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.
Author(s): Ondřej Bothe, Ondřej Kubera, David Bednář, Martin Potančok, Ota Novotný
Publisher: CRC Press/Auerbach
Year: 2022
Language: English
Pages: 163
City: Boca Raton
Cover
Half Title
Title Page
Copyright Page
Table of Contents
Acknowledgement
Foreword
About the Authors
Introduction
The analytics project paradox
Our approach
The focus of the book
The structure of the book
Target audience
Part one ― The Framework Definition
1 The three-axis approach to analytics projects
1.1 Axis 1: Analytics Maturity
1.1.1 Stakeholder Analytics Maturity
1.1.2 Company Analytics Maturity
1.1.3 Analytics Landscape Maturity
1.1.4 Combination of factors
1.1.5 Connection with the type of analytics and time frame
1.2 Axis 2: Data Maturity
1.2.1 Are data available?
1.2.2 Are data integrated?
1.2.3 Are data described?
1.2.4 Is data security considered?
1.2.5 Connection with the type of analytics and time frame
1.3 Axis 3: IT Maturity
1.3.1 Are the tools available?
1.3.2 Are tools integrated?
1.3.3 Are the tools flexible?
1.3.4 Are processes established?
1.3.5 The number of tools paradox
1.3.6 Connection with the type of analytics and time frame
1.4 Ad-hoc x Robust – amplification
1.5 Three axes combined and typical projects
1.5.1 Combination of three axes
1.5.2 Typical projects
1.5.3 Three-axis evaluation and project life cycle
Part two ― The Framework in Context
2 Common attributes of analytics projects
2.1 Agile x Waterfall approach
2.2 Data-driven
2.3 Technology mix
2.4 Data-oriented thinking and effective client communication
2.5 Maintenance of analytics
2.6 Prototyping and Experimentation
3 General areas of risks and challenges
3.1 Key challenges in managing stakeholders’ expectations
3.1.1 Scope definition
3.1.2 Project delivery/implementation
3.1.3 Connection with stakeholder expectation
3.2 Ways of working — WoW
3.2.1 WoW – Advanced analytics ad-hoc project
3.2.2 WoW – Robust descriptive analytics project(data are ready)
3.2.3 WoW – Robust descriptive analytics project (data are not ready)
3.2.4 WoW – Analytics ecosystem (including AA)
3.2.5 WoW – Summary
3.3 Industrialization challenge
3.3.1 Why is the industrialization taking so long?
3.3.2 Why is it not robust from the beginning?
3.3.3 Why do we even need to do industrialization?
3.4 Time impact
3.4.1 Regular re-evaluation of the three axes as a continuous process
3.4.2 Product Life Cycle management
3.4.3 External factors
3.4.4 Vendor lock-in
4 Typical failures and risks per project types
4.1 Advanced analytics projects – Ad-hoc (Predictive)
4.1.1 Moving on the analytics journey for ad-hoc AA projects
4.2 Robust descriptive analytics projects (data are ready)
4.3 Robust descriptive analytics project (data are not ready)
4.4 Industrialization risks
5 Typical questions for analytics projects
Conclusion
Index of Terms
Index
