Business Unintelligence

Download or read book Business unIntelligence written by Barry Devlin and published by . This book was released on 2013-10 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Business intelligence (BI) used to be so simple—in theory anyway. Integrate and copy data from your transactional systems into a specialized relational database, apply BI reporting and query tools and add business users. Job done. No longer. Analytics, big data and an array of diverse technologies have changed everything. More importantly, business is insisting on ever more value, ever faster from information and from IT in general. An emerging biz-tech ecosystem demands that business and IT work together. Business unIntelligence reflects the new reality that in today’s socially complex and rapidly changing world, business decisions must be based on a combination of rational and intuitive thinking. Integrating cues from diverse information sources and tacit knowledge, decision makers create unique meaning to innovate heuristically at the speed of thought. This book provides a wealth of new models that business and IT can use together to design support systems for tomorrow’s successful organizations. Dr. Barry Devlin, one of the earliest proponents of data warehousing, goes back to basics to explore how the modern trinity of information, process and people must be reinvented and restructured to deliver the value, insight and innovation required by modern businesses. From here, he develops a series of novel architectural models that provide a new foundation for holistic information use across the entire business. From discovery to analysis and from decision making to action taking, he defines a fully integrated, closed-loop business environment. Covering every aspect of business analytics, big data, collaborative working and more, this book takes over where BI ends to deliver the definitive framework for information use in the coming years. As the person who defined the conceptual framework and physical architecture for data warehousing in the 1980s, Barry Devlin has been an astute observer of the movement he initiated ever since. Now, in Business unIntelligence, Devlin provides a sweeping view of the past, present, and future of business intelligence, while delivering new conceptual and physical models for how to turn information into insights and action. Reading Devlin’s prose and vision of BI are comparable to reading Carl Sagan’s view of the cosmos. The book is truly illuminating and inspiring. --Wayne Eckerson, President, BI Leader Consulting Author, “Secrets of Analytical Leaders: Insights from Information Insiders”

Download or read book Artificial Unintelligence written by Meredith Broussard and published by MIT Press. This book was released on 2019-01-29 with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt: A guide to understanding the inner workings and outer limits of technology and why we should never assume that computers always get it right. In Artificial Unintelligence, Meredith Broussard argues that our collective enthusiasm for applying computer technology to every aspect of life has resulted in a tremendous amount of poorly designed systems. We are so eager to do everything digitally—hiring, driving, paying bills, even choosing romantic partners—that we have stopped demanding that our technology actually work. Broussard, a software developer and journalist, reminds us that there are fundamental limits to what we can (and should) do with technology. With this book, she offers a guide to understanding the inner workings and outer limits of technology—and issues a warning that we should never assume that computers always get things right. Making a case against technochauvinism—the belief that technology is always the solution—Broussard argues that it's just not true that social problems would inevitably retreat before a digitally enabled Utopia. To prove her point, she undertakes a series of adventures in computer programming. She goes for an alarming ride in a driverless car, concluding “the cyborg future is not coming any time soon”; uses artificial intelligence to investigate why students can't pass standardized tests; deploys machine learning to predict which passengers survived the Titanic disaster; and attempts to repair the U.S. campaign finance system by building AI software. If we understand the limits of what we can do with technology, Broussard tells us, we can make better choices about what we should do with it to make the world better for everyone.

Download or read book Collective Intelligence Development in Business written by Patricia Bouvard and published by John Wiley & Sons. This book was released on 2016-11-23 with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book analyses the development of Collective Intelligence by a better knowledge of the diversity of the temperaments and behavioural and relational processes. The purpose is to help the reader become a better Collective Intelligence Leader, who will be able to capitalize on the specificities and the differences of the individuals present in its collective, and transform these differences into complementarities, which are a source of wealth.

Download or read book The Psychology of Silicon Valley written by Katy Cook and published by Springer Nature. This book was released on 2019-10-15 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt: Misinformation. Job displacement. Information overload. Economic inequality. Digital addiction. The breakdown of democracy, civility, and truth itself. This open access book explores the conscious and unconscious norms, values, and characteristics that drive behaviors within the high-tech capital of the world, Silicon Valley, and the sector it represents. In an era where the reach and influence of a single industry has the potential to define the future of our world, it has become apparent just how little we know about the organizations driving these changes. The Psychology of Silicon Valley offers a revealing look inside the mind of world’s most influential industry and how the identity, culture, myths, and motivations of Big Tech are harming society. The book argues that the bad values and lack of emotional intelligence borne in the vacuum of Silicon Valley will have lasting consequences on everything from social equality to the future of work to our collective mental health. Katy Cook expertly walks us through the psychological landscape of Silicon Valley, including its leadership, ethical, and cultural problems, and artfully explains why we cannot afford to ignore the psychology and values that are behind our technology any longer.

Download or read book Too Big to Ignore written by Phil Simon and published by John Wiley & Sons. This book was released on 2013-03-05 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Residents in Boston, Massachusetts are automatically reporting potholes and road hazards via their smartphones. Progressive Insurance tracks real-time customer driving patterns and uses that information to offer rates truly commensurate with individual safety. Google accurately predicts local flu outbreaks based upon thousands of user search queries. Amazon provides remarkably insightful, relevant, and timely product recommendations to its hundreds of millions of customers. Quantcast lets companies target precise audiences and key demographics throughout the Web. NASA runs contests via gamification site TopCoder, awarding prizes to those with the most innovative and cost-effective solutions to its problems. Explorys offers penetrating and previously unknown insights into healthcare behavior. How do these organizations and municipalities do it? Technology is certainly a big part, but in each case the answer lies deeper than that. Individuals at these organizations have realized that they don't have to be Nate Silver to reap massive benefits from today's new and emerging types of data. And each of these organizations has embraced Big Data, allowing them to make astute and otherwise impossible observations, actions, and predictions. It's time to start thinking big. In Too Big to Ignore, recognized technology expert and award-winning author Phil Simon explores an unassailably important trend: Big Data, the massive amounts, new types, and multifaceted sources of information streaming at us faster than ever. Never before have we seen data with the volume, velocity, and variety of today. Big Data is no temporary blip of fad. In fact, it is only going to intensify in the coming years, and its ramifications for the future of business are impossible to overstate. Too Big to Ignore explains why Big Data is a big deal. Simon provides commonsense, jargon-free advice for people and organizations looking to understand and leverage Big Data. Rife with case studies, examples, analysis, and quotes from real-world Big Data practitioners, the book is required reading for chief executives, company owners, industry leaders, and business professionals.

Download or read book Data Modeling for MongoDB written by Steve Hoberman and published by Technics Publications. This book was released on 2014-06-01 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: Congratulations! You completed the MongoDB application within the given tight timeframe and there is a party to celebrate your application’s release into production. Although people are congratulating you at the celebration, you are feeling some uneasiness inside. To complete the project on time required making a lot of assumptions about the data, such as what terms meant and how calculations are derived. In addition, the poor documentation about the application will be of limited use to the support team, and not investigating all of the inherent rules in the data may eventually lead to poorly-performing structures in the not-so-distant future. Now, what if you had a time machine and could go back and read this book. You would learn that even NoSQL databases like MongoDB require some level of data modeling. Data modeling is the process of learning about the data, and regardless of technology, this process must be performed for a successful application. You would learn the value of conceptual, logical, and physical data modeling and how each stage increases our knowledge of the data and reduces assumptions and poor design decisions. Read this book to learn how to do data modeling for MongoDB applications, and accomplish these five objectives: Understand how data modeling contributes to the process of learning about the data, and is, therefore, a required technique, even when the resulting database is not relational. That is, NoSQL does not mean NoDataModeling! Know how NoSQL databases differ from traditional relational databases, and where MongoDB fits. Explore each MongoDB object and comprehend how each compares to their data modeling and traditional relational database counterparts, and learn the basics of adding, querying, updating, and deleting data in MongoDB. Practice a streamlined, template-driven approach to performing conceptual, logical, and physical data modeling. Recognize that data modeling does not always have to lead to traditional data models! Distinguish top-down from bottom-up development approaches and complete a top-down case study which ties all of the modeling techniques together. This book is written for anyone who is working with, or will be working with MongoDB, including business analysts, data modelers, database administrators, developers, project managers, and data scientists. There are three sections: In Section I, Getting Started, we will reveal the power of data modeling and the tight connections to data models that exist when designing any type of database (Chapter 1), compare NoSQL with traditional relational databases and where MongoDB fits (Chapter 2), explore each MongoDB object and comprehend how each compares to their data modeling and traditional relational database counterparts (Chapter 3), and explain the basics of adding, querying, updating, and deleting data in MongoDB (Chapter 4). In Section II, Levels of Granularity, we cover Conceptual Data Modeling (Chapter 5), Logical Data Modeling (Chapter 6), and Physical Data Modeling (Chapter 7). Notice the “ing” at the end of each of these chapters. We focus on the process of building each of these models, which is where we gain essential business knowledge. In Section III, Case Study, we will explain both top down and bottom up development approaches and go through a top down case study where we start with business requirements and end with the MongoDB database. This case study will tie together all of the techniques in the previous seven chapters. Nike Senior Data Architect Ryan Smith wrote the foreword. Key points are included at the end of each chapter as a way to reinforce concepts. In addition, this book is loaded with hands-on exercises, along with their answers provided in Appendix A. Appendix B contains all of the book’s references and Appendix C contains a glossary of the terms used throughout the text.

Download or read book Automating Inequality written by Virginia Eubanks and published by St. Martin's Press. This book was released on 2018-01-23 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: WINNER: The 2019 Lillian Smith Book Award, 2018 McGannon Center Book Prize, and shortlisted for the Goddard Riverside Stephan Russo Book Prize for Social Justice Astra Taylor, author of The People's Platform: "The single most important book about technology you will read this year." Dorothy Roberts, author of Killing the Black Body: "A must-read." A powerful investigative look at data-based discrimination?and how technology affects civil and human rights and economic equity The State of Indiana denies one million applications for healthcare, foodstamps and cash benefits in three years—because a new computer system interprets any mistake as “failure to cooperate.” In Los Angeles, an algorithm calculates the comparative vulnerability of tens of thousands of homeless people in order to prioritize them for an inadequate pool of housing resources. In Pittsburgh, a child welfare agency uses a statistical model to try to predict which children might be future victims of abuse or neglect. Since the dawn of the digital age, decision-making in finance, employment, politics, health and human services has undergone revolutionary change. Today, automated systems—rather than humans—control which neighborhoods get policed, which families attain needed resources, and who is investigated for fraud. While we all live under this new regime of data, the most invasive and punitive systems are aimed at the poor. In Automating Inequality, Virginia Eubanks systematically investigates the impacts of data mining, policy algorithms, and predictive risk models on poor and working-class people in America. The book is full of heart-wrenching and eye-opening stories, from a woman in Indiana whose benefits are literally cut off as she lays dying to a family in Pennsylvania in daily fear of losing their daughter because they fit a certain statistical profile. The U.S. has always used its most cutting-edge science and technology to contain, investigate, discipline and punish the destitute. Like the county poorhouse and scientific charity before them, digital tracking and automated decision-making hide poverty from the middle-class public and give the nation the ethical distance it needs to make inhumane choices: which families get food and which starve, who has housing and who remains homeless, and which families are broken up by the state. In the process, they weaken democracy and betray our most cherished national values. This deeply researched and passionate book could not be more timely.