# Data Strategy Published articles for Data Strategy. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Evaluation-First AI Agents: How Zepto Scales Customer Support on Databricks and MLflow DevFeed: [Evaluation-First AI Agents: How Zepto Scales Customer Support on Databricks and MLflow]() Original publisher: [Read original article]() Author: Gireesh Sreedhar KP; Deepak Dhankani; Eash Sharma Published: 2026-09-09T03:00:00Z Content type: article Language: en Sources: [Databricks]() Topics: [AI Agent](), [databricks](), [AI Infrastructure](), [real-time]() Tags: [agents](), [ai](), [ai-engineering](), [architecture](), [best-practices](), [blog](), [company](), [customer](), [customers](), [data-science-and-ml](), [data-strategy](), [databricks](), [efficiency](), [engineering](), [evaluation](), [india](), [industries](), [operations](), [platform](), [product](), [production](), [real-time](), [retail-consumer-goods](), [scale](), [workflows]() ### AI overview This Databricks and MLflow case study describes how Zepto uses an evaluation-first, multi-agent AI system to operate customer support at more than 100,000 tickets per day. It focuses on the system architecture, evaluation framework, quality gate, and development and production loops used to improve reliability as volume, product categories, languages, and failure modes expand. ### Source excerpt Zepto's Push for Reliable, Real-Time Customer SupportZepto is one of India's fastest-growing... ## Four pillars of agentic AI success DevFeed: [Four pillars of agentic AI success]() Original publisher: [Read original article]() Author: Simon Sear Published: 2026-07-29T15:47:00Z Content type: article Language: en Sources: [Scott Logic]() Topics: [Artificial Intelligence](), [Software Engineering](), [data](), [legacy](), [systems](), [context]() Tags: [accessibility](), [agentic-ai](), [ai](), [ai-accelerated-development](), [ai-agents](), [architecture](), [artificial-intelligence](), [context](), [data](), [data-architecture](), [data-governance](), [data-strategy](), [delivery](), [engineering](), [governance](), [legacy](), [legacy-modernisation](), [software-engineering](), [systems]() ### AI overview This article argues that organizations need strong foundations before scaling agentic AI. It identifies four pillars: modernizing legacy systems, building reliable data foundations and architecture, managing governance and risk, and democratizing access to data. ### Source excerpt Everyone is talking about agentic AI. That's not surprising. The promise is huge: AI agents that can plan, reason, use tools, work across systems and get real work done. In software engineering, that could mean faster delivery and better quality. In operations, it could mean complex processes moving with less manual effort, fewer handovers and better decisions. ## Why Most Single Source of Truth Initiatives Fail (And What Successful Teams Do Differently) DevFeed: [Why Most Single Source of Truth Initiatives Fail (And What Successful Teams Do Differently)]() Original publisher: [Read original article]() Author: Deepika Saini Published: 2026-07-20T10:07:52Z Content type: article Language: en Sources: [Housing.com]() Topics: [data](), [product analytics]() Tags: [analytics](), [dashboards](), [data](), [data-architecture](), [data-engineering](), [data-strategy](), [databricks](), [governance](), [leadership](), [product-analytics](), [sql](), [technical]() ### AI overview This article argues that Single Source of Truth initiatives often fail because teams use different definitions for shared business metrics. It presents governance, business ownership of KPI definitions, and alignment between teams as more important than centralizing tables, pipelines, or dashboards. ### Source excerpt "We have multiple dashboards showing different numbers. Which one is correct?" If you've worked in data long enough, you've probably heard this question more times than you'd like. Sales reports one revenue figure. Finance reports another. Product Analytics has a third. Executives spend more time debating whose dashboard is correct than discussing what action to take. The natural response is often: "Let's build a Single Source of Truth." Sounds simple. Build a few centralized tables. Move everyone onto the same dashboards. Problem solved. Except...it rarely is. After leading an enterprise-wide Single Source of Truth (SSOT) initiative, I learned an important lesson: The hardest part wasn't building pipelines or writing SQL. It was aligning people. Technology was the easy part. Changing how the organization thought about data was the real challenge. The Biggest Myth About Single Source of Truth Many organizations believe an SSOT is simply a technical project. The thinking usually goes like this: Collect Data ↓ Transform Data ↓ Build Gold Tables ↓ Everyone Uses Them Unfortunately, reality looks more like this: Different Teams ↓ Different Definitions ↓ Different Dashboards ↓ Different Decisions ↓ Lost Trust The problem isn't that data lives in different places. The problem is that different teams define the same business metrics differently. Figure 1: Moving from fragmented metric definitions to a trusted Single Source of Truth is as much about standardization and governance as it is about technology. A table cannot solve that. Only governance can. Technology Doesn't Create Trust Imagine a metric as simple as Revenue. Ask five departments what "Revenue" means, and you might receive five different answers. Finance may recognize revenue after invoicing. Sales may count closed deals. Marketing may include projected pipeline. Product Analytics may track subscription purchases. Customer Success may exclude refunds. None of them are necessarily wrong. They're answering differen ## 16 questions to ask about your real-time data strategy DevFeed: [16 questions to ask about your real-time data strategy]() Original publisher: [Read original article]() Author: Tinybird Published: 2023-07-13T00:00:00Z Content type: article Language: en Sources: [Tinybird]() Topics: [real-time](), [data]() Tags: [data](), [data-strategy](), [real-time](), [scalable-analytics-architecture]() ### AI overview The article presents 16 questions intended to expose gaps in a real-time data strategy before an architecture review. ### Source excerpt 16 questions for your real-time data strategy reveal gaps before they become problems. Answer these before your next architecture review.