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Over half of all businesses are using data science to generate insights and value from big data. How are they doing it? Data Science Strategy For Dummies answers all your questions about how to build a data science capability from scratch, starting with the "what" and the "why" of data science and covering what it takes to lead and nurture a top-notch team of data scientists.
With this book, you'll learn how to incorporate data science as a strategic function into any business, large or small. Find solutions to your real-life challenges as you uncover the stories and value hidden within data.
* Learn exactly what data science is and why it's important
* Adopt a data-driven mindset as the foundation to success
* Understand the processes and common roadblocks behind data science
* Keep your data science program focused on generating business value
* Nurture a top-quality data science team
In non-technical language, Data Science Strategy For Dummies outlines new perspectives and strategies to effectively lead analytics and data science functions to create real value.
Over half of all businesses are using data science to generate insights and value from big data. How are they doing it? Data Science Strategy For Dummies answers all your questions about how to build a data science capability from scratch, starting with the "what" and the "why" of data science and covering what it takes to lead and nurture a top-notch team of data scientists.
With this book, you'll learn how to incorporate data science as a strategic function into any business, large or small. Find solutions to your real-life challenges as you uncover the stories and value hidden within data.
* Learn exactly what data science is and why it's important
* Adopt a data-driven mindset as the foundation to success
* Understand the processes and common roadblocks behind data science
* Keep your data science program focused on generating business value
* Nurture a top-quality data science team
In non-technical language, Data Science Strategy For Dummies outlines new perspectives and strategies to effectively lead analytics and data science functions to create real value.
Ulrika Jägare is an M.Sc. Director at Ericsson AB. With a decade of experience in analytics and machine intelligence and 19 years in telecommunications, she has held leadership positions in R&D and product management. Ulrika was key to the Ericsson s Machine Intelligence strategy and the recent Ericsson Operations Engine launch - a new data and AI driven operational model for Network Operations in telecommunications.
Introduction 1
Part 1: Optimizing Your Data Science Investment 7
Chapter 1: Framing Data Science Strategy 9
Chapter 2: Considering the Inherent Complexity in Data Science 31
Chapter 3: Dealing with Difficult Challenges 41
Chapter 4: Managing Change in Data Science 51
Part 2: Making Strategic Choices for Your Data 65
Chapter 5: Understanding the Past, Present, and Future of Data 67
Chapter 6: Knowing Your Data 85
Chapter 7: Considering the Ethical Aspects of Data Science 97
Chapter 8: Becoming Data-driven 103
Chapter 9: Evolving from Data-driven to Machine-driven 113
Part 3: Building a Successful Data Science Organization 119
Chapter 10: Building Successful Data Science Teams 121
Chapter 11: Approaching a Data Science Organizational Setup 133
Chapter 12: Positioning the Role of the Chief Data Officer (CDO) 145
Chapter 13: Acquiring Resources and Competencies 155
Part 4: Investing in the Right Infrastructure 173
Chapter 14: Developing a Data Architecture 175
Chapter 15: Focusing Data Governance on the Right Aspects 193
Chapter 16: Managing Models During Development and Production 203
Chapter 17: Exploring the Importance of Open Source 213
Chapter 18: Realizing the Infrastructure 223
Part 5: Data as a Business 233
Chapter 19: Investing in Data as a Business 235
Chapter 20: Using Data for Insights or Commercial Opportunities 243
Chapter 21: Engaging Differently with Your Customers 255
Chapter 22: Introducing Data-driven Business Models 265
Chapter 23: Handling New Delivery Models 281
Part 6: The Part of Tens 295
Chapter 24: Ten Reasons to Develop a Data Science Strategy 297
Chapter 25: Ten Mistakes to Avoid When Investing in Data Science 305
Index 315
| Erscheinungsjahr: | 2019 |
|---|---|
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Seiten: | 352 |
| Inhalt: | 352 S. |
| ISBN-13: | 9781119566250 |
| ISBN-10: | 1119566258 |
| Sprache: | Englisch |
| Herstellernummer: | 1W119566250 |
| Einband: | Kartoniert / Broschiert |
| Autor: | Jägare, Ulrika |
| Hersteller: | Wiley |
| Maße: | 233 x 190 x 20 mm |
| Von/Mit: | Ulrika Jägare |
| Erscheinungsdatum: | 11.07.2019 |
| Gewicht: | 0,476 kg |
Ulrika Jägare is an M.Sc. Director at Ericsson AB. With a decade of experience in analytics and machine intelligence and 19 years in telecommunications, she has held leadership positions in R&D and product management. Ulrika was key to the Ericsson s Machine Intelligence strategy and the recent Ericsson Operations Engine launch - a new data and AI driven operational model for Network Operations in telecommunications.
Introduction 1
Part 1: Optimizing Your Data Science Investment 7
Chapter 1: Framing Data Science Strategy 9
Chapter 2: Considering the Inherent Complexity in Data Science 31
Chapter 3: Dealing with Difficult Challenges 41
Chapter 4: Managing Change in Data Science 51
Part 2: Making Strategic Choices for Your Data 65
Chapter 5: Understanding the Past, Present, and Future of Data 67
Chapter 6: Knowing Your Data 85
Chapter 7: Considering the Ethical Aspects of Data Science 97
Chapter 8: Becoming Data-driven 103
Chapter 9: Evolving from Data-driven to Machine-driven 113
Part 3: Building a Successful Data Science Organization 119
Chapter 10: Building Successful Data Science Teams 121
Chapter 11: Approaching a Data Science Organizational Setup 133
Chapter 12: Positioning the Role of the Chief Data Officer (CDO) 145
Chapter 13: Acquiring Resources and Competencies 155
Part 4: Investing in the Right Infrastructure 173
Chapter 14: Developing a Data Architecture 175
Chapter 15: Focusing Data Governance on the Right Aspects 193
Chapter 16: Managing Models During Development and Production 203
Chapter 17: Exploring the Importance of Open Source 213
Chapter 18: Realizing the Infrastructure 223
Part 5: Data as a Business 233
Chapter 19: Investing in Data as a Business 235
Chapter 20: Using Data for Insights or Commercial Opportunities 243
Chapter 21: Engaging Differently with Your Customers 255
Chapter 22: Introducing Data-driven Business Models 265
Chapter 23: Handling New Delivery Models 281
Part 6: The Part of Tens 295
Chapter 24: Ten Reasons to Develop a Data Science Strategy 297
Chapter 25: Ten Mistakes to Avoid When Investing in Data Science 305
Index 315
| Erscheinungsjahr: | 2019 |
|---|---|
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Seiten: | 352 |
| Inhalt: | 352 S. |
| ISBN-13: | 9781119566250 |
| ISBN-10: | 1119566258 |
| Sprache: | Englisch |
| Herstellernummer: | 1W119566250 |
| Einband: | Kartoniert / Broschiert |
| Autor: | Jägare, Ulrika |
| Hersteller: | Wiley |
| Maße: | 233 x 190 x 20 mm |
| Von/Mit: | Ulrika Jägare |
| Erscheinungsdatum: | 11.07.2019 |
| Gewicht: | 0,476 kg |
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