Progress Data Platform boosts answer accuracy with semantic enrichment and governance

Build the output first, 🤩 fix the chaos second. 😅 Most environmental organizations spend years perfecting data collection before building anything useful. 𝗪𝗿𝗼𝗻𝗴 𝗼𝗿𝗱𝗲𝗿. Your decade of field observations sits unusable while you debate validation protocols. Grant deadlines pass while you standardize species nomenclature. Funders need proof of impact and you're still cleaning spreadsheets. I've watched brilliant teams lose competitive grants not because their science was weak, but because they couldn't demonstrate systematic capability fast enough. The pattern is predictable: organizations with worse data but better dashboards win funding. Every time. Build the output first. The dashboard tells you what data matters. When you create the stakeholder view you need, the quarterly report, the funder portal, the impact visualization, you discover immediately which fields are critical and which are nice-to-have. The gaps become obvious. The priorities clarify themselves. A research center I worked with spent 18 days every quarter manually pulling numbers into donor reports. We built one automated dashboard first. Suddenly they knew exactly which data points needed standardization and which didn't. Two months later: 2-hour quarterly reports and stakeholders accessing live updates whenever needed. The infrastructure sprint works backwards from visibility: → What do funders need to see? → What format proves systematic capability? → Which analyses demonstrate impact? → Now: what minimum data makes that possible? Not: collect everything perfectly, then maybe build something someday. 𝗬𝗼𝘂𝗿 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗳𝗶𝗿𝘀𝘁 𝘀𝘁𝗲𝗽: Pick your most painful recurring report. Sketch what an automated version would show. List the 5-10 data points it absolutely requires. Ignore everything else for now. That's your roadmap. Build the dashboard that solves the visible problem, and it reveals exactly how to fix the invisible infrastructure underneath. World-class science deserves infrastructure that makes it visible, not systems that keep it buried 🌊 Which report is costing you the most time right now?

Saw this clip that perfectly illustrates the journey from mostly done to truly complete in any complex project. It’s a humorous nod to the fact that data collection is rarely perfect on the first run. * You might track the core metrics (like 1RM), but the *context* is what truly matters. * Often, the final 10% of insight requires one more API call or a smarter search mechanism. This ongoing iteration—the hunt for that missing variable—is where the real value is built. Keep pushing past the easy data to find the full story! What's one piece of missing data that you consistently have to hunt down in your professional projects? Share your challenges below! #DataDriven #Productivity #BusinessAnalysis #ContinuousImprovement #TechIteration #ProcessOptimization

Most growth-oriented businesses eventually hit a complexity ceiling. ⚙️ The 2026 Cleveland Small Business Data Health Study is designed to help Northeast Ohio owners and strategic partners pinpoint exactly why their revenue engine is grinding. This is a 3-minute diagnostic engineered to categorize your current operations into one of three critical phases: The Analog Operator (paper & gut feel): You are flying on legacy knowledge and gut feel, but you are likely leaving money on the table because you cannot see the leaks. 🔍 The Frustrated Builder (siloed spreadsheets): You have the tools, but you are drowning in the manual labor of keeping them alive through siloed spreadsheets and manual data cleanup. 🛠️ The Modern Optimizer (apps & AI): You have the dashboards, but if profit is flat, you are likely optimizing vanity metrics instead of core growth levers. 📈 Every participant receives an individualized Strategic Blueprint within 48 hours, providing an Engineer’s Insight into your specific system gaps. You will also secure access to the final regional benchmark report to see how your data systems stack up against the Cleveland market. Stop guessing. Start optimizing. 🎯 Take the 3-minute diagnostic and claim your blueprint here: https://lnkd.in/guH7Scde

💭 What does an 18th-century steam engine have to do with modern data governance? Quite a lot, as it turns out. In this article, Dr. Joe Perez draws a clever parallel between James Watt’s definition of horsepower and how today’s data leaders should think about accuracy, completeness, and consistency. Peak metrics impress. Sustainable metrics get funded. A practical, well-framed piece for CDOs who want their numbers to hold up under pressure, across time, teams, and geographies: https://hubs.ly/Q042l-k50

We’re excited to share a new white paper in partnership with Consumer Edge - 🔎 Quantitative Frameworks for Scalable Data Modeling and Alpha Extraction 👉 https://lnkd.in/egSY-YmN Alternative data has become a proven source of alpha - but extracting durable, scalable signal requires more than raw datasets. In this paper, we outline: • The key challenges quants face when evaluating alternative data - including entity mapping, survivorship bias, point-in-time reconstruction, and KPI alignment • How Maiden Century’s platform harmonizes raw alternative data into quant-ready signals • A detailed backtest framework demonstrating 19–25% annualized market-neutral returns across multiple profiles, with Sharpe ratios of 2–3x • How synthetic point-in-time (SPIT) modeling expands backtesting horizons for newer datasets • A structured workflow for converting alternative data into proprietary, production-ready quant signals The takeaway is clear: The next generation of alpha belongs to investors who combine high-quality alternative datasets with robust infrastructure for modeling, validation, and expectation benchmarking. For quant funds, systematic investors, and data-driven portfolio managers, this paper provides a practical framework for moving from raw data to repeatable signal generation. #AlternativeData #QuantInvesting #AlphaGeneration #Backtesting #FinancialData #PointInTime #SystematicInvesting #DataScience #MaidenCentury #ConsumerEdge

Most data projects fail before the first line of code is written. Not because of technology. Because of unclear requirements. In 8 years, I’ve learned: If you don’t define: • The business question • The metric definition • The decision owner • The time horizon • The data source authority You’re building guesswork. Technical excellence cannot compensate for vague thinking. Data professionals who ask better questions build better systems. That’s the difference between execution and strategy. #DataAnalytics #DataEngineering #BusinessIntelligence #CloudData

I opened a spreadsheet today and laughed. Not because it was funny. Because 80% of it was unnecessary. Extra tabs. Old versions. Hidden columns. Complicated formulas solving simple problems. And I thought… We do this in our careers too. We overcomplicate. More tools. More certifications. More dashboards. More noise. When sometimes the real value is: • Clear thinking • Clean data • Simple explanation • Strong decision The most powerful analysts I’ve met don’t impress you with complexity. They make complex things feel simple. That’s the level I’m aiming for. #DataAnalytics #FPandA #FinanceLife #BusinessIntelligence #CareerGrowth #WomenInTech

You open a folder from six months ago, and you’re greeted by  analysis_final_v2_REAL.csvandplot_new_fixed.png Which one was the actual final version? Which script generated it? Bad data organization is the "silent killer" of scientific reproducibility. There is a massive pressure to publish, and we collect more data than ever before, but without a standardized system, that data becomes a graveyard of lost insights. Here, therefore, some practical advice: The Golden Rules - Never modify raw data Treat raw data files as read-only. All transformations go to a separate processed/ folder. - Use consistent naming Pick a convention on day one and follow it for every file in the project. - Document everything Future-you is a stranger. Write README files and data dictionaries. - Automate what you can Scripts are better than memory. If you click 20 times, write a script instead. I’ve compiled these best practices into a complete guide, including copy-paste folder templates and a checklist for your next project. Read the full guide here: https://lnkd.in/d2usDG8X #DataScience #Research #PhDLife #DataVisualization #Plotivy

𝐃𝐚𝐭𝐚 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐈𝐬 𝐖𝐡𝐞𝐫𝐞 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐀𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐁𝐞𝐠𝐢𝐧𝐬 In reporting workflows, raw data rarely arrives in a usable format. It needs structure. Features like 𝐒𝐩𝐥𝐢𝐭 𝐂𝐨𝐥𝐮𝐦𝐧, 𝐑𝐞𝐩𝐥𝐚𝐜𝐞 𝐕𝐚𝐥𝐮𝐞𝐬, and 𝐆𝐫𝐨𝐮𝐩 𝐁𝐲 may seem basic — but their impact is significant when applied correctly. Splitting columns ensures attributes are analysis-ready. Replacing values standardizes business definitions. But the real shift happens with 𝐆𝐫𝐨𝐮𝐩 𝐁𝐲. That’s where transactional-level data turns into structured summaries. Thousands of rows become: • Revenue by segment • Orders by region • Performance by category And that’s where business reporting truly begins. Data cleansing is not cosmetic. It’s foundational. Because structured data doesn’t just improve reports — it improves decisions. Krishna Mantravadi Upendra Gulipilli Ranjith Kalivarapu Harshitha K Rakesh Viswanath Frontlines EduTech (FLM) #PowerQuery #DataTransformation #ExcelForAnalytics #BusinessIntelligence #DataAnalytics #FrontlinesEduTech #FLM

Most data foundation projects start in the wrong place. The first question is usually "what platform should we use?" It should be the fifth. Here's the order that actually works: 1. What decisions do we need this data to support? Everything flows from this. If you can't answer it, stop. 2. Who needs to trust the output? Your stakeholders and their tolerance for uncertainty shape everything downstream. 3. What data do we actually have and how good is it? A reality check before you design anything. Most teams skip this. 4. What does 'good' look like for this use case? Defines your quality bar. Avoids six months of over-engineering. 5. What platform fits our team, our scale, and our budget? Only now does the tooling conversation make sense. The platform choice matters. It's just not the most important choice. Does this match what you're seeing across your data projects?