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Aneesh SatheBeyond the DatasetOn the recent season of the show Clarkson’s farm, J.C. goes through great lengths to buy the right pub. As with any sensible buyer, the team does a thorough tear down followed by a big build up before the place is open for business. They survey how the place is built, located, and accessed. In their refresh they ensure that each part of the pub is built with purpose. Even the tractor on the ceiling. The art is  in answering the question: How was this place put together? A data-scientist should be equally fussy. Until we trace how every number was collected, corrected and cleaned, —who measured it, what tool warped it, what assumptions skewed it—we can’t trust the next step in our business to flourish.<a href="https://aneeshsathe.com/wp-content/uploads/2025/07/image-from-rawpixel-id-3590280-jpeg.jpg" rel="nofollow noopener" target="_blank"></a>Old sound (1925) painting in high resolution by Paul Klee. Original from the Kunstmuseum Basel Museum. Digitally enhanced by rawpixel.Two load-bearing pillarsWhile there are many flavors of data science I’m concerned about the analysis that is done in scientific spheres and startups. In this world, the structure held up by two pillars:<ol><li>How we measure — the trip from reality to raw numbers. Feature extraction.</li><li>How we compare — the rules that let those numbers answer a question. Statistics and causality.</li></ol>Both of these related to having a deep understanding of the data generation process. Each from a different angle. A crack in either pillar and whatever sits on top crumbles. Plots, significance, AI predictions, mean nothing.How we measureA misaligned microscope is the digital equivalent of crooked lumber. No amount of massage can birth a photon that never hit the sensor. In fluorescence imaging, the point-spread function tells you how a pin-point of light smears across neighboring pixels; noise reminds you that light itself arrives from and is recorded by at least some randomness. Misjudge either and the cell you call “twice as bright” may be a mirage.In this data generation process the instrument nuances control what you see. Understanding this enables us to make judgements about what kind of post processing is right and which one may destroy or invent data. For simpler analysis the post processing can stop at cleaner raw data. For developing AI models, this process extends to labeling and analyzing data distributions. Andrew Ng’s approach, in data-centric AI, insists that tightening labels, fixing sensor drift, and writing clear provenance notes often beat fancier models.How we compareNow suppose Clarkson were to test a new fertilizer, fresh goat pellets, only on sunny plots. Any bumper harvest that follows says more about sunshine than about the pellets. Sound comparisons begin long before data arrive. A deep understanding of the science behind the experiment is critical before conducting any statistics. The wrong randomization, controls, and lurking confounder eat away at the foundation of statistics.This information is not in the data. Only understanding how the experiment was designed and which events preclude others enable us to build a model of the world of the experiment. Taking this lightly has large risks for startups with limited budgets and smaller experiments. A false positive result leads to wasted resources while a false negative presents opportunity costs.   The stakes climb quickly. Early in the COVID-19 pandemic, some regions bragged of lower death rates. Age, testing access, and hospital load varied wildly, yet headlines crowned local policies as miracle cures. When later studies re-leveled the footing, the miracles vanished. Why the pillars get skippedSpeed, habit, and misplaced trust. Leo Breiman warned in 2001 that many analysts chase algorithmic accuracy and skip the question of how the data were generated. What he called the “two cultures.” Today’s tooling tempts us even more: auto-charts, one-click models, pretrained everything. They save time—until they cost us the answer.The other issue is lack of a culture that communicates and shares a common language. Only in academic training is it possible to train a single person to understand the science, the instrumentation, and the statistics sufficiently that their research may be taken seriously. Even then we prefer peer review. There is no such scope in startups. Tasks and expertise must be split. It falls to the data scientist to ensure clarity and collecting information horizontally. It is the job of the leadership to enable this or accept dumb risks.Opening dayClarkson’s pub opening was a monumental task with a thousand details tracked and tackled by an army of experts. Follow the journey from phenomenon to file, guard the twin pillars of measure and compare, and reinforce them up with careful curation and open culture. Do that, and your analysis leaves room for the most important thing: inquiry.<a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/ai/" target="_blank">#AI</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/causal-inference/" target="_blank">#causalInference</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/clean-data/" target="_blank">#cleanData</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/data-centric-ai/" target="_blank">#dataCentricAI</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/data-provenance/" target="_blank">#dataProvenance</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/data-quality/" target="_blank">#dataQuality</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/data-science/" target="_blank">#dataScience</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/evidence-based-decision-making/" target="_blank">#evidenceBasedDecisionMaking</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/experiment-design/" target="_blank">#experimentDesign</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/feature-extraction/" target="_blank">#featureExtraction</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/foundation-engineering/" target="_blank">#foundationEngineering</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/instrumentation/" target="_blank">#instrumentation</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/measurement-error/" target="_blank">#measurementError</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/science/" target="_blank">#science</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/startup-analytics/" target="_blank">#startupAnalytics</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/statistical-analysis/" target="_blank">#statisticalAnalysis</a> <a rel="nofollow noopener" class="hashtag u-tag u-category" href="https://aneeshsathe.com/tag/statistics/" target="_blank">#statistics</a>

Bytes EuropeFrom automation to real impact: Creating right environment for SMEs to trust AI <a href="https://www.byteseu.com/1105119/" rel="nofollow noopener" translate="no" target="_blank">https://www.byteseu.com/1105119/</a> <a href="https://pubeurope.com/tags/AgenticAi" class="mention hashtag" rel="nofollow noopener" target="_blank">#AgenticAi</a> <a href="https://pubeurope.com/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#AI</a> <a href="https://pubeurope.com/tags/CleanData" class="mention hashtag" rel="nofollow noopener" target="_blank">#CleanData</a> <a href="https://pubeurope.com/tags/environment" class="mention hashtag" rel="nofollow noopener" target="_blank">#environment</a> <a href="https://pubeurope.com/tags/PeoplePowerAgentic" class="mention hashtag" rel="nofollow noopener" target="_blank">#PeoplePowerAgentic</a> <a href="https://pubeurope.com/tags/SME" class="mention hashtag" rel="nofollow noopener" target="_blank">#SME</a> <a href="https://pubeurope.com/tags/Snowflake" class="mention hashtag" rel="nofollow noopener" target="_blank">#Snowflake</a>

PromptCloudBots don’t scroll — they crawl. 🕷️Today’s <a href="https://mastodon.social/tags/UncomplicateSeries" class="mention hashtag" rel="nofollow noopener" target="_blank">#UncomplicateSeries</a> explains what a web crawler is and why it matters.👉 <a href="https://bit.ly/43In4ur" rel="nofollow noopener" translate="no" target="_blank">https://bit.ly/43In4ur</a><a href="https://mastodon.social/tags/WebScraping" class="mention hashtag" rel="nofollow noopener" target="_blank">#WebScraping</a> <a href="https://mastodon.social/tags/DataQuality" class="mention hashtag" rel="nofollow noopener" target="_blank">#DataQuality</a> <a href="https://mastodon.social/tags/CleanData" class="mention hashtag" rel="nofollow noopener" target="_blank">#CleanData</a> <a href="https://mastodon.social/tags/BigData" class="mention hashtag" rel="nofollow noopener" target="_blank">#BigData</a>

PromptCloudThink Glassdoor is just for salary checks? Smart companies are scraping it for: 🔍 HR sentiment 💰 Pay benchmarks 🧠 Competitor insightsYes, all from public data.👉 Learn how: <a href="https://bit.ly/43YGHxa" rel="nofollow noopener" translate="no" target="_blank">https://bit.ly/43YGHxa</a><a href="https://mastodon.social/tags/WebScraping" class="mention hashtag" rel="nofollow noopener" target="_blank">#WebScraping</a> <a href="https://mastodon.social/tags/DataEngineering" class="mention hashtag" rel="nofollow noopener" target="_blank">#DataEngineering</a> <a href="https://mastodon.social/tags/BigData" class="mention hashtag" rel="nofollow noopener" target="_blank">#BigData</a> <a href="https://mastodon.social/tags/MarketInsights" class="mention hashtag" rel="nofollow noopener" target="_blank">#MarketInsights</a> <a href="https://mastodon.social/tags/CleanData" class="mention hashtag" rel="nofollow noopener" target="_blank">#CleanData</a>

PromptCloudImagine waking up to fresh, structured, compliant data.Every. Single. Day.That’s not a dream. That’s <a href="https://mastodon.social/tags/PromptCloud" class="mention hashtag" rel="nofollow noopener" target="_blank">#PromptCloud</a>!<a href="https://mastodon.social/tags/DataQuality" class="mention hashtag" rel="nofollow noopener" target="_blank">#DataQuality</a> <a href="https://mastodon.social/tags/WebScraping" class="mention hashtag" rel="nofollow noopener" target="_blank">#WebScraping</a> <a href="https://mastodon.social/tags/CleanData" class="mention hashtag" rel="nofollow noopener" target="_blank">#CleanData</a> <a href="https://mastodon.social/tags/BigData" class="mention hashtag" rel="nofollow noopener" target="_blank">#BigData</a> <a href="https://mastodon.social/tags/DataExtraction" class="mention hashtag" rel="nofollow noopener" target="_blank">#DataExtraction</a>

PromptCloudWeb scraping needs vary widely, so should your approach. Should you:• Build your own custom scrapers? • Use a plug-and-play scraping tool? • Go fully managed with a web scraping service?In this blog, we simplify the decision-making process with a no-fluff comparison of: ✅ Cost ✅ Control ✅ Scalability ✅ Maintenance🔗 Read the full blog: <a href="https://bit.ly/3ZHWxL6" rel="nofollow noopener" translate="no" target="_blank">https://bit.ly/3ZHWxL6</a><a href="https://mastodon.social/tags/DataQuality" class="mention hashtag" rel="nofollow noopener" target="_blank">#DataQuality</a> <a href="https://mastodon.social/tags/WebScraping" class="mention hashtag" rel="nofollow noopener" target="_blank">#WebScraping</a> <a href="https://mastodon.social/tags/CleanData" class="mention hashtag" rel="nofollow noopener" target="_blank">#CleanData</a> <a href="https://mastodon.social/tags/BigData" class="mention hashtag" rel="nofollow noopener" target="_blank">#BigData</a> <a href="https://mastodon.social/tags/DataExtraction" class="mention hashtag" rel="nofollow noopener" target="_blank">#DataExtraction</a> <a href="https://mastodon.social/tags/productdata" class="mention hashtag" rel="nofollow noopener" target="_blank">#productdata</a> <a href="https://mastodon.social/tags/DataEngineering" class="mention hashtag" rel="nofollow noopener" target="_blank">#DataEngineering</a> <a href="https://mastodon.social/tags/TechForBusiness" class="mention hashtag" rel="nofollow noopener" target="_blank">#TechForBusiness</a> <a href="https://mastodon.social/tags/MarketInsights" class="mention hashtag" rel="nofollow noopener" target="_blank">#MarketInsights</a>

PromptCloudStill stuck manually copying rows?Somewhere out there, someone’s still copy-pasting 10,000 of them.📊 Schedule a demo to see how easy automated data extraction can be: <a href="https://bit.ly/3ZcTxpS" rel="nofollow noopener" translate="no" target="_blank">https://bit.ly/3ZcTxpS</a><a href="https://mastodon.social/tags/DataQuality" class="mention hashtag" rel="nofollow noopener" target="_blank">#DataQuality</a> <a href="https://mastodon.social/tags/WebScraping" class="mention hashtag" rel="nofollow noopener" target="_blank">#WebScraping</a> <a href="https://mastodon.social/tags/CleanData" class="mention hashtag" rel="nofollow noopener" target="_blank">#CleanData</a> <a href="https://mastodon.social/tags/BigData" class="mention hashtag" rel="nofollow noopener" target="_blank">#BigData</a> <a href="https://mastodon.social/tags/DataExtraction" class="mention hashtag" rel="nofollow noopener" target="_blank">#DataExtraction</a> <a href="https://mastodon.social/tags/productdata" class="mention hashtag" rel="nofollow noopener" target="_blank">#productdata</a> <a href="https://mastodon.social/tags/DataEngineering" class="mention hashtag" rel="nofollow noopener" target="_blank">#DataEngineering</a> <a href="https://mastodon.social/tags/TechForBusiness" class="mention hashtag" rel="nofollow noopener" target="_blank">#TechForBusiness</a> <a href="https://mastodon.social/tags/MarketInsights" class="mention hashtag" rel="nofollow noopener" target="_blank">#MarketInsights</a>

PromptCloudThe real challenge in large-scale data extraction isn’t the code, it’s the strategy behind it.🔹 Building in-house gives you control, but comes with high costs and dependencies.🔹 Partnering with a DaaS provider like PromptCloud offers plug-and-play scalability, zero maintenance, and data on time, every time.📊 Focus on insights, not infrastructure.👉 Schedule a demo to see how it works: <a href="https://bit.ly/4iXlngQ" rel="nofollow noopener" translate="no" target="_blank">https://bit.ly/4iXlngQ</a><a href="https://mastodon.social/tags/Data" class="mention hashtag" rel="nofollow noopener" target="_blank">#Data</a> <a href="https://mastodon.social/tags/WebScraping" class="mention hashtag" rel="nofollow noopener" target="_blank">#WebScraping</a> <a href="https://mastodon.social/tags/CleanData" class="mention hashtag" rel="nofollow noopener" target="_blank">#CleanData</a> <a href="https://mastodon.social/tags/BigData" class="mention hashtag" rel="nofollow noopener" target="_blank">#BigData</a> <a href="https://mastodon.social/tags/DataExtraction" class="mention hashtag" rel="nofollow noopener" target="_blank">#DataExtraction</a> <a href="https://mastodon.social/tags/productdata" class="mention hashtag" rel="nofollow noopener" target="_blank">#productdata</a>

PromptCloudAI models fail not due to bad algorithms, but bad data from day one.Focus on: 🔹 Data sourcing 🔹 Cleaning 🔹 Bias handling 🔹 Feedback loops 🔹 Legal hygieneYour model’s intelligence starts with better data.👉 Unlock AI training data secrets: <a href="https://bit.ly/3RXKK7p" rel="nofollow noopener" translate="no" target="_blank">https://bit.ly/3RXKK7p</a><a href="https://mastodon.social/tags/DataQuality" class="mention hashtag" rel="nofollow noopener" target="_blank">#DataQuality</a> <a href="https://mastodon.social/tags/WebScraping" class="mention hashtag" rel="nofollow noopener" target="_blank">#WebScraping</a> <a href="https://mastodon.social/tags/CleanData" class="mention hashtag" rel="nofollow noopener" target="_blank">#CleanData</a> <a href="https://mastodon.social/tags/BigData" class="mention hashtag" rel="nofollow noopener" target="_blank">#BigData</a> <a href="https://mastodon.social/tags/DataExtraction" class="mention hashtag" rel="nofollow noopener" target="_blank">#DataExtraction</a> <a href="https://mastodon.social/tags/productdata" class="mention hashtag" rel="nofollow noopener" target="_blank">#productdata</a> <a href="https://mastodon.social/tags/DataEngineering" class="mention hashtag" rel="nofollow noopener" target="_blank">#DataEngineering</a> <a href="https://mastodon.social/tags/TechForBusiness" class="mention hashtag" rel="nofollow noopener" target="_blank">#TechForBusiness</a> <a href="https://mastodon.social/tags/MarketInsights" class="mention hashtag" rel="nofollow noopener" target="_blank">#MarketInsights</a>

PromptCloud80%+ of Netflix views come from recommendations.It’s not just streaming - it’s data-driven.What businesses can learn: • Personalize • Predict • Engage👉 <a href="https://bit.ly/3SxEmni" rel="nofollow noopener" translate="no" target="_blank">https://bit.ly/3SxEmni</a><a href="https://mastodon.social/tags/netflix" class="mention hashtag" rel="nofollow noopener" target="_blank">#netflix</a> <a href="https://mastodon.social/tags/WebScraping" class="mention hashtag" rel="nofollow noopener" target="_blank">#WebScraping</a> <a href="https://mastodon.social/tags/ai" class="mention hashtag" rel="nofollow noopener" target="_blank">#ai</a> <a href="https://mastodon.social/tags/BigData" class="mention hashtag" rel="nofollow noopener" target="_blank">#BigData</a> <a href="https://mastodon.social/tags/DataExtraction" class="mention hashtag" rel="nofollow noopener" target="_blank">#DataExtraction</a> <a href="https://mastodon.social/tags/product" class="mention hashtag" rel="nofollow noopener" target="_blank">#product</a> <a href="https://mastodon.social/tags/Data" class="mention hashtag" rel="nofollow noopener" target="_blank">#Data</a> <a href="https://mastodon.social/tags/Tech" class="mention hashtag" rel="nofollow noopener" target="_blank">#Tech</a> <a href="https://mastodon.social/tags/MarketInsights" class="mention hashtag" rel="nofollow noopener" target="_blank">#MarketInsights</a> <a href="https://mastodon.social/tags/DataQuality" class="mention hashtag" rel="nofollow noopener" target="_blank">#DataQuality</a> <a href="https://mastodon.social/tags/WebScraping" class="mention hashtag" rel="nofollow noopener" target="_blank">#WebScraping</a> <a href="https://mastodon.social/tags/CleanData" class="mention hashtag" rel="nofollow noopener" target="_blank">#CleanData</a> <a href="https://mastodon.social/tags/BigData" class="mention hashtag" rel="nofollow noopener" target="_blank">#BigData</a> <a href="https://mastodon.social/tags/DataExtraction" class="mention hashtag" rel="nofollow noopener" target="_blank">#DataExtraction</a> <a href="https://mastodon.social/tags/productdata" class="mention hashtag" rel="nofollow noopener" target="_blank">#productdata</a> <a href="https://mastodon.social/tags/DataEngineering" class="mention hashtag" rel="nofollow noopener" target="_blank">#DataEngineering</a> <a href="https://mastodon.social/tags/TechForBusiness" class="mention hashtag" rel="nofollow noopener" target="_blank">#TechForBusiness</a> <a href="https://mastodon.social/tags/MarketInsights" class="mention hashtag" rel="nofollow noopener" target="_blank">#MarketInsights</a>

PromptCloudIs your data any good? Only one way to know: measure it.Accuracy, completeness, consistency, timeliness — the real health check.🔗 <a href="https://bit.ly/3RQ0XeF" rel="nofollow noopener" translate="no" target="_blank">https://bit.ly/3RQ0XeF</a><a href="https://mastodon.social/tags/PromptCloud" class="mention hashtag" rel="nofollow noopener" target="_blank">#PromptCloud</a> <a href="https://mastodon.social/tags/DataQuality" class="mention hashtag" rel="nofollow noopener" target="_blank">#DataQuality</a> <a href="https://mastodon.social/tags/WebScraping" class="mention hashtag" rel="nofollow noopener" target="_blank">#WebScraping</a> <a href="https://mastodon.social/tags/CleanData" class="mention hashtag" rel="nofollow noopener" target="_blank">#CleanData</a> <a href="https://mastodon.social/tags/BigData" class="mention hashtag" rel="nofollow noopener" target="_blank">#BigData</a> <a href="https://mastodon.social/tags/DataExtraction" class="mention hashtag" rel="nofollow noopener" target="_blank">#DataExtraction</a> <a href="https://mastodon.social/tags/productdata" class="mention hashtag" rel="nofollow noopener" target="_blank">#productdata</a> <a href="https://mastodon.social/tags/DataEngineering" class="mention hashtag" rel="nofollow noopener" target="_blank">#DataEngineering</a> <a href="https://mastodon.social/tags/dataset" class="mention hashtag" rel="nofollow noopener" target="_blank">#dataset</a>

AnkuData Deduplication SoftwareData Deduplication Software: The ultimate merging and purging solution! Effortlessly eliminate duplicates from lists and databases with confidence. Powerful yet user-friendly, with us <a href="https://mastodon.social/tags/DataDeduplication" class="mention hashtag" rel="nofollow noopener" target="_blank">#DataDeduplication</a> <a href="https://mastodon.social/tags/cleandata" class="mention hashtag" rel="nofollow noopener" target="_blank">#cleandata</a>, <a href="https://mastodon.social/tags/dataquality" class="mention hashtag" rel="nofollow noopener" target="_blank">#dataquality</a> <a href="https://mastodon.social/tags/datacleaning" class="mention hashtag" rel="nofollow noopener" target="_blank">#datacleaning</a> <a href="https://mastodon.social/tags/solutions" class="mention hashtag" rel="nofollow noopener" target="_blank">#solutions</a><a href="https://www.melissa.com/in/data-deduplication" rel="nofollow noopener" translate="no" target="_blank">https://www.melissa.com/in/data-deduplication</a>

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