From data collection to evaluation and visualization of prediction results, this book provides a comprehensive overview of the process of predicting demand for retailers. Each step is illustrated with the relevant code and implementation details to demystify how historical data can be leveraged to predict future demand. The tools and methods presented can be applied to most retail settings, both online and brick-and-mortar, such as fashion, electronics, groceries, and furniture. This book is intended to help students in business analytics and data scientists better master how to leverage data for predicting demand in retail applications. It can also be used as a guide for supply chain practitioners who are interested in predicting demand. It enables readers to understand how to leverage data to predict future demand, how to clean and pre-process the data to make it suitable for predictive analytics, what the common caveats are in terms of implementation and how to assess prediction accuracy.

"We have a ton of DATA, now how do we LEVERAGE it?" The data your company generates is enormously valuable. But without the right strategy, you'll never unlock that value - and you might even put your company at risk. Data Leverage is the first comprehensive book on the exploding opportunity of corporate data partnerships. The authors, Christian and Jay Ward, are experts -- a business strategist and a lawyer who, together, have executed hundreds of deals. This book has everything you need to make money from data, starting with the DataSmart Method(TM), a four-step process for building your data partnership strategy. You'll learn: A comprehensive process to >identify your data assets - both the data your company generates and the data about your company that others maintain. A systematic way to value those assets - so you can tell whether it makes more sense to barter them for other valuable data or build them into million-dollar revenue streams. A complete list of deal structures for data partnerships, including how to gain partners for innovative data streams and how to distribute data through large platforms and channels. An analysis of prudent measures you can take to protect your data, with detailed descriptions of how to write contracts and comply with regulations like Europe's GDPR. This book will open your eyes to the power of data with detailed descriptions of real deals. You'll see how companies turned unusual data streams - like aerial photographs of retailers' parking lots, results of customer sales calls, and even their own accounts receivable data - into valuable assets that boosted their companies' bottom lines. Your company is churning out data every day. Your marketing department is generating ads and leads; your HR department is evaluating resumes; your IT group is tracking customer databases and product information. But without a strategy, it's just a bunch of ones and zeroes. To leverage that data, you need to find the right partners, make the right deals, maintain privacy controls, and build contracts that will keep you safe and legal. You'll need the detailed advice in this book as you negotiate with big platforms like Bloomberg, Thomson-Reuters, Dun & Bradstreet, and Amazon. Don't build data partnerships without a detailed map. Data Leverage is the indispensable reference you need to plan for and negotiate data deals. Keep it close by, and you can get started building whole new sources of value for your company with the data you're generating every single day.

Smart sustainable mobility ecosystems promise to address society’s expectation of environmentally friendly on-demand mobility. While the technology stack to build such ecosystems is just around the corner in the form of connected, automated, and electric vehicles, strategies to deploy and operate such fleets in a coordinated manner must still be advanced. Most of such optimization challenges highly depend on the nature of customer demand, vehicle supply, and environmental influences. Hence, this dissertation investigates how available data streams from mobility ecosystems can be leveraged in Information Systems to solve related decision problems. The overarching goal of this work is to generate design knowledge to improve vehicle availability, provider profitability, and environmental sustainability for such ecosystems. Applying quantitative methods to real-world data from shared vehicle systems generates insights into the nature of demand and supply. Combining it with an analysis of empirical research on vehicle relocation algorithms builds the foundation for two artifact designs. The first artifact enables the development and simulation-based evaluation of operation modes for vehicle fleets. The second artifact enables artificial intelligence-based decision support for the vehicle rebalancing problem. The insights are finally incorporated and generalized to a nascent design theory on data-enabled operational decision-making in the context of smart sustainable mobility environments. The findings have multifaceted implications for researchers concerned with data-enabled value creation in Green IS, shared economy and smart mobility, and business analytics and data science. Furthermore, guidance for fleet providers to improve system attractiveness and for society to experience the potential amount of vehicle access without personal ownership is provided.

This open access book explores ways to leverage information technology and machine learning to combat disease and promote health, especially in resource-constrained settings. It focuses on digital disease surveillance through the application of machine learning to non-traditional data sources. Developing countries are uniquely prone to large-scale emerging infectious disease outbreaks due to disruption of ecosystems, civil unrest, and poor healthcare infrastructure – and without comprehensive surveillance, delays in outbreak identification, resource deployment, and case management can be catastrophic. In combination with context-informed analytics, students will learn how non-traditional digital disease data sources – including news media, social media, Google Trends, and Google Street View – can fill critical knowledge gaps and help inform on-the-ground decision-making when formal surveillance systems are insufficient.

Evidence-Based Decision-Making: How to Leverage Available Data and Avoid Cognitive Biases examines how a wide range of factual evidence, primarily derived from a variety of data available to organizations, can be used to improve the quality of business decision-making, by helping decision makers circumvent the various cognitive biases that adversely impact how we all think. The book is built on the following premise: During the past decade, the new ‘data world’ emerged, in which the rush to develop competencies around business analytics and data science can be characterized as nothing less than the new commercial arms race. The ever-expanding volume and variety of data are well known, as are the great advances in data processing/analytics, data visualization, and related information production-focused capabilities. Yet, comparatively little effort has been devoted to how the informational products of business analytics and data science are ‘consumed’ or used in the organizational decision-making processes, as the available evidence shows that only some of that information is used to drive some business decisions some of the time. Evidence-Based Decision-Making details an explicit process describing how the universe of available and applicable evidence, which includes organizational and other data, industry benchmarks, scientific studies, and professional experience, can be assessed, amalgamated, and funneled into an objective driver of key business decisions. Introducing key concepts in relation to data and evidence, and the history of evidence-based management, this new and extremely topical book will be essential reading for researchers and students of data analytics as well as those working in the private and public sectors, and in the voluntary sector.

Utilize R to uncover hidden patterns in your Big Data About This Book Perform computational analyses on Big Data to generate meaningful results Get a practical knowledge of R programming language while working on Big Data platforms like Hadoop, Spark, H2O and SQL/NoSQL databases, Explore fast, streaming, and scalable data analysis with the most cutting-edge technologies in the market Who This Book Is For This book is intended for Data Analysts, Scientists, Data Engineers, Statisticians, Researchers, who want to integrate R with their current or future Big Data workflows. It is assumed that readers have some experience in data analysis and understanding of data management and algorithmic processing of large quantities of data, however they may lack specific skills related to R. What You Will Learn Learn about current state of Big Data processing using R programming language and its powerful statistical capabilities Deploy Big Data analytics platforms with selected Big Data tools supported by R in a cost-effective and time-saving manner Apply the R language to real-world Big Data problems on a multi-node Hadoop cluster, e.g. electricity consumption across various socio-demographic indicators and bike share scheme usage Explore the compatibility of R with Hadoop, Spark, SQL and NoSQL databases, and H2O platform In Detail Big Data analytics is the process of examining large and complex data sets that often exceed the computational capabilities. R is a leading programming language of data science, consisting of powerful functions to tackle all problems related to Big Data processing. The book will begin with a brief introduction to the Big Data world and its current industry standards. With introduction to the R language and presenting its development, structure, applications in real world, and its shortcomings. Book will progress towards revision of major R functions for data management and transformations. Readers will be introduce to Cloud based Big Data solutions (e.g. Amazon EC2 instances and Amazon RDS, Microsoft Azure and its HDInsight clusters) and also provide guidance on R connectivity with relational and non-relational databases such as MongoDB and HBase etc. It will further expand to include Big Data tools such as Apache Hadoop ecosystem, HDFS and MapReduce frameworks. Also other R compatible tools such as Apache Spark, its machine learning library Spark MLlib, as well as H2O. Style and approach This book will serve as a practical guide to tackling Big Data problems using R programming language and its statistical environment. Each section of the book will present you with concise and easy-to-follow steps on how to process, transform and analyse large data sets.

Offers a practical guide for improving schools dramatically that will enable all students from all backgrounds to achieve at high levels. Includes assessment forms, an index, and a DVD.

Discover high-impact leadership strategies for a thriving learning community! This compelling new book provides straightforward guidance and solutions for educators working to transform their school environments. Concrete examples of practical, high-impact, and evidence-based practices help you leverage the “big ideas” of Professional Learning Communities to promote lasting improvement in your school. You’ll learn to: Understand the essential role principals and teacher leaders play in leading PLCs Foster an understanding of how PLCs can support implementation of major instructional shifts such as the new Common Core State Standards Apply high-leverage strategies across your own school and district to improve instruction

Data are an organization's sole, non-depletable, non-degrading, durable asset. Engineered right, data's value increases over time because the added dimensions of time, geography, and precision. To achieve data's full organizational value, there must be dedicated individual to leverage data as assets - a Chief Data Officer or CDO who's three job pillars are: - Dedication solely to leveraging data assets, - Unconstrained by an IT project mindset, and - Reports directly to the business Once these three pillars are set into place, organizations can leverage their data assets. Data possesses properties worthy of additional investment. Many existing CDOs are fatally crippled, however, because they lack one or more of these three pillars. Often organizations have some or all pillars already in place but are not operating in a coordinated manner. The overall objective of this book is to present these pillars in an understandable way, why each is necessary (but insufficient), and what do to about it. - Uncovers that almost all organizations need sophisticated, comprehensive data management education and strategies. - Delivery of organization-wide data success requires a highly focused, full time Chief Data Officer. - Engineers organization-wide data advantage which enables success in the marketplace

Integrating Social Care into the Delivery of Health Care: Moving Upstream to Improve the Nation's Health was released in September 2019, before the World Health Organization declared COVID-19 a global pandemic in March 2020. Improving social conditions remains critical to improving health outcomes, and integrating social care into health care delivery is more relevant than ever in the context of the pandemic and increased strains placed on the U.S. health care system. The report and its related products ultimately aim to help improve health and health equity, during COVID-19 and beyond. The consistent and compelling evidence on how social determinants shape health has led to a growing recognition throughout the health care sector that improving health and health equity is likely to depend â€" at least in part â€" on mitigating adverse social determinants. This recognition has been bolstered by a shift in the health care sector towards value-based payment, which incentivizes improved health outcomes for persons and populations rather than service delivery alone. The combined result of these changes has been a growing emphasis on health care systems addressing patients' social risk factors and social needs with the aim of improving health outcomes. This may involve health care systems linking individual patients with government and community social services, but important questions need to be answered about when and how health care systems should integrate social care into their practices and what kinds of infrastructure are required to facilitate such activities. Integrating Social Care into the Delivery of Health Care: Moving Upstream to Improve the Nation's Health examines the potential for integrating services addressing social needs and the social determinants of health into the delivery of health care to achieve better health outcomes. This report assesses approaches to social care integration currently being taken by health care providers and systems, and new or emerging approaches and opportunities; current roles in such integration by different disciplines and organizations, and new or emerging roles and types of providers; and current and emerging efforts to design health care systems to improve the nation's health and reduce health inequities.