Data Strategy

出版者:Addison-Wesley

作者:Adelman, Sid/ Moss, Larissa T./ Abai, Majid

页数:384

出版时间:2005-6

价格:$ 62.14

装帧:Pap

isbn号码:9780321240996

具体描述

Without a data strategy, the people within an organization have no guidelines for making decisions that are absolutely crucial to the success of the IT organization and to the entire organization. The absence of a strategy gives a blank check to those who want to pursue their own agendas, including those who want to try new database management systems, new technologies (often unproven), and new tools. This type of environment provides no hope for success. Data Strategy should result in the development of systems with less risk, higher quality systems, and reusability of assets. This is key to keeping cost and maintenance down, thus running lean and mean. Data Strategy provides a CIO with a rationale to counter arguments for immature technology and data strategies that are inconsistent with existing strategies. This book uses case studies and best practices to give the reader the tools they need to create the best strategy for the organization.

作者简介

Sid Adelman is a principal in Sid Adelman & Associates, an organization specializing in planning and implementing data warehouses, in data warehouse and BI assessments, and in establishing effective data architectures and strategies. He is a regular speaker at the Data Warehouse Institute and IBM's DB2 and Data Warehouse Conference. Sid also speaks often at DAMA conferences. He chairs the "Ask the Experts" column on http://www.dmreview.com.

Sid is a founding member of the Business Intelligence Alliance. Its members include Colin White, Herb Edelstein, Larry English, David Foote, Douglas Hackney, Pieter Mimno, Neil Raden, and David Marco. Sid is also a frequent contributor to journals that focus on data warehouse and data-related topics. He co-authored Data Warehouse Project Management with Larissa Moss. He is the primary author of Impossible Data Warehouse Situations with Solutions from the Experts.

Sid can be reached at [e-mail]. His web site is http://www.sidadelman.com.

Larissa Moss is president of Method Focus Inc., a corporation specializing in enterprise information management. She frequently lectures at data warehouse and data quality conferences worldwide on the topics of data warehousing, business intelligence, and other enterprise architecture and data strategy topics, such as data integration, data modeling, data quality, and metadata. Larissa is a senior consultant of the Cutter Consortium and a member of Friends of NCR-Teradata and the IBM Gold Group. Her present and past associations also include membership in DAMA, part-time faculty member at the Extended University of California Polytechnic University Pomona, associate of the Relational Institute and the Codd & Date Consulting Group, and lecturer for TDWI, DCI, MIS Training Institute, and PESG.

Larissa has authored and co-authored numerous books, white papers, and articles on business intelligence, project management, information asset management, development methodologies, data quality, and organizational realignments. She can be reached at [e-mail]. Her web-site is http://www.methodfocus.com.

Majid Abai is President of Seena Technologies, a Santa Monica, California consultancy dedicated to delivery of holistic data and enterprise solutions to various organizations. Majid's two decades of IT experience have been primarily focused on solution architecture, data strategies, and business intelligence systems for organizations facing challenges with the management of massive amounts of data. Majid has developed and teaches a class in Business Intelligence at the University of California, Los Angeles (UCLA) and several other seminars and lectures for national and international corporations. He can be reached at [e-mail]. Seena Technologies website is http://www.seenatech.com.

用户评价

坦白讲，我是在一个非常忙碌的时期开始阅读这本书的，时间零碎，但我发现即便是只抽出十分钟，也能从中获得有价值的信息点。这得益于作者精炼的文字和清晰的逻辑分层。书中对“数据资产化”过程的描述，是我读过的最透彻的论述之一。它清晰地界定了从原始数据到可转化为商业价值的“智能资产”所需的关键转化步骤，包括清洗、标注、建模和最终的赋能。作者用非常直观的比喻，比如将数据比作“未开采的矿藏”，将数据团队比作“地质学家和冶炼工程师”，生动地阐释了价值创造的艰辛与回报。更重要的是，它强调了领导力在这一转化过程中的核心作用——没有高层的坚定支持和资源投入，任何数据项目都可能沦为技术部门的“玩具”。这种自上而下的视角，对于希望推动组织变革的管理者来说，无疑是至关重要的指南。读完这本书，我感觉自己对数据战略的理解从“技术问题”升级到了“商业决策”的高度，这种思维上的飞跃是无法用金钱衡量的收获。

这本书的整体阅读体验非常令人振奋，它成功地将一个常常被视为高高在上的“战略”概念，变得触手可及且充满动力。作者在探讨“数据驱动决策”的未来时，没有沉溺于对AI和机器学习的过度美化，而是非常务实地指出了当前组织在接受和采纳数据建议时所面临的心理阻力——人类对直觉的偏爱根深蒂固。书中提出的“渐进式信任培养机制”，即通过小范围、高可见度的成功案例来逐步建立决策者对数据的信任，这个策略极具可操作性。我发现，这本书最可贵之处在于，它没有将数据战略描绘成一条笔直的康庄大道，而是承认了其中的蜿蜒曲折和反复试错。它提供了一张地图，但更重要的是，它教会了读者如何辨别方向、如何在迷雾中保持信心。对于任何正在规划或重塑其数据蓝图的组织领导者而言，这本书都像是一份精心准备的“作战手册”，不仅提供了战术，更重要的是，它帮你磨砺了战略的刀刃。读完后，我迫不及待地想将书中的核心理念分享给我的团队，引发一场深刻的内部对话。

说实话，我原本以为这会是一本枯燥的技术手册，充斥着晦涩难懂的术语和复杂的流程图，但事实完全出乎我的意料。这本书的语言风格极其鲜活，甚至带有一丝幽默感，读起来完全没有阅读障碍。作者在讲解复杂的数据治理原则时，竟然能引用古代哲学家的思想，这种跨界的引用，不仅没有显得突兀，反而极大地丰富了内容的层次感。我特别欣赏书中对“数据伦理”那部分的论述。在当前隐私保护日益成为焦点的大环境下，作者没有回避这个敏感话题，而是将其提升到了战略高度来讨论，强调了信任体系构建对于长期数据价值实现的重要性。他提出的“负责任的创新”理念，给了我很大的启发。很多企业在追求效率最大化的同时，往往忽视了潜在的社会责任风险，这本书像一剂清醒剂，提醒着我们，技术的力量必须被审慎地使用。此外，书中对不同行业数据应用差异化的探讨也相当精彩，例如金融科技与医疗健康领域对数据安全和实时性的不同要求，都被分析得井井有条，显示出作者深厚的行业理解功底。

这本书的结构安排堪称教科书级别的典范。它不像市面上很多同类书籍那样，要么过于侧重技术栈的罗列，要么陷入纯粹的商业故事的堆砌。它巧妙地将战略规划、技术选型、组织变革和文化塑造这四大支柱，编织成一张完整且逻辑严密的网。我尤其喜欢它在“技术路线图”部分的处理方式——它没有盲目推崇某一种特定的数据库或分析工具，而是强调了“适应性”和“可扩展性”的重要性，教导读者如何建立一个能够应对未来未知挑战的弹性架构。这种不拘泥于当下流行技术的态度，体现了作者深远的战略眼光。在阅读过程中，我多次停下来，对照自己公司目前的数据实践进行反思。很多困扰我们许久的部门壁垒和数据质量问题，似乎都在书中找到了理论上的根源和可行的突破口。作者给出的行动建议，不是那种一刀切的“银弹”，而是鼓励读者根据自身的资源禀赋和市场定位，制定出独一无二的路径。这种尊重实践、强调定制化的叙事，让这本书的实操价值大大提升。

这本书的封面设计着实抓人眼球，那种深邃的蓝色调配合着极简的几何图形，让人一眼就能感受到一种专业且前沿的气息。我拿起它的时候，心里充满了期待，希望它能为我打开一个全新的世界，尤其是在信息爆炸的当下，如何驾驭数据已成为企业生存的关键。刚翻开几页，我就被作者那种平实而又极富洞察力的笔触所吸引。他似乎并不急于抛出那些高深的理论，而是循循善诱，通过一个个生动的商业案例，将那些抽象的战略概念具象化。比如，书中对“数据孤岛”问题的剖析，简直是入木三分，直击当下许多大型组织面临的痛点。作者没有停留在问题的表象，而是深入挖掘了组织架构、文化壁垒乃至技术选型背上的深层原因，让人读来不由得拍案叫绝。随后，他提出的解决方案，也并非是那种空泛的口号式指导，而是提供了一套系统性的思维框架，教你如何从高层愿景出发，逐步落地到具体的执行层面。整体阅读体验非常流畅，即便是数据领域的新手，也能在其中找到清晰的指引，而资深人士也能从中汲取新的灵感火花。这种兼顾广度与深度的叙事方式，使得这本书不仅仅是一本“如何做”的指南，更是一本激发思考的“为什么”的哲学著作。