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‎books/Data Strategy and AI Value Creation/summary.en.md‎

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# Book Summary: Data Strategy and AI Value Creation
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* **Author**: Wai Fong Boh, Chee Hua (Neumann) Chew, Thara Ravindran
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* **Genre**: Data Management and AI Business Strategy
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* **Publication Date**: 2025
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This document summarizes the key lessons and insights extracted from the book.
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I highly recommend reading the original book for the full depth and author's perspective.
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## Before You Get Started
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* I summarize key points from useful books to learn and review quickly.
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* Simply click on `Ask AI` links after each section to dive deeper.
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## Introduction
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**Summary**: The book kicks off by highlighting how events like COVID-19 and the rise of generative AI have reshaped our understanding of data and AI. It stresses the critical need for timely, accurate data in decision-making, while pointing out common pitfalls like inconsistent definitions or fragmented datasets. The authors explain that AI, especially generative tools like ChatGPT, democratizes insight extraction, but data leaders must grasp both its potential and risks. Overall, it's about rephrasing timeless data challenges in today's context and offering practical management strategies for data and AI to drive business value.
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**Example**: Think of data as puzzle pieces scattered across different rooms—during COVID-19, we realized that without a way to connect them properly, we couldn't see the full picture, leading to poor decisions. AI acts like a smart assistant that helps assemble those pieces faster, but only if the pieces are clean and reliable.
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**Link for More Details**:
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[Ask AI: Introduction](https://alisol.ir/?ai=Introduction%7CWai%20Fong%20Boh%2C%20Chee%20Hua%20%28Neumann%29%20Chew%2C%20Thara%20Ravindran%7CData%20Strategy%20and%20AI%20Value%20Creation)
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## Overview of the Chapters on Data Strategy and Governance
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**Summary**: This chapter sets the stage for data strategy and governance by emphasizing that data is a core asset needing intentional management for value and integrity. Drawing from COVID-19 lessons, it underscores timely, accurate, and shareable data's role in optimal outcomes. Data strategy focuses on the "why" (vision and goals), while governance handles the "what and how" (operations). It critiques vague definitions from tools like ChatGPT and highlights people/process issues as bigger hurdles than tech. The overview previews upcoming chapters on strategy elements, quality, privacy, security, and EU dataspaces.
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**Example**: Imagine data strategy as plotting a road trip—deciding where to go and why—while governance is maintaining the car, checking fuel, and following traffic rules to ensure you arrive safely without breakdowns.
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**Link for More Details**:
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[Ask AI: Overview of the Chapters on Data Strategy and Governance](https://alisol.ir/?ai=Overview%20of%20the%20Chapters%20on%20Data%20Strategy%20and%20Governance%7CWai%20Fong%20Boh%2C%20Chee%20Hua%20%28Neumann%29%20Chew%2C%20Thara%20Ravindran%7CData%20Strategy%20and%20AI%20Value%20Creation)
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## Data Strategy — Transforming into a Data-Driven Enterprise
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**Summary**: Here, the authors define a data strategy as a plan to use and manage data for business value, balancing offensive (monetization like insights or new products) and defensive (foundation like quality and governance) aspects. It must align with corporate, digital, and IT strategies. Using Philip Morris International's transformation as a case, they show how a dual strategy with a clear roadmap, building blocks (like data architecture and analytics), and top-down/bottom-up development leads to real progress. Recommendations include starting with top management mandate, collaborating across teams, and setting principles like treating data as a shared asset.
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**Example**: A data strategy is like a fitness plan: the offensive side is about building muscle through workouts (monetizing data), while the defensive is the diet and rest (ensuring data quality). PMI's approach was like committing to both gym sessions and healthy eating to achieve a "smoke-free future" goal.
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**Link for More Details**:
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[Ask AI: Data Strategy — Transforming into a Data-Driven Enterprise](https://alisol.ir/?ai=Data%20Strategy%20%E2%80%94%20Transforming%20into%20a%20Data-Driven%20Enterprise%7CWai%20Fong%20Boh%2C%20Chee%20Hua%20%28Neumann%29%20Chew%2C%20Thara%20Ravindran%7CData%20Strategy%20and%20AI%20Value%20Creation)
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## Unlocking the Power of Data: Key Strategies for Success
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**Summary**: Building on real-world examples from companies like Amazon and Google, this chapter explores how data has evolved from a passive tool to a strategic force driving leadership and innovation. It outlines critical success factors, including offensive/defensive strategies and support structures like data literacy and governance. Organizations need to build capabilities in people, processes, and tech while nurturing attributes like adaptability and ethical focus to fully harness data's potential.
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**Example**: Data as a strategic force is like turning a basic bicycle into a high-speed electric bike—companies like Netflix use it to personalize recommendations, speeding ahead of competitors who stick to old paths.
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**Link for More Details**:
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[Ask AI: Unlocking the Power of Data: Key Strategies for Success](https://alisol.ir/?ai=Unlocking%20the%20Power%20of%20Data%3A%20Key%20Strategies%20for%20Success%7CWai%20Fong%20Boh%2C%20Chee%20Hua%20%28Neumann%29%20Chew%2C%20Thara%20Ravindran%7CData%20Strategy%20and%20AI%20Value%20Creation)
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## Challenges and Best Practices Associated with Data Quality and Acceptable Use of Data
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**Summary**: Data quality isn't an IT issue but an organizational one—poor quality leads to unreliable insights and bad decisions. The chapter defines quality issues as defects reducing trustworthiness, explores causes like silos, and recommends a framework with monitoring, KPIs, and governance. It also covers acceptable use, warning against misuse that creates risks, and ties it back to strategy for better outcomes.
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**Example**: Bad data quality is like cooking with spoiled ingredients—no matter the recipe (strategy), the meal (outcomes) will disappoint. A quality framework acts as a freshness check to keep everything palatable.
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**Link for More Details**:
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[Ask AI: Challenges and Best Practices Associated with Data Quality and Acceptable Use of Data](https://alisol.ir/?ai=Challenges%20and%20Best%20Practices%20Associated%20with%20Data%20Quality%20and%20Acceptable%20Use%20of%20Data%7CWai%20Fong%20Boh%2C%20Chee%20Hua%20%28Neumann%29%20Chew%2C%20Thara%20Ravindran%7CData%20Strategy%20and%20AI%20Value%20Creation)
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## Personal Data Privacy, Industry Data Regulations, and Data Security — Three Sides of the Same Coin
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**Summary**: Privacy, regulations, and security are interconnected—overfocusing on compliance can create silos and false security. The chapter proposes six simple questions for effective data access control, aiming for true security rather than just ticking boxes. Maturity is evaluated by how quickly and accurately organizations answer these, ensuring protection without hindering authorized use.
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**Example**: Think of data privacy, regs, and security as locks on a house: compliance is installing them, but real security means knowing who has keys and why, preventing break-ins while letting family in easily.
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**Link for More Details**:
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[Ask AI: Personal Data Privacy, Industry Data Regulations, and Data Security — Three Sides of the Same Coin](https://alisol.ir/?ai=Personal%20Data%20Privacy%2C%20Industry%20Data%20Regulations%2C%20and%20Data%20Security%20%E2%80%94%20Three%20Sides%20of%20the%20Same%20Coin%7CWai%20Fong%20Boh%2C%20Chee%20Hua%20%28Neumann%29%20Chew%2C%20Thara%20Ravindran%7CData%20Strategy%20and%20AI%20Value%20Creation)
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## Privacy-First Design — The Significant Role of Privacy-Enhancing Technologies
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**Summary**: Privacy by design ensures trust in digital transactions without sacrificing data utility. The chapter dives into technologies like homomorphic encryption (computing on encrypted data), secure multi-party computation (joint analysis without sharing raw data), federated learning (sharing model updates, not data), and differential privacy (adding noise for anonymity). These allow insights from sensitive data across organizations, like in healthcare billing, while protecting identities.
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**Example**: Homomorphic encryption is like working on a locked safe—you can add or multiply contents inside without opening it, keeping secrets safe but still getting useful results.
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**Link for More Details**:
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[Ask AI: Privacy-First Design — The Significant Role of Privacy-Enhancing Technologies](https://alisol.ir/?ai=Privacy-First%20Design%20%E2%80%94%20The%20Significant%20Role%20of%20Privacy-Enhancing%20Technologies%7CWai%20Fong%20Boh%2C%20Chee%20Hua%20%28Neumann%29%20Chew%2C%20Thara%20Ravindran%7CData%20Strategy%20and%20AI%20Value%20Creation)
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## Data Strategy and AI Value Creation: Dataspaces — Opportunities with ESG
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**Summary**: EU initiatives push dataspaces as trusted platforms for large-scale data sharing, linking to ESG and sustainability. The chapter discusses benefits like better insights from combined data, but highlights barriers like privacy concerns. For non-EU leaders, key takeaways include preparing for data-sharing ecosystems to enhance strategy and AI value.
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**Example**: Dataspaces are like a shared community garden—companies contribute and harvest data collectively for ESG goals, but trust issues (like who picks the fruits) must be resolved first.
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**Link for More Details**:
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[Ask AI: Data Strategy and AI Value Creation: Dataspaces — Opportunities with ESG](https://alisol.ir/?ai=Data%20Strategy%20and%20AI%20Value%20Creation%3A%20Dataspaces%20%E2%80%94%20Opportunities%20with%20ESG%7CWai%20Fong%20Boh%2C%20Chee%20Hua%20%28Neumann%29%20Chew%2C%20Thara%20Ravindran%7CData%20Strategy%20and%20AI%20Value%20Creation)
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## Overview — AI Value Creation
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**Summary**: Shifting to AI, this overview explains how AI extracts non-obvious insights from data, boosting strategy. It covers non-generative (decision-support via machine learning) and generative AI (content creation), skipping math/code to focus on business applications, potential, and limitations.
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**Example**: AI is the refinery turning crude oil (data) into fuel (insights)—non-generative refines predictions, while generative creates new blends like auto-generated reports.
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**Link for More Details**:
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[Ask AI: Overview — AI Value Creation](https://alisol.ir/?ai=Overview%20%E2%80%94%20AI%20Value%20Creation%7CWai%20Fong%20Boh%2C%20Chee%20Hua%20%28Neumann%29%20Chew%2C%20Thara%20Ravindran%7CData%20Strategy%20and%20AI%20Value%20Creation)
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## The Rise of Artificial Intelligence
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**Summary**: AI's evolution from rule-based to machine learning is unpacked, with successes in areas like fraud detection and failures from biases or overhyping. The chapter clarifies variants (supervised/unsupervised) and stresses AI's role in everyday decisions, urging leaders to understand its limits for effective use.
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**Example**: Machine learning is like teaching a child patterns—supervised uses labeled examples (e.g., "this is fraud"), unsupervised finds hidden groups without labels, like clustering similar behaviors.
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**Link for More Details**:
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[Ask AI: The Rise of Artificial Intelligence](https://alisol.ir/?ai=The%20Rise%20of%20Artificial%20Intelligence%7CWai%20Fong%20Boh%2C%20Chee%20Hua%20%28Neumann%29%20Chew%2C%20Thara%20Ravindran%7CData%20Strategy%20and%20AI%20Value%20Creation)
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## Generative AI for Advanced Value Creation
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**Summary**: Generative AI's boom, via tools like ChatGPT, is explored for industries: healthcare (drug discovery), finance (reports), entertainment (content), manufacturing (designs). It advises assessing if content generation adds business value before adopting, noting rapid evolution.
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**Example**: In manufacturing, generative AI is like a brainstorming partner—input specs, and it suggests optimized designs, speeding innovation without starting from scratch.
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[Personal note: Generative AI has advanced quickly since 2023; in 2026, I'd check for integrated tools in CAD software that handle real-time collaboration better.]
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**Link for More Details**:
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[Ask AI: Generative AI for Advanced Value Creation](https://alisol.ir/?ai=Generative%20AI%20for%20Advanced%20Value%20Creation%7CWai%20Fong%20Boh%2C%20Chee%20Hua%20%28Neumann%29%20Chew%2C%20Thara%20Ravindran%7CData%20Strategy%20and%20AI%20Value%20Creation)
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## Tipping the Scales with AI: Harnessing Data and AI to Enhance Business Value
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**Summary**: Likening data to oil and AI to a refinery, this chapter focuses on optimizing the data-insight-action cycle. Key areas: integrate data effectively, generate tailored insights, build feedback loops, and strengthen cycles for efficiency. Mastering this sustains long-term AI success.
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**Example**: In railways, AI refines sensor data into predictive maintenance insights, acting to prevent breakdowns—like oil refined into gas powering a smooth ride.
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**Link for More Details**:
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[Ask AI: Tipping the Scales with AI: Harnessing Data and AI to Enhance Business Value](https://alisol.ir/?ai=Tipping%20the%20Scales%20with%20AI%3A%20Harnessing%20Data%20and%20AI%20to%20Enhance%20Business%20Value%7CWai%20Fong%20Boh%2C%20Chee%20Hua%20%28Neumann%29%20Chew%2C%20Thara%20Ravindran%7CData%20Strategy%20and%20AI%20Value%20Creation)
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## Using AI to Power a Digital Bank with a Human Touch
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**Summary**: A case study of a digital bank blending AI for efficiency (e.g., personalization) with human elements for trust. AI augments processes like customer service, but humans handle empathy—redesigning workflows to leverage strengths of both.
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**Example**: In banking, AI is the efficient calculator for loan approvals, but humans add the reassuring conversation, creating a "human touch" experience that's fast yet personal.
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**Link for More Details**:
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[Ask AI: Using AI to Power a Digital Bank with a Human Touch](https://alisol.ir/?ai=Using%20AI%20to%20Power%20a%20Digital%20Bank%20with%20a%20Human%20Touch%7CWai%20Fong%20Boh%2C%20Chee%20Hua%20%28Neumann%29%20Chew%2C%20Thara%20Ravindran%7CData%20Strategy%20and%20AI%20Value%20Creation)
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## Generative AI Output for Business Organizations: Legal Perspectives from Copyright Law
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**Summary**: Generative AI creates value through intangibles like copyright, but outputs may not qualify without human input—jurisdictions like US/Australia require human authorship and originality. The chapter warns of infringement risks from training data and suggests modifying outputs or using public domain prompts to mitigate.
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**Example**: Using AI for a company logo is like hiring an artist who copies others—without your tweaks (human touch), it might not be yours legally, and could infringe existing works.
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**Link for More Details**:
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[Ask AI: Generative AI Output for Business Organizations: Legal Perspectives from Copyright Law](https://alisol.ir/?ai=Generative%20AI%20Output%20for%20Business%20Organizations%3A%20Legal%20Perspectives%20from%20Copyright%20Law%7CWai%20Fong%20Boh%2C%20Chee%20Hua%20%28Neumann%29%20Chew%2C%20Thara%20Ravindran%7CData%20Strategy%20and%20AI%20Value%20Creation)
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## Concluding Thoughts
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**Summary**: Wrapping up, the book ties data strategy as the vision guiding governance and AI for insights. It recaps chapters, emphasizing human orchestration over tech for success, and urges integrating these for better decisions and growth in a tech-driven era.
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**Example**: The data-AI synergy is a loop: strategy as the map, governance the vehicle maintenance, AI the accelerator—humans drive it all to the destination of business success.
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**Link for More Details**:
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[Ask AI: Concluding Thoughts](https://alisol.ir/?ai=Concluding%20Thoughts%7CWai%20Fong%20Boh%2C%20Chee%20Hua%20%28Neumann%29%20Chew%2C%20Thara%20Ravindran%7CData%20Strategy%20and%20AI%20Value%20Creation)
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---
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**About the summarizer**
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I'm *Ali Sol*, a Backend Developer. Learn more:
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* Website: [alisol.ir](https://alisol.ir)
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* LinkedIn: [linkedin.com/in/alisolphp](https://www.linkedin.com/in/alisolphp)

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