ابتکارات تحلیل داده: مدیریت تحلیل‌ها برای موفقیت

The categorisation of analytical projects could help to simplify complexity reasonably and, at the same time, clarify the critical aspects of analytical initiatives. But how can this complex work be categorized? What makes it so complex? Data Analytics Initiatives: Managing Analytics for Success emphasizes that each analytics project is different. At the same time, analytics projects have many common aspects, and these features make them unique compared to other projects. Describing these commonalities helps to develop a conceptual understanding of analytical work. However, features specific to each initiative affects the entire analytics project lifecycle. Neglecting them by trying to use general approaches without tailoring them to each project can lead to failure. In addition to examining typical characteristics of the analytics project and how to categorise them, the book looks at specific types of projects, provides a high-level assessment of their characteristics from a risk perspective, and comments on the most common problems or challenges. The book also presents examples of questions that could be asked of relevant people to analyse an analytics project. These questions help to position properly the project and to find commonalities and general project challenges. The three-axis approach to analytics projects The categorisation of analytical initiatives could help us leverage the knowledge we have already gained and reflect it in our work. Correctly defined categories could help us to simplify the complexity reasonably and, at the same time, understand the critical aspects of analytical work. But how can we do it, and what can make it so complex? Common attributes of analytics projects Throughout the book, we reiterate that each analytics project is different. At the same time, analytics projects have a lot in common, and these features make them unique compared to other projects. Describing these commonalities could move us further in the conceptual understanding of analytical work. These specific features impact the entire project lifecycle, and neglecting them (trying to use general approaches without tailoring them to analytics projects) can lead to failure. General ideas of risks and challenges Challenges and risks - another critical aspect of analytical initiatives that could be the same from the overall definition perspective, but the realisation and mitigation could significantly differ based on the previously described project categorisation. Typical failures and risks per project types To provide a more tangible point of view, we would like to look at things from the opposite angle. So far, we have been looking at the typical characteristics of the analytics project (and how to categorise them). We will look at specific types of projects, provide a high-level assessment of their characteristics from a risk perspective (highly generalised), and comment on the most common problems or challenges. Typical questions for analytics projects As the last chapter of the book, we will try to provide you with some examples of questions that could be asked of relevant people in order to analyse the project. These questions may help you properly pos5ition the project on to each axis and understand the commonalities and general project challenges. This serves only as an example and may differ a great deal based on your company and environment. Analytics projects have a great deal of complexity, which can be hidden and needs to be considered. This book is a guide to evaluating and setting up analytics projects. It gives a holistic view of all aspects of analytical project that can be easily leveraged before a project starts or when it needs to be re-evaluated. Cover 1 Half Title 2 Title Page 4 Copyright Page 5 Table of Contents 8 Acknowledgement 11 Foreword 12 About the Authors 14 Introduction 16 The analytics project paradox 16 Our approach 17 The focus of the book 18 The structure of the book 18 Target audience 20 Part one ― The Framework Definition 21 1 The three-axis approach to analytics projects 22 1.1 Axis 1: Analytics Maturity 24 1.1.1 Stakeholder Analytics Maturity 28 1.1.2 Company Analytics Maturity 29 1.1.3 Analytics Landscape Maturity 30 1.1.4 Combination of factors 31 1.1.5 Connection with the type of analytics and time frame 32 1.2 Axis 2: Data Maturity 34 1.2.1 Are data available? 35 1.2.2 Are data integrated? 39 1.2.3 Are data described? 42 1.2.4 Is data security considered? 45 1.2.5 Connection with the type of analytics and time frame 48 1.3 Axis 3: IT Maturity 49 1.3.1 Are the tools available? 50 1.3.2 Are tools integrated? 52 1.3.3 Are the tools flexible? 54 1.3.4 Are processes established? 55 1.3.5 The number of tools paradox 60 1.3.6 Connection with the type of analytics and time frame 62 1.4 Ad-hoc x Robust – amplification 65 1.5 Three axes combined and typical projects 71 1.5.1 Combination of three axes 72 1.5.2 Typical projects 73 1.5.3 Three-axis evaluation and project life cycle 77 Part two ― The Framework in Context 81 2 Common attributes of analytics projects 82 2.1 Agile x Waterfall approach 83 2.2 Data-driven 85 2.3 Technology mix 86 2.4 Data-oriented thinking and effective client communication 88 2.5 Maintenance of analytics 90 2.6 Prototyping and Experimentation 90 3 General areas of risks and challenges 94 3.1 Key challenges in managing stakeholders’ expectations 95 3.1.1 Scope definition 96 3.1.2 Project delivery/implementation 97 3.1.3 Connection with stakeholder expectation 98 3.2 Ways of working — WoW 99 3.2.1 WoW – Advanced analytics ad-hoc project 102 3.2.2 WoW – Robust descriptive analytics project(data are ready) 104 3.2.3 WoW – Robust descriptive analytics project (data are not ready) 108 3.2.4 WoW – Analytics ecosystem (including AA) 112 3.2.5 WoW – Summary 114 3.3 Industrialization challenge 114 3.3.1 Why is the industrialization taking so long? 115 3.3.2 Why is it not robust from the beginning? 116 3.3.3 Why do we even need to do industrialization? 117 3.4 Time impact 117 3.4.1 Regular re-evaluation of the three axes as a continuous process 118 3.4.2 Product Life Cycle management 122 3.4.3 External factors 123 3.4.4 Vendor lock-in 124 4 Typical failures and risks per project types 128 4.1 Advanced analytics projects – Ad-hoc (Predictive) 129 4.1.1 Moving on the analytics journey for ad-hoc AA projects 131 4.2 Robust descriptive analytics projects (data are ready) 133 4.3 Robust descriptive analytics project (data are not ready) 137 4.4 Industrialization risks 143 5 Typical questions for analytics projects 148 Conclusion 154 Index of Terms 156 Index 161 Data,Science;,Big,data;,Project,management;,Project,risk;,Project,success Data Science,Big data,Project management,Project risk,Project success