later, I can confirm most of your time will be spent on a relentless grind of unglamorous tasks.  • The hours spent refreshing and validating data in the backend.  • The repetition of updating documents and presentations.  • The drain of hours spent to troubleshoot tiny errors  • The constant communication to chase down stakeholders.  • The Monotony of endless meetings with no clear purpose.  • Working late to wrap up tasks that feel never-ending. These tasks will eventually feel like a burnout or boredom. And when they do, even your "high salary" won't feel enough to get you through. Hence, before you commit to a data career, it's vital to have a strong "why." Your motivation too needs to be rooted in a genuine passion for problem-solving, a tolerance for complexity, and the resilience to handle the demanding, unglamorous tasks. - Find purpose in repetitive work by understanding its critical role in the final outcome. - Turn repetitive work into a challenge by automating where you can. - Use downtime to practice new skills or explore emerging technologies. - Showcase your value by explaining the rigorous work that goes into your final product. Money and hype only take you so far before your true passion—or lack thereof, gets revealed. Follow Varun Sagar Theegala for resources & insights on life in analytics, data science, AI trends, and more #DataAnalytics #CareerAdvice #ProfessionalDevelopment #DataTransition

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Your data model is only as good as your understanding of the business problem you're trying to solve. Model the business first, then optimize the technology. "But how do I do this in the real world?” You need a framework, and the good news? You don’t have to reinvent the wheel. At Data Culture, we use a practical, 3-step approach to data modelling: ✅ Understand the Why, When, and Who (Conceptual Model) ✅ Design the What (Logical Model) ✅ Optimize the How (Physical Model) In Opeyemi Fabiyi’s latest blog post, he breaks down this framework and shows how to build scalable, business-aligned data models that work. 👉 Explore the full framework on the blog https://lnkd.in/dyvBUAUc #datamodelling #dataculture #kimball #dimensionalmodelling

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