About the author |
|
xiv | |
Preface |
|
xv | |
Acknowledgements |
|
xvii | |
|
PART ONE How data and analytics can help you grow your business |
|
|
1 | (72) |
|
01 How can this book help you? |
|
|
3 | (19) |
|
|
3 | (4) |
|
CDOs hold the future of their organizations in their hands |
|
|
7 | (1) |
|
Task: Ten questions to ask about your business |
|
|
7 | (12) |
|
|
19 | (1) |
|
|
20 | (2) |
|
02 The business case for data |
|
|
22 | (11) |
|
|
23 | (1) |
|
The cost of doing nothing |
|
|
24 | (2) |
|
The value of data is only as good as the value of your business case for it |
|
|
26 | (2) |
|
Identifying the most pressing data problems |
|
|
28 | (2) |
|
|
30 | (1) |
|
Aligning the business case with the business |
|
|
31 | (1) |
|
|
32 | (1) |
|
03 Your data and analytics strategy |
|
|
33 | (18) |
|
|
33 | (2) |
|
What is a `data and analytics strategy'? |
|
|
35 | (1) |
|
|
36 | (1) |
|
Task: Setting priorities using the data periodic table |
|
|
37 | (5) |
|
Task: Using the data periodic table to design projects |
|
|
42 | (2) |
|
Five waves of transformation |
|
|
44 | (3) |
|
|
47 | (1) |
|
|
48 | (2) |
|
|
50 | (1) |
|
|
51 | (22) |
|
|
51 | (2) |
|
Cracking the code as a CDO |
|
|
53 | (2) |
|
|
55 | (1) |
|
Discovering problem-solvers |
|
|
56 | (1) |
|
Task: Recruiting for PQ and AQ |
|
|
57 | (3) |
|
Beware of ready-made data teams |
|
|
60 | (1) |
|
|
61 | (2) |
|
Data and analytics capability model |
|
|
63 | (7) |
|
|
70 | (3) |
|
|
73 | (24) |
|
|
75 | (11) |
|
|
75 | (2) |
|
|
77 | (2) |
|
Task: Identifying the right project |
|
|
79 | (4) |
|
Make sure it's a quick win for everyone |
|
|
83 | (1) |
|
Quick wins are not strategic wins |
|
|
84 | (1) |
|
|
85 | (1) |
|
|
86 | (11) |
|
|
86 | (1) |
|
Why build a repeat-and-learn culture? |
|
|
87 | (2) |
|
Listen to what data is telling you |
|
|
89 | (2) |
|
Task: Define a data process |
|
|
91 | (1) |
|
Task: Develop a business change process |
|
|
92 | (1) |
|
Learning and innovating through experimentation |
|
|
93 | (2) |
|
|
95 | (2) |
|
PART THREE Wave 2: Mature |
|
|
97 | (80) |
|
|
99 | (17) |
|
|
99 | (3) |
|
|
102 | (3) |
|
The importance of accountability |
|
|
105 | (2) |
|
Data stewards, data owners and the data executive |
|
|
107 | (4) |
|
Task: Implementing data governance |
|
|
111 | (4) |
|
|
115 | (1) |
|
|
115 | (1) |
|
|
116 | (19) |
|
|
116 | (1) |
|
The risks of low-quality data |
|
|
117 | (2) |
|
The upside of high-quality data |
|
|
119 | (3) |
|
The four principles of data quality |
|
|
122 | (2) |
|
|
124 | (1) |
|
Task: Setting a baseline and a target |
|
|
125 | (4) |
|
Task: Build a data quality team |
|
|
129 | (1) |
|
Task: Improving data quality in the short term |
|
|
130 | (2) |
|
Task: Improving data quality in the long term |
|
|
132 | (2) |
|
|
134 | (1) |
|
|
134 | (1) |
|
09 A single customer view |
|
|
135 | (16) |
|
|
135 | (2) |
|
What is a single customer view? |
|
|
137 | (6) |
|
|
143 | (1) |
|
|
144 | (2) |
|
|
146 | (2) |
|
Shadow data is the enemy of the SCV |
|
|
148 | (1) |
|
|
149 | (1) |
|
|
150 | (1) |
|
10 Reports and dashboards |
|
|
151 | (17) |
|
|
151 | (1) |
|
|
152 | (4) |
|
From static to dynamic decision support |
|
|
156 | (2) |
|
Task: Designing your dashboard |
|
|
158 | (2) |
|
Task: Dashboard implementation |
|
|
160 | (2) |
|
From reporting to insight |
|
|
162 | (1) |
|
Task: Information architecture |
|
|
163 | (2) |
|
|
165 | (2) |
|
|
167 | (1) |
|
|
167 | (1) |
|
11 Data risk management and ethics |
|
|
168 | (9) |
|
|
168 | (1) |
|
|
169 | (5) |
|
Task: Working with a regulator |
|
|
174 | (2) |
|
|
176 | (1) |
|
PART FOUR Wave 3: Industrialize |
|
|
177 | (36) |
|
12 Automation, automation, automation |
|
|
179 | (13) |
|
|
179 | (1) |
|
|
180 | (2) |
|
How much can we automate? |
|
|
182 | (2) |
|
Task: The business case for automation |
|
|
184 | (3) |
|
Task: Manageable automation projects |
|
|
187 | (2) |
|
|
189 | (2) |
|
|
191 | (1) |
|
13 Scaling up and scaling out |
|
|
192 | (12) |
|
|
192 | (1) |
|
From quick wins to big wins |
|
|
193 | (1) |
|
|
194 | (1) |
|
Task: Choosing how and when to scale |
|
|
195 | (2) |
|
Use your resource multipliers |
|
|
197 | (3) |
|
Task: Implementing your hackathon |
|
|
200 | (2) |
|
The dividend from scaling |
|
|
202 | (1) |
|
|
203 | (1) |
|
|
204 | (9) |
|
|
204 | (1) |
|
Best intentions are not optimal |
|
|
205 | (1) |
|
Task: Plot a path to optimization |
|
|
206 | (2) |
|
Task: Overcoming resistance |
|
|
208 | (1) |
|
|
209 | (1) |
|
|
210 | (1) |
|
|
211 | (2) |
|
PART FIVE Wave 4: Realize |
|
|
213 | (42) |
|
15 The voice of the customer |
|
|
215 | (17) |
|
|
215 | (1) |
|
|
216 | (2) |
|
Reasons to use other sources of data |
|
|
218 | (1) |
|
Understanding competitors |
|
|
219 | (2) |
|
|
221 | (1) |
|
|
222 | (1) |
|
Task: Applying insights from social listening |
|
|
223 | (3) |
|
Task: Creating a reliable Net Promoter Score |
|
|
226 | (3) |
|
|
229 | (1) |
|
|
229 | (1) |
|
|
230 | (2) |
|
16 Maximizing data science |
|
|
232 | (10) |
|
|
232 | (1) |
|
Data science, not data magic |
|
|
233 | (1) |
|
How not to do data science |
|
|
234 | (2) |
|
Task: Integrating data science |
|
|
236 | (2) |
|
Task: Sustaining data science |
|
|
238 | (2) |
|
Embrace the potential for failure |
|
|
240 | (1) |
|
|
241 | (1) |
|
17 Sharing data with suppliers and customers |
|
|
242 | (13) |
|
|
242 | (1) |
|
|
243 | (1) |
|
Exposing data to business partners and suppliers |
|
|
244 | (2) |
|
Blockchain in the supply chain |
|
|
246 | (1) |
|
|
247 | (2) |
|
Exposing data to customers |
|
|
249 | (1) |
|
|
250 | (2) |
|
Exposure is inevitable, so do it your way |
|
|
252 | (1) |
|
|
253 | (2) |
|
PART SIX Wave 5: Differentiate |
|
|
255 | (34) |
|
18 From data-driven to Al-driven |
|
|
257 | (21) |
|
|
257 | (3) |
|
|
260 | (1) |
|
A hierarchy of data value |
|
|
261 | (2) |
|
|
263 | (1) |
|
|
264 | (2) |
|
|
266 | (2) |
|
Task: Improved clustering |
|
|
268 | (1) |
|
|
269 | (1) |
|
Task: Complex analysis and prediction |
|
|
270 | (3) |
|
The limits of AI as a guide or manager |
|
|
273 | (1) |
|
Task: Creating commitment to AI for independent real-time decision-making |
|
|
274 | (1) |
|
From data-driven transformation to Al-driven business |
|
|
275 | (1) |
|
|
276 | (2) |
|
|
278 | (8) |
|
|
278 | (2) |
|
|
280 | (1) |
|
A data and analytics centre of excellence (CoE) |
|
|
281 | (2) |
|
Task: Creating a data CoE |
|
|
283 | (1) |
|
Three functions of research |
|
|
284 | (1) |
|
A continuous improvement life cycle |
|
|
284 | (1) |
|
|
285 | (1) |
|
20 Right leadership, right time |
|
|
286 | (3) |
|
|
286 | (1) |
|
Leading a sustainable data culture |
|
|
287 | (1) |
|
|
288 | (1) |
|
|
288 | (1) |
Epilogue: Data success |
|
289 | (2) |
Glossary |
|
291 | (7) |
Abbreviations and acronyms |
|
298 | (2) |
Index |
|
300 | |