Common Data Analysis Mistakes In Engineering

Data Engineer's Guide to Avoiding Common Pitfalls: Data Fallacies! Common Data Fallacies in Data Engineering Practice can be further grouped as - 🔧 Pipeline Design Fallacies: # Cherry Picking: Reporting 99.9% pipeline uptime by excluding scheduled maintenance windows and known outages # Data Dredging: Running multiple ML models on your ETL logs until finding a "significant" pattern that predicts failures # Survivorship Bias: Analyzing only successful data migrations while ignoring failed ones to design "best practices" # Cobra Effect: Setting strict SLAs on pipeline completion time, leading to teams bypassing data quality checks 🏗️ Infrastructure Fallacies: # False Causality: Assuming system slowdown is due to recent code deployment when it's actually regular peak load # Gerrymandering: Adjusting time window boundaries to make batch processing metrics look better than streaming # Sampling Bias: Testing data pipeline performance using only weekday data, missing weekend traffic patterns # Gambler's Fallacy: Assuming after three job failures, the next run will definitely succeed without fixing root cause 📊 Monitoring Fallacies: # Hawthorne Effect: System performance improving during monitoring setup because teams are paying extra attention # Regression Towards Mean: Overcorrecting resource allocation after one extreme pipeline latency spike # Simpson's Paradox: Overall pipeline success rate decreasing despite improvements in each individual data source # McNamara Fallacy: Focusing solely on data throughput while ignoring data quality and business value 🛠️ Development Fallacies: # Overfitting: Creating overly specific data validation rules based on current data that fail with new sources # Publication Bias: Documenting only successful architectural patterns while hiding failed approaches # Danger of Summary Metrics: Using average latency instead of percentiles to monitor pipeline performance It’s important to always validate assumptions, consider full context, and remember that data tells a story—make sure you're telling the complete one. Image Credits: Gina Acosta Gutiérrez #data #engineering #analytics #sql #python #storytelling

One of the biggest mistakes I see among data analysts (including me :D) is jumping straight into writing SQL queries or applying formulas in Excel without first understanding 𝐰𝐡𝐚𝐭 𝐭𝐡𝐞 𝐝𝐚𝐭𝐚 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐫𝐞𝐩𝐫𝐞𝐬𝐞𝐧𝐭𝐬. I've encountered analysts who write complex joins, aggregations, and filters—only to realize later that they misunderstood how the data was structured. The result? 𝐈𝐧𝐚𝐜𝐜𝐮𝐫𝐚𝐭𝐞 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬, 𝐰𝐫𝐨𝐧𝐠 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬, 𝐚𝐧𝐝 𝐰𝐚𝐬𝐭𝐞𝐝 𝐞𝐟𝐟𝐨𝐫𝐭𝐬. 𝐋𝐞𝐭 𝐦𝐞 𝐬𝐡𝐚𝐫𝐞 𝐚 𝐫𝐞𝐚𝐥 𝐞𝐱𝐚𝐦𝐩𝐥𝐞: At a previous company, a junior analyst was tasked with analyzing customer refund rates. He pulled data from multiple tables, applied filters, and calculated the refund percentage. His conclusion? 𝐓𝐡𝐞 𝐫𝐞𝐟𝐮𝐧𝐝 𝐫𝐚𝐭𝐞 𝐰𝐚𝐬 𝐚𝐥𝐚𝐫𝐦𝐢𝐧𝐠𝐥𝐲 𝐡𝐢𝐠𝐡—𝐚𝐥𝐦𝐨𝐬𝐭 35%. The leadership team was concerned. But when we revisited his analysis, we found a major issue: 👉 He had included 𝐜𝐚𝐧𝐜𝐞𝐥𝐞𝐝 𝐨𝐫𝐝𝐞𝐫𝐬 in the refund calculation. 👉 He didn't know that the system stored cancellations and refunds in the same column with different status codes. 👉 After cleaning the data properly, the actual refund rate was just 5%. A single misunderstanding could have led to misguided strategies and unnecessary panic. 𝐇𝐨𝐰 𝐒𝐡𝐨𝐮𝐥𝐝 𝐘𝐨𝐮 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬? 🔹 𝐑𝐞𝐚𝐝 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚 𝐅𝐢𝐫𝐬𝐭: Understand what each row and column represents. Ask, "What process generated this data?" 🔹 𝐊𝐧𝐨𝐰 𝐭𝐡𝐞 𝐒𝐲𝐬𝐭𝐞𝐦: Learn how data is stored, updated, and linked across tables. 🔹 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐞 𝐁𝐞𝐟𝐨𝐫𝐞 𝐀𝐧𝐚𝐥𝐲𝐳𝐢𝐧𝐠: Before applying formulas or queries, check for duplicates, missing values, and inconsistencies. 🔹 𝐀𝐬𝐤 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬: If you're unsure about a field, reach out to engineers, product managers, or domain experts. Mastering SQL or Excel is important—but understanding data deeply is what separates great analysts from average ones. Have you ever encountered a situation where misunderstanding the data led to wrong insights? Let’s discuss in the comments! 👇

Everyone’s posting their data analytics wins. Today, I'm sharing my losses. Courses didn't make me a data analyst. Real-world experience did with every failure along the way. Here’s my mistakes: • Scheduled a report with SQL errors that sent blank data to essential managers • Accidentally emailed key stakeholders the wrong file • Rushed a report with a critical formula mistake that had to be retracted and corrected • Updated a dashboard in production without proper testing, breaking visualizations for executive teams These failures taught me to: - Slow down when it matters most - Build consistent checks and processes - Test obsessively before releasing - Create safety nets for mistakes I owned those errors AND the required solutions. The truth? Every failure is an opportunity to grow. The best analysts I know aren't those who never make mistakes. Instead, it’s those who learn from them faster. What mistake taught you the most? Share below 👇 #DataAnalytics #FailForward #ProfessionalGrowth #DataLessons

When the data doesn’t fit the data model: Is it the data’s fault or the data model’s? Yesterday I had a fascinating conversation with my friend Dan Gschwend about a scenario that might sound all too familiar to data engineers: A team had a table in the data model that relied on a single identifier—let's call it a BatchID. Everything worked fine with internal data, but when external data was added, the assumptions broke down. The BatchID wasn't unique anymore. So, the data engineer took action, creating a composite key to make it work. Problem solved, right? Not quite. By forcing the data to fit the model, rather than re-evaluating the model itself, the team was about to create multiple downstream issues. The pipeline was green, but the meaning of the data was wrong. Applications would have started to receive data where they would need to  make arbitrary decisions—pick the max, min, random —you name it. Ultimately, this would have led to incorrect insights and bad business decisions. How did we get here? 1) Siloed team structures: The data modeling team worked independently of the data engineering team. They didn’t collaborate on sourcing or truly understanding the data. 2) Static assumptions: The model was designed for internal data but didn’t account for the evolving reality of external data sources. 3) Lack of communication: There wasn’t a safe space for the data engineer to raise questions or challenge the assumptions baked into the model. So what can we do differently? 1) Encourage collaboration: Data modeling and data engineering should go hand in hand. The people designing the model need to understand the data they’re working with. 2) Create a safe space: If something doesn’t look right, team members should feel empowered to raise their concerns—even if the pipeline is “green.” 3) Acknowledge shortcuts and debt: Not every solution will be perfect, but it’s crucial to document decisions and trade-offs so they can be revisited later. The best shortcuts balance near term needs while leaving a clean path to the ideal representation. At the end of the day, data and knowledge work takes a village. It’s not just about moving data or building models—it’s about fostering a shared understanding and creating systems that can evolve as reality changes. This is an example of why we need to invest in semantics and knowledge. Have you faced a similar challenge? How do you ensure collaboration between data modeling and engineering teams?

One major issue with Data Science is that, in the real world, if you have two teams competing to build some model and judge them based on some arbitrary metric like Precision or Accuracy or RSME, it's very likely that the winning team will build a model that fails once it goes into production. This is entirely due to data leakage, which is quite common, even in published PhD papers, but it's really hard to know if you have a data leakage problem in your dataset until you put your model in production. There are, however, a few things you can do to mitigate this problem. 1. Be suspicious. If your model behaves well, assume it's because of data leakage first. That should be your default hypothesis. 2. Know what every single variable you throw into your model means, how it was collected, and how it was calculated. 3. Use SHAP values in every project. If one column (or a collection of columns derived from that one column) shows a very high SHAP value compared to everything else, assume it's a target leakage problem (where information about your target variable entered the system, like future sales) and investigate. 4. Build models consisting only of variables you absolutely are sure do not have data leakage first. 5. Think very carefully about your cross-validation strategy. Doing out-of-the-box cross validation out of habit often introduces data leakage. 6. Rigorously test the model on data it's never seen before (i.e. data that was never used to train OR score the model). 7. Always do data-preprocessing and featurization after you split the data never before, i.e. don't impute means on the whole dataset first. 8. Only use data that would be available at the time you'd want to predict your target, so don't use data like November GDP to predict something in November because it's not released until mid-December. 9. Avoid identical or nigh-identical rows in train and test, as your model will memorize rather than generalize. 10. Correlate your variables with the target variable at the onset of your project and investigate variables that are highly correlated for target leakage. #datascience #datascientist #machinelearning #dataleakage #ai

*** Blind Spots in Statistics *** Blind spots in statistics are a rich topic, and it’s more interesting than just “common mistakes.” These are the places where even smart analysts, researchers, and data‑driven people routinely mislead themselves without realizing it. Below is a map of the major blind spots. Major Blind Spots in Statistics 1. Confusing Correlation, Causation, and Mechanism • People often treat a statistical association as if it reveals the underlying mechanism. • Even when analysts know correlation ≠ causation, they still slip into causal language. • Blind spot: forgetting that most datasets are observational, not experimental. Example: A model predicts that people who buy diapers also buy beer. The blind spot is assuming a psychological cause rather than recognizing the structural mechanism (parents running errands). 2. Overtrusting Models Without Checking Assumptions Many statistical tools rely on assumptions that are rarely verified: • Normality • Independence • Linearity • Homoscedasticity • Random sampling Blind spot: analysts often treat these assumptions as “default truths” rather than hypotheses that need checking. 3. Survivorship Bias We see the winners, not the failures. • Companies that succeed look like they followed a formula. • Athletes who “made it” seem to validate a training method. 4. The Base Rate Fallacy People ignore the underlying prevalence of an event. • A test with 95% accuracy doesn’t mean a 95% chance the result is true. • Rare events produce many false positives. 5. Misinterpreting p‑values • p < 0.05 does not mean “there’s only a 5% chance the result is due to chance.” • p-values don’t measure effect size or importance. • They’re sensitive to sample size. 6. Overfitting Disguised as Insight Models can memorize noise and present it as structure. • Especially common in machine learning. • Humans then interpret the noise as a meaningful pattern. 7. Ignoring Measurement Error Every variable is a shadow of the real thing. • Self-reported data • Sensor drift • Survey wording • Proxy variables 8. Simpson’s Paradox A trend appears in subgroups but reverses when groups are combined. • Happens when a lurking variable shifts group sizes. Blind spot: assuming aggregated data tells the same story as disaggregated data. 9. The Multiple Comparisons Problem If you test enough hypotheses, some will appear significant by accident. • A dataset with 1,000 variables can produce dozens of “significant” results by chance. Blind spot: forgetting that searching for patterns creates patterns. 10. Human Pattern‑Seeking Even with perfect math, humans: • Overinterpret randomness • See trends in noise • Prefer simple stories • Anchor on first impressions --- B. Noted

Your dashboard isn’t broken. Your data quality is. And the worst part? Most issues don’t show up in meetings, they start quietly inside your pipelines. Here are the 𝗺𝗼𝘀𝘁 𝗰𝗼𝗺𝗺𝗼𝗻 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 𝗱𝗯𝘁 𝗰𝗮𝗻 𝗰𝗮𝘁𝗰𝗵 𝗲𝗮𝗿𝗹𝘆 : 𝟭. 𝗗𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗲 𝗥𝗲𝗰𝗼𝗿𝗱𝘀 𝗜𝗻𝗳𝗹𝗮𝘁𝗲 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 The same entity appears multiple times, making revenue or user counts look bigger than reality. 𝙙𝙗𝙩 𝙛𝙞𝙭: Unique tests on primary/surrogate keys. 𝟮. 𝗠𝗶𝘀𝘀𝗶𝗻𝗴 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗙𝗶𝗲𝗹𝗱𝘀 Nulls in things like user_id or order_id break analysis downstream. 𝙙𝙗𝙩 𝙛𝙞𝙭: not_null tests on essential columns. 𝟯. 𝗕𝗿𝗼𝗸𝗲𝗻 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽𝘀 𝗕𝗲𝘁𝘄𝗲𝗲𝗻 𝗧𝗮𝗯𝗹𝗲𝘀 Join keys don’t match, leaving gaps in fact–dimension relationships. 𝙙𝙗𝙩 𝙛𝙞𝙭: Relationship tests for foreign-key integrity. 𝟰. 𝗦𝗶𝗹𝗲𝗻𝘁 𝗗𝗮𝘁𝗮 𝗟𝗼𝘀𝘀 𝗔𝗳𝘁𝗲𝗿 𝗨𝗽𝘀𝘁𝗿𝗲𝗮𝗺 𝗖𝗵𝗮𝗻𝗴𝗲𝘀 Pipelines “succeed,” but row counts mysteriously drop. 𝙙𝙗𝙩 𝙛𝙞𝙭: Row counts + volume-based anomaly tests. 𝟱. 𝗟𝗮𝘁𝗲-𝗔𝗿𝗿𝗶𝘃𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗦𝗸𝗲𝘄𝘀 𝗥𝗲𝗽𝗼𝗿𝘁𝘀 Historical records never load, causing trend distortion. 𝙙𝙗𝙩 𝙛𝙞𝙭: Incremental models with lookback windows. 𝟲. 𝗦𝗰𝗵𝗲𝗺𝗮 𝗖𝗵𝗮𝗻𝗴𝗲𝘀 𝗕𝗿𝗲𝗮𝗸 𝗗𝗼𝘄𝗻𝘀𝘁𝗿𝗲𝗮𝗺 𝗠𝗼𝗱𝗲𝗹𝘀 A renamed column silently breaks your entire stack. 𝙙𝙗𝙩 𝙛𝙞𝙭: Schema + freshness tests across sources. 𝟳. 𝗦𝘁𝗮𝗹𝗲 𝗗𝗮𝘁𝗮 𝗶𝗻 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀 Reports “look fine” but run on outdated tables. 𝙙𝙗𝙩 𝙛𝙞𝙭: Freshness tests inside SLAs. 𝟴. 𝗜𝗻𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗟𝗼𝗴𝗶𝗰 𝗔𝗰𝗿𝗼𝘀𝘀 𝗧𝗲𝗮𝗺𝘀 Teams calculate the same metric differently. 𝙙𝙗𝙩 𝙛𝙞𝙭: Centralize logic inside dbt models. 𝟵. 𝗜𝗻𝘃𝗮𝗹𝗶𝗱 𝗼𝗿 𝗢𝘂𝘁-𝗼𝗳-𝗥𝗮𝗻𝗴𝗲 𝗩𝗮𝗹𝘂𝗲𝘀 Negative revenue, impossible dates, or status mismatches. 𝙙𝙗𝙩 𝙛𝙞𝙭: Custom tests for ranges, enums, and rules. 𝟭𝟬. 𝗘𝗿𝗿𝗼𝗿𝘀 𝗙𝗼𝘂𝗻𝗱 𝗢𝗻𝗹𝘆 𝗔𝗳𝘁𝗲𝗿 𝗦𝗼𝗺𝗲𝗼𝗻𝗲 𝗖𝗼𝗺𝗽𝗹𝗮𝗶𝗻𝘀 Stakeholders notice problems long after the pipeline runs. 𝙙𝙗𝙩 𝙛𝙞𝙭: Run dbt tests on every job, every deployment. Most data issues aren’t engineering problems, they’re visibility problems. dbt turns silent failures into loud alerts before dashboards break.

I Almost Lost a Client Because of These 7 Data Mistakes A quick story: Last Month, I was analyzing a wholesale dataset for a client. I built a beautiful dashboard that showed sales trends, customer segments, and forecasts. But here’s the problem: When I presented it, the sales manager looked at me and said: “This doesn’t reflect what’s actually happening on the ground.” 😳 Turns out, I had skipped a critical step: Validating my assumptions with the business team. I was tracking revenue per order, while they cared about revenue per customer. A single oversight nearly derailed the project. That experience reminded me that in data analysis, it’s not just about knowing SQL, Excel, or Power BI. The real challenge is avoiding mistakes that waste hours and weaken trust. Here are 7 data mistakes you should avoid at all costs: 1️⃣ Skipping data cleaning → Dirty data = dirty insights. Always check for duplicates, nulls, and inconsistencies before analysis. 2️⃣ Rushing into visualization without clarifying the business question. → A colorful chart is useless if it doesn’t answer what the stakeholder is really asking. 3️⃣ Overcomplicating visuals → If the client can’t understand it, it’s not useful. 4️⃣ Not validating results with stakeholders → What looks correct to you might not align with business reality. Always cross-check assumptions. 5️⃣ Skipping documentation → Today you may remember your steps, but in 3 months when they ask “how did you get this number?”, you’ll struggle. 📌Document your process 6️⃣ Relying only on one tool → Each tool has strengths. SQL for querying, Excel for quick checks, Power BI/Tableau for visuals. Blend them for the best outcome. 7️⃣ Presenting numbers without a story → Leaders don’t just want metrics; they want a narrative: What happened? Why? What should we do next? 📌That near-miss taught me that data mistakes aren’t just technical. They affect trust, reputation, and career growth. 📌If you’re in data (or any role that handles reports), watch out for these mistakes. #DataAnalytics #PowerBI #DataVisualization #DashboardDesign #AnalyticsTips #DataDriven #BusinessIntelligence #DataStorytelling #MistakesToAvoid #LearnWithData

After years of working with engineering teams around the world, we’ve noticed a pattern: even highly skilled engineers can fall into the same traps when it comes to Finite Element Analysis. Let’s bring them into the light - because avoiding these mistakes can save projects from major delays and cost overruns. 𝟭. 𝗕𝗹𝗶𝗻𝗱𝗹𝘆 𝘁𝗿𝘂𝘀𝘁𝗶𝗻𝗴 𝗱𝗲𝗳𝗮𝘂𝗹𝘁 𝘀𝗲𝘁𝘁𝗶𝗻𝗴𝘀 FEA software is powerful, but it’s not a magic wand. Relying on default mesh sizes, solver parameters, or boundary conditions without questioning them is one of the fastest ways to get inaccurate results. 𝟮. 𝗣𝗼𝗼𝗿𝗹𝘆 𝗱𝗲𝗳𝗶𝗻𝗲𝗱 𝗯𝗼𝘂𝗻𝗱𝗮𝗿𝘆 𝗰𝗼𝗻𝗱𝗶𝘁𝗶𝗼𝗻𝘀 This one’s a classic. If your boundary conditions don’t reflect the real-world physical constraints, your results won’t reflect reality either. 𝟯. 𝗜𝗴𝗻𝗼𝗿𝗶𝗻𝗴 𝗰𝗼𝗻𝘃𝗲𝗿𝗴𝗲𝗻𝗰𝗲 𝗰𝗵𝗲𝗰𝗸𝘀 A beautiful contour plot doesn’t mean your results are correct. Too many engineers skip the mesh convergence study - and end up with answers that change when the mesh is refined. 𝟰. 𝗢𝘃𝗲𝗿𝗰𝗼𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹 I usually follow a simple rule: if the structure is simple enough to represent it with 1D model - go for beams, it's easy to adjust cross sections if required. If you need weld checks, or detailed connections - use plate elements, and if the model is too complex to be represented with those - go for solid finite elements. 𝟱. 𝗙𝗼𝗿𝗴𝗲𝘁𝘁𝗶𝗻𝗴 𝘃𝗲𝗿𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 Even a perfect simulation is just a model. If it’s not validated against test data or design codes, it’s a guess — not an answer. At SDC Verifier, we’ve built tools and workflows to help engineers avoid these pitfalls and get accurate results faster. But more importantly, we encourage a mindset: question your assumptions, and never stop questioning.