Data Science Strategy

Title:

Personal Author:

Jagare, Ulrika, author

Series:

for dummies

Physical Description:

xvi, 332 pages : illustrations ; 24 cm

ISBN:

9781119566250

Abstract:

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.

Subject Term:

Big data

Data mining

Information science

Available:*

Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ KL	33000000017338	QA76.9.D343 J34 2019	Open Access Book	Book	Searching... Unknown

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Summary

All the answers to your data science questions

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.

Author Notes

Ulrika Jgare 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 RD and product management. Ulrika was key to the Ericsson's Machine Intelligence strategy and the recent Ericsson Operations Engine launch - a new data and Al driven operational model for Network Operations in telecommunications.

Foreword	p. xv
Introduction	p. 1
About This Book	p. 2
Foolish Assumptions	p. 3
How This Book Is Organized	p. 3
Icons Used In This Book	p. 4
Beyond The Book	p. 4
Where To Go From Here	p. 5
Part 1 Optimizing Your Data Science Investment	p. 7
Chapter 1 Framing Data Science Strategy	p. 9
Establishing the Data Science Narrative	p. 10
Capture	p. 11
Maintain	p. 12
Process	p. 13
Analyze	p. 14
Communicate	p. 16
Actuate	p. 17
Sorting Out the Concept of a Data-driven Organization	p. 19
Approaching data-driven	p. 20
Being data obsessed	p. 21
Sorting Out the Concept of Machine Learning	p. 22
Defining and Scoping a Data Science Strategy	p. 26
Objectives	p. 26
Approach	p. 27
Choices	p. 27
Data	p. 27
Legal	p. 28
Ethics	p. 28
Competence	p. 28
Infrastructure	p. 29
Governance and security	p. 29
Commercial/business models	p. 30
Measurements	p. 30
Chapter 2 Considering the Inherent Complexity in Data Science	p. 31
Diagnosing Complexity in Data Science	p. 32
Recognizing Complexity as a Potential	p. 33
Enrolling in Data Science Pitfalls 101	p. 34
Believing that all data is needed	p. 34
Thinking that investing in a data lake will solve all your problems	p. 35
Focusing on Al when analytics is enough	p. 36
Believing in the 1-tool approach	p. 37
Investing only in certain areas	p. 37
Leveraging the infrastructure for reporting rather than exploration	p. 38
Underestimating the need for skilled data scientists	p. 39
Navigating the Complexity	p. 40
Chapter 3 Dealing with Difficult Challenges	p. 41
Getting Data from There to Here	p. 41
Handling dependencies on data owned by others	p. 42
Managing data transfer and computation across-country borders	p. 43
Managing Data Consistency Across the Data Science Environment	p. 44
Securing Explainability in Al	p. 45
Dealing with the Difference between Machine Learning and Traditional Software Programming	p. 47
Managing the Rapid Al Technology Evolution and Lack of Standardization	p. 50
Chapter 4 Managing Change in Data Science	p. 51
Understanding Change Management in Data Science	p. 52
Approaching Change in Data Science	p. 53
Recognizing what to avoid when driving change in data science	p. 56
Using Data Science Techniques to Drive Successful Change	p. 59
Using digital engagement tools	p. 59
Applying social media analytics to identify stakeholder sentiment	p. 60
Capturing reference data in change projects	p. 61
Using data to select people for change roles	p. 61
Automating change metrics	p. 62
Getting Started	p. 62
Part 2 Making Strategic Choices for Your Data	p. 65
Chapter 5 Understanding the Past, Present, and Future of Data	p. 67
Sorting Out the Basics of Data	p. 68
Explaining traditional data versus big data	p. 69
Knowing the value of data	p. 71
Exploring Current Trends in Data	p. 73
Data monetization	p. 73
Responsible Al	p. 74
Cloud-based data architectures	p. 75
Computation and intelligence in the edge	p. 75
Digital twins	p. 77
Blockchain	p. 78
Conversational platforms	p. 79
Elaborating on Some Future Scenarios	p. 80
Standardization for data science productivity	p. 80
From data monetization scenarios to a data economy	p. 82
An explosion of human/machine hybrid systems	p. 82
Quantum computing will solve the unsolvable problems	p. 83
Chapter 6 Knowing Your Data	p. 85
Selecting Your Data	p. 85
Describing Data	p. 87
Exploring Data	p. 89
Assessing Data Quality	p. 93
Improving Data Quality	p. 95
Chapter 7 Considering the Ethical Aspects of Data Science	p. 97
Explaining Al Ethics	p. 98
Addressing trustworthy artificial intelligence	p. 99
Introducing Ethics by Design	p. 101
Chapter 8 Becoming Data-driven	p. 103
Understanding Why Data-Driven Is a Must	p. 103
Transitioning to a Data-Driven Model	p. 105
Securing management buy-in and assigning a chief data officer (CDO)	p. 106
Identifying the key business value aligned with the business maturity	p. 107
Developing a Data Strategy	p. 108
Caring for your data	p. 109
Democratizing the data	p. 109
Driving data standardization	p. 110
Structuring the data strategy	p. 110
Establishing a Data-Driven Culture and Mindset	p. 111
Chapter 9 Evolving from Data-driven to Machine-driven	p. 113
Digitizing the Data	p. 114
Applying a Data-driven Approach	p. 115
Automating Workflows	p. 116
Introducing AI/ML capabilities	p. 116
Part 3 Building a Successful Data Science Organization	p. 119
Chapter 10 Building Successful Data Science Teams	p. 121
Starting with the Data Science Team Leader	p. 121
Adopting different leadership approaches	p. 122
Approaching data science leadership	p. 124
Finding the right data science leader or manager	p. 124
Defining the Prerequisites for a Successful Team	p. 125
Developing a team structure	p. 125
Establishing an infrastructure	p. 126
Ensuring data availability	p. 126
Insisting on interesting projects	p. 127
Promoting continuous learning	p. 127
Encouraging research studies	p. 128
Building the Team	p. 128
Developing smart hiring processes	p. 129
Letting your teams evolve organically	p. 130
Connecting the Team to the Business Purpose	p. 131
Chapter 11 Approaching a Data Science Organizational Setup	p. 133
Finding the Right Organizational Design	p. 134
Designing the data science function	p. 134
Evaluating the benefits of a center of excellence for data science	p. 136
Identifying success factors for a data science center of excellence	p. 137
Applying a Common Data Science Function	p. 138
Selecting a location	p. 138
Approaching ways of working	p. 139
Managing expectations	p. 141
Selecting an execution approach	p. 142
Chapter 12 Positioning the Role of the Chief Data Officer (CDO)	p. 145
Scoping the Role of the Chief Data Officer (CDO)	p. 146
Explaining Why a Chief Data Officer Is Needed	p. 149
Establishing the CDO Role	p. 150
The Future of the CDO Role	p. 152
Chapter 13 Acquiring Resources and Competencies	p. 155
Identifying the Roles in a Data Science Team	p. 156
Data scientist	p. 157
Data engineer	p. 157
Machine learning engineer	p. 158
Data architect	p. 159
Business analyst	p. 159
Software engineer	p. 159
Domain expert	p. 160
Seeing What Makes a Great Data Scientist	p. 160
Structuring a Data Science Team	p. 163
Hiring and evaluating the data science talent you need	p. 165
Retaining Competence in Data Science	p. 167
Understanding what makes a data scientist leave	p. 169
Part 4 Investing in the Right Infrastructure	p. 173
Chapter 14 Developing a Data Architecture	p. 175
Defining What Makes Up a Data Architecture	p. 176
Describing traditional architectural approaches	p. 176
Elements of a data architecture	p. 177
Exploring the Characteristics of a Modern Data Architecture	p. 178
Explaining Data Architecture Layers	p. 181
Listing the Essential Technologies for a Modern Data Architecture	p. 184
NoSQL databases	p. 184
Real-time streaming platforms	p. 185
Docker and containers	p. 185
Container repositories	p. 186
Container orchestration	p. 187
Microservices	p. 187
Function as a service	p. 188
Creating a Modern Data Architecture	p. 189
Chapter 15 Focusing Data Governance on the Right Aspects	p. 193
Sorting Out Data Governance	p. 194
Data governance for defense or offense	p. 195
Objectives for data governance	p. 196
Explaining Why Data Governance is Needed	p. 197
Data governance saves money	p. 197
Bad data governance is dangerous	p. 198
Good data governance provides clarity	p. 198
Establishing Data Stewardship to Enforce Data Governance Rules	p. 198
Implementing a Structured Approach to Data Governance	p. 199
Chapter 16 Managing Models During Development and Production	p. 203
Unfolding the Fundamentals of Model Management	p. 203
Working with many models	p. 204
Making the case for efficient model management	p. 206
Implementing Model Management	p. 207
Pinpointing implementation challenges	p. 208
Managing model risk	p. 210
Measuring the risk level	p. 211
Identifying suitable control mechanisms	p. 211
Chapter 17 Exploring the Importance of Open Source	p. 213
Exploring the Role of Open Source	p. 213
Understanding the importance of open source in smaller companies	p. 214
Understanding the trend	p. 215
Describing the Context of Data Science Programming Languages	p. 215
Unfolding Open Source Frameworks for AI/ML Models	p. 218
TensorFlow	p. 219
Theano	p. 219
Torch	p. 219
Caffe and Caffe2	p. 220
The Microsoft Cognitive Toolkit (previously known as Microsoft CNTK)	p. 220
Keras	p. 220
Scikit-learn	p. 221
Spark MLlib	p. 221
Azure ML Studio	p. 221
Amazon Machine Learning	p. 221
Choosing Open Source or Not?	p. 222
Chapter 18 Realizing the Infrastructure	p. 223
Approaching Infrastructure Realization	p. 223
Listing Key Infrastructure Considerations for Al and ML Support	p. 226
Location	p. 226
Capacity	p. 227
Data center setup	p. 227
End-to-end management	p. 227
Network infrastructure	p. 228
Security and ethics	p. 228
Advisory and supporting services	p. 229
Ecosystem fit	p. 229
Automating Workflows in Your Data Infrastructure	p. 229
Enabling an Efficient Workspace for Data Engineers and Data Scientists	p. 230
Part 5 Data as a Business	p. 233
Chapter 19 Investing in Data as a Business	p. 235
Exploring How to Monetize Data	p. 236
Approaching data monetization is about treating data as an asset	p. 237
Data monetization in a data economy	p. 238
Looking to the Future of the Data Economy	p. 240
Chapter 20 Using Data for Insights or Commercial Opportunities	p. 243
Focusing Your Data Science Investment	p. 243
Determining the Drivers for Internal Business insights	p. 244
Recognizing data science categories for practical implementation	p. 245
Applying data-science-driven internal business insights	p. 247
Using Data for Commercial Opportunities	p. 248
Defining a data product	p. 249
Distinguishing between categories of data products	p. 250
Balancing Strategic Objectives	p. 252
Chapter 21 Engaging Differently with Your Customers	p. 255
Understanding Your Customers	p. 255
Step 1 Engage your customers	p. 256
Step 2 Identify what drives your customers	p. 257
Step 3 Apply analytics and machine learning to customer actions	p. 258
Step 4 Predict and prepare for the next step	p. 259
Step 5 Imagine your customer's future	p. 260
Keeping Your Customers Happy	p. 261
Serving Customers More Efficiently	p. 263
Predicting demand	p. 263
Automating tasks	p. 264
Making company applications predictive	p. 264
Chapter 22 Introducing Data-Driven Business Models	p. 265
Defining Business Models	p. 265
Exploring Data-driven Business Models	p. 267
Creating data-centric businesses	p. 268
Investigating different types of data-driven business models	p. 268
Using a Framework for Data-driven Business Models	p. 275
Creating a data-driven business model using a framework	p. 276
Key resources	p. 277
Key activities	p. 277
Offering/value proposition	p. 278
Customer segment	p. 278
Revenue model	p. 279
Cost structure	p. 280
Putting it all together	p. 280
Chapter 23 Handling New Delivery Models	p. 281
Defining Delivery Models for Data Products and Services	p. 282
Understanding and Adapting to New Delivery Models	p. 282
Introducing New Ways to Deliver Data Products	p. 284
Self-service analytics environments as a delivery model	p. 285
Applications, websites, and product/service interfaces as delivery models	p. 287
Existing products and services	p. 289
Downloadable files	p. 290
APIs	p. 290
Cloud services	p. 291
Online marketplaces	p. 291
Downloadable licenses	p. 292
Online services	p. 293
Onsite services	p. 293
Part 6 The Part of Tens	p. 295
Chapter 24 Ten Reasons to Develop a Data Science Strategy	p. 297
Expanding Your View on Data Science	p. 297
Aligning the Company View	p. 298
Creating a Solid Base for Execution	p. 299
Realizing Priorities Early	p. 299
Putting the Objective into Perspective	p. 300
Creating an Excellent Base for Communication	p. 300
Understanding Why Choices Matter	p. 301
Identifying the Risks Early	p. 301
Thoroughly Considering Your Data Need	p. 302
Understanding the Change Impact	p. 303
Chapter 25 Ten Mistakes to Avoid When Investing in Data Science	p. 305
Don't Tolerate Top Management's Ignorance of Data Science	p. 305
Don't Believe That Al Is Magic	p. 306
Don't Approach Data Science as a Race to the Death between Man and Machine	p. 307
Don't Underestimate the Potential of Al	p. 308
Don't Underestimate the Needed Data Science Skill Set	p. 308
Don't Think That a Dashboard Is the End Objective	p. 309
Don't Forget about the Ethical Aspects of Al	p. 310
Don't Forget to Consider the Legal Rights to the Data	p. 311
Don't Ignore the Scale of Change Needed	p. 312
Don't Forget the Measurements Needed to Prove Value	p. 313
Index	p. 315

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Summary

Summary

Author Notes

Table of Contents