ИИ-помощник для получения ответов из базы данных: как внедрить и добиться реальной пользы

Copilots (AI assistants) in data platforms: a new data interface or an expensive toy? Why do 99% of pilots fail to scale, and who is really at risk? In the new episode of the Pro Data podcast, we explore with MWS Data experts what copilots are in modern platforms and how to get real business value from them: • What is a copilot in a data platform: how a chatbot with a large language model is changing the way we work with data—from generating worksheets to finding duplicates and automatically describing metadata. • Why a "bare" language model without a semantic layer produces beautiful, but completely incorrect information: about the importance of integrating business terminology and data quality. • Where to begin implementation: why a unified data catalog and semantic layer are needed. • Who is at risk: how the role of the analyst is changing—routine is disappearing, the demand for complex knowledge and the ability to distinguish hallucinations from truth is growing. • Why it's difficult to scale a copy-pilot across a company: a "zoo" of approaches, closed loops, and varying data sensitivities. • How to measure the impact: instead of reducing your team size, you'll see increased speed, democratized access to data, and new business opportunities. • Case studies: agents for searching for tenders and decision makers, collecting metrics from Jira and Confluence. Guests: • Denis Titov, MWS Product Owner, Host • Maxim Bartenev, CTO, MWS Data • Dmitry Amosov, Product Director, MWS Data Timecodes: 00:03:21 – Copy-pilots as a new level of communication with data. 00:06:19 – How copy-pilots help businesses: from interpreting dashboards to generating reports. 00:08:48 – Copy-pilots in data management: finding duplicate information, describing a data catalog. 00:10:55 – Entry-level specialist vs. copy-pilot: why it's cheaper to set up AI than to hire interns for scaling. 00:13:35 – The problem of trust and hallucinations: a language model produces a beautiful but incorrect answer without a semantic layer. 00:18:47 – How an analyst's work is changing: from manual system queries to AI setup and complex analysis. 00:23:40 – How to calculate the ROI of copy-pilot implementation: not laying off people, but increasing speed and new opportunities. 00:27:36 – The problems of closed loops and varying data sensitivity. 00:32:28 – The key to success: a sponsor within the organization who understands the value. 00:33:22 – Where copy-pilots won't replace humans: in physical labor and non-standard tasks. 00:40:26 – Personal use cases: a tender search agent, collecting metrics from Jira and Confluence. 00:46:17 – Preparing data for copy-and-paste: three layers – raw data, metadata, and a semantic layer. 00:51:25 – Errors due to poor data description: one request – four different responses. 00:52:23 – Brief summary and a look into the future: the risks of an infrastructure bubble and a decline in critical thinking. Join us on social media: Telegram — https://t.me/MTCweb_s VK — https://vk.com/mtswebservices Other episodes of the Pro Data podcast:    • Подкаст Pro Данные