Big Data for Big Decisions (Record no. 6426)
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| 000 -LEADER | |
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| fixed length control field | 07486cam a2200565Mu 4500 |
| 001 - CONTROL NUMBER | |
| control field | 9781003321347 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | FlBoTFG |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240213122835.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS | |
| fixed length control field | m o d |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr cnu---unuuu |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 221231s2022 xx o ||| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | OCoLC-P |
| Language of cataloging | eng |
| Transcribing agency | OCoLC-P |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781000816891 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 1000816893 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781003321347 |
| Qualifying information | (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 1003321348 |
| Qualifying information | (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781000816969 |
| Qualifying information | (electronic bk. : EPUB) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 1000816966 |
| Qualifying information | (electronic bk. : EPUB) |
| 024 7# - OTHER STANDARD IDENTIFIER | |
| Standard number or code | 10.1201/9781003321347 |
| Source of number or code | doi |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (OCoLC)1356008456 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (OCoLC-P)1356008456 |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | HD58.8 |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | BUS |
| Subject category code subdivision | 042000 |
| Source | bisacsh |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | COM |
| Subject category code subdivision | 021030 |
| Source | bisacsh |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | COM |
| Subject category code subdivision | 032000 |
| Source | bisacsh |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | UN |
| Source | bicssc |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 658.4013 |
| Edition number | 23/eng/20230112 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Pera, Krishna. |
| 245 10 - TITLE STATEMENT | |
| Title | Big Data for Big Decisions |
| Medium | [electronic resource] : |
| Remainder of title | Building a Data-Driven Organization. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Milton : |
| Name of publisher, distributor, etc. | Auerbach Publishers, Incorporated, |
| Date of publication, distribution, etc. | 2022. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 1 online resource (266 p.) |
| 500 ## - GENERAL NOTE | |
| General note | Description based upon print version of record. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Acknowledgments -- Author -- Introduction -- I.1 Inception -- I.2 Data-Driven Organization: The Stakeholders' Expectations -- I.2.1 Stakeholders' Expectations -- I.2.2 The Other Stakeholders' Dilemma -- I.3 Setting Up a Data-Driven Organization -- Constraints and Experiences -- I.4 What This Book Covers -- Chapter 1: Quo Vadis: Before the Transformational Journey -- 1.1 Data-Driven Organization: Refining the Meaning and the Purpose -- 1.1.1 From Data-Driven, to Insights-Driven |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 1.2 Before the Journey: Deconstructing the Data-to-Decisions Flow -- 1.2.1 The Data Manifest -- 1.2.2 Data Catalog and Data Dictionary -- 1.2.3 Data Logistics: Information Supply and Demand -- 1.2.3.1 DDO's and the Theory of Asymmetric Information -- 1.3 Data-Driven Organization: Defining the Scope, Vision, and Maturity Models -- 1.3.1 Maturity Models -- 1.3.2 What is Missing? -- Bibliography -- Chapter 2: Decision-Driven before Data-Driven -- 2.1 The Three Good Decisions -- 2.2 Decision-Driven before Data-Driven -- 2.3 The "Big" Decisions Need to Be Process-Driven |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 2.3.1 Decision Modeling and Limitations -- 2.4 Conclusion -- Bibliography -- Chapter 3: Knowns, Unknowns, and the Elusive Value From Analytics -- 3.1 The Unknown-Unknowns -- 3.2 Decisions That You Are Making and the Data That You Need -- 3.3 A Johari Window For an Organization -- 3.3.1 Customers' Perspective -- 3.3.2 Employees' Perspective -- 3.4 In Search of Value From Analytics -- 3.4.1 In Theory -- 3.4.2 In Reality -- Bibliography -- Chapter 4: Toward a Data-Driven Organization: A Roadmap For Analytics -- 4.1 The Challenge of Making Analytics Work |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 4.1.1 Investing in Analytics: The Fear of Being Left Behind -- 4.2 Decision-Oriented Analytics: From Decisions to Data -- 4.3 The Importance of Beginning From the End -- 4.4 Deciphering the Data behind the Decisions -- 4.5 Meet the Ad Hoc Manager! -- 4.6 Local vs. Global Solutions -- 4.7 Problem vs. Opportunity Mindset -- 4.8 A Roadmap for Data-Driven Organization -- 4.9 Summary -- Bibliography -- Chapter 5: Identifying the "Big" Decisions -- 5.1 Taking Stock: Existing Analytics Assets -- 5.1.1 Project Trigger -- 5.1.2 Business Value Targeted -- 5.1.3 Ad Hoc-ism |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 5.2 The Lost Art of Decision-Making -- 5.3 Prioritizing Decisions: In Search of an Objective Methodology -- 5.4 Learning from the Bain Model -- 5.5 Decision Analysis -- 5.6 Decision Prioritization: Factors to Consider -- 5.7 Decision Prioritization: Creating a Process Framework -- 5.7.1 Cross-Dimensional Comparison -- 5.7.2 The Process Framework: Identifying and Prioritizing the "Big" Decisions -- Bibliography -- Chapter 6: Decisions to Data: Building a "Big" Decision Roadmap and Business Case -- 6.1 Toward a Data-Driven Organization: Building a "Big" Decision Roadmap |
| 500 ## - GENERAL NOTE | |
| General note | 6.1.1 Identifying and Prioritizing the Decisions |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Building a data-driven organization (DDO) is an enterprise-wide initiative that may consume and lock up resources for the long term. Understandably, any organization considering such an initiative would insist on a roadmap and business case to be prepared and evaluated prior to approval. This book presents a step-by-step methodology in order to create a roadmap and business case, and provides a narration of the constraints and experiences of managers who have attempted the setting up of DDOs. The emphasis is on the big decisions - the key decisions that influence 90% of business outcomes - starting from decision first and reengineering the data to the decisions process-chain and data governance, so as to ensure the right data are available at the right time, every time. Investing in artificial intelligence and data-driven decision making are now being considered a survival necessity for organizations to stay competitive. While every enterprise aspires to become 100% data-driven and every Chief Information Officer (CIO) has a budget, Gartner estimates over 80% of all analytics projects fail to deliver intended value. Most CIOs think a data-driven organization is a distant dream, especially while they are still struggling to explain the value from analytics. They know a few isolated successes, or a one-time leveraging of big data for decision making does not make an organization data-driven. As of now, there is no precise definition for data-driven organization or what qualifies an organization to call itself data-driven. Given the hype in the market for big data, analytics and AI, every CIO has a budget for analytics, but very little clarity on where to begin or how to choose and prioritize the analytics projects. Most end up investing in a visualization platform like Tableau or QlikView, which in essence is an improved version of their BI dashboard that the organization had invested into not too long ago. The most important stakeholders, the decision-makers, are rarely kept in the loop while choosing analytics projects. This book provides a fail-safe methodology for assured success in deriving intended value from investments into analytics. It is a practitioners' handbook for creating a step-by-step transformational roadmap prioritizing the big data for the big decisions, the 10% of decisions that influence 90% of business outcomes, and delivering material improvements in the quality of decisions, as well as measurable value from analytics investments. The acid test for a data-driven organization is when all the big decisions, especially top-level strategic decisions, are taken based on data and not on the collective gut feeling of the decision makers in the organization. |
| 588 ## - SOURCE OF DESCRIPTION NOTE | |
| Source of description note | OCLC-licensed vendor bibliographic record. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Organizational change. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Business planning. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Big data. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Computer storage devices. |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | BUSINESS & ECONOMICS / Management Science |
| Source of heading or term | bisacsh |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | COMPUTERS / Database Management / Data Mining |
| Source of heading or term | bisacsh |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | COMPUTERS / Information Technology |
| Source of heading or term | bisacsh |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Materials specified | Taylor & Francis |
| Uniform Resource Identifier | <a href="https://www.taylorfrancis.com/books/9781003321347">https://www.taylorfrancis.com/books/9781003321347</a> |
| 856 42 - ELECTRONIC LOCATION AND ACCESS | |
| Materials specified | OCLC metadata license agreement |
| Uniform Resource Identifier | <a href="http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf">http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf</a> |
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