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Author: Christian J. Ward
Publisher: Ward Pllc
ISBN: 9781732991705
Category : Big data
Languages : en
Pages : 220
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Book Description
"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.
Author: Christian J. Ward
Publisher: Ward Pllc
ISBN: 9781732991705
Category : Big data
Languages : en
Pages : 220
Get Book Here
Book Description
"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.
Author: Maxime C. Cohen
Publisher: Springer Nature
ISBN: 3030858553
Category : Business & Economics
Languages : en
Pages : 166
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Book Description
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.
Author: Lowell Fryman
Publisher: Morgan Kaufmann
ISBN: 0128025476
Category : Computers
Languages : en
Pages : 294
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Book Description
The Data and Analytics Playbook: Proven Methods for Governed Data and Analytic Quality explores the way in which data continues to dominate budgets, along with the varying efforts made across a variety of business enablement projects, including applications, web and mobile computing, big data analytics, and traditional data integration. The book teaches readers how to use proven methods and accelerators to break through data obstacles to provide faster, higher quality delivery of mission critical programs. Drawing upon years of practical experience, and using numerous examples and an easy to understand playbook, Lowell Fryman, Gregory Lampshire, and Dan Meers discuss a simple, proven approach to the execution of multiple data oriented activities. In addition, they present a clear set of methods to provide reliable governance, controls, risk, and exposure management for enterprise data and the programs that rely upon it. In addition, they discuss a cost-effective approach to providing sustainable governance and quality outcomes that enhance project delivery, while also ensuring ongoing controls. Example activities, templates, outputs, resources, and roles are explored, along with different organizational models in common use today and the ways they can be mapped to leverage playbook data governance throughout the organization. - Provides a mature and proven playbook approach (methodology) to enabling data governance that supports agile implementation - Features specific examples of current industry challenges in enterprise risk management, including anti-money laundering and fraud prevention - Describes business benefit measures and funding approaches using exposure based cost models that augment risk models for cost avoidance analysis and accelerated delivery approaches using data integration sprints for application, integration, and information delivery success
Author: Chandan Mukherjee
Publisher: Routledge
ISBN: 1136144684
Category : Business & Economics
Languages : en
Pages : 518
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Book Description
Getting accurate data on less developed countries has created great problems for studying these areas. Yet until recently students of development economics have relied on standard econometrics texts, which assume a Western context. Econometrics and Data Analysis for Developing Countries solves this problem. It will be essential reading for all advanced students of development economics.
Author: Andrew D. Banasiewicz
Publisher: Routledge
ISBN: 1351050060
Category : Business & Economics
Languages : en
Pages : 283
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Book Description
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.
Author: Paul Bambrick-Santoyo
Publisher: John Wiley & Sons
ISBN: 1119524768
Category : Education
Languages : en
Pages : 72
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Book Description
The bestselling guide for school leaders—updated in a new edition Data-driven instruction is the philosophy that schools should focus on two simple questions: how do you know if are students learning? And when they are not, what do you do about it? Driven by Data 2.0 is a practical guide that answers these questions to empower schools to achieve significant gains in student achievement. Rooted in a proven framework that has been implemented in thousands of schools, the book presents what makes schools successful along with tools to put the framework into place to make data work for your schools: Assess—set the roadmap for learning Analyze—identify why students struggle Act—teach more effectively what students need Build the culture—train and develop your staff so that data-driven instruction can thrive If you’re a K – 12 leader, coach, or teacher looking to implement data-driven instruction in your school district, Driven by Data 2.0 has the tools to train your staff: PD materials, videos of exemplar practice and all the resources you need to achieve remarkable results.
Author: Paul Bambrick-Santoyo
Publisher: John Wiley & Sons
ISBN: 0470548746
Category : Education
Languages : en
Pages : 336
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Book Description
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.
Author: William Johnson
Publisher: HiTeX Press
ISBN:
Category : Business & Economics
Languages : en
Pages : 564
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Book Description
"Data-Driven Trading: Leveraging Big Data for Quantitative Advantage" offers a transformative guide through the modern complexities of financial markets. As the landscape of trading evolves with unprecedented speed, this book equips readers with the essential principles and tools to harness the power of big data, quantitative finance, and advanced analytics. Each chapter methodically unpacks core concepts, from foundational financial instruments and exploratory data analysis to regulatory considerations and emerging technological trends, ensuring a comprehensive understanding for novices and seasoned traders alike. Readers will uncover the intricacies of building successful algorithmic trading strategies, employing machine learning techniques, and mastering risk management to optimize their trading portfolios. The book also ventures into real-world case studies, providing tangible examples of how data-driven methodologies are reshaping the financial domain. With a strong emphasis on both knowledge acquisition and practical application, "Data-Driven Trading" serves as a vital handbook for anyone aspiring to excel in the dynamic sphere of trading by leveraging quantitative insights and technological advancements.
Author: Simon Walkowiak
Publisher: Packt Publishing Ltd
ISBN: 1786463725
Category : Computers
Languages : en
Pages : 498
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Book Description
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.
Author: Nitin Seth
Publisher: Penguin Random House India Private Limited
ISBN: 9357087842
Category : Computers
Languages : en
Pages : 381
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Book Description
There are two remarkable phenomena that are unfolding almost simultaneously. The first is the emergence of a data-first world, where data has become a central driving force, shaping industries and fueling innovation. The second is the dawn of the AI age, propelled by the advent of Generative AI, that has created the possibility to leverage the data of the world for the first time. The convergence of these two, with data as the common denominator, holds immense promise and the opportunities are boundless. This book provides us with opportunities to push our thinking, to innovate, to transform and to create a better future at all levels—individual, enterprise and the world.
