Common Mistakes in Data Management to Avoid

After years building event-driven systems. Here are the top 4 mistakes I have seen: 1. Duplication Events often get re-delivered due to retries or system failures. Without proper handling, duplicate events can: • Charge a customer twice for the same transaction. • Cause duplicate inventory updates, messing up stock levels. • Create inconsistent or broken system states. Solution: • Assign unique IDs to every event so consumers can track and ignore duplicates. • Design event processing to be idempotent, ensuring repeated actions don’t cause harm. 2. Not Guaranteeing Order Events can arrive out of order when distributed across partitions or queues. This can lead to: • Processing a refund before the payment. • Breaking logic that relies on correct sequence. Solution: • Use brokers that support ordering guarantees (e.g., Kafka). • Add sequence numbers or timestamps to events so consumers can detect and reorder them if needed. 3. The Dual Write Problem When writing to a database and publishing an event, one might succeed while the other fails. This can: • Lose events, leaving downstream systems uninformed. • Cause mismatched states between the database and event consumers. Solution: • Use the Transactional Outbox Pattern: Store events in the database as part of the same transaction, then publish them separately. • Adopt Change Data Capture (CDC) tools to track and publish database changes as events automatically. 4. Non-Backward-Compatible Changes Changing event schemas without considering existing consumers can break systems. For example: • Removing a field might cause missing data for consumers. • Renaming or changing field types can trigger runtime errors. Solution: • Maintain versioned schemas to allow smooth migration for consumers. • Use formats like Avro or Protobuf that support schema evolution. • Add adapters to translate new schema versions into older ones for compatibility. "Every schema change is a test of your system’s resilience—don’t fail it." What other mistakes have you seen out there?

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

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

Over the last 5 years, I've spoken to 100+ Data Engineering leaders. They all struggle with the same data quality issues: 1. 𝐈𝐧𝐜𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐭 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐃𝐚𝐭𝐚 𝐀𝐜𝐫𝐨𝐬𝐬 𝐒𝐲𝐬𝐭𝐞𝐦𝐬: Matching customers across various systems is a major challenge, especially when data sources use different formats, identifiers, or definitions for the same customer information. 2. 𝐋𝐚𝐜𝐤 𝐨𝐟 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐚𝐧𝐝 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠: Organizations often lack sufficient resources or clear foresight from management, leading to poorly designed data architectures that contribute to data quality problems over time. 3. 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐒𝐜𝐡𝐞𝐦𝐚 𝐂𝐡𝐚𝐧𝐠𝐞𝐬: Frequent and undocumented schema changes, especially in production databases, disrupt data pipelines and lead to data integrity issues. 4. 𝐎𝐯𝐞𝐫𝐮𝐬𝐞 𝐨𝐟 𝐅𝐥𝐞𝐱𝐢𝐛𝐥𝐞 𝐃𝐚𝐭𝐚 𝐓𝐲𝐩𝐞𝐬: In some cases, converting everything to flexible data types (e.g., varchar) is a quick fix that can mask underlying data quality issues but makes the system difficult to maintain and troubleshoot over time. These common challenges underscore the importance of #datagovernance, #datamodeling, and overall #datastrategy. Anything I missed?

💥Your data pipeline is only as strong as its weakest assumption Even the most elegant data pipelines can break if you're not careful. I’ve broken more pipelines than I’d like to admit - and learned them the hard way. After years of building and scaling pipelines - especially at high-throughput environments like TikTok and my previous companies - I’ve learned that small oversights can lead to massive downstream pain. I’ve seen beautiful code break in production because of avoidable mistakes, let's see how to avoid them: ❌ 1. No Data Validation: ➡️ Do not assume upstream systems always send clean data. ✅ Add schema checks, null checks, and value thresholds before processing and triggering your downstreams ❌ 2. Hardcoding Logic ➡️ Writing the same transformation for 10 different tables? ✅ Move to a metadata-driven or parametrized ETL framework. Believe me, you will save hours. ❌ 3. Over-Shuffling in Spark ➡️ groupby, join, or distinct without proper partitioning - it's a disaster. ✅ Use broadcast joins instead, and monitor Exchange nodes in the execution plan. ❌ 4. No Observability ➡️ A silent failure is worse than a visible crash. ✅ Always implement logging, alerts, and data quality checks (e.g: row counts, null rates etc) ❌ 5. Failure to Design for Re-runs ➡️ Rerunning your job shouldn’t duplicate or corrupt data. ✅  Ensure that your logic is repeat-safe using overwrite modes or deduplication keys #dataengineering #etl #datapipeline #bigdata #sparktips #databricks #moderndatastack #engineering #datareliability #tiktok #data #dataengineering

I've analyzed 200+ lender tech stacks. And I've identified the data mistakes that are silently killing your business. Most mortgage companies think they have a technology problem. But what they really have is a data problem. So today, I'm breaking down the 6 most expensive data mistakes destroying your margins. Let's walk through each one. Mistake 1: Relying on static data instead of source data. Here's what's killing your margins: • PDFs and bank statements are dead • Manual verification burns cash and slows processing • Forward-thinking lenders use direct source data via PEO platforms • Result: 60% faster processing, 40% lower costs Source data changes the game entirely. Mistake 2: Giving away your most valuable IP. Large lenders surrendered their pricing logic to PPEs. Optimal Blue, Polly, and Lender Price now control the industry's pricing intelligence. What was once proprietary is now a commodity. The only remaining differentiator? The loan officer in the last mile. You handed your competitive advantage to vendors who sell it to your competitors. And you're paying them for it. Mistake 3: Fragmenting data across disparate systems. The average lender has 15+ systems that don't talk to each other: • CRM data stays in the CRM • LOS data stays in the LOS • Marketing data stays isolated • Critical insights lost in the gaps You're sitting on gold but can't access it because your systems operate in silos. Mistake 4: Treating servicing data as a retention play only. Most lenders view servicing data through a marketing lens. But it's actually your most valuable asset for future marketplace participation. Performance metrics, payment behaviors, and geographic patterns determine how your loans will be traded in the future. You're undervaluing the data that will define your business model in 5 years. Mistake 5: Failing to capture decisioning expertise. Your top talent makes complex decisions intuitively: • Years of experience in split-second calls • Tribal knowledge in their heads • Expertise that walks out when they retire • IP competitors can't replicate Leading lenders use decision modeling to capture this and turn human judgment into technological advantage. You're letting your edge retire. Mistake 6: Having no real mobile data strategy. Borrowers spend 5+ hours daily on their phones. Yet mortgage apps have less than 20% adoption. Top servicers reach 80% adoption and collect behavioral data you can't access. You're blind to how customers behave because you're not meeting them where they are. In 5 years, winners won't be the ones with the best rates. They'll be the ones who built their data strategy today. Are you building for 2025 or 2015?

My team has 50+ years of collective experience in healthcare analytics. Here's one mistake we agree is a silent killer in this industry: Skipping documentation and data cataloging. When no one knows the “official” definition of a metric, every team ends up making their own version. And suddenly… • Finance’s “Net Revenue” doesn’t match Operations’ • Two dashboards show different patient counts for the same month • Meetings turn into “Which number is right?” debates instead of decision-making We've seen this exact problem stall million-dollar initiatives. Just imagine it... The same “Readmission Rate” metric in three different reports... all calculated differently. Nobody can agree on the correct measure. Analysts waste days tracing logic instead of delivering insights. Leadership loses confidence in the reports altogether. This stuff is all too common. Here’s the fix we recommend (and what we do on every project): 𝟭/ 𝗕𝘂𝗶𝗹𝗱 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝗱𝗶𝗰𝘁𝗶𝗼𝗻𝗮𝗿𝘆 𝗮𝘀 𝘆𝗼𝘂 𝗺𝗼𝗱𝗲𝗹 - Not afterward. Waiting until the end means you’ll forget important details and create inconsistencies. 𝟮/ 𝗔𝗻𝘀𝘄𝗲𝗿 𝘁𝗵𝗲 𝗸𝗲𝘆 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝗲𝘃𝗲𝗿𝘆 𝗳𝗶𝗲𝗹𝗱: • What does it mean? • Where does it come from? (source system) • How is it calculated? (logic in the source + reporting logic) • How often is it refreshed? 𝟯/ 𝗠𝗮𝗸𝗲 𝗶𝘁 𝗮𝗰𝗰𝗲𝘀𝘀𝗶𝗯𝗹𝗲 So a finance director can quickly confirm which “Net Revenue” field to use for a quarterly report, or a clinician can check the exact definition of “Follow-up Visit.” 4/ 𝗚𝗼 𝗯𝗲𝘆𝗼𝗻𝗱 𝘀𝘁𝗮𝘁𝗶𝗰 𝗱𝗼𝗰𝘀 Record short videos, add in-platform guides, and train teams in person so they can self-serve without relying on analysts for every request. I know it sounds like a lot. But robust documentation should never be considered as "busywork." It's the difference between messy, conflicting reports that nobody trusts, and a system that delivers consistent, reliable insights across the business. ♻️ Share this with someone building a healthcare data platform. Follow for weekly lessons from real-world healthcare data projects.

“Why doesn’t your dashboard match my spreadsheet?” Every data leader has heard this question. Every business leader has asked it. And that’s when you know the alignment is off. Here are the biggest mistakes I see: 1️⃣ Data is treated as a service, not a partner If data teams are just report builders, they’ll never drive real impact. Embed them early in decision-making, not just when a dashboard is needed. 2️⃣ No shared definitions Does “customer” mean an active user or a paying account? Does “churn” include downgrades or just cancellations? If teams don’t agree on definitions, reporting is a mess. 3️⃣ Insights without action A great analysis is worthless if no one acts on it. Tie every data request to a decision. If there’s no action, question the need. 4️⃣ Speed vs. accuracy debates Rubbish in = Rubbish out Business teams need fast answers. Data teams need to understand the business impact, need better data, models, definitions, context and more to provide better insights Find a balance—80% right today is better than 100% right too late. 5️⃣ Siloed tools, siloed truths Business teams rely on spreadsheets and static reports. Data teams pull from warehouses, SQL queries, and BI tools. Different sources. Different numbers. And suddenly, no one trusts the data. If everyone’s using different numbers without context, trust erodes fast. Fix this by: ✅ Aligning on definitions and key metrics ✅ Embedding data teams in business conversations early ✅ Building analytics with clear governance ✅ Prioritizing action over analysis paralysis When business and data teams work together, they don’t just report on the past. They shape the future.

3 Mistakes I Made Early in My #DataEngineering Career (And What I Learned) When I started my data engineering journey, I thought knowing #Python, #SQL, and #AWS was enough. I was wrong. Here are 3 painful mistakes I made — so you don’t have to. 1. 𝐎𝐯𝐞𝐫𝐜𝐨𝐦𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐧𝐠 𝐄𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠 I thought: "If my pipeline looks complex, it will look impressive." Reality: - Complex = fragile. - Complex = impossible to debug under pressure. - Complex = slower teams. 💡Lesson: Make it simple first. Then make it scalable. Clarity wins over cleverness. 2. 𝐈𝐠𝐧𝐨𝐫𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐄𝐚𝐫𝐥𝐲 "We’ll fix data issues later." "Let’s just focus on getting the pipeline running." I made this mistake more times than I can count. Reality: - Bad data = bad decisions. - Debugging dirty pipelines is 10x harder than building clean ones. 💡Lesson: Validate your data as early as possible. Always assume upstream data will break — because one day, it will. 3. 𝐍𝐨𝐭 𝐓𝐡𝐢𝐧𝐤𝐢𝐧𝐠 𝐀𝐛𝐨𝐮𝐭 𝐒𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐔𝐧𝐭𝐢𝐥 𝐈𝐭 𝐖𝐚𝐬 𝐓𝐨𝐨 𝐋𝐚𝐭𝐞 "We’ll worry about scaling when the traffic increases." Nope. When you don’t plan for scale, small problems become fire alarms later. 💡Lesson: Design like your pipeline will have 10x data tomorrow. Even simple partitioning, indexing, and retries can save future you. 💬 If you’re early in your data engineering career, remember: - Simple > Complex - Quality > Speed - Future-proofing > Quick fixes ✔️ Save this post — you’ll thank yourself 6 months from now. ✔️ Share it with someone starting their #datajourney

Top 5 Mistakes I Made Early in My Data Career (And How You Can Avoid Them)! When I first started working in data, I made a number of missteps that cost me valuable time, energy, and at times, confidence. Looking back, these are the five mistakes I wish I had avoided: 🔹Skipping SQL fundamentals: I assumed I could rely on Python alone and still get by. That approach quickly fell apart. SQL is foundational to almost every data engineering task. It is where much of the actual work begins and ends. 🔹Delaying hands-on cloud experience: I spent too long working with local datasets. The reality is that most data lives in the cloud-on platforms like AWS, GCP, or Azure. Getting hands-on with cloud services early on would have made a major difference in my learning curve. 🔹Avoiding orchestration tools like Apache Airflow: I found tools like Airflow intimidating and put off learning them. In truth, they simplify complex workflows and add a level of professionalism and efficiency to your pipelines that manual scripting cannot match. 🔹Not using version control for SQL and pipelines: I used to think Git was only for software developers. But in practice, version controlling your SQL scripts and pipeline logic is essential for collaboration and debugging. Learning Git alongside tools like dbt would have saved me countless hours. 🔹Relying solely on unstructured learning: I jumped between blog posts and tutorials without a clear learning path. What I really needed was structured, project-based learning. A guided program like the Associate Data Engineer in SQL track on DataCamp would have helped me build both confidence and competence much faster. Check it out here: https://lnkd.in/dBcnAWUx If you are early in your data career (or pivoting into it), I hope these lessons help you avoid some of the common pitfalls. I would be happy to dive deeper into any of these areas if helpful. #dataengineer #technology #sql #python #programming