How to Analyze Data for Valuable Insights

In a sea of possible insights, how do you know which are worth reporting? As a data analyst, there are two types of insights you will report: 1) Ones that are directly aligned to a business question or priority 2) Ones that nobody is asking for… but should be 90% of the time, you should be focusing on the first one. But when done right, the second can be very powerful. So… how do you find those hidden insights? How do you know which ones truly matter? ➤ Explore high-level trends Scan dashboards, reports, or raw data for unexpected patterns. Look for sudden spikes, dips, or emerging trends that don’t have an obvious explanation. ➤ Slice the data by different dimensions Break data down by different categories (customer segments, time periods, product lines, etc.). Where are things changing the most? Which groups are behaving unlike the others? ➤  Identify outliers Look at the extremes. What’s happening with your best customers? Worst-performing regions? Most productive employees? Outliers often reveal inefficiencies or hidden opportunities. ➤ Tie insights to business impact Before reporting, ask: Would knowing this change a decision? If it doesn’t, it’s probably not worth surfacing. ➤ Pressure-test with stakeholders Run your findings by a manager or friendly stakeholder. Ask them if the finding resonates with other trends they've seen, whether they see potential value, and whether it could influence strategy. In other words: - Start broad - Dig deep - Sense-check —-— 👋🏼 I’m Morgan. I share my favorite data viz and data storytelling tips to help other analysts (and academics) better communicate their work.

People usally start data analysis with dashboards. Good analysts start with questions. Data doesn’t create insights on its own. The quality of analysis depends on the clarity of thinking before any query is written or chart is built. This framework highlights the key questions experienced analysts ask before analyzing any dataset - ensuring analysis leads to decisions, not just reports. 👇 • Define the real business problem before touching the data, because unclear decisions lead to meaningless analysis. • Clearly understand what success looks like by identifying metrics, benchmarks, and expected outcomes. • Verify what data is actually available to avoid building analysis on incomplete or misunderstood sources. • Assess data reliability early, since poor data quality weakens even the best analytical models. • Challenge assumptions continuously to prevent bias, false correlations, and misleading conclusions. • Choose the right dimensions for segmentation to uncover patterns hidden inside aggregated numbers. • Identify the target audience so insights match the level of technical depth and business context required. • Decide the output format intentionally, because how insights are presented shapes how they are used. • Focus on the action the analysis should drive - because analysis without decisions creates no impact. Great analysis isn’t about tools or dashboards. It’s about asking better questions before searching for answers. What’s the first question you ask before starting a data analysis project? 👇

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! 👇

Nobody cares how many dashboards you build. Nobody cares how complex your SQL queries are. Nobody cares if your reports have the fanciest visualizations. Hiring manager cares about one thing—how your insights drive business impact. If your analysis isn’t helping increase revenue or cut costs, then it’s just numbers on a screen. As data analysts, our job is not to report data—it’s to change business outcomes. That means: Finding revenue leaks before they drain profits. Identifying cost inefficiencies and optimizing processes. Predicting trends that help the business stay ahead. If your analysis doesn’t lead to action, you’re just a data librarian. Advice for any data professional: 1. Understand the business, not just the data. 2. Focus on insights that lead to decisions. 3. Measure the impact of your recommendations. 4. Make data a business asset, not just a report. Data without action is just decoration. Let’s change that.

Behind every great insight is a solid statistical foundation. Here are the 4 methods every data analyst must master: 𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐲 𝐢𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬: Data visualization is just the tip of the iceberg. The real power comes from understanding the statistical methods that reveal relationships, patterns, and predictive insights. 𝐓𝐡𝐞𝐬𝐞 4 𝐬𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐚𝐥 𝐦𝐞𝐭𝐡𝐨𝐝𝐬 𝐩𝐨𝐰𝐞𝐫 𝐞𝐯𝐞𝐫𝐲 𝐝𝐚𝐭𝐚-𝐝𝐫𝐢𝐯𝐞𝐧 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧: 1. 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 → Predict outcomes and identify what drives them → "How does marketing spend impact revenue?" → Master: R² for model fit, RMSE for prediction accuracy → Pro tip: Always check residuals - they tell the real story 2. 𝐇𝐲𝐩𝐨𝐭𝐡𝐞𝐬𝐢𝐬 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 → Make confident, evidence-based decisions → "Is this A/B test result actually significant?" → Master: t-tests for comparing means, ANOVA for multiple groups → Remember: Statistical significance ≠ business significance 3. 𝐂𝐨𝐫𝐫𝐞𝐥𝐚𝐭𝐢𝐨𝐧 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 → Measure relationships between variables → "How strongly do these factors move together?" → Master: Pearson for linear, Spearman for non-linear → Warning: Correlation ≠ causation (but you knew that) 4. 𝐓𝐢𝐦𝐞 𝐒𝐞𝐫𝐢𝐞𝐬 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 → Uncover trends, cycles, and seasonality → "What will demand look like next quarter?" → Master: ARIMA for trends, Exponential Smoothing for patterns → Always: Decompose first to understand components 𝐖𝐡𝐲 𝐦𝐚𝐬𝐭𝐞𝐫 𝐭𝐡𝐞𝐬𝐞 𝐧𝐨𝐰: ↳ Every dashboard needs statistical validation ↳ Every recommendation requires evidence ↳ Every model must be interpretable ↳ Master these = become indispensable The best part? Once you think statistically, data tells stories you never noticed before. Master the stats. Master the insights. Get 150+ real data analyst interview questions with solutions from actual interviews at top companies: https://lnkd.in/dyzXwfVp ♻️ Save this for your next analysis 𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 18,000+ readers here → https://lnkd.in/dUfe4Ac6

Still struggling with where to start when you are given a project? I have got you! Below is a step-by-step breakdown of key tasks to complete on a data analytics project. 1. Define The Project Objectives and Deliverables 🔹Identify the key questions or goals Why? A clear goal directs what data you need and how you will analyze it.   2. Understand the Structure of your Tables 🔹Examine each table's schema: columns, data types, relationships, and keys Why? This is helpful before any meaningful combination or analysis. Note: Most of the time, your project's data is located in different tables.   3. Prepare and Clean the Data 🔹Handle missing values 🔹Remove duplicates 🔹Fix formatting issues 🔹Ensure consistent units/currency/date formats Why? Data cleaning is often the most time-consuming part, but it is essential for ensuring accuracy and reliability in your analysis. 4. Combine/Merge the Tables 🔹Use keys or common fields to combine tables Why? It creates a complete dataset by bringing together relevant information from all the tables. It improves data quality and ensures that the analysis is comprehensive. 6. Data Enrichment (Optional) 🔹Create new variables or derive new metrics 🔹Create a date table using the date column from your table Why? It provides additional context and improves the power of your analysis by revealing deeper insights. 5. Conduct Exploratory Data Analysis (EDA) 🔹Run summary statistics 🔹Explore patterns, trends, and anomalies in your dataset Why? EDA helps you uncover patterns, spot errors, and decide which variables matter for analysis. 7. Perform Analysis 🔹Compare trends across time, regions, or segments 🔹Apply analytical techniques to answer initially defined questions 🔹Build KPIs Why? Here, you extract actionable insights from your prepared dataset and test hypotheses, directly addressing your project’s objectives. 8. Visualize Results 🔹Create different charts 🔹Use any visualization tool Why? It helps stakeholders understand results more easily through clear visuals. 9. Interpret and Report your Results 🔹Tell the story behind the data to communicate findings through reports or presentations tailored to your audience 🔹Explain what the analysis reveals, what it means, and why it matters 🔹Use concise reports, presentations, or dashboards Why? It converts technical output into business-relevant insights. This helps stakeholders make informed decisions based on your analysis. 10. Make Data-Driven Recommendations 🔹Validate your findings by checking for errors, testing assumptions, and possibly seeking feedback from others 🔹Suggest actions to be taken Why? Validation ensures the credibility and robustness of your conclusions before they are used in decision-making. 11. Monitor & Iterate 🔹Evaluate the impact of implemented changes 🔹Re-analyze periodically 🔹Update data pipelines or dashboards as needed Why? It ensures your analysis stays useful and responsive to changes. PS: What step can you add?

Data is everywhere. But useful data? That’s rare. Here’s the truth most people don’t say out loud: Collecting data doesn’t create results. Acting on it does. Leaders don’t need more dashboards. They need clarity, insight, and execution. Here’s a simple 8-step approach to turn data into real action: 1/ Collect Relevant Data • Strong decisions start with accurate information • Identify key metrics, gather from trusted sources, organize for easy analysis 2/ Clean and Validate • Messy data leads to messy decisions • Remove duplicates, verify accuracy, standardize formats 3/ Analyze Patterns and Trends • Trends reveal opportunities and hidden risks • Visualize data, segment it, and flag outliers for deeper review 4/ Derive Actionable Insights • Insights are where numbers become decisions • Ask what the data implies, rank insights by impact, document clearly 5/ Translate Insights Into Strategy • Strategy turns insight into outcomes • Align with goals, define clear objectives, map required resources 6/ Communicate Findings Clearly • If people don’t understand it, they won’t act on it • Use simple visuals, tailor the message, outline next steps 7/ Implement and Track Results • What gets measured, improves • Set KPIs, adjust based on performance, review progress regularly 8/ Iterate and Improve • Data gets more valuable with refinement • Apply lessons learned, update metrics, encourage feedback Data isn’t the goal. Better decisions are. What’s the last insight you turned into action? Follow Mark Mehok for more Business Insights like this

Turning Raw Data into Actionable Business Insights – A Data Analyst’s Perspective. As Data Analysts, our mission is to uncover actionable insights from vast and messy data to drive informed business decisions. But before we can analyze, visualize, or derive insights, the data needs to be carefully prepared, structured, and connected. The dataflow diagram above demonstrates a typical yet powerful data preparation workflow designed to calculate and aggregate key business metrics from transactional data. Here’s the process breakdown from a Data Analyst’s point of view: 1. Data Reading & Filtering We start by reading the raw transactional data (e.g., orders data). The raw data is often unstructured and massive, so applying precise filters like selecting data for a particular year (orderdate = 2016 or >= 2015) is critical to narrow down our focus for the analysis at hand. 2. Data Aggregation Next, we perform targeted aggregations: Score Dataset: Aggregate total price or other key metrics at the year level to observe temporal trends. State-level Aggregation: Calculate the average total price grouped by state, which helps in understanding regional patterns and performance. Default-level Aggregation: Similar aggregation logic, but perhaps focusing on default categories or other grouping criteria relevant to the business context. 3. Data Integration via Joins After filtering and aggregating different perspectives of the data, we merge these datasets using JOIN operations (on fields like zipcode) to create a unified dataset. This enables cross-dimensional analysis such as combining state-level trends with year-over-year performance. Handling Nulls with COALESCE A crucial step to ensure clean and reliable analysis: we use functions like COALESCE to manage missing values by filling them with the most appropriate defaults (e.g., average amounts). This prevents errors in downstream analysis or biased insights caused by incomplete data. Final Output – Ready for Insights At the end of this robust pipeline, we obtain a well-structured, clean dataset where meaningful comparisons can be drawn. This dataset is now primed for visualizations, statistical analysis, and predictive modeling. Why This Matters for Data Analysts: Without a solid data preparation workflow, our insights risk being misleading or incomplete. Data Engineering empowers us with clean, accurate, and integrated data, letting us focus purely on generating insights, spotting trends, and providing actionable recommendations. Data Analysts + Robust Data Pipelines = Confident Business Decisions #DataAnalytics hashtag #DataPreparation hashtag #DataIntegration hashtag #ETL hashtag #DataInsights hashtag #DataDrivenDecisions hashtag #BusinessAnalytics hashtag #STLacademy hashtag #DataPipeline hashtag #DataAggregation hashtag #CleanData hashtag #DataVisualization

🚀 A Life-Changing Lesson I Learned at Google — That Every Analyst Needs to Hear At Google, I learned the fastest way to generate impact isn't writing code. It's mastering conceptual reasoning before you touch a tool. Let's take Exploratory Data Analysis (EDA). 🙅♀️ Most analysts treat it as a technical race. A checklist of commands to run. 💡 But EDA isn't a coding competition. It's a framework for thinking. It’s not about the commands you run; it’s about the questions you ask. Here’s the framework we used 👇 Notice how the "So What?" is built in from the very beginning. 1. Find the Shape (Observe, Don't Analyze) Before you run a single command, get the 30,000-foot view. Ask: What's the scale (thousands or millions)? What are the extremes? Is the data skewed by a few massive values? Purpose: To understand the landscape before you get lost in the details. 2. Understand the Components (Univariate) Now, zoom in on one variable at a time. Ask: How is this metric distributed? Is it stable, volatile, or clustered? Are outliers mistakes, or are they your most valuable insights? Purpose: To understand the behavior of each individual character in the story. 3. Connect the Dots (Bivariate) Step back and see how the characters interact. Ask: When one metric goes up, what does another do? Which relationships are worth paying attention to — and which are noise? Are you seeing signs of dependency (e.g., engagement rises, then conversions follow)? Purpose: To identify potential cause-and-effect patterns—not to prove them, but to know where to look deeper. 4. Add Context (Time & Segments) Data doesn't exist in a vacuum. Ask: How has this changed over time? What's driving it (seasonality, a product launch)? Which segments (geographies, demographics) behave differently? Purpose: To connect abstract patterns to real-world business decisions. 5. Deliver the "So What" (The Decision) This is the only step that matters. An analysis is useless until it forces a decision. Ask: What does this mean for the business? What should we do next? Purpose: To move from description ("what")->>> interpretation ("so what") ->>> action ("now what"). 💬 The Takeaway: You don’t need a complex tool to master analytics. You need to learn how to observe, connect, and reason. Tools can compute. Analysts must interpret. Comment 👍 if you need my full EDA framework guide

Python, SQL, and dashboards? They're just the start. What truly sets you apart is how you connect the dots—how you turn data into a story that drives real value. Here are 5 lessons I’ve learned: 1. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗶𝘀 𝗸𝗶𝗻𝗴. A tool is only as powerful as the person using it, and their understanding of the business domain. Data cleaning, modeling, and interpretation must always align with the bigger picture. 2. 𝗔𝗰𝘁𝗶𝗼𝗻𝗮𝗯𝗹𝗲 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗼𝘃𝗲𝗿 𝗳𝗮𝗻𝗰𝘆 𝗺𝗼𝗱𝗲𝗹𝘀. It doesn’t matter how advanced your analysis is if it doesn’t lead to a decision or provide clarity. Data must be digestible and usable. 3. 𝗦𝗲𝗹𝗹𝗶𝗻𝗴 𝘁𝗵𝗲 𝘀𝘁𝗼𝗿𝘆 𝗶𝘀 𝗽𝗮𝗿𝘁 𝗼𝗳 𝘁𝗵𝗲 𝗷𝗼𝗯. No one will connect the dots for you. It’s your responsibility to make it clear how your work impacts the business and solves real problems. 4. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻. A perfectly optimized model won't matter if you're solving the wrong problem. Always focus on defining the problem correctly in a way that aligns with business goals. 5. 𝗣𝗲𝗼𝗽𝗹𝗲 𝗮𝗿𝗲 𝘀𝘁𝗶𝗹𝗹 𝗮𝘁 𝘁𝗵𝗲 𝗵𝗲𝗮𝗿𝘁 𝗼𝗳 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴. Data is great, but human emotions and biases play a huge role in decisions. No model can eliminate that, so learn to work with people and adapt your approach. What’s your top tip for delivering meaningful insights?