The Self-Service Data Roadmap: Democratize Data and Reduce Time to Insight (自助數據藍圖：數據民主化與縮短洞察時間)

Uttamchandani, Sandeep

The Self-Service Data Roadmap: Democratize Data and Reduce Time to Insight

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商品描述

Data-driven insights are a key competitive advantage for any industry today, but deriving insights from raw data can still take days or weeks. Most organizations can't scale data science teams fast enough to keep up with the growing amounts of data to transform. What's the answer? Self-service data.

With this practical book, data engineers, data scientists, and team managers will learn how to build a self-service data science platform that helps anyone in your organization extract insights from data. Sandeep Uttamchandani provides a scorecard to track and address bottlenecks that slow down time to insight across data discovery, transformation, processing, and production. This book bridges the gap between data scientists bottlenecked by engineering realities and data engineers unclear about ways to make self-service work.

  • Build a self-service portal to support data discovery, quality, lineage, and governance
  • Select the best approach for each self-service capability using open source cloud technologies
  • Tailor self-service for the people, processes, and technology maturity of your data platform
  • Implement capabilities to democratize data and reduce time to insight
  • Scale your self-service portal to support a large number of users within your organization

商品描述(中文翻譯)

數據驅動的洞察力是當今任何行業的關鍵競爭優勢，但從原始數據中獲取洞察力仍然需要花費數天甚至數週的時間。大多數組織無法快速擴展數據科學團隊，以應對不斷增長的數據轉換需求。答案是什麼？自助式數據。

通過這本實用書，數據工程師、數據科學家和團隊經理將學習如何構建一個自助式數據科學平台，幫助組織中的任何人從數據中提取洞察力。Sandeep Uttamchandani 提供了一個評分卡，用於追踪和解決在數據發現、轉換、處理和生產過程中降低洞察力所需時間的瓶頸。這本書填補了數據科學家受到工程現實限制的瓶頸和數據工程師對於如何實現自助式工作方式的不清楚之間的差距。

- 構建一個自助式門戶，支持數據發現、質量、譜系和治理
- 使用開源雲技術選擇最佳的自助式能力方法
- 根據數據平台的人員、流程和技術成熟度，量身定制自助式方式
- 實施能力以實現數據民主化並減少洞察力所需時間
- 擴展自助式門戶，支持組織內大量用戶的使用

作者簡介

Dr. Sandeep Uttamchandani is the Chief Data Officer and VP of Product Engineering at Unravel Data Systems. He brings nearly two decades of experience building enterprise data products as well as running petabyte-scale data platforms for business-critical analytics and ML applications. Most recently he was at Intuit, where he ran the data platform team powering analytics and ML for Intuit's financial accounting, payroll, and payments products. Previously in his career, Sandeep was co-founder and CEO of a startup using ML for managing security vulnerabilities of open-source products. He has played engineering leadership roles at VMware and IBM for 15+ years.

Sandeep holds more than 40 issued patents, has 25+ publications in key technical conferences, and has received several product innovation and management excellence awards. He is a regular speaker in data conferences and a guest lecturer at universities. He advises startups and has served as a program/steering committee member for several conferences, including serving as Co-chair of Gartner's SF CDO Executive Summit, and Usenix Operational ML (OpML) conference. Sandeep holds a Ph.D and a Master's in Computer Science from the University of Illinois at Urbana-Champaign.

作者簡介(中文翻譯)

Dr. Sandeep Uttamchandani是Unravel Data Systems的首席數據官兼產品工程副總裁。他擁有近20年的企業數據產品建設經驗，並運營過用於業務關鍵分析和機器學習應用的PB級數據平台。最近，他在Intuit擔任數據平台團隊的負責人，為Intuit的財務會計、工資和支付產品提供分析和機器學習支持。在職業生涯的早期，Sandeep是一家使用機器學習來管理開源產品安全漏洞的初創公司的聯合創始人和首席執行官。他在VMware和IBM擔任了15年以上的工程領導職位。

Sandeep擁有40多項專利，發表了25多篇重要技術會議的論文，並獲得了多個產品創新和管理卓越獎項。他經常在數據會議上演講，並擔任大學的客座講師。他為初創企業提供諮詢，並擔任多個會議的計劃/指導委員會成員，包括擔任Gartner的SF CDO Executive Summit和Usenix Operational ML (OpML) conference的聯合主席。Sandeep擁有伊利諾伊大學香檳分校的計算機科學博士學位和碩士學位。

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