Data Analysis For Project Managers

Risk Assessment. Risk assessment is “The process of quantifying the probability of a risk occurring and its likely impact on the project”. It is often undertaken, at least initially, on a qualitative basis by which I mean the use of a subjective method of assessment rather than a numerical or stochastic (probablistic) method. Such methods seek to assess risk to determine severity or exposure, recording the results in a probability and impact grid or ‘risk assessment matrix'. The infographic provides one example which usefully visually communicates the assessment to the project team and interested parties. Probability may be assessed using labels such as: Rare, unlikely, possible, likely and almost certain; whilst impact considered using labels: Insignificant, minor, medium, major and severe. Each label is assigned a ‘scale value’ or score with the values chosen to align with the risk appetite of the project and sponsoring organisation. The product of the scale values (i.e. probability x impact) resulting in a ranking index for each risk. Thresholds should be established early in the life cycle of the project for risk acceptance and risk escalation to aid decision-making and establish effetive governance principles. Risk assessment matrices are useful in the initial assessment of risk, providing a quick prioritisation of the project’s risk environment. It does not, however, give a full analysis of risk exposure that would be accomplished by quantitative risk analysis methods. Quantitative risk analysis may be defined as: “The estimation of numerical values of the probability and impact of risks on a project usually using actual or estimated values, known relationships between values, modelling, arithmetical and/or statistical techniques”. Quantitative methods assign a numerical value (e.g. 60%) to the probability of the risk occurring, where possible based on a verifiable data source. Impact is considered by means of more than one deterministic value (using at least 3-point estimation techniques) applying a distribution (uniform, normal or skewed) across the impact values. Quantitative risk methods provide a means of understanding how risk and uncertainty affect a project’s objectives and a view of its full risk exposure. It can also provide an assessment of the probability of achieving the planned schedule and cost estimate as well as a range of possible out-turns, helping to inform the provision of contingency reserves and time buffers. #projectmanagement #businesschange #roadmap

Root Cause Analysis (RCA) Methods – Overview, Comparison & Tips 🔍 In quality, safety, engineering, and problem-solving domains, Root Cause Analysis (RCA) is a cornerstone of sustainable improvement. Here’s a quick overview and comparison of the top RCA methods, their strengths, and where they shine: 🎯 Popular RCA Tools & Techniques: ❶5 Whys – Simple yet powerful. Keep asking “why” to drill down to the root cause. ✅ Quick, intuitive | ❌ May oversimplify complex issues ❷Fishbone (Ishikawa) Diagram – Visualizes potential causes across categories (People, Methods, Machines, etc.) ✅ Great for brainstorming | ❌ Needs team consensus ❸Pareto Analysis – Based on the 80/20 rule. Focuses on the most frequent causes. ✅ Prioritization | ❌ Doesn’t show causality ❹FMEA (Failure Modes and Effects Analysis) – Proactive method to assess risk of potential failures. ✅ Risk-based | ❌ Time-consuming ❺Fault Tree Analysis (FTA) – Logical, top-down approach using boolean logic. ✅ Detailed and structured | ❌ Requires expertise ❻DMAIC (Six Sigma) – Structured problem-solving (Define, Measure, Analyze, Improve, Control). ✅ Data-driven | ❌ Can be resource-heavy ❼8D (Eight Disciplines) – Team-based, process-driven RCA with containment and corrective action. ✅ Widely used in automotive/manufacturing | ❌ May be too rigid for some issues ❽Shainin Red X Method – Focuses on dominant cause using progressive elimination. ✅ Fast for repetitive issues | ❌ Less known, needs training ❾Bowtie Analysis – Combines risk assessment with RCA, visualizing threats, controls, and consequences. ✅ Holistic | ❌ More qualitative ❿Cause & Effect Matrix – Prioritizes inputs based on impact on key outputs (CTQs). ✅ Links causes to outcomes | ❌ Needs solid process understanding ⓫AI/ML-Based RCA – Uses data mining and algorithms to detect patterns and predict root causes. ✅ Scalable, modern | ❌ Requires quality data & digital maturity 🔥 Challenges in Using RCA: -Bias and assumptions -Lack of data or poor data quality -Over-reliance on a single tool -Team misalignment -Skipping validation of root cause(s) 🧿 New Additions & Tips: ✅ Combine methods: e.g., Fishbone + 5 Whys or Pareto + FMEA ✅ Train teams on when/how to use each tool ✅ Always validate the root cause with data/evidence ✅ Document learnings for future prevention ✅ Embrace digital tools where appropriate 🧭 Choosing the Right RCA Tool: Ask yourself: ✔ Is the problem complex or simple? ✔ Do we have data? ✔ Is time a constraint? ✔ Are multiple stakeholders involved? ✔ Is this recurring or a one-time issue? 📊 Sometimes, a hybrid approach works best! 📢 What RCA tool do you use most often, and why? Share your experience or tips in the comments! ====== 🔔 Consider following me at Govind Tiwari,PhD #RootCauseAnalysis #QualityManagement #ContinuousImprovement #ProblemSolving #LeanSixSigma #FMEA #8D #DMAIC #Shainin #AIinQuality #CQI #QMS #RiskManagement #OperationalExcellence

Ever wondered how a real AI project actually works ? A successful AI project goes through 7 structured steps, each led by different experts. From defining the business problem to continuous improvement after deployment, every role plays a part in making AI work in the real world. Here’s a cheat sheet that breaks down the end-to-end AI project lifecycle with clear steps, leaders, and responsibilities. ✅ AI Project Steps Covered: 🔹Step 1: Defining the Problem → Led by business analysts & product managers. Identify real problems, set objectives, align business & tech needs. 🔹Step 2: Preparing the Data → Led by data engineers & analysts. Collect raw data, clean, standardize, and split into training, validation, and test sets. 🔹Step 3: Building the Model → Led by ML engineers & data scientists. Choose algorithms, engineer features, train models, tune hyperparameters, and compare best fits. 🔹Step 4: Testing & Evaluation → Led by data scientists & ML researchers. Validate with unseen data, use metrics (accuracy, recall, AUC), stress-test, and decide if model is production-ready. 🔹Step 5: Deployment → Led by MLOps engineers & software developers. Package models into APIs, use Docker/Kubernetes, integrate with apps, enable predictions, and ensure reliability before going live. 🔹Step 6: Validation & Monitoring → Led by validators, ethicists, QA teams. Monitor accuracy, detect drift, check bias, log failures, and trigger alerts if performance drops. 🔹Step 7: Continuous Improvement → Led by data scientists, PMs, domain experts. Gather feedback, add new data sources, retrain, optimize pipelines, and push regular updates. Save this guide and share with others, and hopefully this will help to understand how AI projects work, step by step, role by role! #AI

Way too many data projects fail. Not because the analysis was wrong but because the goal was never clear to begin with. Before you dive into the data, make sure you understand what problems you actually try to solve and for whom. 𝗔𝘀𝗸 𝘁𝗵𝗲𝘀𝗲 𝟴 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗯𝗲𝗳𝗼𝗿𝗲 𝘀𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝗮𝗻𝘆 𝗱𝗮𝘁𝗮 𝗽𝗿𝗼𝗷𝗲𝗰𝘁: 1. What is the actual business question? 2. Who are the stakeholders? 3. What decisions will this analysis support? 4. What data is available? 5. What pieces are missing? 6. What format is expected? 7. What does success look like? 8. What is the timeline and urgency? Answering these upfront can save hours of rework and ensure your results will get used. What’s the one question you wish you had asked before your last data project? ---------------- ♻️ 𝗦𝗵𝗮𝗿𝗲 if you find these questions helpful. ➕ 𝗙𝗼𝗹𝗹𝗼𝘄 for more daily insights on how to grow your career in the data field. #dataanalytics #datascience #dataproject #stakeholdermanagement  #careergrowth

🚀 From Data to Decisions: Technical Support Insights with Power BI One of the things I love about data analytics is its ability to transform raw numbers into actionable insights. Over the years, I’ve worked on multiple IT ticketing dashboards, helping teams track support tickets, monitor agent performance, and improve resolution times. In my previous organization, I worked extensively with BigQuery and various visualization tools to automate ticketing dashboards, eliminating manual reporting efforts and ensuring teams had real-time insights at their fingertips. That experience played a key role in shaping my latest project: developing a Power BI dashboard to analyze IT support ticket trends. 🔑 Key Insights from This Project: 📌 Ticket Volume Trends – Analyzed peak hours and ticket distribution across different sources and countries, providing clarity on workload patterns. 📌 Resolution Efficiency – Measured resolution times across ticket sources, identifying opportunities to enhance response speed and optimize workflows. 📌 Agent Workload Balance – Assessed ticket distribution among agents to ensure an even workload and improve overall efficiency in handling support requests. A huge thank you to Anh Leimer and Hien Tran for your invaluable feedback and support throughout this project. It made a world of difference! 🙌 And a special shoutout to Injae Park for sharing the amazing IT Service Ticket Overview dashboard. I loved the design and took inspiration from it for my Resolved Summary Page. ✨ Every project, every tool, and every challenge has been a stepping stone. Whether it was working with BigQuery and Looker Studio in my previous role or diving deep into Power BI now, the goal remains the same --> turning data into insights that drive real change. Have you worked on a dashboard or automation project that made a big impact? Let’s share insights! Interactive Dashboard Link: https://lnkd.in/gpAKgCec #DataAnalytics #PowerBI #DashboardDesign #Automation #BusinessIntelligence #ProblemSolving #Efficiency #DataVisualization

Think your dataset is clean? 🤔 The 3 types of outliers silently sabotaging your model say otherwise... | Most teams focus on model architecture while ignoring dataset hygiene. They discover too late that quality outliers, content anomalies, and annotation errors are destroying their model's reliability. Traditional data cleaning methods miss these critical issues. | By combining embedding spaces with Local Outlier Factor analysis, you can catch these issues early and systematically clean your datasets. 🔑 KEY LEARNINGS: → Dataset outliers directly impact model metrics by skewing confidence thresholds and reducing precision/recall → Dense embeddings from your model's penultimate layer provide rich feature representations for outlier detection → UMAP visualization reveals clusters of similar images—isolated points are your first outlier candidates ⚡ TRY THIS NOW: Start with a small subset of your data (~1000 images): extract their embeddings and apply LOF to get an outlier score for each image. The highest scoring samples are your priority investigation targets. 🔬 Ready to level up? My Coursera course on computer vision quality is free to audit and includes complete notebooks on embedding-based outlier detection. 💭 What's your biggest dataset quality challenge? Let me know in the comments! #deeplearning #data #computervision #objectdetection

Data is only powerful if people understand and act on it That’s why just pulling numbers isn’t enough. A good report tells a story, answers key business questions, and helps decision-makers take action. To ensure your analysis actually gets used: ✅ Start with the right question – If you don’t understand what stakeholders really need, you’ll spend hours on the wrong metrics. It’s okay to ask clarifying questions. ✅ Make it simple, not just accurate – Clean tables, clear charts, and insights that anyone (not just data people) can understand. ✅ Provide context, not just numbers – A 20% drop in sales is scary… unless you also show seasonality trends and explain why it’s normal. ✅ Anticipate follow-up questions – The best reports answer the next question before it's asked. ✅ Know your audience – A C-suite executive and a product manager don’t need the same level of detail. Tailor accordingly. Your work should make decision-making easier. If stakeholders are confused, they won’t use your report No matter how technically correct it is. The best data professionals don’t just crunch numbers. They translate data into impact. Have you ever spent hours on an analysis only for no one to use it?

Crafting a Data and Analytics Strategy That Really Resonates For many organizations, articulating the tangible value of a data strategy can be a significant challenge. It's common to default to a technology-centric approach, leading to skepticism about solving a "problem" with a "hammer". 🔵 Strategy First, Technology Second Gaining buy-in for your data and analytics vision before diving into the technical details of the operating model. This prevents stakeholders from questioning the need for proposed technology solutions. Communication is key, and it must be segmented based on your audience – whether you're educating or informing (sideways; business partners), persuading (upwards; sponsors), or instructing (downwards; D&A teams). Each approach demands different content, length, and emphasis in your presentations. 🔵 Concise, Outcome-Led Vision Your vision statement should be remarkably concise, ideally 20-40 words, deliverable as an "elevator pitch". It should clearly state how your data and analytics team contributes to the top three organizational goals, identifies the specific stakeholders you aim to help, and outlines three mechanisms for delivering value. This also includes explicitly stating what you won't focus on, ensuring clarity and preventing dilution of effort. 🔵 Align with Business Transformations and Culture To ensure relevance, your strategy must connect with ongoing major business transformations within the organization. Furthermore, addressing cultural barriers to data-driven decision-making is paramount. I suggest framing the culture as "outcome-led" / "value-driven" and "decision-centric" rather than merely "data-driven". 🔵 Broaden The Appeal and Resonate, Wider Incorporate contemporary drivers and trends (e.g. how DA& teams are responding to Generative and Agentic AI), categorizing them as technology, internal, or market/societal factors, to demonstrate your strategy's forward-looking nature. 🔵 Defining Value and Measurable Impact Prioritize your primary stakeholders (ideally three), and for each, define the top three goals your team will help them achieve. For each goal, identify three measurable metrics, creating a "metrics tree" that clearly tracks your contribution to their success. Gartner defines three core value propositions for data and analytics: 1️⃣ Utility: Providing enterprise reporting as a service for common questions. Central team, allocated budget, data warehouse, etc. 2️⃣ Enabler: Facilitating business outcomes through self-service analytics, coaching, and projects based on business cases. 3️⃣ Innovation: Driving new initiatives like AI for decision making and prescriptive analytics. Each value prop requires a different delivery model, from service desks for utility to portfolio management for innovation, and these should be aligned. Collaborating with leaders like CIO, CISO, CAIO is also crucial for innovation efforts. Develop a D&A strategy that demonstrates tangible business value.

In today’s data-driven world, the ability to quickly understand and act on data is more critical than ever. One of the most powerful tools to achieve this is data visualization, especially when using Excel. By transforming raw data into visual representations, we can not only identify trends and patterns but also communicate insights in a more digestible format. 𝐿𝑒𝑡’𝑠 𝑑𝑖𝑣𝑒 𝑖𝑛𝑡𝑜 ℎ𝑜𝑤 𝑦𝑜𝑢 𝑐𝑎𝑛 𝑙𝑒𝑣𝑒𝑟𝑎𝑔𝑒 𝐸𝑥𝑐𝑒𝑙’𝑠 𝑓𝑒𝑎𝑡𝑢𝑟𝑒𝑠 𝑡𝑜 𝑒𝑛ℎ𝑎𝑛𝑐𝑒 𝑦𝑜𝑢𝑟 𝑑𝑎𝑡𝑎 𝑎𝑛𝑎𝑙𝑦𝑠𝑖𝑠 𝑎𝑛𝑑 𝑑𝑒𝑐𝑖𝑠𝑖𝑜𝑛-𝑚𝑎𝑘𝑖𝑛𝑔 𝑝𝑟𝑜𝑐𝑒𝑠𝑠𝑒𝑠: 📈 Charts and Graphs: Visualizing data with charts and graphs helps highlight important trends and patterns at a glance. Whether it’s a bar chart, line graph, or pie chart, these visuals are perfect for simplifying complex data and making it easier to interpret. ℹ️ Conditional Formatting: Want to quickly spot outliers or key data points? Conditional formatting is your go-to tool. By applying color scales, data bars, or icon sets, you can instantly identify critical information without having to sift through every row of data. 📊 Pivot Charts: Pivot charts allow you to create dynamic visual summaries of your data, giving you the flexibility to explore different perspectives on the fly. With the ability to adjust and manipulate the data, you can uncover insights that might have been overlooked in static tables. 🌟 Sparklines: These mini-charts inside a cell are perfect for showcasing trends within a single row of data. Use sparklines to get a snapshot of trends without taking up too much space on your sheet. 〰️ Dashboard Integration: A dashboard consolidates multiple visualizations into one interactive view, making it easier to track key metrics and make informed decisions. With Excel, you can integrate different charts and graphs into a dashboard that provides a holistic view of your data. Data visualization isn’t just about creating pretty pictures—it’s about making data more accessible, understandable, and actionable. Whether you’re tracking business performance or analyzing trends, these tools can turn raw numbers into strategic insights that drive decisions. How do you currently use data visualization to inform your decision-making process, and which Excel feature do you find most effective? Share your thoughts in the comments below! #DataVisualization #ExcelTips #ExcelDashboards #DataInsights #DataDrivenDecisionMaking

𝗠𝗮𝗸𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 𝗧𝗵𝗮𝘁 𝗖𝗼𝗻𝗻𝗲𝗰𝘁 𝗪𝗶𝘁𝗵 𝗬𝗼𝘂𝗿 𝗔𝘂𝗱𝗶𝗲𝗻𝗰𝗲 How can you create data visualizations that are dynamic, intuitive, and impactful for diverse audiences? Whether you're a beginner or a seasoned data analyst, effective visualizations tell compelling stories and deliver clear, actionable insights. Before diving into the details, consider: • Who is your audience? • What questions are they asking? • What answers are you providing? • What story do you want to tell? 𝗪𝗵𝗲𝗻 𝘆𝗼𝘂’𝗿𝗲 𝗿𝗲𝗮𝗱𝘆 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝘆𝗼𝘂𝗿 𝘃𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀, 𝘁𝗵𝗲𝘀𝗲 𝗯𝗲𝘀𝘁 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 𝗰𝗮𝗻 𝗵𝗲𝗹𝗽: • Choose the right charts for the story you want to tell. • Use intuitive layouts and clear color cues. • Incorporate contextual elements like shapes and sizes. Check out these slides for 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝘁𝗶𝗽𝘀 𝗮𝗻𝗱 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 𝗼𝗻 𝗽𝗼𝗽𝘂𝗹𝗮𝗿 𝗰𝗵𝗮𝗿𝘁 𝘁𝘆𝗽𝗲𝘀, from bar charts to heatmaps, and take your data storytelling to the next level!