Analytics Project Management

If your analytics ends at visualization, it hasn’t started yet. Charts are the surface. Real analytics is the system behind them - the questions, the data work, the validation, and the decisions that follow. Here is how data analytics projects actually work end-to-end: 🔹 Input Clarity Every strong analytics initiative begins with a clear business objective. Stakeholder expectations, success metrics, and data sources are aligned before a single query is written. 🔹 Data Processing Raw data is extracted, cleaned, explored, and structured. KPIs are defined carefully, assumptions are tested, and patterns are validated to ensure the analysis is trustworthy. 🔹 Preparation & Validation Dashboards are designed for clarity, but accuracy comes first. Validation checks, performance tuning, and stakeholder feedback refine the insights before decisions are made. 🔹 Action & Monitoring Insights must translate into execution. Teams implement decisions, monitor KPIs continuously, and measure whether real change is happening. 🔹 Business Impact The real output of analytics is not a report - it is measurable improvement in revenue, efficiency, cost reduction, or customer experience. Visualization communicates insight. Analytics drives outcomes. If you’re building analytics projects, ask yourself - are you delivering dashboards or decisions?

As we look back on the last few years of ambitious analytics initiatives in B2B, the emerging narrative isn't about the 30x ROI from analytics COEs anymore—it's about pragmatic solutions, realistic implementations, and sustainable growth. A common theme, underscored by former AI/ML executive and current hedge fund manager Pratik Kodial's insights, concerns last-mile delivery (i.e., adoption and impact, with a wide gap between strategic analytics initiatives and actual end-user uptake). Despite successful AI/ML-enabled commercial analytics deployments across functions like Pricing, Supply Chain, and Marketing, actual ROI was often negative. Many Analytics/ Data Science teams set out with a broad scope influenced by high expectations from CXOs, hoping to address various business challenges through AI/ML. However, this often leads to an overcommitment that might impress on paper and make for a good section on the Annual Report but needs to improve substantially in practice. The crucial lesson here is the importance of focused, smaller-scale projects directly influencing Revenue and Gross Profit drivers. For Analytics leaders, the challenge is dual: Balancing the pressure to engage in transformational, high-visibility projects against smaller projects they know will deliver immediate, measurable value to commercial teams. It is imperative to spearhead practical, scalable analytics solutions that stakeholders will adopt that will demonstrably impact the bottom line. For those new to leading Analytics or Data Science teams, consider this approach: 1. Narrow Focus: Select fewer initiatives with a high potential impact on key financial metrics. Work your way down the Income Statement, and those areas will be your most significant opportunities to attack with AI/ML-enabled solutions and strategies. 2. Stakeholder Engagement: Ensure that projects are supported by senior executives at all levels, fostering broader buy-in. 3. Expert Partnerships: Differentiate between what can be outsourced and what should be developed internally, leveraging experienced external firms where beneficial. 4. Collaborative Development: Engage a core team from various organizational levels to build solutions that are as much 'theirs' as 'yours.' Value-Driven Development: Delay coding until the problem and its value are fully understood and broken down into manageable parts. Areas for early focus include Price Optimization, Customer Churn Reduction, Cross-Sell Optimization, Promotion and Discount Management, and Procurement/Logistics Optimization. These areas promise immediate returns and build a strong foundation for more extensive, transformative projects. Dive deeper into this approach in our previous LinkedIn article, and subscribe to join over 3,500 revenue management and commercial analytics professionals who regularly read our content. https://lnkd.in/eszpvrp4 #revenue_growth_analytics

After speaking with hundreds of analytics leaders this year, one theme kept coming up. The reason so many data projects underperform isn’t a lack of skill or budget. It’s that: Nobody Cared. The people who’d be benefiting from the new solution (the business users) had no interest in the project. Mainly because the analytics leaders, admittedly so, had not done a great job of selling the value of the project. Nobody likes to be told what to do, especially without an explanation. But that’s exactly how so many data projects are executed. Higher-ups say they have to participate, so people accept the tasks. But that’s it. They will do the bare minimum (and sometimes not even that). The leaders who pivoted their approach to build buy-in from the beginning. And throughout the project. Regularly saw the highest levels of success. Because the users saw how their jobs would get easier. I am making it sound easy, but it’s not. But with the right approach, it becomes repeatable over time. Things that worked well for the successful leaders were: -Meeting 1-on-1 with team leaders to sell the value and gather feedback before starting the project. -Proving out the most difficult aspects of the project through a POC and getting feedback. -Having a project kickoff with the entire team and building excitement around the vision. -Running regular touchpoints through the project to continue gathering feedback and iterating. -Getting team members involved by having them take ownership of SME tasks, especially those who want to step up and grow. People want to feel heard. Creating a framework allows you to listen and execute. And it will significantly increase the chances of your project's success. What approaches have worked well for you on your most successful projects? #data #analytics

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?

Want to tackle the most impactful data projects? Use the RICE scoring model to sort them by priority! RICE stands for Reach, Impact, Confidence, and Effort. It’s a useful framework to prioritize tasks and projects effectively. 1. 𝗥𝗲𝗮𝗰𝗵: Estimate how many people your project will affect. For example, how many teams will make decisions based on my results?     2. 𝗜𝗺𝗽𝗮𝗰𝘁: Estimate the potential benefit. Will this project bring significant improvements or minor enhancements? Rate it on a scale e.g., 1 to 5.     3. 𝗖𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝗰𝗲: Assess how confident you are in your estimates. High confidence boosts the project’s score, while low confidence lowers it. Be honest about your uncertainties regarding data quality and model complexity (0.0 to 1.0).     4. 𝗘𝗳𝗳𝗼𝗿𝘁: Calculate the time and resources required to complete the project. Measure it in person-hours or team-days. Less effort means a higher score. C͟a͟l͟c͟u͟l͟a͟t͟i͟o͟n͟ 𝗥𝗜𝗖𝗘 𝗦𝗰𝗼𝗿𝗲 = (Reach × Impact × Confidence) / Effort E͟x͟a͟m͟p͟l͟e͟ You will reach 50 sales managers with your model and estimate an impact of 4 out 5 on their work. You're fairly certain about achieving your goal with a rate of 0.8. It will take you about 80 hours of work to build the model. 𝗥𝗜𝗖𝗘 𝗦𝗰𝗼𝗿𝗲 = (50 × 4 × 0.8) / 80 𝗥𝗜𝗖𝗘 𝗦𝗰𝗼𝗿𝗲 =  2 You can compare this score of 2 versus the other project scores and select the one with the highest value. Use the RICE model to sort and prioritize your data projects. It ensures you’re focusing on high-impact tasks that require reasonable effort and have solid confidence behind them. Regularly revisit and adjust your scores as new data or insights become available. This keeps your priorities aligned with changing business goals. By applying the RICE scoring model, you’ll increase the efficiency of your project management, ensuring you’re working on what truly matters. How do you currently prioritize your data projects? ---------------- ♻️ Share if you find this post useful ➕ Follow for more daily insights on how to grow your career in the data field #dataanalytics #datascience #rice #projectmanagement #prioritization

Why most predictive analytics projects fail and the simple way to fix them The promise of predictive analytics in healthcare is huge like helping accurate patient forecasts, optimised resource allocation, reduced costs and improved outcomes. Yet, industry data shows that up to 85% of data projects fail to deliver measurable ROI. Because most projects treat analytics like a “tech installation” rather than a “problem-solving system.” Here’s what usually goes wrong: 1) No clear business problem defined – Teams jump straight to modeling without knowing exactly what success looks like. 2) Poor data quality – Inconsistent or incomplete patient and operational data means predictions are flawed from day one. 3) Lack of operational integration – Insights stay on dashboards instead of driving real-time decision-making. 4) Change resistance – Staff are not trained or involved in using the insights, so adoption remains low. 5) The fix is surprisingly simple: Start with one specific, high-impact use case (e.g., forecasting ER patient inflow for next week). Use clean, relevant data even if it’s limited before scaling. Integrate predictions directly into operational workflows so they trigger action automatically. Involve frontline teams early so they trust and use the output. Predictive analytics is solving one real problem at a time, then scaling what works. We’ve helped teams turn analytics into real decisions. If you want the same, connect with us. #healthcare #tech #hospital

The best analysis in the world will fail if the client’s environment rejects it. Large organizations have an immune system designed to kill change. Your project is a foreign object. Unless you engineer the environment for adoption, the organization will bury your work. A project is not a success when the code runs. It is a success when the organization is reshaped to sustain it. Here's how to make sure your deliverables create real change: * Arm the Champion: Your stakeholder must sell your work to their bosses. Provide the ROI calculations and executive soundbites they need to win that fight. * Bridge the Skill Gap: Identify the skill gaps. If the team cannot wield the tool you've built, it is worthless. Deliver a hiring or training roadmap alongside your data. * Hard-code Ethics: Move beyond compliance. Build interpretability, data privacy, and social impact into the architecture. If your work lacks a moral compass or a transparent logic, it is a business liability. * Architect the Roadmap: Do not just show a vision. Deliver a three-phase blueprint—from frictionless wins to strategic integration to total transformation. When you obsess over your client's reality instead of the elegance of your code, you are no longer just an analyst. You are a strategic partner who creates real value and positive outcomes. Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics #DataStorytelling

SQL, Excel, Python… All are useless if you don’t follow the analytics framework. Everyone thinks data analytics = tools. But tools are only 10%. The real work is a FRAMEWORK. Here’s the 5–step flow you should know 👇 1. Define the Problem Don’t jump into Excel or SQL yet. Ask: What’s the business question? 2. Collect Data From databases, APIs, surveys, or logs. Garbage in = garbage out. 3. Clean & Prepare Fix missing values. Remove duplicates. Make the dataset analysis-ready. 4. Analyze & Explore Use statistics, SQL, Excel, or Python. Look for trends, patterns, and insights. 5. Communicate Findings Dashboards, reports, or storytelling. Because insights are useless if no one understands them. This is the core framework. Whether you’re in Excel, Power BI, or Python, the steps remain the same. Master this flow → you can adapt to any tool. P.S. I still don’t get why so many beginners depend only on tools… 🤔 Do you want me to show how to complete a full analytics project from start to finish?

Most analytics work falls into two buckets: reactive, and proactive. Here's a short guide on how to master both and become a more trusted partner to your stakeholders: 1. Reactive requests: - Understand the business context - Uncover the real reason for the request - Dig into what decisions might be made - Consider tradeoffs with your time & priorities - Deliver insights, not information, wherever possible And remember that while a quick "yes, I can pull that for you" can help you build relationship capital and trust, saying it all of the time (without pushing back) actually erodes your ability to make an impact and advance in your career. 2. Proactive projects: - Study the business you're in, become an expert - Study your stakeholder's quarterly & annual goals - Look at what truly drives results against those goals - Ask yourself: is there analytics we could be doing? The goal is to build up your domain knowledge to the point that you start identifying the right analytics projects without being asked. This is transformational for Data Analyst careers.