Data 1. What is the data? 2. Can data be validated? Is it accurate? 3. How do we store the data? 4. Is there a way to make money on the data? 5. How does changing expectations of data change your companys future? In this book, we will be reviewing these issues to help business leaders create a path to protecting, using, and storing data that makes sense and to save money, time, and effort.

Are algorithms friend or foe? The human mind is evolutionarily designed to take shortcuts in order to survive. We jump to conclusions because our brains want to keep us safe. A majority of our biases work in our favor, such as when we feel a car speeding in our direction is dangerous and we instantly move, or when we decide not take a bite of food that appears to have gone bad. However, inherent bias negatively affects work environments and the decision-making surrounding our communities. While the creation of algorithms and machine learning attempts to eliminate bias, they are, after all, created by human beings, and thus are susceptible to what we call algorithmic bias. In Understand, Manage, and Prevent Algorithmic Bias, author Tobias Baer helps you understand where algorithmic bias comes from, how to manage it as a business user or regulator, and how data science can prevent bias from entering statistical algorithms. Baer expertly addresses some of the 100+ varieties of natural bias such as confirmation bias, stability bias, pattern-recognition bias, and many others. Algorithmic bias mirrors—and originates in—these human tendencies. Baer dives into topics as diverse as anomaly detection, hybrid model structures, and self-improving machine learning. While most writings on algorithmic bias focus on the dangers, the core of this positive, fun book points toward a path where bias is kept at bay and even eliminated. You’ll come away with managerial techniques to develop unbiased algorithms, the ability to detect bias more quickly, and knowledge to create unbiased data. Understand, Manage, and Prevent Algorithmic Bias is an innovative, timely, and important book that belongs on your shelf. Whether you are a seasoned business executive, a data scientist, or simply an enthusiast, now is a crucial time to be educated about the impact of algorithmic bias on society and take an active role in fighting bias. What You'll Learn Study the many sources of algorithmic bias, including cognitive biases in the real world, biased data, and statistical artifact Understand the risks of algorithmic biases, how to detect them, and managerial techniques to prevent or manage them Appreciate how machine learning both introduces new sources of algorithmic bias and can be a part of a solutionBe familiar with specific statistical techniques a data scientist can use to detect and overcome algorithmic bias Who This Book is For Business executives of companies using algorithms in daily operations; data scientists (from students to seasoned practitioners) developing algorithms; compliance officials concerned about algorithmic bias; politicians, journalists, and philosophers thinking about algorithmic bias in terms of its impact on society and possible regulatory responses; and consumers concerned about how they might be affected by algorithmic bias

If your are interested in the power of Big Data to drive improvement in your business, then this book will help you build and initiate a project for positive change.

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.

Learn to create an effective business strategy using Microsoft's BI stack Microsoft Business Intelligence tools are among the most widely used applications for gathering, providing access to, and analyzing data to enable the enterprise to make sound business decisions. The tools include SharePoint Server, the Office Suite, PerformancePoint Server, and SQL Server, among others. With so much jargon and so many technologies involved, Microsoft Business Intelligence For Dummies provides a much-needed step-by-step explanation of what's involved and how to use this powerful package to improve your business. Microsoft Business Intelligence encompasses a broad collection of tools designed to help business owners and managers direct the enterprise effectively This guide provides an overview of SharePoint, PerformancePoint, the SQL Server suite, Microsoft Office, and the BI development technologies Explains how the various technologies work together to solve functional problems Translates the buzzwords and shows you how to create your business strategy Examines related technologies including data warehousing, data marts, Online Analytical Processing (OLAP), data mining, reporting, dashboards, and Key Performance Indicators (KPIs) Simplifies this complex package to get you up and running quickly Microsoft Business Intelligence For Dummies demystifies these essential tools for enterprise managers, business analysts, and others who need to get up to speed.

Find the right big data solution for your business or organization Big data management is one of the major challenges facing business, industry, and not-for-profit organizations. Data sets such as customer transactions for a mega-retailer, weather patterns monitored by meteorologists, or social network activity can quickly outpace the capacity of traditional data management tools. If you need to develop or manage big data solutions, you'll appreciate how these four experts define, explain, and guide you through this new and often confusing concept. You'll learn what it is, why it matters, and how to choose and implement solutions that work. Effectively managing big data is an issue of growing importance to businesses, not-for-profit organizations, government, and IT professionals Authors are experts in information management, big data, and a variety of solutions Explains big data in detail and discusses how to select and implement a solution, security concerns to consider, data storage and presentation issues, analytics, and much more Provides essential information in a no-nonsense, easy-to-understand style that is empowering Big Data For Dummies cuts through the confusion and helps you take charge of big data solutions for your organization.

Describing quantitative measurements and statistical techniques in marketing, this work contains examples and study applications. It is intended for any student hoping to enter the world of marketing.

Successful strategic management of small business isn't always as easy as it looks, given the dynamic changes, turbulent business environments, and in many cases severely restricted room for maneuver that prevail nowadays in Western economies. Which is why strategic and sustainable business governance and operational management are taking on growing importance, especially in sectors consisting mainly of small businesses. Although much has been written on these subjects, a book that resolutely focuses on the nuts and bolts of strategically focused sustainability management is needed, especially for smaller entities. More than 200 illustrative examples of best practice and other scenarios drawn from actual practice in the wine industry and elsewhere; plus more than 160 graphics, and a wealth of empirical data serve to answer the "why and how" of strategy and sustainability in the small business sector.

In recent years, the science of managing and analyzing large datasets has emerged as a critical area of research. In the race to answer vital questions and make knowledgeable decisions, impressive amounts of data are now being generated at a rapid pace, increasing the opportunities and challenges associated with the ability to effectively analyze this data.

Data Mining: Opportunities and Challenges presents an overview of the state of the art approaches in this new and multidisciplinary field of data mining. The primary objective of this book is to explore the myriad issues regarding data mining, specifically focusing on those areas that explore new methodologies or examine case studies. This book contains numerous chapters written by an international team of forty-four experts representing leading scientists and talented young scholars from seven different countries.