| 000 | 07486cam a2200565Mu 4500 | ||
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| 001 | 9781003321347 | ||
| 003 | FlBoTFG | ||
| 005 | 20240213122835.0 | ||
| 006 | m o d | ||
| 007 | cr cnu---unuuu | ||
| 008 | 221231s2022 xx o ||| 0 eng d | ||
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_aOCoLC-P _beng _cOCoLC-P |
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| 020 | _a9781000816891 | ||
| 020 | _a1000816893 | ||
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_a9781003321347 _q(electronic bk.) |
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_a1003321348 _q(electronic bk.) |
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_a9781000816969 _q(electronic bk. : EPUB) |
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_a1000816966 _q(electronic bk. : EPUB) |
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| 024 | 7 |
_a10.1201/9781003321347 _2doi |
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| 035 | _a(OCoLC)1356008456 | ||
| 035 | _a(OCoLC-P)1356008456 | ||
| 050 | 4 | _aHD58.8 | |
| 072 | 7 |
_aBUS _x042000 _2bisacsh |
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_aCOM _x021030 _2bisacsh |
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_aCOM _x032000 _2bisacsh |
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_aUN _2bicssc |
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| 082 | 0 | 4 |
_a658.4013 _223/eng/20230112 |
| 100 | 1 | _aPera, Krishna. | |
| 245 | 1 | 0 |
_aBig Data for Big Decisions _h[electronic resource] : _bBuilding a Data-Driven Organization. |
| 260 |
_aMilton : _bAuerbach Publishers, Incorporated, _c2022. |
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| 300 | _a1 online resource (266 p.) | ||
| 500 | _aDescription based upon print version of record. | ||
| 505 | 0 | _aCover -- 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 | _a1.2 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 | _a2.3.1 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 | _a4.1.1 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 | _a5.2 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 | _a6.1.1 Identifying and Prioritizing the Decisions | ||
| 520 | _aBuilding 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 | _aOCLC-licensed vendor bibliographic record. | ||
| 650 | 0 | _aOrganizational change. | |
| 650 | 0 | _aBusiness planning. | |
| 650 | 0 | _aBig data. | |
| 650 | 0 | _aComputer storage devices. | |
| 650 | 7 |
_aBUSINESS & ECONOMICS / Management Science _2bisacsh |
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| 650 | 7 |
_aCOMPUTERS / Database Management / Data Mining _2bisacsh |
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| 650 | 7 |
_aCOMPUTERS / Information Technology _2bisacsh |
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| 856 | 4 | 0 |
_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9781003321347 |
| 856 | 4 | 2 |
_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf |
| 999 |
_c6426 _d6426 |
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