The Self-Service Data Roadmap: Democratize Data and Reduce Time to Insight
暫譯: 自助數據路線圖：民主化數據並縮短洞察時間

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)

資料驅動的洞察力是當今任何行業的關鍵競爭優勢，但從原始數據中提取洞察力仍然可能需要數天或數週的時間。大多數組織無法快速擴展數據科學團隊，以跟上不斷增長的數據轉換需求。解決方案是什麼？自助式數據。

在這本實用的書中，數據工程師、數據科學家和團隊經理將學習如何建立一個自助式數據科學平台，幫助您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，以追蹤和解決在數據發現、轉換、處理和生產過程中減慢洞察時間的瓶頸。這本書彌合了因工程現實而受到限制的數據科學家與對如何實現自助式運作感到困惑的數據工程師之間的鴻溝。

- 建立一個自助式入口網站，以支持數據發現、質量、來源和治理
- 使用開源雲技術為每個自助式功能選擇最佳方法
- 根據您的數據平台的使用者、流程和技術成熟度量身定制自助式服務
- 實施功能以民主化數據並縮短洞察時間
- 擴展您的自助式入口網站，以支持組織內大量用戶

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 的首席數據官及產品工程副總裁。他擁有近二十年的經驗，專注於構建企業數據產品以及運行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、薪資和支付產品提供分析和機器學習支持。在他的職業生涯早期，Sandeep 是一家初創公司的共同創辦人及首席執行官，該公司利用機器學習來管理開源產品的安全漏洞。他在 VMware 和 IBM 擔任工程領導角色超過 15 年。

Sandeep 擁有超過 40 項已授權專利，在重要的技術會議上發表了 25 篇以上的論文，並獲得多項產品創新和管理卓越獎。他是數據會議的常客演講者，也是多所大學的客座講師。他為初創公司提供諮詢，並曾擔任多個會議的計劃/指導委員會成員，包括擔任 Gartner 的 SF CDO 執行峰會和 Usenix Operational ML (OpML) 會議的共同主席。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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