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| data analytics and product management: Data Science For Dummies Lillian Pierson, 2021-08-20 Monetize your company’s data and data science expertise without spending a fortune on hiring independent strategy consultants to help What if there was one simple, clear process for ensuring that all your company’s data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that’s most prime for achieving profitability while also moving your company closer to its business vision? There is. Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework – A simple, proven process for leading profit-forming data science projects. Not sure what data science is yet? Don’t worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you’re already a data science expert? Then you really won’t want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book. Data Science For Dummies demonstrates: The only process you’ll ever need to lead profitable data science projects Secret, reverse-engineered data monetization tactics that no one’s talking about The shocking truth about how simple natural language processing can be How to beat the crowd of data professionals by cultivating your own unique blend of data science expertise Whether you’re new to the data science field or already a decade in, you’re sure to learn something new and incredibly valuable from Data Science For Dummies. Discover how to generate massive business wins from your company’s data by picking up your copy today. |
| data analytics and product management: Data Analytics in Project Management Seweryn Spalek, J. Davidson Frame, Yanping Chen, Carl Pritchard, Alfonso Bucero, Werner Meyer, Ryan Legard, Michael Bragen, Klas Skogmar, Deanne Larson, Bert Brijs, 2019-01-01 Data Analytics in Project Management. Data analytics plays a crucial role in business analytics. Without a rigid approach to analyzing data, there is no way to glean insights from it. Business analytics ensures the expected value of change while that change is implemented by projects in the business environment. Due to the significant increase in the number of projects and the amount of data associated with them, it is crucial to understand the areas in which data analytics can be applied in project management. This book addresses data analytics in relation to key areas, approaches, and methods in project management. It examines: • Risk management • The role of the project management office (PMO) • Planning and resource management • Project portfolio management • Earned value method (EVM) • Big Data • Software support • Data mining • Decision-making • Agile project management Data analytics in project management is of increasing importance and extremely challenging. There is rapid multiplication of data volumes, and, at the same time, the structure of the data is more complex. Digging through exabytes and zettabytes of data is a technological challenge in and of itself. How project management creates value through data analytics is crucial. Data Analytics in Project Management addresses the most common issues of applying data analytics in project management. The book supports theory with numerous examples and case studies and is a resource for academics and practitioners alike. It is a thought-provoking examination of data analytics applications that is valuable for projects today and those in the future. |
| data analytics and product management: The Product Book: How to Become a Great Product Manager Product School, Josh Anon, 2017-05 Nobody asked you to show up. Every experienced product manager has heard some version of those words at some point in their career. Think about a company. Engineers build the product. Designers make sure it has a great user experience and looks good. Marketing makes sure customers know about the product. Sales get potential customers to open their wallets to buy the product. What more does a company need? What does a product manager do? Based upon Product School's curriculum, which has helped thousands of students become great product managers, The Product Book answers that question. Filled with practical advice, best practices, and expert tips, this book is here to help you succeed! |
| data analytics and product management: Building Products for the Enterprise Blair Reeves, Benjamin Gaines, 2018-03-09 If you’re new to software product management or just want to learn more about it, there’s plenty of advice available—but most of it is geared toward consumer products. Creating high-quality software for the enterprise involves a much different set of challenges. In this practical book, two expert product managers provide straightforward guidance for people looking to join the thriving enterprise market. Authors Blair Reeves and Benjamin Gaines explain critical differences between enterprise and consumer products, and deliver strategies for overcoming challenges when building for the enterprise. You’ll learn how to cultivate knowledge of your organization, the products you build, and the industry you serve. Explore why: Identifying customer vs user problems is an enterprise project manager’s main challenge Effective collaboration requires in-depth knowledge of the organization Analyzing data is key to understanding why users buy and retain your product Having experience in the industry you’re building products for is valuable Product longevity depends on knowing where the industry is headed |
| data analytics and product management: Product Analytics Joanne Rodrigues, 2020-08-27 Use Product Analytics to Understand Consumer Behavior and Change It at Scale Product Analytics is a complete, hands-on guide to generating actionable business insights from customer data. Experienced data scientist and enterprise manager Joanne Rodrigues introduces practical statistical techniques for determining why things happen and how to change what people do at scale. She complements these with powerful social science techniques for creating better theories, designing better metrics, and driving more rapid and sustained behavior change. Writing for entrepreneurs, product managers/marketers, and other business practitioners, Rodrigues teaches through intuitive examples from both web and offline environments. Avoiding math-heavy explanations, she guides you step by step through choosing the right techniques and algorithms for each application, running analyses in R, and getting answers you can trust. Develop core metrics and effective KPIs for user analytics in any web product Truly understand statistical inference, and the differences between correlation and causation Conduct more effective A/B tests Build intuitive predictive models to capture user behavior in products Use modern, quasi-experimental designs and statistical matching to tease out causal effects from observational data Improve response through uplift modeling and other sophisticated targeting methods Project business costs/subgroup population changes via advanced demographic projection Whatever your product or service, this guide can help you create precision-targeted marketing campaigns, improve consumer satisfaction and engagement, and grow revenue and profits. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. |
| data analytics and product management: Lean Analytics Alistair Croll, Benjamin Yoskovitz, 2024-02-23 Whether you're a startup founder trying to disrupt an industry or an entrepreneur trying to provoke change from within, your biggest challenge is creating a product people actually want. Lean Analytics steers you in the right direction. This book shows you how to validate your initial idea, find the right customers, decide what to build, how to monetize your business, and how to spread the word. Packed with more than thirty case studies and insights from over a hundred business experts, Lean Analytics provides you with hard-won, real-world information no entrepreneur can afford to go without. Understand Lean Startup, analytics fundamentals, and the data-driven mindset Look at six sample business models and how they map to new ventures of all sizes Find the One Metric That Matters to you Learn how to draw a line in the sand, so you'll know it's time to move forward Apply Lean Analytics principles to large enterprises and established products |
| data analytics and product management: HBR Guide to Data Analytics Basics for Managers (HBR Guide Series) Harvard Business Review, 2018-03-13 Don't let a fear of numbers hold you back. Today's business environment brings with it an onslaught of data. Now more than ever, managers must know how to tease insight from data--to understand where the numbers come from, make sense of them, and use them to inform tough decisions. How do you get started? Whether you're working with data experts or running your own tests, you'll find answers in the HBR Guide to Data Analytics Basics for Managers. This book describes three key steps in the data analysis process, so you can get the information you need, study the data, and communicate your findings to others. You'll learn how to: Identify the metrics you need to measure Run experiments and A/B tests Ask the right questions of your data experts Understand statistical terms and concepts Create effective charts and visualizations Avoid common mistakes |
| data analytics and product management: Deep Data Analytics for New Product Development Walter R. Paczkowski, 2020-02-19 This book presents and develops the deep data analytics for providing the information needed for successful new product development. Deep Data Analytics for New Product Development has a simple theme: information about what customers need and want must be extracted from data to effectively guide new product decisions regarding concept development, design, pricing, and marketing. The benefits of reading this book are twofold. The first is an understanding of the stages of a new product development process from ideation through launching and tracking, each supported by information about customers. The second benefit is an understanding of the deep data analytics for extracting that information from data. These analytics, drawn from the statistics, econometrics, market research, and machine learning spaces, are developed in detail and illustrated at each stage of the process with simulated data. The stages of new product development and the supporting deep data analytics at each stage are not presented in isolation of each other, but are presented as a synergistic whole. This book is recommended reading for analysts involved in new product development. Readers with an analytical bent or who want to develop analytical expertise would also greatly benefit from reading this book, as well as students in business programs. |
| data analytics and product management: Escaping the Build Trap Melissa Perri, 2018-11-01 To stay competitive in today’s market, organizations need to adopt a culture of customer-centric practices that focus on outcomes rather than outputs. Companies that live and die by outputs often fall into the build trap, cranking out features to meet their schedule rather than the customer’s needs. In this book, Melissa Perri explains how laying the foundation for great product management can help companies solve real customer problems while achieving business goals. By understanding how to communicate and collaborate within a company structure, you can create a product culture that benefits both the business and the customer. You’ll learn product management principles that can be applied to any organization, big or small. In five parts, this book explores: Why organizations ship features rather than cultivate the value those features represent How to set up a product organization that scales How product strategy connects a company’s vision and economic outcomes back to the product activities How to identify and pursue the right opportunities for producing value through an iterative product framework How to build a culture focused on successful outcomes over outputs |
| data analytics and product management: Big Data Analytics Kim H. Pries, Robert Dunnigan, 2015-02-05 With this book, managers and decision makers are given the tools to make more informed decisions about big data purchasing initiatives. Big Data Analytics: A Practical Guide for Managers not only supplies descriptions of common tools, but also surveys the various products and vendors that supply the big data market.Comparing and contrasting the dif |
| data analytics and product management: Data Analytics for Organisational Development Uwe H. Kaufmann, Amy B. C. Tan, 2021-07-26 A practical guide for anyone who aspires to become data analytics–savvy Data analytics has become central to the operation of most businesses, making it an increasingly necessary skill for every manager and for all functions across an organisation. Data Analytics for Organisational Development: Unleashing the Potential of Your Data introduces a methodical process for gathering, screening, transforming, and analysing the correct datasets to ensure that they are reliable tools for business decision-making. Written by a Six Sigma Master Black Belt and a Lean Six Sigma Black Belt, this accessible guide explains and illustrates the application of data analytics for organizational development and design, with particular focus on Customer and Strategy Analytics, Operations Analytics and Workforce Analytics. Designed as both a handbook and workbook, Data Analytics for Organisational Development presents the application of data analytics for organizational design and development using case studies and practical examples. It aims to help build a bridge between data scientists, who have less exposure to actual business issues, and the non-data scientists. With this guide, anyone can learn to perform data analytics tasks from translating a business question into a data science hypothesis to understanding the data science results and making the appropriate decisions. From data acquisition, cleaning, and transformation to analysis and decision making, this book covers it all. It also helps you avoid the pitfalls of unsound decision making, no matter where in the value chain you work. Follow the “Five Steps of a Data Analytics Case” to arrive at the correct business decision based on sound data analysis Become more proficient in effectively communicating and working with the data experts, even if you have no background in data science Learn from cases and practical examples that demonstrate a systematic method for gathering and processing data accurately Work through end-of-chapter exercises to review key concepts and apply methods using sample data sets Data Analytics for Organisational Development includes downloadable tools for learning enrichment, including spreadsheets, Power BI slides, datasets, R analysis steps and more. Regardless of your level in your organisation, this book will help you become savvy with data analytics, one of today’s top business tools. |
| data analytics and product management: The Lean Product Playbook Dan Olsen, 2015-05-21 The missing manual on how to apply Lean Startup to build products that customers love The Lean Product Playbook is a practical guide to building products that customers love. Whether you work at a startup or a large, established company, we all know that building great products is hard. Most new products fail. This book helps improve your chances of building successful products through clear, step-by-step guidance and advice. The Lean Startup movement has contributed new and valuable ideas about product development and has generated lots of excitement. However, many companies have yet to successfully adopt Lean thinking. Despite their enthusiasm and familiarity with the high-level concepts, many teams run into challenges trying to adopt Lean because they feel like they lack specific guidance on what exactly they should be doing. If you are interested in Lean Startup principles and want to apply them to develop winning products, this book is for you. This book describes the Lean Product Process: a repeatable, easy-to-follow methodology for iterating your way to product-market fit. It walks you through how to: Determine your target customers Identify underserved customer needs Create a winning product strategy Decide on your Minimum Viable Product (MVP) Design your MVP prototype Test your MVP with customers Iterate rapidly to achieve product-market fit This book was written by entrepreneur and Lean product expert Dan Olsen whose experience spans product management, UX design, coding, analytics, and marketing across a variety of products. As a hands-on consultant, he refined and applied the advice in this book as he helped many companies improve their product process and build great products. His clients include Facebook, Box, Hightail, Epocrates, and Medallia. Entrepreneurs, executives, product managers, designers, developers, marketers, analysts and anyone who is passionate about building great products will find The Lean Product Playbook an indispensable, hands-on resource. |
| data analytics and product management: Big Data Analytics in Supply Chain Management Iman Rahimi, Amir H. Gandomi, Simon James Fong, M. Ali Ülkü, 2020-12-20 In a world of soaring digitization, social media, financial transactions, and production and logistics processes constantly produce massive data. Employing analytical tools to extract insights and foresights from data improves the quality, speed, and reliability of solutions to highly intertwined issues faced in supply chain operations. From procurement in Industry 4.0 to sustainable consumption behavior to curriculum development for data scientists, this book offers a wide array of techniques and theories of Big Data Analytics applied to Supply Chain Management. It offers a comprehensive overview and forms a new synthesis by bringing together seemingly divergent fields of research. Intended for Engineering and Business students, scholars, and professionals, this book is a collection of state-of-the-art research and best practices to spur discussion about and extend the cumulant knowledge of emerging supply chain problems. |
| data analytics and product management: Big Data Management Peter Ghavami, 2020-11-09 Data analytics is core to business and decision making. The rapid increase in data volume, velocity and variety offers both opportunities and challenges. While open source solutions to store big data, like Hadoop, offer platforms for exploring value and insight from big data, they were not originally developed with data security and governance in mind. Big Data Management discusses numerous policies, strategies and recipes for managing big data. It addresses data security, privacy, controls and life cycle management offering modern principles and open source architectures for successful governance of big data. The author has collected best practices from the world’s leading organizations that have successfully implemented big data platforms. The topics discussed cover the entire data management life cycle, data quality, data stewardship, regulatory considerations, data council, architectural and operational models are presented for successful management of big data. The book is a must-read for data scientists, data engineers and corporate leaders who are implementing big data platforms in their organizations. |
| data analytics and product management: Cracking the PM Career Jackie Bavaro, Gayle Laakmann McDowell, 2022-04 Product management is a big role, and this is a big book. This comprehensive guide teaches new PMs and experienced PMs the skills, frameworks, and practices to become great product managers. ?Product skills: Drive better product decisions by conducting user research, performing data analysis, prototyping, writing product docs, and understanding technology.?Execution skills: Run your team well and deliver your projects quickly, smoothly, and effectively with project management, incremental development, launch processes, and good time management.?Strategic skills: Set a better direction for your team and optimize for long-term impact with vision, strategy, roadmapping, and team goals. Learn what it means to be more strategic.?Leadership skills: Lead more effectively by developing your personal mindset, collaboration, communication, inspiration, and mentorship skills.?People management: Learn leadership skills for managers, including coaching, recruiting, interviewing, and creating organizational structures.?Careers: Navigate your career by understanding the career ladder, setting goals, and translating your accomplishments into advancement. |
| data analytics and product management: Management in the Era of Big Data Joanna Paliszkiewicz, 2020-06-18 This book is a wonderful collection of chapters that posits how managers need to cope in the Big Data era. It highlights many of the emerging developments in technologies, applications, and trends related to management’s needs in this Big Data era. —Dr. Jay Liebowitz, Harrisburg University of Science and Technology This book presents some meaningful work on Big Data analytics and its applications. Each chapter generates helpful guidance to the readers on Big Data analytics and its applications, challenges, and prospects that is necessary for organizational strategic direction. —Dr. Alex Koohang, Middle Georgia State University Big Data is a concept that has caught the attention of practitioners, academicians, and researchers. Big Data offers organizations the possibility of gaining a competitive advantage by managing, collecting, and analyzing massive amounts of data. As the promises and challenges posed by Big Data have increased over the past decade, significant issues have developed regarding how data can be used for improving management. Big Data can be understood as large amounts of data generated by the Internet and a variety of connected smart devices and sensors. This book discusses the main challenges posed by Big Data in a manner relevant to both practitioners and scholars. It examines how companies can leverage Big Data analytics to act and optimize the business. This book brings together the theory and practice of management in the era of Big Data. It offers a look at the current state of Big Data, including a comprehensive overview of both research and practical applications. By bringing together conceptual thinking and empirical research on the nature, meaning, and development of Big Data in management, this book unifies research on Big Data in management to stimulate new directions for academic investigation as well as practice. |
| data analytics and product management: Seeing What Others Don't Gary Klein, 2013-06-25 Insights -- like Darwin's understanding of the way evolution actually works, and Watson and Crick's breakthrough discoveries about the structure of DNA -- can change the world. We also need insights into the everyday things that frustrate and confuse us so that we can more effectively solve problems and get things done. Yet we know very little about when, why, or how insights are formed -- or what blocks them. In Seeing What Others Don't, renowned cognitive psychologist Gary Klein unravels the mystery. Klein is a keen observer of people in their natural settings -- scientists, businesspeople, firefighters, police officers, soldiers, family members, friends, himself -- and uses a marvelous variety of stories to illuminate his research into what insights are and how they happen. What, for example, enabled Harry Markopolos to put the finger on Bernie Madoff? How did Dr. Michael Gottlieb make the connections between different patients that allowed him to publish the first announcement of the AIDS epidemic? What did Admiral Yamamoto see (and what did the Americans miss) in a 1940 British attack on the Italian fleet that enabled him to develop the strategy of attack at Pearl Harbor? How did a smokejumper see that setting another fire would save his life, while those who ignored his insight perished? How did Martin Chalfie come up with a million-dollar idea (and a Nobel Prize) for a natural flashlight that enabled researchers to look inside living organisms to watch biological processes in action? Klein also dissects impediments to insight, such as when organizations claim to value employee creativity and to encourage breakthroughs but in reality block disruptive ideas and prioritize avoidance of mistakes. Or when information technology systems are dumb by design and block potential discoveries. Both scientifically sophisticated and fun to read, Seeing What Others Don't shows that insight is not just a eureka! moment but a whole new way of understanding. |
| data analytics and product management: Product Leadership Richard Banfield, Martin Eriksson, Nate Walkingshaw, 2017-05-12 In today’s lightning-fast technology world, good product management is critical to maintaining a competitive advantage. Yet, managing human beings and navigating complex product roadmaps is no easy task, and it’s rare to find a product leader who can steward a digital product from concept to launch without a couple of major hiccups. Why do some product leaders succeed while others don’t? This insightful book presents interviews with nearly 100 leading product managers from all over the world. Authors Richard Banfield, Martin Eriksson, and Nate Walkingshaw draw on decades of experience in product design and development to capture the approaches, styles, insights, and techniques of successful product managers. If you want to understand what drives good product leaders, this book is an irreplaceable resource. In three parts, Product Leadership helps you explore: Themes and patterns of successful teams and their leaders, and ways to attain those characteristics Best approaches for guiding your product team through the startup, emerging, and enterprise stages of a company’s evolution Strategies and tactics for working with customers, agencies, partners, and external stakeholders |
| data analytics and product management: Self-Service Data Analytics and Governance for Managers Nathan E. Myers, Gregory Kogan, 2021-06-02 Project governance, investment governance, and risk governance precepts are woven together in Self-Service Data Analytics and Governance for Managers, equipping managers to structure the inevitable chaos that can result as end-users take matters into their own hands Motivated by the promise of control and efficiency benefits, the widespread adoption of data analytics tools has created a new fast-moving environment of digital transformation in the finance, accounting, and operations world, where entire functions spend their days processing in spreadsheets. With the decentralization of application development as users perform their own analysis on data sets and automate spreadsheet processing without the involvement of IT, governance must be revisited to maintain process control in the new environment. In this book, emergent technologies that have given rise to data analytics and which form the evolving backdrop for digital transformation are introduced and explained, and prominent data analytics tools and capabilities will be demonstrated based on real world scenarios. The authors will provide a much-needed process discovery methodology describing how to survey the processing landscape to identify opportunities to deploy these capabilities. Perhaps most importantly, the authors will digest the mature existing data governance, IT governance, and model governance frameworks, but demonstrate that they do not comprehensively cover the full suite of data analytics builds, leaving a considerable governance gap. This book is meant to fill the gap and provide the reader with a fit-for-purpose and actionable governance framework to protect the value created by analytics deployment at scale. Project governance, investment governance, and risk governance precepts will be woven together to equip managers to structure the inevitable chaos that can result as end-users take matters into their own hands. |
| data analytics and product management: Data Analytics Initiatives Ondřej Bothe, Ondřej Kubera, David Bednář, Martin Potančok, Ota Novotný, 2022-04-20 The categorisation of analytical projects could help to simplify complexity reasonably and, at the same time, clarify the critical aspects of analytical initiatives. But how can this complex work be categorized? What makes it so complex? Data Analytics Initiatives: Managing Analytics for Success emphasizes that each analytics project is different. At the same time, analytics projects have many common aspects, and these features make them unique compared to other projects. Describing these commonalities helps to develop a conceptual understanding of analytical work. However, features specific to each initiative affects the entire analytics project lifecycle. Neglecting them by trying to use general approaches without tailoring them to each project can lead to failure. In addition to examining typical characteristics of the analytics project and how to categorise them, the book looks at specific types of projects, provides a high-level assessment of their characteristics from a risk perspective, and comments on the most common problems or challenges. The book also presents examples of questions that could be asked of relevant people to analyse an analytics project. These questions help to position properly the project and to find commonalities and general project challenges. |
| data analytics and product management: The Signal and the Noise Nate Silver, 2015-02-03 One of the more momentous books of the decade. —The New York Times Book Review Nate Silver built an innovative system for predicting baseball performance, predicted the 2008 election within a hair’s breadth, and became a national sensation as a blogger—all by the time he was thirty. He solidified his standing as the nation's foremost political forecaster with his near perfect prediction of the 2012 election. Silver is the founder and editor in chief of the website FiveThirtyEight. Drawing on his own groundbreaking work, Silver examines the world of prediction, investigating how we can distinguish a true signal from a universe of noisy data. Most predictions fail, often at great cost to society, because most of us have a poor understanding of probability and uncertainty. Both experts and laypeople mistake more confident predictions for more accurate ones. But overconfidence is often the reason for failure. If our appreciation of uncertainty improves, our predictions can get better too. This is the “prediction paradox”: The more humility we have about our ability to make predictions, the more successful we can be in planning for the future. In keeping with his own aim to seek truth from data, Silver visits the most successful forecasters in a range of areas, from hurricanes to baseball to global pandemics, from the poker table to the stock market, from Capitol Hill to the NBA. He explains and evaluates how these forecasters think and what bonds they share. What lies behind their success? Are they good—or just lucky? What patterns have they unraveled? And are their forecasts really right? He explores unanticipated commonalities and exposes unexpected juxtapositions. And sometimes, it is not so much how good a prediction is in an absolute sense that matters but how good it is relative to the competition. In other cases, prediction is still a very rudimentary—and dangerous—science. Silver observes that the most accurate forecasters tend to have a superior command of probability, and they tend to be both humble and hardworking. They distinguish the predictable from the unpredictable, and they notice a thousand little details that lead them closer to the truth. Because of their appreciation of probability, they can distinguish the signal from the noise. With everything from the health of the global economy to our ability to fight terrorism dependent on the quality of our predictions, Nate Silver’s insights are an essential read. |
| data analytics and product management: Data Analytics with Hadoop Benjamin Bengfort, Jenny Kim, 2016-06 Ready to use statistical and machine-learning techniques across large data sets? This practical guide shows you why the Hadoop ecosystem is perfect for the job. Instead of deployment, operations, or software development usually associated with distributed computing, you’ll focus on particular analyses you can build, the data warehousing techniques that Hadoop provides, and higher order data workflows this framework can produce. Data scientists and analysts will learn how to perform a wide range of techniques, from writing MapReduce and Spark applications with Python to using advanced modeling and data management with Spark MLlib, Hive, and HBase. You’ll also learn about the analytical processes and data systems available to build and empower data products that can handle—and actually require—huge amounts of data. Understand core concepts behind Hadoop and cluster computing Use design patterns and parallel analytical algorithms to create distributed data analysis jobs Learn about data management, mining, and warehousing in a distributed context using Apache Hive and HBase Use Sqoop and Apache Flume to ingest data from relational databases Program complex Hadoop and Spark applications with Apache Pig and Spark DataFrames Perform machine learning techniques such as classification, clustering, and collaborative filtering with Spark’s MLlib |
| data analytics and product management: Data Analytics for Accounting Vernon J. Richardson, Ryan Teeter, Katie L. Terrell, 2018-05-23 |
| data analytics and product management: Winning with Data Tomasz Tunguz, Frank Bien, 2016-06-20 Crest the data wave with a deep cultural shift Winning with Data explores the cultural changes big data brings to business, and shows you how to adapt your organization to leverage data to maximum effect. Authors Tomasz Tunguz and Frank Bien draw on extensive background in big data, business intelligence, and business strategy to provide a blueprint for companies looking to move head-on into the data wave. Instrumentation is discussed in detail, but the core of the change is in the culture—this book provides sound guidance on building the type of organizational culture that creates and leverages data daily, in every aspect of the business. Real-world examples illustrate these important concepts at work: you'll learn how data helped Warby-Parker disrupt a $13 billion monopolized market, how ThredUp uses data to process more than 20 thousand items of clothing every day, how Venmo leverages data to build better products, how HubSpot empowers their salespeople to be more productive, and more. From decision making and strategy to shipping and sales, this book shows you how data makes better business. Big data has taken on buzzword status, but there is little real guidance for companies seeking everyday business data solutions. This book takes a deeper look at big data in business, and shows you how to shift internal culture ahead of the curve. Understand the changes a data culture brings to companies Instrument your company for maximum benefit Utilize data to optimize every aspect of your business Improve decision making and transform business strategy Big data is becoming the number-one topic in business, yet no one is asking the right questions. Leveraging the full power of data requires more than good IT—organization-wide buy-in is essential for long-term success. Winning with Data is the expert guide to making data work for your business, and your needs. |
| data analytics and product management: EMPOWERED Marty Cagan, 2020-12-03 Great teams are comprised of ordinary people that are empowered and inspired. They are empowered to solve hard problems in ways their customers love yet work for their business. They are inspired with ideas and techniques for quickly evaluating those ideas to discover solutions that work: they are valuable, usable, feasible and viable. This book is about the idea and reality of achieving extraordinary results from ordinary people. Empowered is the companion to Inspired. It addresses the other half of the problem of building tech products?how to get the absolute best work from your product teams. However, the book's message applies much more broadly than just to product teams. Inspired was aimed at product managers. Empowered is aimed at all levels of technology-powered organizations: founders and CEO's, leaders of product, technology and design, and the countless product managers, product designers and engineers that comprise the teams. This book will not just inspire companies to empower their employees but will teach them how. This book will help readers achieve the benefits of truly empowered teams-- |
| data analytics and product management: Practical Web Analytics for User Experience Michael Beasley, 2013-06-21 Practical Web Analytics for User Experience teaches you how to use web analytics to help answer the complicated questions facing UX professionals. Within this book, you'll find a quantitative approach for measuring a website's effectiveness and the methods for posing and answering specific questions about how users navigate a website. The book is organized according to the concerns UX practitioners face. Chapters are devoted to traffic, clickpath, and content use analysis, measuring the effectiveness of design changes, including A/B testing, building user profiles based on search habits, supporting usability test findings with reporting, and more. This is the must-have resource you need to start capitalizing on web analytics and analyze websites effectively. - Discover concrete information on how web analytics data support user research and user-centered design - Learn how to frame questions in a way that lets you navigate through massive amounts of data to get the answer you need - Learn how to gather information for personas, verify behavior found in usability testing, support heuristic evaluation with data, analyze keyword data, and understand how to communicate these findings with business stakeholders |
| data analytics and product management: End-to-end Data Analytics for Product Development Rosa Arboretti Giancristofaro, Mattia De Dominicis, Chris Jones, Luigi Salmaso, 2020-04-06 An interactive guide to the statistical tools used to solve problems during product and process innovation End to End Data Analytics for Product Development is an accessible guide designed for practitioners in the industrial field. It offers an introduction to data analytics and the design of experiments (DoE) whilst covering the basic statistical concepts useful to an understanding of DoE. The text supports product innovation and development across a range of consumer goods and pharmaceutical organizations in order to improve the quality and speed of implementation through data analytics, statistical design and data prediction. The book reviews information on feasibility screening, formulation and packaging development, sensory tests, and more. The authors – noted experts in the field – explore relevant techniques for data analytics and present the guidelines for data interpretation. In addition, the book contains information on process development and product validation that can be optimized through data understanding, analysis and validation. The authors present an accessible, hands-on approach that uses MINITAB and JMP software. The book: • Presents a guide to innovation feasibility and formulation and process development • Contains the statistical tools used to solve challenges faced during product innovation and feasibility • Offers information on stability studies which are common especially in chemical or pharmaceutical fields • Includes a companion website which contains videos summarizing main concepts Written for undergraduate students and practitioners in industry, End to End Data Analytics for Product Development offers resources for the planning, conducting, analyzing and interpreting of controlled tests in order to develop effective products and processes. |
| data analytics and product management: The Analytics Lifecycle Toolkit Gregory S. Nelson, 2018-03-07 An evidence-based organizational framework for exceptional analytics team results The Analytics Lifecycle Toolkit provides managers with a practical manual for integrating data management and analytic technologies into their organization. Author Gregory Nelson has encountered hundreds of unique perspectives on analytics optimization from across industries; over the years, successful strategies have proven to share certain practices, skillsets, expertise, and structural traits. In this book, he details the concepts, people and processes that contribute to exemplary results, and shares an organizational framework for analytics team functions and roles. By merging analytic culture with data and technology strategies, this framework creates understanding for analytics leaders and a toolbox for practitioners. Focused on team effectiveness and the design thinking surrounding product creation, the framework is illustrated by real-world case studies to show how effective analytics team leadership works on the ground. Tools and templates include best practices for process improvement, workforce enablement, and leadership support, while guidance includes both conceptual discussion of the analytics life cycle and detailed process descriptions. Readers will be equipped to: Master fundamental concepts and practices of the analytics life cycle Understand the knowledge domains and best practices for each stage Delve into the details of analytical team processes and process optimization Utilize a robust toolkit designed to support analytic team effectiveness The analytics life cycle includes a diverse set of considerations involving the people, processes, culture, data, and technology, and managers needing stellar analytics performance must understand their unique role in the process of winnowing the big picture down to meaningful action. The Analytics Lifecycle Toolkit provides expert perspective and much-needed insight to managers, while providing practitioners with a new set of tools for optimizing results. |
| data analytics and product management: How to Lead in Product Management: Practices to Align Stakeholders, Guide Development Teams, and Create Value Together Roman Pichler, 2020-03-10 This book will help you become a better product leader. Benefitting from Roman Pichler's extensive experience, you will learn how to align stakeholders and guide development teams even in challenging circumstances, avoid common leadership mistakes, and grow as a leader. Written in an engaging and easily accessible style, How to Lead in Product Management offers a wealth of practical tips and strategies. Through helpful examples, the book illustrates how you can directly apply the techniques to your work. Coverage includes: * Choosing the right leadership style * Cultivating empathy, building trust, and influencing others * Increasing your authority and empowering others * Directing stakeholders and development teams through common goals * Making decisions that people will support and follow through * Successfully resolving disputes and conflicts even with senior stakeholders * Listening deeply to discover and address hidden needs and interests * Practising mindfulness and embracing a growth mindset to develop as a leader Praise for How to Lead in Product Management: Roman has done it again, delivering a practical book for the product management community that appeals to both heart and mind. How to Lead in Product Management is packed with concise, direct, and practical advice that addresses the deeper, personal aspects of the product leadership. Roman's book shares wisdom on topics including goals, healthy interactions with stakeholders, handling conflict, effective conversations, decision-making, having a growth mindset, and self-care. It is a must read for both new and experienced product people. ~Ellen Gottesdiener, Product Coach at EBG Consulting Being a great product manager is tough. It requires domain knowledge, industry knowledge, technical skills, but also the skills to lead and inspire a team. Roman Pichler's How to Lead in Product Management is the best book I've read for equipping product managers to lead their teams. ~Mike Cohn, Author of Succeeding with Agile, Agile Estimating and Planning, and User Stories Applied This is the book that has been missing for product people. Roman has created another masterpiece, a fast read with lots of value. It's a must read for every aspiring product manager. ~Magnus Billgren, CEO of Tolpagorni Product Management How Lead in Product Management is for everyone who manages a product or drives important business decisions. Roman lays out the key challenges of product leadership and shows us ways of thoughtfully working with team members, stakeholders, partners, and the inevitable conflicts. ~Rich Mironov, CEO of Mironov Consulting and Smokejumper Head of Product |
| data analytics and product management: Python Data Science Handbook Jake VanderPlas, 2016-11-21 For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms |
| data analytics and product management: Data Analytics and AI Jay Liebowitz, 2020-08-06 Analytics and artificial intelligence (AI), what are they good for? The bandwagon keeps answering, absolutely everything! Analytics and artificial intelligence have captured the attention of everyone from top executives to the person in the street. While these disciplines have a relatively long history, within the last ten or so years they have exploded into corporate business and public consciousness. Organizations have rushed to embrace data-driven decision making. Companies everywhere are turning out products boasting that artificial intelligence is included. We are indeed living in exciting times. The question we need to ask is, do we really know how to get business value from these exciting tools? Unfortunately, both the analytics and AI communities have not done a great job in collaborating and communicating with each other to build the necessary synergies. This book bridges the gap between these two critical fields. The book begins by explaining the commonalities and differences in the fields of data science, artificial intelligence, and autonomy by giving a historical perspective for each of these fields, followed by exploration of common technologies and current trends in each field. The book also readers introduces to applications of deep learning in industry with an overview of deep learning and its key architectures, as well as a survey and discussion of the main applications of deep learning. The book also presents case studies to illustrate applications of AI and analytics. These include a case study from the healthcare industry and an investigation of a digital transformation enabled by AI and analytics transforming a product-oriented company into one delivering solutions and services. The book concludes with a proposed AI-informed data analytics life cycle to be applied to unstructured data. |
| data analytics and product management: Swipe to Unlock Neel Mehta, Parth Detroja, Aditya Agashe, 2017 WANT A NON-CODING JOB AT A TECH COMPANY? Interested in product management, marketing, strategy, or business development? The tech industry is the place to be: nontechnical employees at tech companies outnumber their engineering counterparts almost 3 to 1 (Forbes, 2017). You might be worried that your lack of coding skills or tech industry knowledge will hold you back. But here's the secret: you don't need to learn how to code to break into the tech industry. Written by three former Microsoft PMs, Swipe to Unlock gives you a breakdown of the concepts you need to know to crush your interviews, like software development, big data, and internet security. We'll explain how Google's ad targeting algorithm works, but Google probably won't ask you how to explain it in a non-technical interview. But they might ask you how you could increase ad revenue from a particular market segment. And if you know how Google's ad platform works, you'll be in a far stronger position to come up with good growth strategies. We'll show you how Robinhood, an app that lets you trade stocks without commission, makes money by earning interest on the unspent money that users keep in their accounts. No one will ask you to explain this. But if someone asks you to come up with a new monetization strategy for Venmo (which lets you send and receive money without fees), you could pull out the Robinhood anecdote to propose that Venmo earn interest off the money sitting in users' accounts. We'll talk about some business cases like why Microsoft acquired LinkedIn. Microsoft interviewers probably won't ask you about the motive of the purchase, but they might ask you for ideas to improve Microsoft Outlook. From our case study, you'll learn how the Microsoft and LinkedIn ecosystems could work together, which can help you craft creative, impactful answers. You could propose that Outlook use LinkedIn's social graph to give salespeople insights about clients before meeting them. Or you could suggest linking Outlook's organizational tree to LinkedIn to let HR managers analyze their company's hierarchy and figure out what kind of talent they need to add. (We'll further explore both ideas in the book.) Either way, you're sure to impress. Learn the must know concepts of tech from authors who have received job offers for Facebook's Rotational Product Manager, Google's Associate Product Marketing Manager, and Microsoft's Program Manager to get a competitive edge at your interviews! |
| data analytics and product management: Testing Business Ideas David J. Bland, Alexander Osterwalder, 2019-11-06 A practical guide to effective business model testing 7 out of 10 new products fail to deliver on expectations. Testing Business Ideas aims to reverse that statistic. In the tradition of Alex Osterwalder’s global bestseller Business Model Generation, this practical guide contains a library of hands-on techniques for rapidly testing new business ideas. Testing Business Ideas explains how systematically testing business ideas dramatically reduces the risk and increases the likelihood of success for any new venture or business project. It builds on the internationally popular Business Model Canvas and Value Proposition Canvas by integrating Assumptions Mapping and other powerful lean startup-style experiments. Testing Business Ideas uses an engaging 4-color format to: Increase the success of any venture and decrease the risk of wasting time, money, and resources on bad ideas Close the knowledge gap between strategy and experimentation/validation Identify and test your key business assumptions with the Business Model Canvas and Value Proposition Canvas A definitive field guide to business model testing, this book features practical tips for making major decisions that are not based on intuition and guesses. Testing Business Ideas shows leaders how to encourage an experimentation mindset within their organization and make experimentation a continuous, repeatable process. |
| data analytics and product management: Outcomes Over Output Joshua Seiden, 2019-04-08 A project has to have a goal, otherwise, how do you know you're done? In the old days of engineering, setting project goals wasn't that hard. But when you're making software products, done is less obvious. When is Microsoft Word done? When is Google done? Or Facebook? In reality, software systems are never done. So then how do we give teams a goal that they can work on? Mostly, we simply ask teams to build features-but features are the wrong way to go. We often build features that create no value. Instead, we need to give teams an outcome to achieve. Setting goals as outcomes sounds simple, but it can be hard to do in practice. This book is a practical guide to using outcomes to guide the work of your team--Publisher's website. |
| data analytics and product management: Guide to Business Data Analytics Iiba, 2020-08-07 The Guide to Business Data Analytics provides a foundational understanding of business data analytics concepts and includes how to develop a framework; key techniques and application; how to identify, communicate and integrate results; and more. This guide acts as a reference for the practice of business data analytics and is a companion resource for the Certification in Business Data Analytics (IIBA(R)- CBDA). Explore more information about the Certification in Business Data Analytics at IIBA.org/CBDA. About International Institute of Business Analysis International Institute of Business Analysis(TM) (IIBA(R)) is a professional association dedicated to supporting business analysis professionals deliver better business outcomes. IIBA connects almost 30,000 Members, over 100 Chapters, and more than 500 training, academic, and corporate partners around the world. As the global voice of the business analysis community, IIBA supports recognition of the profession, networking and community engagement, standards and resource development, and comprehensive certification programs. IIBA Publications IIBA publications offer a wide variety of knowledge and insights into the profession and practice of business analysis for the entire business community. Standards such as A Guide to the Business Analysis Body of Knowledge(R) (BABOK(R) Guide), the Agile Extension to the BABOK(R) Guide, and the Global Business Analysis Core Standard represent the most commonly accepted practices of business analysis around the globe. IIBA's reports, research, whitepapers, and studies provide guidance and best practices information to address the practice of business analysis beyond the global standards and explore new and evolving areas of practice to deliver better business outcomes. Learn more at iiba.org. |
| data analytics and product management: Creating Value with Data Analytics in Marketing Peter C. Verhoef, Edwin Kooge, Natasha Walk, Jaap E. Wieringa, 2021-11-07 The key competing texts are practitioner-focused ‘how to’ guides, whilst our book combines rigorous theory with practical insight and examples, with authors from both the academic and business world, making it more adoptable as a student text; Unlike other books on the subject, this has a customer focus and an exploration of how big data can add value to customers as well as organisations; Enables readers to move from big data to big solutions by demonstrating how to integrate data analytics into specific goals and processes for implementation; Highly successful and well regarded both for students and practitioners |
| data analytics and product management: Agile Data Science 2.0 Russell Jurney, 2017-06-07 Data science teams looking to turn research into useful analytics applications require not only the right tools, but also the right approach if they’re to succeed. With the revised second edition of this hands-on guide, up-and-coming data scientists will learn how to use the Agile Data Science development methodology to build data applications with Python, Apache Spark, Kafka, and other tools. Author Russell Jurney demonstrates how to compose a data platform for building, deploying, and refining analytics applications with Apache Kafka, MongoDB, ElasticSearch, d3.js, scikit-learn, and Apache Airflow. You’ll learn an iterative approach that lets you quickly change the kind of analysis you’re doing, depending on what the data is telling you. Publish data science work as a web application, and affect meaningful change in your organization. Build value from your data in a series of agile sprints, using the data-value pyramid Extract features for statistical models from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future via classification and regression Translate predictions into actions Get feedback from users after each sprint to keep your project on track |
| data analytics and product management: Cracking the PM Interview Gayle Laakmann McDowell, Jackie Bavaro, 2013 How many pizzas are delivered in Manhattan? How do you design an alarm clock for the blind? What is your favorite piece of software and why? How would you launch a video rental service in India? This book will teach you how to answer these questions and more. Cracking the PM Interview is a comprehensive book about landing a product management role in a startup or bigger tech company. Learn how the ambiguously-named PM (product manager / program manager) role varies across companies, what experience you need, how to make your existing experience translate, what a great PM resume and cover letter look like, and finally, how to master the interview: estimation questions, behavioral questions, case questions, product questions, technical questions, and the super important pitch. |
| data analytics and product management: Enterprise Analytics Thomas H. Davenport, 2013 International Institute for Analytics--Dust jacket. |
| data analytics and product management: Impact Mapping Gojko Adzic, 2012-10 A practical guide to impact mapping, a simple yet incredibly effective method for collaborative strategic planning that helps organizations make an impact with software. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
