126,99 €
Mehr erfahren.
- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: ISTE Invoiced
- Sprache: Englisch
The human mind is endowed with a remarkable capacity for creative synthesis between intuition and reason; this mental alchemy is the source of genius. A new synergy is emerging between human ingenuity and the computational capacity of generative AI models.
Automated Data Analytics focuses on this fruitful collaboration between the two to unlock the full potential of data analysis. Together, human ethics and algorithmic productivity have created an alloy stronger than the sum of its parts. The future belongs to this symbiosis between heart and mind, human and machine. If we succeed in harmoniously combining our strengths, it will only be a matter of time before we discover new analytical horizons.
This book sets out the foundations of this promising partnership, in which everyone makes their contribution to a common work of considerable scope. History is being forged before our very eyes. It is our responsibility to write it wisely, and to collectively pursue the ideal of augmented intelligence progress.
Sie lesen das E-Book in den Legimi-Apps auf:
Seitenzahl: 354
Veröffentlichungsjahr: 2024
Ähnliche
Table of Contents
Cover
Table of Contents
Dedication Page
Title Page
Copyright Page
Preface
Introduction
1 Artificial Intelligence (AI) and Automated Data Analytics
1.1. The emergence of automated data analytics and the potential of generative AI
1.2. Revolutionizing the data analytics process with ChatGPT
1.3. Harmony between human creativity and automated analysis: the winning duo
1.4. Unlocking the secrets of prompt engineering for powerful results
2 ChatGPT for Data Analytics
2.1. Exploring the ChatGPT universe: history, presentation and capabilities
2.2. Powerful features for intelligent data analytics: natural language at your service
2.3. ChatGPT versus data scientists: an intelligence battle that looks like an alliance
2.4. Benefits and challenges of integrating ChatGPT into data analytics workflows
3 Data Preparation for Analysis with ChatGPT
3.1. ChatGPT in charge of preparing our datasets
3.2. Automated cleaning and pre-processing for optimum results with ChatGPT
3.3. Handling missing data, outliers and other common data issues
3.4. Using ChatGPT for data transformation, feature engineering and beyond
4 Intuitive Query Creation with ChatGPT
4.1. The discovery of patterns, trends and insights through interactive conversations
4.2. Creating natural language queries to analyze your data
4.3. The art of transforming analysis questions into SQL queries with ChatGPT
4.4. Generating efficient and optimized queries: the key to your success with ChatGPT
5 ChatGPT: The Advanced Analysis Wizard
5.1. Exploring new horizons: ChatGPT for exploratory data analysis
5.2. Simplifying your analysis: automation tasks for increased efficiency
5.3. From statistics to predictions: ChatGPT as the partner of choice
5.4. Deciphering feelings: text and sentiment analysis with ChatGPT
6 Prediction and Modeling with ChatGPT
6.1. Automating the data analysis process with ChatGPT
6.2. ChatGPT for accurate and reboust modeling
6.3. Continuous improvement: optimizing model capabilities through feedback loops
6.4. Trend and time series analysis
7 ChatGPT at the Service of Machine Learning
7.1. Machine learning in the functional fabric of ChatGPT
7.2. Creating new machine learning approaches with ChatGPT
7.3. Boosting machine learning algorithms with ChatGPT
7.4. Enhancing the potential of machine learning algorithms with ChatGPT
8 Narrative Fascination: Data-driven Stories and Reports
8.1. ChatGPT for generating data storytelling plans
8.2. The bewitchment of words: automating for writing data-driven stories
8.3. Interactive dashboards and ChatGPT’s ingenuity
8.4. Humans at the heart of protocols: the imprint of human ingenuity in generative AI
9 Power within Hands: Ethics, Orientation and Use
9.1. Understanding the limits of AI-generated analysis
9.2. Ethical harmony: ChatGPT in data analytics workflows
9.3. Providing iterative feedback to improve ChatGPT
9.4. Addressing ethical concerns and biases when using ChatGPT in data analytics
9.5. Ensuring fairness, transparency and accountability in automated data analytics
Conclusion
Appendices
Appendix 1: Prompt Repositories for the Analysis Process Using ChatGPT
A1.1. Data collection
A1.2. Data preparation
A1.3. Coding
A1.4. SQL queries
A1.5. Data processing and analysis
A1.6. Feature engineering
A1.7. Modeling
A1.8. Data visualization
A1.9. Analysis documentation
A1.10. Forecasting trends and time series
A1.11. Machine learning
Appendix 2: GPT-4 versus GPT-3.5: Feature Comparison
Appendix 3: Basic Terminology Cheat Sheet
References
Index
Other titles from ISTE in Information Systems, Web and Pervasive Computing
End User License Agreement
List of Tables
Chapter 1
Table 1.1. ChatGPT: a programming and code debugging tool
Table 1.2. From basic to generative AI: a timeline
Table 1.3. The human–AI analytical duo
Chapter 2
Table 2.1. Applications of ChatGPT in various fields
Table 2.2. Conversational versus generative capabilities
Table 2.3. Evolution of the data analyst’s role with generative AI
Table 2.4. List of ChatGPT plugins for data analytics
Chapter 3
Table 3.1. How ChatGPT improves cleaning and pre-processing of data
Table 3.2. Processing and identification of missing data and outliers with C...
Table 3.3. Valuing ChatGPT for data transformation, feature engineering and ...
Chapter 5
Table 5.1. ChatGPT and analyst roles in advanced analytical tasks
Table 5.2. Examples of ChatGPT application in sentiment analysis
Table 5.3. Cultural challenges to consider in ChatGPT sentiment analysis
Chapter 6
Table 6.1. Enhanced data analytics workflows with ChatGPT
Table 6.2. Using ChatGPT to reduce bias, assess generalizability and build c...
Table 6.3. Performance of GPT-3.5 and GPT-4 for data analytics
Chapter 7
Table 7.1. ChatGPT and machine learning algorithms: a virtuous circle of co-...
Table 7.2. Boosting machine learning performance with ChatGPT
Table 7.3. The various uses of algorithms versus the role of ChatGPT
Chapter 8
Table 8.1. Automated data storytelling with ChatGPT
Table 8.2. AI-based collaborative data analytics to improve ChatGPT
Chapter 9
Table 9.1. Comparison of roles between humans and Al
Table 9.2. Framework for the ethical deployment of ChatGPT in data workflows
Table 9.3. Strategies for fair and ethical automated data analytics
Appendix 2
Table A2.1. Comparison between GPT-3.5 and GPT-4
List of Illustrations
Chapter 1
Figure 1.1. Benefits of generative AI in data analytics
Figure 1.2. How does ChatGPT work?
Figure 1.3. Basic prompts versus role definition
Figure 1.4. Guide to strategic prompting with ChatGPT
Chapter 2
Figure 2.1. Chronological progression of GPT
Chapter 3
Figure 3.1. ChatGPT’s role in data preparation tasks
Chapter 4
Figure 4.1. ChatGPT’s advantages for natural language queries
Figure 4.2. Using ChatGPT for SQL queries
Chapter 5
Figure 5.1. Using ChatGPT for data processing, analysis and hypothesis generat...
Figure 5.2. ChatGPT’s data visualization response
Figure 5.3. ChatGPT’s response for sentiment analysis
Chapter 6
Figure 6.1. ChatGPT’s capabilities for time series analysis
Chapter 8
Figure 8.1. ChatGPT’s potential in the narrative data planning process
Chapter 9
Figure 9.1. Responsible use of ChatGPT in data analytics
Guide
Cover Page
Dedication Page
Title Page
Copyright Page
Preface
Introduction
Table of Contents
Begin Reading
Conclusion
Appendix 1 Prompt Repositories for the Analysis Process Using ChatGPT
Appendix 2 GPT-4 versus GPT-3.5: Feature Comparison
Appendix 3 Basic Terminology Cheat Sheet
References
Index
Other titles from iSTE in Information Systems, Web and Pervasive Computing
WILEY END USER LICENSE AGREEMENT
Pages
ii
iii
iv
ix
x
xi
xii
xiii
xiv
xv
xvi
xvii
xviii
xix
xx
xxi
xxii
xxiii
xxiv
xxv
xxvi
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
203
This book is dedicated to my little family, who light my way.May it give back to you even a fraction of what you bring me.
With all my gratitude and affection.
Soraya Sedkaoui
Series Editor
Jean-Charles Pomerol
Automated Data Analytics
Combining Human Creativity and AI Power using ChatGPT
Soraya Sedkaoui
First published 2024 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.
Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:
ISTE Ltd27-37 St George’s RoadLondon SW19 4EUUK
www.iste.co.uk
John Wiley & Sons, Inc.111 River StreetHoboken, NJ 07030USA
www.wiley.com
© ISTE Ltd 2024The rights of Soraya Sedkaoui to be identified as the author of this work have been asserted by her in accordance with the Copyright, Designs and Patents Act 1988.
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s), contributor(s) or editor(s) and do not necessarily reflect the views of ISTE Group.
Library of Congress Control Number: 2024941677
British Library Cataloguing-in-Publication DataA CIP record for this book is available from the British LibraryISBN 978-1-78630-978-5
Preface
Comparing the capacity of computers to the capacity of the human brain, I’ve often wondered, where does our success come from? The answer is synthesis, the ability to combine creativity and calculation… into a whole that is much greater than the sum of its parts.
How Life Imitates Chess, Garry Kasparov (2007)
Data analytics is a crucial process in today’s data-driven world. It involves collecting, cleaning, transforming and analyzing data to uncover useful information, insights, trends and patterns which inform business strategy, decision-making and process optimization. Traditionally, data analytics was a manual process requiring data scientists and analysts to prepare and process data before analyzing them. This was both tedious and time-consuming. The advent of machine learning and artificial intelligence (AI) has transformed data analytics by automating some parts of the process.
Generative AI models, like ChatGPT, are at the forefront of this automation revolution in data analytics. These large language models can understand human prompts and generate coherent and human-like textual responses. They are trained with massive text datasets, enabling them to perform a variety of language-based tasks. Generative models, such as ChatGPT, can be fine-tuned for specific applications, including data analytics.
We can consider these generative AI models as children with unlimited potential. The data scientist’s role is to nurture these models, train them and help them grow – just as parents do with their children. In the beginning, these models are like children – they have immense capabilities, but need guidance to realize their potential.
The data scientist trains them step-by-step, teaching them the different tasks, operations and functionalities required for data analytics. This includes data preprocessing, cleaning, feature engineering, modeling, evaluation and explanation. Models are trained thoroughly, across diverse datasets, learning the nuances of each analytical task.
Gradually, just as a toddler learns to walk, talk and eat solid foods, generative models become capable with regard to various data analytics workflows. With each iteration, their skills improve: they learn to handle diverse datasets, manage missing values, transform features, select optimal models, critically assess performance and generate data-driven insights.
After extensive training on a wide range of datasets and analytical tasks, these generative models evolve from toddlers into mature analytical assistants. They move from simple memorization of problem-solving techniques to the development of true conceptual understanding. The models understand why particular data transformations, models and evaluations are appropriate for given scenarios.
In a way, they develop inductive reasoning and deductive logic, just like humans. They understand the principles and evidence-based principles that underlie data analytics workflows, rather than simply memorizing mechanical instructions. This conceptual understanding is what distinguishes generative AI from prior rule-based expert systems.
Thus, when a data scientist prompts a mature, well-trained model like ChatGPT to perform analysis, it deeply understands the request, rather than simply matching keywords. It draws on its conceptual knowledge to analyze the dataset, select optimal techniques, generate insights and explain the reasoning behind them. And it does so at superhuman speeds, leveraging the computing power of AI.
But does that make these generative models smarter than humans? The answer is no. At least not yet. Although they can outperform humans on narrow tasks within their training domain, these AI models lack generalized intelligence. Human cognition crucially remains far more advanced.
Unlike generative models, humans possess common sense, intuition, imagination, social intelligence, sensitivity and generalized reasoning capacities. We can creatively solve new and open problems that intersect several domains. Humans also have better judgment, wisdom and morality, which temper our technical capabilities with ethics and responsibility.
So, while ChatGPT can analyze datasets and quickly generate information, it lacks the general critical thinking skills to deeply understand implications and assess ethics. Its intelligence is circumscribed by its training data and purpose. It cannot reason its way through completely new scenarios, as humans can through transfer learning.
That said, narrow AI models offer complementary advantages to human intelligence. Their prodigious memory and computational speed enable exhaustive data analytics. Their lack of bias and fatigue ensure consistent performance. In this way, they endow humans with superhuman data processing capabilities.
Rather than competing with AI, we can collaborate with it – combining human wisdom and ethics with AI’s productivity and precision. Together, we evolve data analytics and make it more insightful and responsible. But humans must remain in the loop to provide guidance, assess implications and ensure alignment with ethics.
An ideal symbiosis is one where humans manage creative and strategic tasks requiring reason, ethics and imagination, while AI accelerates repetitive analytical tasks requiring memory, computation and precision. Similar to Iron Man deploying the AI assistant JARVIS to enhance his human capabilities.
So, while the gap between human intelligence and AI persists, narrow AI models like ChatGPT are still in their infancy. Their capabilities will continue to grow exponentially thanks to increased data size, computing power and algorithmic advances. One day, they may even cross the threshold into artificial general intelligence (AGI).
But for the time being, generative AI enhances rather than replaces humans when it comes to data analytics process. It takes care of the tedious parts, allowing data scientists to focus on creative, high-added-value tasks. It is becoming an indispensable analytical assistant that continues to learn – like a child who grows into an adult over the years with careful attention.
The key is for humans to guide the development of these generative models in a thoughtful and ethical way. We need to focus on beneficial objectives and monitor for harmful biases or abuse. With judicious care and training, AI can usher in an era of augmented analytics – where human and machine intelligences meet and converge for more powerful, yet ethical, data insights. But the human must remain the supervising parent for the AI child.
Rather than wondering when AI will surpass human intelligence, we should focus on how to cultivate beneficial and ethical AI applications today. Generative models like ChatGPT are impressionable children that will shape the future based on the guidance they receive. Data scientists have a profound opportunity and responsibility: to educate these AI “children”, so that they become responsible and collaborative allies instead of impenetrable adversaries.
Just as teaching helps humans to consolidate our own knowledge, the training of AI models requires us to thoroughly evaluate our hypotheses, biases and best practices. AI development is as much about advancing our own intelligence – codifying disciplines into coherent frameworks, evidence-based principles and methodologies.
Collective training of generative models advances human knowledge across domains. This requires distilling nebulous issues into structured frameworks; formalizing messy tasks into step-by-step workflows; and crystallizing weakly defined domains into rigorous first principles. Teaching AI models through examples helps us to better evaluate solutions, generalize insights and formalize ethics for humans as well.
The future of data analytics is humans and AI working together – combining the human’s imaginative definition of problems, ethics and strategic judgment with AI’s vast memory, exhaustive computation and high-velocity analytical workflows. Neither can match the synergistic value of the two intelligences combined. Data science augments both human and artificial intelligence.
The time has come to actively train this child prodigy: ChatGPT! It holds enormous potential to enhance human capabilities if nurtured properly. We need to nurture it carefully – teaching analytical skills while emphasizing ethics, exposing ChatGPT to a variety of data and scenarios under supervision, so that it moves from mechanical regurgitation to contextual comprehension.
So, let us therefore guide these generative models with wisdom and kindness. Let us instill analytical techniques with values and ethics, guiding them from innocence to maturity. And let us develop artificial intelligence that makes individuals responsible instead of replacing them. Models such as ChatGPT are still in their maturing stages. With the careful supervision of researchers concerned with data ethics, they could evolve into assistants, opening up new horizons of ethically guided discovery, and collaborating more fruitfully with human than they could achieve on their own.
Just as Garry Kasparov’s quote states, human success arises from our ability to synthesize the creative and intuitive aspects of cognition with the computational and analytical aspects. When we combine these complementary modes of thinking and reasoning, the result is an emergent intelligence that simply exceeds the linear sum of creativity and calculation. There are synergies and amplifying effects that result from the fusion of different thinking styles, which makes us unique as human beings.
Synthesis creates something with expanded potential and capabilities beyond what creativity or calculation could achieve individually. It is this integration that allows us to shine. This is what Kasparov believes the human brain excels at, compared to computers.
Kasparov’s quote eloquently captures the spirit of the collaboration between humans and AI that we advocate for in this book – combining complementary strengths for amplified potential. Just as the synthesis of creativity and computation expands human cognition, the integration of human ingenuity with the analytical power of AI opens up new frontiers in data science.
When we design responsible workflows that harness both modes of intelligence, the results can far exceed their individual contributions. A capacity emerges from the thoughtful combination of human ethics and supervision with artificial productivity and rigor. This book provides frameworks for harnessing these synergies to advance data analytics. The future holds exciting possibilities as we forge partnerships between hearts and minds.
July 2024
Introduction
Imagine this: a seamless and interactive experience where you communicate effortlessly with your data, unravel their mysteries and uncover hidden insights through an engaging conversation. This vision can become a reality thanks to recent advances in artificial intelligence (AI) in data analytics.
The data-driven world is increasingly turning to AI to speed up and improve analysis. More specifically generative AI models, such as ChatGPT, are automating the process of interacting with data to discover insights. These generative models have immense potential as analytical assistants if developed responsibly under human guidance.
Just like a curious child who continually asks questions, generative AI enables fluid and intuitive exploration of datasets without the constraints of predefined queries or static reports. Users can engage in an ad hoc and open-ended dialogue, where the path is guided by emerging insights rather than being limited to pre-planned paths.
This creative symbiosis between humans and machine remarkably amplifies the analytical process. Domain experts provide strategic thinking, intuition, monitoring and ethical judgment. Meanwhile, intelligent AI agents handle the difficult computational tasks – rapid analysis of plain-language questions, analyzing huge datasets and generating interactive responses in natural language.
For example, prompted by a human analyst to evaluate sales patterns, ChatGPT could respond with high-level trends, correlations and high-level hypotheses it has identified by rapidly analyzing sales data. The analyst can then interpret these insights critically, ask follow-up questions to validate hypotheses and direct the AI to explore new perspectives.
This collaborative analysis is far more exploratory and multidimensional than static queries or predefined reports. It combines the framing and monitoring of human imagination with the exhaustive computational power of AI for an exhaustive and open exploration. Insights emerge more organically, guiding data analysts towards unexpected paths and serendipitous discoveries.
Generative AI augments the human analyst by automatically aggregating, processing and visualizing volumes of data exponentially faster than manual analysis. But it is human creativity that sparks new threads of inquiry, asks “why” and “how”, and connects analytical dots to extract meaning and implications.
The AI assistant finds signals buried in the massive noise. The human provides the contextual framework for interpreting signals into meaningful information. Together, they can unravel complex phenomena by intimately engaging with the raw data through a natural conversational flow.
This fluid and unconstrained approach enables data scientists to seamlessly traverse granular detail and broad trends as required. They can quickly zoom in on micro-level data points if something appears anomalous, then zoom back out to visualize macro-trends – spotting unusual events within wider contexts.
A key advantage is that generative AI allows humans to guide the analysis intuitively using natural language, without the constraints of structured queries or predefined analyses. No need to know programming languages or database schema. Users can engage in conversation as the process evolves.
This makes data exploration more accessible to a wider audience beyond just data scientists. Business leaders, frontline staff and others can participate, encouraging diverse analytical perspectives. The AI assistant becomes the great equalizer, enabling more stakeholders to unlock insights from data via a natural conversation.
Of course, like any child learning a skill, an AI assistant requires extensive training under human supervision to become proficient. It must ingest a myriad of datasets and scenarios to move from the mechanical analysis of queries to a true understanding of the principles and trade-offs involved when interacting with data.
Data scientists must teach these conversational models the nuances of analytical workflows – how to manage ambiguity, validate hypotheses, identify limitations, avoid bias, present information responsibly, etc. Ethics and responsibility must be emphasized, as models progress from innocence to maturity.
But once properly developed, generative AI assistants, like ChatGPT, can automate the laborious aspects of data analytics, while humans focus on high-level creative supervision. This enables exponential scaling of data exploration while maintaining a human-led direction. With sufficient guardrails, AI can even suggest new analytical paths for humans to evaluate.
Of course, generative AI has its limitations. Unlike humans, these models lack any real semantic understanding, beyond pattern recognition in training data. They also lack human judgment, intuition and ethics. Unconstrained automation could lead to misleading insights or generalized bias.
As a result, human guidance is crucial when deploying generative AI. Data scientists must assess the ethics, assumptions and blind spots behind any insights generated by AI. Generative conversational models are still, ultimately, narrow AIs, contrary to generalized human cognition. They excel only in their trained domain.
The ideal symbiosis is rapidly prompted, human-supervised, generative AI mechanical analysis. But humans provide the global framing, subjective judgment and moral responsibility for interpreting, validating and acting on ideas, both critically and ethically.
Neither humans nor machines alone can match the amplified intelligence of their collaboration. Together, they can mine both granular detail and big-picture meaning to uncover hidden insights in massive and complex data. AI expands the scope of analysis exponentially, while human creativity and ethics anchor it responsibly.
Generative AI promises to take data exploration to new frontiers by enabling a natural, fluid form of interactive analysis. But careful design and monitoring are essential if such automation is to be developed ethically and responsibly. When combined with human creativity and ethics, generative AI can open up new horizons of amplified analytical intelligence and accelerated discovery.
As data grows exponentially, AI will become an indispensable partner to augment human analysts. These models are still maturing children. Under the tutelage of responsible data scientists, they could become trusted analytical allies operating under the compass of human values to extract the maximum value from data for the benefit of society.
Why this book?
The emergence of AI has given rise to visions of a future where analytical tasks are fully automated by intelligent algorithms far exceeding human capabilities. This narrative evokes both excitement at the possibilities and apprehension at the implications of handing over decision-making power to AI systems devoid of human values. It imagines a world where generative models like ChatGPT rapidly displace humans’ footing in data science.
But this dystopian vision fails to take into account the enduring nature of human ingenuity and ignores the narrowness of contemporary AI technologies. As analytical automation is indeed accelerating, thanks to models like ChatGPT, they remain beneficial enhancements to human intelligence, more than adversaries. When developed responsibly, AI systems can amplify human potential to unlock new frontiers in data-driven insights and value creation.
However, careless implementation, focused solely on efficiency, risks undermining human action and responsibility. Maximizing the transformative power of AI-driven analytics requires respect for human ethics and surveillance throughout the process. This balance remains crucial, but precarious in the age of automation.
That is why this timely book, Automated Data Analytics: Combining Human Creativity and AI Power using ChatGPT, serves as an indispensable guide to the modern analytical frontier. It charts a prudent course, one where human imagination and ethics harness automation to elevate, rather than overwhelm, human capabilities. The book advocates thoughtful collaboration with AI, and not abandonment.
And this collaborative approach is, indeed, the wisest course. Because at their core, contemporary AI models like ChatGPT remain narrow and limited by the patterns contained in their training data. Although they can perform defined tasks at superhuman speeds, they lack generalized intelligence capabilities.
What we need is a harmonious fusion of the forces of human creativity, ethics and surveillance with the relentless analytical excellence of AI on vast datasets. Neither humans nor machines alone can match the amplified intelligence unleashed by their symbiosis. The book provides frameworks for productively structuring this collaboration for maximum collective benefit.
By shedding light on the inner workings, development processes and inherent limitations of AI systems, the book enables smoother integration into analytical workflows. Laying bare their technical realities helps to delimit optimal roles that emphasize the specialized strengths of humans versus machines. We can design complementary human–AI partnerships by assigning appropriate tasks based on contextual capabilities.
Humans must take the lead in framing problems, interpreting solutions and thinking globally, while AI undertakes data processing, calculation and in-depth analysis. This book discusses various interaction models that enable a continuous flow between human creativity and the machine’s productivity to constantly improve the discovery process.
It highlights techniques to better expose the model’s decision logic, in order to increase trust and accountability. Continuous surveillance of model behavior and fail-safe guardrails are emphasized to ensure safety and prevent harmful abuse. The book also strongly advocates for maintaining sustainable human values and ethics at every stage of analytical automation to keep progress aligned with social benefits.
It explores frameworks for adapting to society and developing specialized human capabilities that unlock unique synergies with AI. All this is geared towards developing AI-driven analytics in a responsible way for collective human elevation. The book envisions an augmented intelligence, wherein humans and machines elevate each other, not AI supremacy over humanity.
Mainly, it lays the foundations for the ethical integration of automation into the analytical workflow – from strategic problem definition where humans lead, to iterative collaboration where both participate seamlessly, to evaluative surveillance where human judgment remains essential. This makes it possible to benefit from productivity gains without giving up creativity or responsibility.
The book comes at an opportune moment when AI is transforming analysis, but its capacities remain nascent and impressionable, like a young child. It underlines our responsibility as developers, leaders and citizens to wisely guide AI’s progress from these formative stages to broader social benefit. The choices we make today will determine the long-term trajectory of artificial analytical intelligence.
And the book equips us to make these choices wisely by demystifying AI, highlighting its strengths and weaknesses, and providing ethically based normative advice. Whether you are excited or anxious about the revolution of automation, this book is an indispensable decision-making tool for navigating the future analytically.
Its frameworks, examples and principles enable us to integrate analysis responsibly with a human-centered perspective. You will get comprehensive, balanced clarity on collaborative opportunities that elevate without undermining human agency. And it champions respect for ethics as a guiding compass in the development and application of analytical AI.
This book inspires a future where AI makes data insights available to the whole of society for the common enrichment of human life. Its sound advice steers analytical automation in a prudent direction that amplifies human potential exponentially while keeping humanity firmly in control. The possibilities are breathtaking if we move forward guided by the human values and spirit of collaboration that this book seeks to inspire.
Who this book is for
This book serves as an indispensable guide for the various stakeholders navigating the early stages of amplified analysis.
For data scientists and analytics teams, it provides actionable frameworks for working responsibly with AI while retaining human creativity and human supervisions. The book’s insights will enable thoughtful development of analytical AI to unlock productivity while maintaining accountability. With illuminating case studies and prescriptive recommendations, data scientists can future-proof their skills and workflows to thrive in the age of automation.
The book is also extremely important for business leaders and decision-makers evaluating the adoption of AI-based analytics. It grounds discussions in ethical considerations beyond mere efficiency, helping leaders to make wise choices when integrating automated analytics into their organizations. Pragmatic advice allows leaders to select analytical workflows that wisely enhance the capabilities of their teams, rather than ruthlessly replacing them.
For policy-makers and regulators, the book highlights the need for adaptive governance to encourage responsible AI innovation in data analytics. Its forwardlooking perspectives reveal how today’s prudent policies can proactively shape the trajectory of automation in a direction that benefits society collectively. The book provides insight into the development of governance frameworks that are responsive to rapid technological change, while remaining grounded in ethics and inclusion.
In academic institutions, this book offers an indispensable program both in data science programs and for interdisciplinary discussions. It enriches understanding of how best to develop AI-based techniques founded on moral principles and responsibility. The book sheds light on research avenues into reliable and transparent analytical algorithms. And it enables a nuanced discourse on the balance between productivity and prudence in the age of automation.
The book also serves civil society and general interest readers looking for balanced perspectives on the integration of analytical AI. It dispels alarmist fears of AI, while emphasizing the need for thoughtful monitoring and adaptation. By making technical concepts accessible through clear metaphors and examples, the book enables the public to participate in the ethical development of the discourse on automation. It contributes to the crucial dialogue on the direction of technology for collective human upliftment.
Students and young professionals will find this book invaluable in preparing for the future of analysis. It provides a fundamental understanding of how best to develop collaborative skills in AI from a technical and ethical perspective. The information will help students cultivate human-centered values and creativity even as they develop technical prowess in AI systems. This fosters comprehensive capabilities suited to the judicious integration of automation between roles.
But perhaps most critically, this work highlights the essential human responsibility in the development and deployment of analytical AI. It emphasizes that all stakeholders have an active role to play in shaping progress on the basis of moral principles and compassion. Only through collective foresight and wisdom can we nurture a beneficial intelligence that amplifies rather than diminishes human potential.
Overall, this book is aimed at anyone interested in or affected by the integration of automation and AI in data analytics. Its mix of technical foundations, ethical direction and pragmatic advice offers indispensable guidance for moving cautiously into the era of amplified analytics. The book awakens our collective consciousness to the responsible orientation of technology use towards the expansion of human capabilities. And it provides ways to synergize human and artificial intelligence under conscientious human management, so that automated analysis benefits, rather than harms, society.
The challenge of the book
The emergence of generative AI models such as ChatGPT has triggered a revolution in data analytics. These powerful technologies automate parts of the analytical process that previously required significant human effort. Tasks such as data cleaning, aggregation and basic reporting are rapidly transformed thanks to these AI systems.
An appropriate analogy compares the education of these AI models to the education of children. Like children, they start out as blank slates and gradually acquire complex skills through careful guidance. Data scientists act as parents, slowly exposing them to various datasets and analytical tasks during the training process.
Initially, the models are only capable of mechanically imitating operations based on training data. They do not have a deeper understanding of the concepts, principles and implications behind the workflows. Models can repeat data processing steps with precision, but without any notion of meaning.
This is similar to a young child reciting multiplication tables by heart without grasping the deeper mathematical concepts of multiplication. They can perform the calculation accurately, but without the reasoning behind it. Early training of AI models also focuses on the repetitive execution of instructed tasks.
But with enough care over large datasets, these AI systems can move on to a true understanding of the patterns, relationships and rules governing analytical workflows. Just as varied exposure to the real world enables a child to deduce abstract concepts from concrete examples, extensive training helps models generalize.
Gradually, the models evolve from the simple memorization of instructions to the development of an inductive understanding of the principles underlying data analytics workflows. They understand why particular techniques are appropriate in different contexts on the basis of conceptual understanding, rather than blind implementation.
This transition reflects a student’s move from simply memorizing mathematical facts to understanding how mathematical operations work on the basis of logical reasoning. Through further maturation, the models acquire contextual awareness to adaptively select the optimal analytical approaches for new datasets and scenarios.
In essence, they develop a trained intuition similar to that of human analysts. Models like ChatGPT acquire the ability to critically evaluate, explain and improve their analysis based on experience and learned patterns, rather than on simple, rigid instructions. Their analytical capabilities evolve in sophistication from basic obedience to contextual understanding.
As models develop proficiencies in a variety of datasets, they are transformed from passive analysis tools into intelligent collaborators in the information extraction process. They move from simply analyzing data to engaging in intelligent dialogue with human analysts.
The models provide the computational speed needed to rapidly process, crossreference and visualize complex data on a superhuman scale. Their patternrecognition capacities uncover relationships that humans may miss or take much longer to discern simply because of the sheer volume of data.
But human analysts provide the strategic framing, imagination and supervision needed to ensure appropriate interpretation and extraction of meaningful information, rather than simple blind correlations. Analysts evaluate the assumptions, ethics and implications guiding the appropriate application of model results.
This creative synergy combines the power of data processing with nuanced judgment. Powerful AI models can generate new analytical avenues, but perceptive humans still need to guide the investigation by asking the right questions, through prompts. Models quickly reveal insights, but humans discern their meaning and applicability.
Together, humans and AI models can reach higher analytical heights than either of them can separately. But role divisions need to be carefully structured to benefit from this symbiosis. Just as shared parental responsibilities create specialization, the separation of analytical tasks promotes human–AI complementarity.
Delineating clear roles while collaborating closely avoids confusion and ensures that human creativity and the power of AI can amplify each other. But care must be taken to structure the human–machine partnership in such a way as to highlight their complementarities.
Uncontrolled automation without human supervision risks losing contextual judgment and responsibility. Giving full control to autonomous AI models ignores their limitations in relation to generalized human cognition. Although powerful, these models still lack certain distinctly human mental capacities.
For example, AI is currently struggling with complex abstraction, causality, transfer learning and innovative analytical techniques. Models like ChatGPT also lack human judgment, intuition, common sense, morality and appreciation of ethics or biases. They have limited capacities for imagination and social awareness beyond their training.
We must recognize these technological limitations to properly integrate AI into the analytical workflow as assistants rather than sole replacements. Their specialized capacity makes them productivity amplifiers for specific tasks rather than fully capable analysts themselves. Sound human guidance prevents misleading ideas or exaggeration of correlations.
Some predict a dystopian future in which AI will rapidly surpass all human intelligence – the so-called technological singularity, (or simply the singularity). But for now, AI remains limited to narrow applications, unable to compete with the multi-faceted reasoning, ethics and creativity of the human mind. Their capabilities, while prodigious, are constrained by the limits of their data training.
These models should be seen as powerful tools, not omniscient oracles. Like earlier innovations, such as calculators or computers, they extend certain analytical capacities without reproducing human cognition in the broadest sense. Their performance is exceptional, but narrowly delimited within a trained domain.
This is why this book, entitled Automated Data Analytics: Combining Human Creativity and AI Power using ChatGPT, rightly advocates developing and exploiting these technologies with caution, rather than entrusting them with complete analytical controls. It warns against focusing solely on efficiency gains, without considering social impacts or the loss of human control.
The aim should be to synergize and advance human and artificial intelligence to better understand data, faster and more responsibly. This involves establishing strong human safeguards and emphasizing ethics when applying AI models to avoid abuse or harmful bias.
But this also requires the development of mechanisms to interact seamlessly with AI, in order to improve, rather than simply automate, the analysis. This means structuring intuitive interfaces, intelligent workflows, and complementary human–AI team structures.
The optimal way forward is neither domination nor rejection of AI, but rather assimilation of AI under the aegis of human wisdom. This involves embracing AI’s productivity gains while instilling human values, creativity and supervision through continuous training.
If we nurture AI wisely, avoiding the perils of uncontrolled automation, the future promises new frontiers of creative data-driven insights far beyond what humans or machines could realize independently. AI development must balance social wellbeing and human dignity, not sacrifice them on the altar of efficiency and capability.
The wise integration of hearts and minds with artificial analytical power can propel progress at an unprecedented pace and scale. But this requires setting ethical boundaries and priorities beyond simply maximizing analytical power using AI, for example, ChatGPT.
Human stewardship is essential to guide these technologies towards automation, rather than the replacement of human capabilities and responsibilities. We need to proactively shape their evolution based on moral considerations and social benefits.
With AI transforming data analytics, we have reached a decisive turning point, where our choices and priorities will define the trajectory of the future of ethical AI. Treading the path cautiously, yet boldly, with compassion and creativity, jointly guiding analytical rigor, the dawn of hybrid intelligence promises an unprecedented, yet humanistic, discovery.
Navigating content
The contemporary world overflows with data, and in-depth analysis holds untold potential for individuals and organizations. However, traditional methods are struggling to unlock these nuggets of value. AI is emerging as a promising response, and in particular, generative models like ChatGPT automate entire sections of the analytical process, increasing our ability to extract impactful insights.
This book explores the fascinating world of AI-enhanced data analytics. Through a step-by-step exploration, it reveals the exciting possibilities of this symbiosis of human ingenuity and algorithmic power. The book demystifies these emerging technologies and provides a practical framework for integrating them ethically into modern analytical workflows. It presents an essential roadmap for the ethical and collaborative integration of automation in data analytics.
The interconnected chapters guide the reader through the process of training, preparing and supporting ChatGPT, in order to ensure the automation of the various tasks involved in the analytical process.
Chapter 1 sets the scene by examining how AI-driven automation is transforming data analytics. It highlights the immense potential of generative models such as ChatGPT to accelerate and deepen the extraction of insights. The chapter highlights the importance of combining human creativity and automated analysis in a harmonious way, in order to achieve augmented intelligence. It explains how to create effective prompts to get the most out of ChatGPT.
Chapter 2
