Nicole Janeway Bills is the Founder & CEO of Data Strategy Professionals. She has a proven track record of applying data strategy and related disciplines to solve clients’ challenges, and has worked as a data scientist and project manager for federal and commercial consulting teams. Nicole’s specialisms include NLP, cloud computing, statistical testing and web and application development.
In today’s data-driven economy, how can leaders optimise the ever-changing relationship between emerging tech and human collaboration? For our latest post Nicole gives us a run-down of her top five essential reads for digital leaders. Featuring influential authors such as Ethan Mollick and Tiankai Feng, Nicole’s curated list offers crucial insights into data quality, data strategy, AI and business performance:
Emergent technologies toe the line between overhyped and revolutionary. Over the last few years, generative AI has played jump rope with that line. Nevertheless, as GenAI graduates from its infancy, it has become clear that this technology will have a lasting impact on the world.
In this article, we look at five books that explore the evolving relationship between data, technology, and human factors, offering insights into data quality, strategy, and the rise of AI.
Authors: Laura Sebastian-Coleman
Time to read: 6 hrs 16 mins (376 pages)
Rating: 4.1/5 (39 total ratings)
Measuring Data Quality for Ongoing Improvemen t by Laura Sebastian-Coleman is an excellent resource for data practitioners and organisations grappling with maintaining high-quality data. This guide explores the challenges of measuring and monitoring data quality over time, providing a structured approach that bridges the gap between business needs and IT capabilities.
Sebastian-Coleman begins by laying a solid foundation in the general concepts of measurement before introducing a detailed framework comprising over three dozen measurement types. These are mapped to five objective dimensions of data quality: completeness, timeliness, consistency, validity, and integrity. By focusing on ongoing measurement rather than one-time assessments, the book emphasises the importance of continuous improvement in data quality – a critical factor for organisations aiming to leverage data as a strategic asset.
One of the standout features of this book is its plainlanguage approach. Sebastian-Coleman avoids technical jargon, making the content accessible to both business stakeholders and IT professionals. This fosters more effective communication and collaboration across departments.
The guidance offered is not just theoretical; it provides actionable strategies for applying the Data Quality Assessment Framework within any organisation. Readers will find valuable insights on prioritising measurement types, determining their placement within data flows, and establishing appropriate measurement frequencies. The inclusion of common conceptual models for defining and storing data quality results is particularly useful for trend analysis and detecting anomalies over time.
Furthermore, the book addresses generic business requirements for ongoing measuring and monitoring. It details the calculations and comparisons necessary to make measurements meaningful, helping organisations understand trends and make informed decisions based on their data quality metrics.
Laura Sebastian-Coleman’s extensive experience as a data quality practitioner shines throughout the book. With a career spanning over two decades, she brings a wealth of knowledge in implementing data quality metrics, facilitating stewardship groups, and establishing data standards.
Measuring Data Quality for Ongoing Improvement is more than just a guide – it’s a roadmap for organisations seeking to elevate their data quality practices. By leveraging this technology-independent framework, businesses can initiate meaningful discussions about data quality, prioritise improvements, and ultimately achieve a higher level of data integrity and trustworthiness.
TL;DR: Measuring Data Quality for Ongoing Improvement offers a practical, accessible approach to measuring and monitoring data quality over time, providing organisations with the tools to implement continuous improvement across five key dimensions.
Author: Thomas C. Redman
Time to read: 4 hrs 24 mins (264 pages)
Rating: 4.8/5 (22 total ratings)
People and Data by Thomas C. Redman explores the relationship between non-data professionals and data in achieving organisational success. Redman, known as the ‘Data Doc,’ sheds light on why many companies have yet to harness the full value that data offers. He identifies a common misstep – regular employees are often excluded from data-driven initiatives, resulting in structures and processes that fail to capitalise on data’s potential.
The book underscores the importance of prioritising data quality improvement. Redman tackles challenging organisational issues such as departmental silos that impede the effective use of data, and he advocates for upskilling the entire workforce, demonstrating how employees at all levels can leverage data insights to enhance business performance.
A standout feature of this book is its practical advice, complemented by real-world examples from organisations like AT&T and Morgan Stanley. Redman doesn’t just theorise; he provides resources such as a curriculum for employee training and tools to help companies align their data efforts with business goals.
TL;DR: People and Data by Thomas C. Redman emphasises the crucial role of non-data professionals in maximising data quality and organisational success, offering practical guidance on uniting people and data to unlock a company’s full potential.
Authors: Tiankai Feng
Time to read: 2 hrs 4 mins (124 pages)
Rating: 4.5/5 (38 total ratings)
In Humanizing Data Strategy , Tiankai Feng offers a refreshing exploration into the often overlooked human elements of data strategy. Drawing from his experience in data analytics, data governance, and data strategy, Feng bridges the gap between technical expertise and the emotional, irrational, yet crucial human behaviours that influence data initiatives.
The book stands out by combining relentless optimism with a candid reflection on common mistakes made in data strategy and practical solutions to address them. Feng introduces the ‘Five Cs’ framework – competence, collaboration, communication, creativity, and conscience – to link human needs with business objectives. This framework provides guidance for C-suite executives, operational staff, data experts, business users, and even students aiming to become change-makers in creating sustainable data strategies.
One of the compelling aspects of Feng’s approach is his acknowledgement of the role that emotions and impulses play in data strategy work. Rather than positioning these elements of the work as obstacles, Feng encourages the reader to see them as opportunities to enhance data strategy by making it more inclusive and impactful. The author’s focus on empathy and compassion adds a human touch to data-driven decision-making.
The writing is accessible and engaging, enriched by personal anecdotes and professional wisdom. Feng’s unique use of humour, music, and even memes to explain complex data-related concepts adds an unconventional yet effective dimension to the learning experience.
Humanizing Data Strategy is a call to action for organisations to recognise and leverage their most significant asset – their people. By integrating the Five Cs into data strategy, Feng provides a roadmap for creating a more collaborative, ethical, and effective data culture.
TL;DR: Humanizing Data Strategy by Tiankai Feng delves into the essential human aspects of data strategy, offering an actionable Five Cs framework to create more inclusive and impactful data initiatives by linking human needs with business objectives.
Author: Ethan Mollick
Time to read: 4 hrs 16 mins (256 pages)
Rating: 4.5/5 (1459 total ratings
Co-Intelligence by Ethan Mollick explores AI’s transformative impact on work, learning, and life. As a Wharton professor specialising in innovation and entrepreneurship, Mollick brings a unique perspective to discussing AI as a ‘co-intelligence’ that can augment or even replace human thinking.
Mollick begins by sharing his journey with AI, particularly his experience with the release of ChatGPT in November 2022. He describes the profound realisation that AI technologies are not just tools but entities capable of creative and innovative tasks once reserved for humans. This sets the stage for a deeper examination of how AI is reshaping industries, education, and personal productivity.
Diving into ethical and societal implications, Mollick also addresses concerns about job displacement and the future of human work. He challenges readers to harness AI’s power responsibly – learning from it without being misled and using it to create a better future. His balanced approach encourages proactive engagement with AI, emphasising collaboration over fear.
Mollick explores key themes throughout the book, such as understanding AI fundamentals, its impact on business and education, and navigating the ethical landscape surrounding AI and data privacy.
Endorsements from notable figures such as Angela Duckworth and Daniel H. Pink underscore the book’s significance. Duckworth describes it as ‘the very best book I know about the ins, outs, and ethics of generative AI,’ while Pink praises its lucid explanations and insightful analysis.
Co-Intelligence is for anyone curious about how AI is changing our world and what that means for individuals and organisations. Mollick combines expertise, practical advice, and a thought-provoking perspective to help readers understand and thrive in this new era.
TL;DR: Co-Intelligence by Ethan Mollick explores the transformative role of AI as a ‘co-intelligence,’ offering practical guidance on collaborating with intelligent machines to enhance work, learning, and life without losing human identity.
Authors: Laura Madsen
Time to read: 3 hrs 29 mins (209 pages)
Rating: 5/5 (15 total ratings)
In AI & The Data Revolution , Laura Madsen delivers a practical and insightful guide for data leaders navigating the complexities of today’s technology landscape. Businesses are increasingly relying on data-driven decision-making and AI-powered solutions. In her book, Madsen addresses the challenges and opportunities that come with this rapid evolution.
Drawing from her extensive experience as a global data strategist, Madsen provides a comprehensive roadmap to harness the power of AI while mitigating risks. The book offers effective strategies that data practitioners can implement immediately, making it an excellent resource for practitioners at any stage of their careers.
Key themes explored include understanding the fundamentals of AI and its implications for data leadership, navigating the ethical and regulatory landscape surrounding AI and data privacy, and creating sustainable models for technology disruption within organisations. Madsen also tackles the critical issue of technical debt that can hinder innovation, offering guidance on addressing it effectively.
One of the book’s strengths is its blend of theoretical frameworks with real-world case studies. This approach equips readers with the knowledge and tools needed to thrive in the age of AI disruption. Madsen emphasises the importance of creating an AI framework with dynamic innovation in mind, ensuring that organisations remain adaptable in a fastpaced environment.
The inclusion of a step-by-step guide on how to get started further enhances the book’s practicality. Madsen’s writing is accessible and engaging, avoiding unnecessary jargon without sacrificing the depth of the content.
In addressing both the opportunities and the challenges, AI & The Data Revolution empowers readers to unlock the full potential of their data assets and drive innovation within their organisations.
TL;DR: AI & The Data Revolution by Laura Madsen offers practical advice and strategic insights for data leaders, providing guidance on leveraging AI, addressing challenges like technical debt, and creating sustainable models for innovation in today’s fast-paced, technological world.
From data quality to AI collaboration, these five books cut through the tech hype to reveal a surprising truth: the future of digital leadership isn’t about the deployment of better algorithms – it’s about better collaboration between humans. Through frameworks for measurement, strategies for teamwork, and hard-won insights about AI adoption, these books offer a masterclass in what really matter s when it comes to pushing forward effectively with emerging technologies. By integrating these insights into their work, leaders can effectively balance technology with human values, ensuring their organisations thrive.
