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Jordan S: Data Leader

Data Leader

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

The new Data Act is not only the centrepiece of the new EU data regulation, but probably also the most complex piece within the mosaic created by the EU Data Strategy. The regulation establishes b2b and b2c data access rights, stipulates rules for 'fair' data contracts, and tackles b2g data access in exceptional circumstances. The new rules are especially directed at IoT devices, smart contracts, and cloud services. 

The work offers an introduction to the new rules. Genealogy, structure, and practical consequences of the new Act are laid down - nonetheless with regard to the existing legal framework (e. g. data protection law). 

The authors' research is dedicated to European and International Information and Data Law.
Dr. Gordian Konstantin Ebner | Prof. Dr. Moritz Hennemann, M.Jur. (Oxon.) | Benedikt Karsten | Gregor Lienemann, LL.M. (Reading) | Marie Wienroeder

The new Data Act is not only the centrepiece of the new EU data regulation, but probably also the most complex piece within the mosaic created by the EU Data Strategy. The regulation establishes b2b and b2c data access rights, stipulates rules for ‘fair’ data contracts, and tackles b2g data access in exceptional circumstances. The new rules are especially directed at IoT devices, smart contracts, and cloud services. 

The work offers an introduction to the new rules. Genealogy, structure, and practical consequences of the new Act are laid down – nonetheless with regard to the existing legal framework (e. g. data protection law). 

The authors’ research is dedicated to European and International Information and Data Law.
Dr. Gordian Konstantin Ebner, LL.M. | Prof. Dr. Moritz Hennemann, M.Jur. (Oxon.) | Benedikt Karsten | Gregor Lienemann, LL.M. (Reading) | Marie Wienroeder

An Introduction

Data Act

Implement trusted AI systems that deliver measurable business value while managing risk, compliance and long-term performance. Decision-Quality AI 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 Decision-Quality AI 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 you need to build 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

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!

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

HIMSS Book Series

This book entitled "How Data Can Manage Global Health Pandemics: Analyzing and Understanding COVID-19" provides guidance on these topics. While there are a few books on pandemics and COVID-19, role of technologies in pandemics, none of the books talk about all the above topics with special emphasis on data.

"This book bridges the fields of health care and data to clarify how to use data to manage pandemics. Written while COVID-19 was raging, it identifies both effective practices and misfires, and is grounded in clear, research-based explanations of pandemics and data strategy….The author has written an essential book for students and professionals in both health care and data. While serving the needs of academics and experts, the book is accessible for the general reader." – Eileen Forrester, CEO of Forrester Leadership Group, Author of CMMI for Services, Guidelines for Superior Service"…Rupa Mahanti explores the connections between data and the human response to the spread of disease in her new book,... She recognizes the value of data and the kind of insight it can bring, while at the same time recognizing that using data to solve problems requires not just technology, but also leadership and courage. This is a book for people who want to better understand the role of data and people in solving human problems."-- Laura Sebastian-Coleman, Author of Meeting the Challenges of Data Quality ManagementIn contrast to the 1918 Spanish flu pandemic which occurred in a non-digital age, the timing of the COVID-19 pandemic intersects with the digital age, characterized by the collection of large amounts of data and sophisticated technologies. Data and technology are being used to combat this digital age pandemic in ways that were not possible in the pre-digital age.Given the adverse impacts of pandemics in general and the COVID-19 pandemic in particular, it is imperative that people understand the meaning, origin of pandemics, related terms, trajectory of a new disease, butterfly effect of contagious diseases, factors governing the pandemic potential of a disease, strategies to combat a pandemic, role of data, data sharing, data strategy, data governance, analytics, and data visualization in managing pandemics, pandemic myths, critical success factors in managing pandemics, and lessons learned. How Data Can Manage Global Health Pandemics: Analyzing and Understanding COVID-19 discusses these elements with special reference to COVID-19.Dr. Rupa Mahanti is a business and data consultant and has expertise in different data management disciplines, business process improvement, regulatory reporting, quality management, and more. She is the author of Data Quality (ASQ Quality Press) and the series Data Governance: The Way Forward (Springer).

Analyzing and Understanding COVID-19

How Data Can Manage Global Health Pandemics

The changing digital landscape has many organizations grappling with the challenge of adapting to unprecedented changes and innovation. The absence of a universally accepted definition of digital strategy exacerbates the complexity, leaving leaders uncertain about how to navigate this journey. Inertia becomes an untenable response to the urgent questions organizations face in a digitized world-how to seize digital opportunities, mitigate threats, and transform traditional values into digital assets. New Strategy Models in Digital Entrepreneurship emerges as the definitive solution for academic scholars seeking clarity and guidance in the realm of digital strategy. The book offers a comprehensive collection of papers that explore traditional paradigms, contemporary approaches, and innovative research methodologies. It serves as a beacon for practitioners, academicians, postgraduate and undergraduate students, and librarians, providing a roadmap for formulating and executing robust digital strategies. From theoretical foundations to the application of innovative business models, the book presents a holistic approach to navigating the complexities of digital transformation.

New Strategy Models in Digital Entrepreneurship

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

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

- A comprehensive overview of the various fields of application of data science and artificial intelligence.
- Case studies from practice to make the described concepts tangible.
- Practical examples to help you carry out simple data analysis projects.
- NEW in the 3rd edition: Chapters on Vibe Coding and AI Agents

Data Science, Big Data, Artificial Intelligence and Generative AI are currently some of the most talked-about concepts in industry, government, and society, and yet also the most misunderstood. This book will clarify these concepts and provide you with practical knowledge to apply them.
Using exercises and real-world examples, it will show you how to apply data science methods, build data platforms, and deploy data- and ML-driven projects to production. It will help you understand-and explain to various stakeholders-how to generate value from such endeavors. Along the way, it will bring essential data science concepts to life, including statistics, mathematics, and machine learning fundamentals, and explore crucial topics like critical thinking, legal and ethical considerations, and building high-performing data teams.
Readers of all levels of data familiarity-from aspiring data scientists to expert engineers to data leaders-will ultimately learn: how can an organization become more data-driven, what challenges might it face, and how can individuals help make that journey a success.

The Team of Authors consists of data professionals from business and academia, including data scientists, engineers, business leaders and legal experts. All are members of the Vienna Data Science Group (VDSG), an NGO that aims to establish a platform for exchanging knowledge on the application of data science, AI and machine learning, and raising awareness of the opportunities and potential risks of these technologies. 

WHAT'S INSIDE //
- Critical Thinking and Data Culture: How evidence driven decision making is the base for effective AI.
- Machine Learning Fundamentals: Foundations of mathematics, statistics, and ML algorithms and architectures.
- Natural Language Processing and Computer Vision: How to extract valuable insights from text, images and video data, for real world applications.
- Foundation Models and Generative AI: Understand the strengths and challenges of generative models for text, images, video, and more.
- ML and AI in Production: Turning experimentation into a working data science product.
- AI Agents and Vibe Coding: Using AI in practice and creating new solutions.
- Presenting your Results: Essential presentation techniques for data scientists.

A comprehensive overview of the various fields of application of data science and artificial intelligence.
Case studies from practice to make the described concepts tangible.
Practical examples to help you carry out simple data analysis projects.
New in 3rd edition: AI Agents, RAGs, MCP, Vibe Coding

Data Science, Big Data, and Artificial Intelligence are currently some of the most talked-about concepts in industry, government, and society, and yet also the most misunderstood. This book will clarify these concepts and provide you with practical knowledge to apply them. Featuring: 
The book approaches the topic of data science from several sides. Crucially, it will show you how to build data platforms and apply data science tools and methods. Along the way, it will help you understand - and explain to various stakeholders - how to generate value from these techniques, such as applying data science to help organizations make faster decisions, reduce costs, and open up new markets. Furthermore, it will bring fundamental concepts related to data science to life, including statistics, mathematics, and legal considerations. Finally, the book outlines practical case studies that illustrate how knowledge generated from data is changing various industries over the long term.

- Mathematics basics: Mathematics for Machine Learning to help you understand and utilize various ML algorithms.
- Machine Learning: From statistical to neural and from Transformers and GPT-3 to AutoML, we introduce common frameworks for applying ML in practice
- Natural Language Processing: Tools and techniques for gaining insights from text data and developing language technologies 
- Computer vision: How can we gain insights from images and videos with data science?
- Modeling and Simulation: Model the behavior of complex systems, such as the spread of COVID-19, and do a What-If analysis covering different scenarios.
- ML and AI in production: How to turn experimentation into a working data science product?
- Presenting your results: Essential presentation techniques for data scientists

Generate Value from Data with Machine Learning and Data Analytics

The Handbook of Data Science and AI

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

Data Leader

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