Mistakes to Avoid in Data Graph Projects

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

3 small design choices that turn your charts into liars Good data visualization is more than just aesthetics or making your takeaway obvious - it's about communicating data honestly. And whether intentional or not, small design choices can distort insights, mislead your audience, and even call your credibility into question. Here are three common pitfalls to watch out for: ➤ Not starting x- or y-axes at 0 on bar charts Bar charts rely on bar length (or height) to compare values. If the x-axis (for horizontal bar charts) or y-axis (for vertical bar charts) don’t start at zero, differences can be exaggerated, making a small change look more dramatic than it really is. Rule: Always start bar chart axes at zero to ensure accurate comparisons. ➤ Using inconsistent time intervals on line charts Line charts show trends over time, but uneven time spacing can distort those trends. For example, if one gap represents a month and the next represents a year, the chart might suggest a sudden shift or flatten a real change. Rule: Always use equal time intervals and label them clearly. ➤ Ignoring margin of error If the margin of error between two or more values overlaps, the difference might not be meaningful - but a bar chart’s crisp edges can suggest otherwise. Rule: If the margin of error overlaps, use error bars or annotations to highlight uncertainty. To recap: - Start axis at 0 for bar charts - Use consistent time intervals - Mind the margin of error —-— 👋🏼 I’m Morgan. I share my favorite data viz and data storytelling tips to help other analysts (and academics) better communicate their work.

Teams waste $500K on vector databases because they optimize for the wrong thing. 💸 Not the database cost. The architectural mismatch cost. 💥 Expensive Mistake #1: Write-Heavy on HNSW HNSW degrades with constant writes. Random inserts fragment the graph. → Cost: Index rebuilds on 100M vectors = 6-12 hours. Weekly or monthly. Performance drops between rebuilds. → Fix: IVF with buffering or disk-backed indexes for incremental updates. 🐌 Expensive Mistake #2: Unindexed Filters Query: "Similar products under $50 posted this week" → Without indexed filters: Retrieve 1000 → scan all for price/date → discard 950 → return 50. Your 50ms query just became 400ms. → Fix: Index metadata or encode constraints into vectors. 💸 Expensive Mistake #3: Managed Service at Scale Without Math → $500K+/year managed vs. $150K/year self-hosted infrastructure. But self-hosted needs DevOps (2-3 engineers = $400K/year). → Fix: Run the math based on your ops capacity. 📈 Expensive Mistake #4: Memory-Only at Billion-Vector Scale → 100M vectors = 400-460 GB RAM. Aggressive sharding = $$$. → Fix: Disk-backed indexes trade speed for scale. Don't start with features. Start with: → write rate, scale, ops capacity, query needs, latency target. Full breakdown of expensive mistakes + fixes 👉 https://lnkd.in/gDHW7g-g

Why Million-Dollar Knowledge Graph Projects Fail - How Knowledge Graphs are really Built #9.1 I've seen organizations spend millions on knowledge graph projects that never get used. Here's why they fail. A biotech company once showed me their KG. Two years in development. Every major data source integrated. Beautiful ontology. Sophisticated infrastructure. Zero active users. The team had built what they thought scientists needed. They never asked what scientists actually wanted. By the time they launched, the problems had changed and the interface didn't match anyone's workflow. This isn't rare. It's the norm for failed KG projects. The Common Mistakes Starting too big is the most frequent failure mode. "We'll integrate all our data sources into one unified KG." Sounds ambitious. Usually means nothing launches. The scope expands. Requirements multiply. Technical challenges compound. Two years later, you're still building and users have found workarounds. Lack of clear use case or user kills projects slowly. "KGs will help with drug discovery" isn't a use case. "Help medicinal chemists find structurally similar compounds with activity data across all historical screens" is. Without specific users solving specific problems, you're building in a vacuum. Lack of rigorous validation benchmarks means you don't know if your graph is actually good. Teams deploy entity recognition without measuring precision and recall. What percentage of drug-target relationships are correct? Without benchmark datasets testing each pipeline component, you're flying blind. Users discover quality issues in production, lose trust, and abandon the system. Over-engineering the ontology before testing delays value. Teams spend months debating whether "inhibits" and "antagonizes" should be separate relationship types. They design comprehensive schemas covering every possible entity. Then they discover users only care about three entity types and five relationship types. The perfect ontology sits unused. Ignoring data quality from the start creates technical debt that becomes insurmountable. "We'll clean the data later" means you won't. Building in isolation from end users guarantees misalignment. Technical teams make assumptions about what scientists need. They design interfaces engineers like, not interfaces scientists will actually use. When you finally show it to users, they say "this doesn't fit how I work." Underestimating maintenance requirements causes post-launch failure. Knowledge graphs aren't build-once projects. Data sources update. Ontologies evolve. Extraction methods need retraining. Relationships become outdated. Without dedicated maintenance resources, your graph decays and users drift away. No governance or ownership model creates chaos. Who decides what data gets added? Who validates quality? Who prioritizes new features? Without clear ownership, knowledge graphs become data dumping grounds with no accountability.

Data analysis mistakes rarely look dangerous at first. A wrong metric here. A messy join there. A dashboard with too many charts. A report without validation. But small mistakes can quickly turn into wrong business decisions. Here are the most common mistakes data analysts should avoid: 𝗪𝗿𝗼𝗻𝗴 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 Tracking vanity metrics instead of business KPIs, using the wrong denominator, or mixing averages, totals, and rates incorrectly. 𝗕𝗮𝗱 𝗷𝗼𝗶𝗻𝘀 Joining data at the wrong grain can create duplicates, inflate revenue, remove records, or change the entire result. 𝗠𝗲𝘀𝘀𝘆 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀 Too many visuals, weak hierarchy, inconsistent colors, and unclear takeaways make insights harder to understand. 𝗜𝗴𝗻𝗼𝗿𝗶𝗻𝗴 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 Numbers without context can mislead. Seasonality, promotions, pricing changes, and customer segments matter. 𝗣𝗼𝗼𝗿 𝘀𝘁𝗼𝗿𝘆𝘁𝗲𝗹𝗹𝗶𝗻𝗴 Presenting numbers without a clear narrative, headline, recommendation, or decision support weakens the analysis. 𝗡𝗼 𝘃𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻 Skipping QA means errors go unnoticed. Always check row counts, nulls, duplicates, date types, joins, outliers, and KPI definitions. A simple modeling mistake can change the entire business story: Wrong join → inflated revenue → misleading dashboard → bad decision The fix is not complicated: Start with the business question. Define metrics clearly. Profile the data early. Test joins carefully. Validate before sharing. Keep dashboards simple. Document assumptions. End with recommendations. Great analysts do more than find numbers. They find the right numbers, verify them, and explain them clearly. Which data analyst mistake have you seen most often?

I lost (too many) BI projects because of these mistakes Quick story: Early in my career, I delivered what I thought was my “masterpiece” Power BI dashboard. Sleek interface, accurate to the decimal, loaded with features. The sales director looked at it for ten seconds and told me: “This isn’t how my team works. Our reality isn’t in your numbers.” Painful, yes. But it forced me to see the 7 mistakes that kill real BI value – none of them in the docs: 1️⃣ Designing for “what should happen” – not what does → Your data model matches process maps, not real-life workarounds. If the dashboard looks perfect but nobody uses it, you missed the human factor. 2️⃣ Mistaking “requirements” for alignment → Did every decision-maker actually agree? Or did they just say “fine” to wrap the call? Budget-killer: silent misalignment. 3️⃣ Making visuals for impressing, not convincing → Showy charts nobody trusts will get you praise (and zero adoption). 4️⃣ Skipping ownership handover → If only one person understands the logic, you are one vacation away from chaos. 5️⃣ Ignoring how people really download, export, copy, and paste → If your users are still slicing Excel exports, your dashboard isn’t solving their actual job. 6️⃣ Dodging metric definitions “because politics” → If you avoid clarifying a controversial number, you’re just banking a future crisis. 7️⃣ Hiding complicated logic, hoping nobody asks → If you need a 14-tab DAX walkthrough to explain a single KPI, you will lose trust long-term. PS. What’s the silent mistake that nearly killed your most important report? Let’s compare below.

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

Is everyone on the same page? Beware misalignment that could be derailing your data, analytics or AI projects. One of the first causes of misalignment happens in the project ideation phase. It's not uncommon for projects to begin with enthusiasm but suffer from vague goals, that not everyone understands or agrees on. It's easy for people to just decide they need an analytics platform like #PowerBI without any thought as to how they will use it. Then there's the matter of stakeholders. Too often, crucial players who need to be involved from the start are overlooked or identified too late in the process. This oversight leads to missed requirements and unexpected resistance later on, which can drop a bomb into an otherwise healthy project. Another misstep I think is the lack of alignment process. Without effective early alignment meetings that clearly outline the project’s drivers, impact, scope, benefits and timeline, stakeholders might not fully commit to the direction or outcomes of the project. It's easy to fall into the trap of "we need this Copilot" with no consideration of why it's important and what value it has. Finally, handling objections is a common stumbling block. Misalignment caused by the above issues leads to objections that aren't addressed effectively, causing further delays and, in some cases, jeopardizing the project's success. I think we could all do better in data and AI at anticipating and managing business stakeholder objections proactively. Some tips I've learned and observed over the years for effective project alignment in Data and AI projects: 💡 Size doesn't matter. Even small projects like a simple report can be undermined or suffer blowout due to a misalignment issue. Don't underestimate the potential impact of skipping this step. 💡 Identify your stakeholders. This could be as simple as a list of key people or as complex as a comprehensive stakeholder map that includes individuals at all levels of the organization. In Data and AI projects these are often IT, the end users, source system admins, managers and executives driving the initiative to name a few. 💡 Set up alignment workshops early on that focus on detailed discussions about project drivers like the challenges faced by the org and their impact, the scope or objectives and the new capabilities the org will receive. 💡 Use visual tools like diagrams, whiteboards, kanban and timelines to help stakeholders understand and agree on the project attributes. 💡 Proactively identify potential stakeholder objections, put yourself in their shoes and prepare clear, well thought out responses. Common objections to Data and AI projects are around cost, data privacy and security, resistance to using new tools (e.g. Excel vs Power BI), unclear benefits and doubts about data accuracy and quality to name a few. What do you think are the first steps we should take in a Data or AI project and what challenges can we expect? #Data #AI #ProjectManagement

10 Things You Should Never Do As a Data Scientist Here are the habits I see often derail projects, told with love, I've made most of these mistakes myself. 10. Skip EDA because "it's all about the algorithm." It isn't. It never was. The algorithm is maybe 10% of the outcome; the other 90% is knowing your data: its shape, its leaks, the column called status_2 that nobody can explain. Spend more time on EDA. 9. Reach for XGBoost because "it wins on Kaggle." Kaggle hands you clean labels and zero stakeholders. Your work is drift, latency budgets, and a PM who has to explain to legal in words that aren't "gradient boosted." Sometimes the right answer is logistic regression. The best model is the simplest model that solves the problem. 8. Throw a deep neural net at raw data because "it learns the features itself." In practice, with a 40,000-row dataset and messy categoricals, thoughtful feature engineering will beat a DNN on every axis that matters, including explaining to a regulator why a loan was denied without saying "the network felt strongly about it." 7. Treat df.dropna() as "data cleaning." Missingness is information. Is it missing at random, or missing because of what you're predicting? Dropping rows silently rewrites your population. 6. Report a single accuracy number on an imbalanced problem. "97% accurate!" on a dataset that's 97% one class is a rounding error wearing a tuxedo. 5. Call a notebook production-ready. Notebooks are for thinking. The moment a model affects real people, it needs version control, tests, and a deployment path that doesn't involve "just rerun cells 1 through 47, but skip 23." "It works on my machine" is a confession, not a strategy. 4. Ship a model without a monitoring plan. Models decay. Inputs drift, an upstream team renames a column on a Wednesday and doesn't tell you. 3. Optimize for the metric instead of the decision. AUC went up 0.02 — beautiful, frame it. Did the business outcome actually improve? The metric is a proxy, and proxies have a charming habit of betraying you the moment you optimize them. 2. Present results without translating them. A confusion matrix is not a deliverable for an executive. It's a punishment. Your job is to convert model output into a decision in the language your audience already speaks — dollars, risk, time, customers. 1. Just use AI. AI solves everything. No sentence has launched more doomed projects. "AI" is not a strategy. Wrapping a GPT call around an undefined problem doesn't make it go away. It just makes the failure mode more expensive, now with hallucinations. Answer the boring questions first: What decision are we improving? What's the baseline? What happens when it's wrong? Answer those, you'll often discover you didn't need AI — you needed a clear problem statement and a SQL query. The pattern underneath all ten: the craft is in the thinking, not the tooling. Algorithms are commodities now. Judgment isn't. #DataScience #MachineLearning #AI #Analytics #IBM

After $100M in AI projects, I noticed a pattern. This is the difference between success and $2M mistakes. It's not the AI models that fail. It's the data foundation nobody talks about. Here's the exact data audit that separates winners from expensive mistakes: 1. High-Value Data 🎯 Pick 3 KPIs that matter. Find the data that moves them. Skip everything else. 2. Map Data Flows 🗺 Where does it start? Where does it go? One visual > 50 reports. 3. Quality Check 🔍 Score each dataset 0-100. Bad data = Dead AI. No exceptions. 4. Clean-up Rules 🧹 Set standards. Kill duplicates. Prevent mess, don't fix it. 5. Build Pipeline 🔄 Automate everything. Assign owners. Stop firefighting. 6. Test Before Scale ✅ Clean data first. Compare results. Catch mistakes early. Key Insights: → 80% of AI success happens before the first model → Clean data compounds: Fix it once, benefit forever → Most skip steps 4-5. That's where projects die. 📊 Companies doing this right see: - 2-4× faster AI adoption - 70% fewer failed projects - 3× ROI on data investments 📌 Save this before your next AI project ♻️ Repost to help another leader avoid a $2M mistake 🎯 Follow Gabriel Millien for more no-BS AI playbooks