Confusing data training for new analysts

Data Doesn’t Lie — But Analysts Can Mislead Most times, the challenge is not the data itself… It’s how we choose to present it. Same numbers. Different charts. Different stories. A good data analyst must not only analyze — But also stay honest. The real skill is clarity, not manipulation. Learn the craft. Respect the truth

A few weeks ago, I was working on a project where multiple data sources were supposed to align perfectly… but of course, they were not. Duplicate entries, missing fields, inconsistent formats — the classic data nightmare. 😅 Instead of rushing into analysis, I paused and reframed the problem: “How can I make this data reliable enough to trust the insights?” Here’s what I did step-by-step: 1️⃣ Created a clear data cleaning checklist identify, remove, and standardize. 2️⃣ Used SQL for quick validation queries and Excel for spot-checking anomalies. 3️⃣ Documented every assumption so the team understood what changed and why. The result? ✅ A dashboard with 100% accurate KPIs ✅ A 25% faster reporting process ✅ Stakeholders who finally trusted the data again Data analysis isn’t about fancy visuals or tools — it’s about building trust in the numbers first. If you’re working with data, slow down and fix the foundation before you visualize the outcome. What’s one challenge you’ve faced recently that taught you a valuable lesson? #dataanalyst

A lot of people assume data quality belongs to the data team. In reality, it starts long before anything reaches a database. Quality is shaped at the point of entry, which means it lives with the people closest to the work. The form that gets filled out. The label someone selects. The category chosen in the moment. Data teams can clean and validate, but they cannot fix upstream context that was never captured in the first place. When front-line inputs improve, trust in the numbers improves. That is when dashboards become useful instead of questioned. Data quality is an ownership problem.

🎭 “Beauty is in the eye of the beholder” — and so is Data Quality! What’s “good data” for one person might be “garbage” for another 🤷♂️ Because let’s face it — data quality is subjective and depends heavily on who’s using it and for what purpose. 💡 Why Data Quality Is Never One-Size-Fits-All Different consumers look at the same dataset in very different ways: 🔹 Use-case matters: What’s “accurate enough” for analytics might not pass a compliance audit. 🔹 Rules vary: Each team has its own validation logic, business definitions, and thresholds. 🔹 Speed vs precision: Some care about real-time freshness, others about historical completeness. 🧩 The Real Challenge: Enterprise-Wide Consistency 🔹 Building data that makes sense across the organization? 🔹 That’s where things get interesting (read: painful 😅). 🔹 Analytics systems often inherit upstream chaos — schema drift, missing fields, inconsistent definitions — you name it. 🔧 Enter the “Data Fixers” Sometimes, you just have to: 🔹 Derive missing values to make sense of it all 🔹 Apply heavy business logic to clean up source flaws 🔹 Build a “master” dataset that finally makes sense to everyone (well… almost everyone) So next time someone says, “Our data quality is bad”, ask them — “Bad… for whom exactly?” 😏

As a Data Analyst, attention to detail is essential — but so is maintaining perspective. Focusing too much on the small things can make us lose sight of the bigger picture. True analytical excellence lies in knowing when to dive deep and when to step back.

Most beginner data analysts get stuck in "analysis paralysis". That means analysing to death and never having any impact. Great analysts spend as little time analysing as possible. Instead, they focus on three things: 1. Problem root cause 2. Stakeholder needs 3. Delivery approach This way, they are targeted, efficient, and impactful. It seems obvious, right? Maybe it is, but most data professionals overlook these points. They spend way too much time analysing. When the time to present finally comes, they're burnt out, confused, and mixed up. If that's the case, how are stakeholders expected to be clear on what to do next and why? Take some time to get clear on the problem. Move forward with your stakeholders in mind. And remember, the idea is to use data to help guide action, not simply describe the state of play. What do you find is the best way to create impact with your analysis?

Most beginner data analysts get stuck in "analysis paralysis". That means analysing to death and never having any impact. Great analysts spend as little time analysing as possible. Instead, they focus on three things: 1. Problem root cause 2. Stakeholder needs 3. Delivery approach This way, they are targeted, efficient, and impactful. It seems obvious, right? Maybe it is, but most data professionals overlook these points. They spend way too much time analysing. When the time to present finally comes, they're burnt out, confused, and mixed up. If that's the case, how are stakeholders expected to be clear on what to do next and why? Take some time to get clear on the problem. Move forward with your stakeholders in mind. And remember, the idea is to use data to help guide action, not simply describe the state of play. What do you find is the best way to create impact with your analysis?

Avoid Flawed Analysis ❌🚫 I've seen how messy data can ruin a good project. It starts small, then suddenly your insight don't match reality. Messy data can lead to flawed analysis and flawed analysis leads to bad decision which can cause serious losses. That's why data cleaning is so important and why data analyst play such a key role. The quality of your data determines the quality of your insights. Common Data Cleaning Issues: 1. Missing Values: Most messy Datasets come with missing values often from poor data entry or incomplete records. You can drop or remove rows/columns if they're not important to your analysis or fill them using mean, median or more imputation. 💡 Always check how much data is missing sometimes some missing values can mean something. 2. Duplicate Values Duplicates appear when the same record is repeated. Best way to handle them is using Excel's Remove Duplicates or the UNIQUE function. 💡 Always check duplicates with unique identifiers (like IDs) some repetition might be valid. Anything you do during data cleaning affects your final results. Handle each step deliberately, document your process (very important) and keep a copy of the raw data.

Data Migration: Not Always a Piece of Cake When clients describe their policy data, it often sounds like a masterpiece: structured, clean, and ready to migrate. But as this meme (adapted from a well-known format cataloged on Know Your Meme) humorously illustrates, the reality can be far from it. The first cake (labeled "How the client described their data") is a Pinterest-worthy pup made of cupcakes. The second ("Their data") is a chaotic attempt at the same idea, complete with Excel, XML, TXT, and PDF icons - representing the fragmented formats we often receive. Why is policy data migration with system changes not always possible or pretty? 🧩 Data Fragmentation: Legacy systems store data in inconsistent formats, making it hard to unify. Hidden Gaps: What looks complete on the surface may have missing fields, outdated values, or duplicated records. 🔄 Structural Mismatches: New systems often require different data models, and mapping isn’t always straightforward. 🧠 Human Interpretation: What one team calls “clean” data might be riddled with manual edits and workarounds. The takeaway? Data migration is a technical and strategic process - not just a copy-paste job. It requires collaboration, validation, and often, a reality check. Let’s keep the conversation going: Have you ever seen a “cake” like this in your migration journey? #InsuranceTech #DataMigration #SystemChange #TialTechnologies

🔹 Role Highlight: Why Data Cleaning Matters More Than You Think Before beautiful dashboards and insights come to life, there’s one step that defines the quality of analysis — Data Cleaning. It’s the hidden backbone of every successful data project. A well-cleaned dataset ensures accuracy, consistency, and trust in decision-making. Behind every polished chart lies hours of fixing duplicates, handling missing values, and validating formats — because bad data leads to bad decisions. 📊 Key things done in Data Cleaning: 👉 Removing duplicates and irrelevant entries. 👉 Handling missing or incomplete data. 👉 Correcting data types and formatting inconsistencies. 👉 Detecting and treating outliers. 👉 Ensuring data accuracy before visualization or modeling. ✨ In short: A Data Analyst spends nearly 70% of their time cleaning data — because clean data means clear insights, and clear insights mean smarter decisions.