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Jordan S: Data Leader

Data Leader

A Practical Guide to Building and Leading a Data Organization at Any Level

Data and AI present a tremendous opportunity to improve business performance by increasing operational efficiency, revenues and customer satisfaction. An effective data strategy leveraging the power of AI allows businesses to create a competitive advantage.

Data and Analytics Strategy for Business is a practical guide for business, technology and data leaders building a data, analytics and AI strategy for their organization. Starting by identifying the value you can obtain from data, analytics and AI, this book shows you how to maximise that value to support your organization's goals and mission. It covers the importance of having high quality data to generate trust, getting the whole organization on board as well as all the other essential elements required to complete your strategy. This book provides the keys to using data to drive improved business results. Incorporating the latest developments in AI, this new edition of Data and Analytics Strategy for Business shows how leaders can use AI right away to get value from their existing strategy. It provides practical guidance and recommendations for implementing AI and machine learning to maximize performance. Filled with real-world examples from organizations including Tesco and Facebook, this book is a step-by-step guide to designing and implementing a results-driven data strategy.

Leverage Data and AI to Achieve Your Business Goals

Data and Analytics Strategy for Business

A clear, engaging, evidence-based guide to monetizing data, for everyone from employee to board member. Most organizations view data monetization—converting data into money—too narrowly: as merely selling data sets. But data monetization is a core business activity for both commercial and noncommercial organizations, and, within organizations, it’s critical to have wide-ranging support for this pursuit. In Data Is Everybody’s Business, the authors offer a clear and engaging way for people across the entire organization to understand data monetization and make it happen. The authors identify three viable ways to convert data into money—improving work with data, wrapping products with data, and selling information offerings—and explain when to pursue each and how to succeed. Key features of the book: •&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Grounded in twenty-eight years of academic research, including nine years of research at the MIT Sloan Center for Information Systems Research (MIT CISR) •&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Definitions of key terms, self-reflection questions, appealing graphics, and easy-to-use frameworks •&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Rich with detailed case studies •&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Supplemented by free MIT CISR website resources (cisr.mit.edu) Ideal for organizations engaged in data literacy training, data-driven transformation, or digital transformation, Data Is Everybody’s Business is the essential guide for helping everybody in the organization—not just the data specialists—understand, get excited about, and participate in data monetization.

A clear, engaging, evidence-based guide to monetizing data, for everyone from employee to board member. Most organizations view data monetization—converting data into money—too narrowly: as merely selling data sets. But data monetization is a core business activity for both commercial and noncommercial organizations, and, within organizations, it’s critical to have wide-ranging support for this pursuit. In Data Is Everybody’s Business, the authors offer a clear and engaging way for people across the entire organization to understand data monetization and make it happen. The authors identify three viable ways to convert data into money—improving work with data, wrapping products with data, and selling information offerings—and explain when to pursue each and how to succeed. Key features of the book: •&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Grounded in twenty-eight years of academic research, including nine years of research at the MIT Sloan Center for Information Systems Research (MIT CISR) •&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Definitions of key terms, self-reflection questions, appealing graphics, and easy-to-use frameworks •&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Rich with detailed case studies •&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Supplemented by free MIT CISR website resources (cisr.mit.edu) Ideal for organizations engaged in data literacy training, data-driven transformation, or digital transformation, Data Is Everybody’s Business is the essential guide for helping everybody in the organization—not just the data specialists—understand, get excited about, and participate in data monetization.

The Fundamentals of Data Monetization

Data Is Everybody's Business

Le guide pratique du Chuchoteur de données pour expliquer et comprendre la valeur stratégique de la gestion des données. Le POURQUOI, pas le COMMENT.


Le besoin de gestion des données est partout dans votre entreprise. La valeur de chaque initiative de transformation numérique, de chaque projet de science des données et d'analyse, de chaque offre de service en ligne, de chaque incursion dans le commerce électronique et de chaque mise en ¿uvre de logiciels d'entreprise est inextricablement liée à la réussite des efforts de gestion des données. Bien qu'il s'agisse d'une simple fonction de "ordures à l'entrée, ordures à la sortie", ce slogan est rarement à l'origine d'une action durable. Nous devons raconter une meilleure histoire de données.


Le narration des données est probablement la tendance non technique la plus présente dans la technologie. Mais elle ne soutient pas directement la gestion des données, car elle est axée sur comment raconter des histoires avec des données. Il est donc temps d'élargir le domaine du récit des données pour reconnaître le rôle de la gestion des données en racontant des histoires à propos des données.
Apprenez comment obtenir l'implication des parties prenantes et l'engagement de la direction pour financer et soutenir la gestion des données en tant que partie systématique, cohérente et fondamentale de vos activités.

Histoires de données pour la gestion des données

Raconter Vos Données

Turn raw data into real traction—and real revenue.
This hands-on guide is built for the realities of startup life—where time is short, resources are limited, and every decision counts. Whether you're just getting started or scaling fast, this book is your essential toolkit for building a data-driven startup ready for growth and innovation—and for turning insight into impact, and data into dollars.
Drawing from years of experience as the first data hire at multiple high-growth startups across the US and Europe, author Piotr Sidoruk shares practical insights into building data systems that are both affordable and scalable, agile and reliable. You’ll explore how to create a strong data foundation free from the rigidity of traditional corporate models, and how to use analytics and AI to inform product development, customer insights, and investor communication—ultimately turning data into measurable business outcomes and revenue growth.
What sets this book apart is its unified approach. Instead of siloing data engineering, analytics, and machine learning, it brings them together into one actionable playbook tailored for startup environments. With real-world examples from companies such as Spotify, Airbnb, and Stripe, you’ll see how data can become a strategic asset from day one. From your first data hire to your next funding round, this book is your manual for building a startup where data drives every decision.
What You Will Learn
<ul>
<li>Develop a data-driven strategy aligned with startup goals, using rapid, iterative methods to support fast growth</li>
<li>Set up and manage data warehouses, build pipelines, and integrate diverse data sources seamlessly</li>
<li>Design and scale flexible, cost-effective data infrastructure tailored to startup constraints</li>
<li>Use data to drive decision making that directly impacts customer retention and revenue</li>
<li>Build dashboards and reports in BI tools to track the metrics that matter most for customer engagement, retention, and growth</li>
<li>Master data storytelling to influence stakeholders and secure investor funding</li>
<li>Demystify and apply advanced techniques in AI and machine learning to enhance innovation and competitiveness</li>
<li>Calculate and monitor essential startup metrics, such as Customer Acquisition Cost (CAC), Lifetime Value (LTV), retention, and growth KPIs</li>
<li>Combine quantitative data with qualitative insights to deeply understand user behavior and optimize product experience</li>
</ul>
&nbsp;

Turn raw data into real traction—and real revenue
 .&nbsp;
 This practical hands-on guide is built for the realities of startup life—where time is short, resources are limited, and every decision counts. Whether you're just getting started or scaling fast, this book is your essential playbook for building a data-driven startup ready for growth and innovation—and for turning insight into impact and data into dollars.&nbsp;Drawing from his hard-won lessons as the first data hire in both venture-backed successes and failures, author Piotr Sidoruk shares battle-tested strategies for building data systems that are both affordable and scalable, agile and reliable. You’ll explore how to create a strong data foundation free from the rigidity of traditional corporate models, and how to use analytics and AI to inform product development, customer insights, and investor communication—ultimately turning data into measurable business outcomes and revenue growth.&nbsp;What makes this book different? It unites data engineering, analytics, and machine learning into one actionable framework tailored for startups. With real-world examples from companies like Spotify, Airbnb, and Stripe, you’ll see how data can become a strategic asset from day one. From your first data hire to your next funding round, this book is your roadmap to making data your competitive advantage.&nbsp;What You Will Learn
 &nbsp;
 <ul><li>Develop a lean data-driven strategy to fuel startup growth&nbsp;</li></ul><ul><li>Build a scalable data stack for AI and machine learning without overspending&nbsp;</li></ul><ul><li>Discover user insights with analytics and qualitative research&nbsp;</li></ul><ul><li>Turn data into financial value and build narratives that win investors&nbsp;</li></ul><ul><li>Track key startup metrics including CAC, LTV, retention, and growth KPIs&nbsp;</li></ul><ul><li>Build dashboards and reports that drive decisions and revenue&nbsp;</li></ul>Who This Book Is ForData engineers, analysts, and scientists who are the first data hires at startups—or preparing to work in fast-paced, high-growth startup environments. Ideal for professionals looking to build scalable data systems, drive product insights, and grow their careers in early-stage tech companies.

Getting started with Data Analytics and AI in Startups

From Data to Dollars

Why do organisations still struggle to realise meaningful value from data and artificial intelligence, despite decades of technological progress?Despite decades of investment in data platforms, analytics tools and artificial intelligence, many organisations still struggle to turn data into real business impact. Too often, data initiatives become trapped in cycles of pilots, fragmented platforms and growing technical complexity, while the promised value remains just out of reach. This book explores why that happens and what organisations must do differently to unlock the true potential of data and AI.At the centre of the book is the Enabling Data Model, a new mental model for designing organisations where data, analytics and AI can scale. Rather than focusing solely on technology or isolated innovation projects, the model highlights the organisational foundations required for data work to succeed, aligning strategy, governance, technology and people around the shared goal of generating value from data.Accompanied by a Data Manifesto that articulates the first principles of enabling data, the book helps leaders rethink how data initiatives should be structured, governed and sustained in complex organisations. Readers will gain:<ul> <li>A clear explanation of why many data and AI initiatives fail to deliver impact</li> <li>A new mental model for aligning data capabilities with business strategy and innovation</li> <li>Practical tools including diagnostic rubrics, data personas, use case templates and the Platform Fit Canvas to translate ideas into action</li> </ul>Combining strategic insight with practical guidance, this book equips senior data leaders, CDOs, product leaders and analysts with the perspectives and tools needed to move beyond experimentation and build the foundations required for data, analytics and AI to generate lasting business value.

A New Model for Data-Driven Success in Your Organization

Enabling Data

Creating Success series

The best business decisions and strategies are always data-driven. Improve your understanding of data and your digital literacy with How to Interpret Data. Data is a cornerstone of our decision-making, but the ability to effectively interpret data is challenging to master. This book demystifies the process of data interpretation, providing you with the tools and knowledge you need to develop smart data-driven strategies. Packed full of fascinating interviews and stories which uncover the potential of data-driven strategies, this book will show you not only how to effectively interpret data, but will also uncover how you can use this knowledge to improve your decision-making and influence at work.

Using Data to Improve Your Influence and Decision-making

How to Interpret Data

Is your company struggling to get real value from data? Problem solved. This book is your guide to transforming an organization from one that treats data as an afterthought or merely a support function into one that makes data a key driver of product development and business innovation. In doing so, you will be able to measure outcomes that matter, rather than just tracking features shipped.
In a world where products are increasingly driven by data and AI, traditional approaches to product development and data management have become barriers to growth rather than enablers of success. At its core, this book establishes two fundamental principles for success: autonomous, outcome/data-driven product teams and the need for data assets to be managed as products. These principles are then expanded into practical frameworks, step-by-step implementation guides, and maturity models that cross-functional teams in any industry can incorporate in their decision-making.
Whether you are a product manager wanting to become more data-fluent, a data professional aiming to increase your product impact, or a leader trying to break down silos in your organization, Data as a Product Driver provides practical steps to transform how your company uses data.

What You Will Learn

Reorganize your teams around business problems instead of technical disciplines.

Manage the transition from centralized data teams to domain-driven decentralized data ownership.

Build effective data platform teams that enable product teams while maintaining consistent standards across your data ecosystem.

Create data assets that provide lasting value across your organization.

Implement the right operating model for your company's size and maturity level.




Who This Book Is For

Product and data leads driving organizational transformation, product managers, team accountable leads, and data practitioners such as data engineers, data analysts, data scientists, and ML engineers who are willing to evolve their team's operating model to maximize value from data.

Is your company struggling to get real value from data? Problem solved. This book is your guide to transforming an organization from one that treats data as an afterthought or merely a support function into one that makes data a key driver of product development and business innovation. In doing so, you’ll be able to measure outcomes that matter, rather than just tracking features shipped.In a world where products are increasingly driven by data and AI, traditional approaches to product development and data management have become barriers to growth rather than enablers of success. At its core, this book establishes two fundamental principles for success: autonomous, outcome/data-driven product teams and the need for data assets to be managed as products. These principles are then expanded into practical frameworks, step-by-step implementation guides, and maturity models, that cross-functional teams in any industry can incorporate in their decision-making.
 Whether you're a product manager wanting to become more data-fluent, a data professional aiming to increase your product impact, or a leader trying to break down silos in your organization, 
 Data as a Product Driver
 provides practical steps to transform how your company uses data. 
 What You Will Learn<ul><li>Reorganize your teams around business problems instead of technical disciplines.</li><li>Manage the transition from centralized data teams to domain-driven decentralized data ownership.</li><li>Build effective data platform teams that enable product teams while maintaining consistent standards across your data ecosystem.</li><li>Create data assets that provide lasting value across your organization.</li><li>Implement the right operating model for your company's size and maturity level.</li></ul>Who This Book is ForProduct and data leads driving organizational transformation, product managers, team accountable leads, and data practitioners, such as data engineers, data analysts, data scientists, and ML engineers, who are willing to evolve their team's operating model to maximize value from data.

Strategies for Aligning Data and Product Teams to Transform Organizations

Data as a Product Driver

Implement trusted AI systems that deliver measurable business value while managing risk, compliance and long-term performance. AI Development by Eva-Marie Muller-Stuler is a practical guide for data, product and technology leaders responsible for turning AI ambition into real-world outcomes. Covering the complete AI lifecycle from strategy and team design to deployment and governance, it provides the frameworks and technical foundations needed to develop AI products that are effective, responsible and aligned with business goals. Designed for leaders implementing AI at scale, this book helps you move beyond experimentation and hype to build AI solutions that solve genuine business challenges while meeting regulatory and operational requirements. You will learn how to: - Define AI use cases that address real business needs - Build teams, processes and governance for AI delivery - Develop, test and deploy AI with confidence - Manage risk, compliance, security and transparency - Monitor performance throughout the AI lifecycle AI Development combines responsible AI principles with the practical disciplines required for successful implementation. It explores areas such as MLOps, ModelOps, AIOps, DevSecOps and cybersecurity, giving you a complete framework for delivering AI that creates lasting impact. Whether you are launching your first AI initiative or scaling enterprise-wide adoption, this book provides the tools, processes and leadership guidance needed to develop trusted AI systems that deliver results. Themes include: AI development, AI governance, AI security, MLOps, ModelOps, risk management, DevSecOps, data architecture, AI strategy

Grasp how to deliver fully deployed, scalable, trusted and useful AI and avoid expensive failed solutions and limited proof-of-concepts with this practical guide covering the lifecycle of AI development.

From AI Ambition to Operational Success

Decision-Quality AI

Praise for The Soul of Strategy "The Soul of Strategy is a straightforward and practical book on customer-centricity; a topic that is easy to conceptualize yet extraordinarily difficult to implement in practice, and at scale." -Mick Farrell, CEO, Resmed "The Soul of Strategy shares abundant guidance for firms wise enough to grasp the importance of orienting their operations around the soul of the customer. That is no easy feat, yet this book is a clear recipe for achieving that goal." -Gerald Zaltman, Joseph C. Wilson Professor Emeritus, Harvard Business School and Author, Dare to Think Differently "This is the definitive guide to becoming customer centric. Compelling, comprehensive, and thoroughly practical." -Rajesh Chandy, Tony and Maureen Wheeler Chair in Entrepreneurship, London Business School

Building Customer Centric Organizations

The Soul of Strategy

Every business needs data to survive. Build and implement a strategy to take advantage of data and generate critical insights with renowned data expert and one of the world's leading voices in business Bernard Marr.

Data Strategy is the key to building a robust approach to data that will give business leaders the competitive advantage they need to succeed in the digital economy. From small organizations to huge multinationals, a data-driven strategy gives every organization the tools to revolutionize their uses of data to deliver impact and profits. By building the right strategy to collect, use and manage data, this book will prepare any organization for the data-driven future. In its fully updated third edition, this bestselling title covers the latest advances in data and AI. With updates covering synthetic data, generative AI, the potential of quantum computing and expanded content on cybersecurity, regulations and ethics, this book shows how to craft your own strategy. Bernard Marr uses his unrivalled expertise to show how you can gain an edge in the rapidly changing data-driven world.

How to Use Data and Artificial Intelligence to Transform Your Business

Data Strategy

Praise for Data &amp; AI Imperative "In Data &amp; AI Imperative, Lillian Pierson masterfully breaks down the complexities and challenges of data and AI strategy. The book offers practical guidance like the STAR Framework, as well as real-world examples, that are essential for any leader looking to stay ahead of the fast-moving curve of AI." -JOE REIS, Best-selling Author of Fundamentals of Data Engineering and Data Practitioner "Imagine standing at the crossroads of technology and growth, where every decision is underpinned by the silent yet powerful pulse of AI. Lillian Pierson's Data &amp; AI Imperative emerges not just as a guide, but also a pivotal narrative for the modern marketer and product leader in this new landscape. Pierson deftly walks us through this landscape, illustrating how data-driven strategies transcend mere practicality to become truly transformative forces. A must-read for anyone in marketing looking to use data and AI to drive product-led growth." - MADHUKAR KUMAR, CMO, SingleStore(TM), Named 'Leading CMO to Watch in 2024' by USA TODAY "This book is solid gold. This content is a must for teams upskilling their knowledge of data and AI builds. With concrete examples, illustrations, and exceptional details, this is a resource I wish I'd had when starting to work on Fortune 500 AI product deployments." -DR. JOAN PALMITER BAJOREK, Founder, Clarity AI &amp; Author of Your AI Roadmap (Wiley 2025) "Sometimes you just need a smart person to really think through a new and challenging subject from A to Z, so that the rest of us can get a head start. Lillian has done that with the Data &amp; AI Imperative and provides an actionable roadmap for organizations who want to do business in the 21st century." -ULRIK LEHRSKOV-SCHMIDT, Global expert on B2B SaaS pricing, Author of The Pricing Roadmap &amp; Managing Partner, Willingness To Pay "Lillian Pierson has collected her many years of insights gained working with data-drenched organizations who are trying to keep up with the data and AI revolutions. The result is this comprehensive book, Data &amp; AI Imperative, which provides frameworks, assessments, guidelines, and growth-focused actions that are both strategic and tactical. This is a must-read for anyone leading data-driven transformations in their company." -KIRK BORNE, PH.D., Founder and Owner, Data Leadership Group LLC

Designing Strategies for Exponential Growth

Data & AI Imperative

Frag doch einfach!

Alle sammeln Daten. Doch warum eigentlich? Die Autor:innen gehen dieser Frage auf den Grund. Sie verraten, was genau hinter dem Schlagwort Big Data steckt und welche Chancen und Risiken sich aus der Kombination von großen Datenvolumina und künstlicher Intelligenz ergeben. 

Auch auf das Management, die Verarbeitung, Visualisierung und die Haltung von Daten gehen sie ein und lassen darüber hinaus die rechtlichen Rahmenbedingungen des Datensammelns nicht außer Acht. Im Frage-Antwort-Stil leicht verständlich!

Ein Buch für Studium und Praxis – ideal für Studierende der Wirtschafts-, Sozial- und Naturwissenschaften. 

Frag doch einfach! Die utb-Reihe geht zahlreichen spannenden Themen im Frage-Antwort-Stil auf den Grund. Ein Must-have für alle, die mehr wissen und verstehen wollen.

Alle sammeln Daten. Doch warum eigentlich? Die Autoren gehen dieser Frage auf den Grund. Sie verraten, was genau hinter dem Schlagwort „Big Data“ steckt und welche Chancen und Risiken sich aus der Kombination von großen Datenvolumina und künstlicher Intelligenz ergeben. Auch auf das Management, die Verarbeitung und die Haltung von Daten gehen sie ein und lassen darüber hinaus den rechtlichen Rahmen nicht außer Acht. Im Frage-Antwort-Stil leicht verständlich!

Die utb-Reihe „Frag doch einfach!“ beantwortet Fragen, die sich nicht nur Studierende stellen. Im Frage-Antwort-Stil geben Expert*innen kundig Auskunft und verraten alles Wissenswerte rund um ein Thema.

Die wichtigsten Fachbegriffe werden zudem prägnant vorgestellt und es wird verraten, welche Websites, YouTube-Videos und Bücher das Wissen aus diesem Band vertiefen können.

Klare Antworten aus erster Hand

Big Data? Frag doch einfach!

Data Leader

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