# Data & Insights Published articles for Data & Insights. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Building an AI-native data & insights operating system at Webflow DevFeed: [Building an AI-native data & insights operating system at Webflow]() Original publisher: [Read original article]() Author: Ashwini Chaube Published: 2026-09-16T00:00:00Z Content type: article Language: en Sources: [Webflow Blog]() Topics: [data](), [Artificial Intelligence](), [Platform Engineering](), [Data Science](), [decision-making](), [Authorization]() Tags: [agentic](), [ai](), [data](), [data-insights](), [inside-webflow](), [permissions](), [review](), [self-service](), [skills]() ### AI overview Webflow describes how its Data & Insights team built an AI-native operating system for trusted self-service analytics. The approach combines governed data, encoded business context, reusable skills and agents, permissions, architectural controls, review practices, and human judgment, while also changing how the team works through agent-first workflows, learning, and experimentation. ### Source excerpt How we built the governed foundations for trusted self-service analytics while transforming the way our own team works. ## Testing Trust in Prediction Markets | Part 1 DevFeed: [Testing Trust in Prediction Markets | Part 1]() Original publisher: [Read original article]() Author: David Phillips Published: 2026-09-03T13:15:00Z Content type: opinion Language: en Sources: [Sift Science]() Topics: [integrity]() Tags: [data-insights](), [fairness](), [fraud](), [integrity](), [prediction-markets](), [prediction-markets-legislation](), [trust]() ### AI overview Part 1 examines how reported insider trading, irregularities, settlement issues, and operational design choices in prediction markets can undermine claims about fairness, market integrity, collective intelligence, and truth. It argues that gaps between provider claims and market realities can erode public trust and invite political or regulatory responses. ### Source excerpt This April, U.S. federal prosecutors charged an Army sergeant with using sensitive classified information to bet on Polymarket that U.S. forces would enter Venezuela and remove Maduro from power. About the same time, Kalshi disclosed that it had fined and suspended three congressional candidates for five years after they traded on prediction markets tied to [...] The post Testing Trust in Prediction Markets | Part 1 appeared first on Sift. ## Testing Trust in Prediction Markets | Part 2 DevFeed: [Testing Trust in Prediction Markets | Part 2]() Original publisher: [Read original article]() Author: David Phillips Published: 2026-09-10T17:44:43Z Content type: opinion Language: en Sources: [Sift Science]() Topics: [integrity](), [data]() Tags: [analysis](), [comparison](), [data-insights](), [digital-trust](), [fraud](), [integrity](), [prediction-markets](), [trust]() ### AI overview Part 2 examines whether prediction markets can reliably produce trustworthy information. It compares Kalshi's centralized contract resolution with Polymarket's decentralized, oracle-based model and discusses risks involving contract interpretation, voting power, concentrated profits, and market integrity. ### Source excerpt Missed Part 1? Read it here. Prediction markets make a bold claim that they are engines of truth discovery. Proponents argue that by aggregating collective judgment, prediction markets generate forecasts that are far more valuable than traditional gambling or sports betting. While that claim has merit, a prediction market's output is not merely a well [...] The post Testing Trust in Prediction Markets | Part 2 appeared first on Sift. ## Operational Analytics for Real-Time Business Decisions DevFeed: [Operational Analytics for Real-Time Business Decisions]() Original publisher: [Read original article]() Author: Jorge Sancha Published: 2020-08-26T00:00:00Z Content type: opinion Language: en Sources: [Tinybird]() Topics: [data](), [real-time](), [dashboards]() Tags: [analytics](), [dashboards](), [data](), [data-insights](), [operational](), [real-time](), [scalable-analytics-architecture](), [time]() ### AI overview The article discusses how operational analytics support real-time business decisions and why hourly-refreshing dashboards may be insufficient. ### Source excerpt Operational analytics power real-time business decisions. Dashboards that refresh hourly aren't enough anymore. Here's what works. ## Tricks and tips for feature engineering DevFeed: [Tricks and tips for feature engineering]() Original publisher: [Read original article]() Author: Mrinal Parekh Published: 2016-07-20T18:32:44Z Content type: tutorial Language: en Sources: [CleverTap]() Topics: [Feature Engineering](), [data]() Tags: [ai-and-ml](), [ai-and-ml-data-and-analytics-data-insights-engineering-data-science](), [data](), [data-and-analytics](), [data-insights](), [data-science](), [engineering](), [feature-engineering](), [tips](), [tricks]() ### AI overview The article explains feature engineering as the creation of meaningful new input variables from existing fields for predictive modeling. It describes how domain expertise, reasoning, intuition, or automation can improve data insights and predictive model performance. ### Source excerpt Predictive modeling is a formula that transforms a list of input fields or variables into some output of interest. Feature The post Tricks and tips for feature engineering first appeared on CleverTap. ## How to remove duplicates in large datasets DevFeed: [How to remove duplicates in large datasets]() Original publisher: [Read original article]() Author: Suresh Kondamudi Published: 2016-04-19T17:17:05Z Content type: tutorial Language: en Sources: [CleverTap]() Topics: [datasets](), [Data structures](), [hash]() Tags: [bloom-filter](), [data-and-analytics](), [data-and-analytics-data-insights-engineering-data-science](), [data-insights](), [data-science](), [data-structures](), [datasets](), [engineering](), [hash](), [how-to](), [push-notifications]() ### AI overview This tutorial explains how probabilistic data structures, especially Bloom filters, can reduce memory requirements when identifying duplicate push notification tokens in datasets ranging from hundreds of millions to billions of records. ### Source excerpt Dealing with large datasets is often daunting. With limited computing resources, particularly memory, it can be challenging to perform even The post How to remove duplicates in large datasets first appeared on CleverTap.