The Definitive Guide to DAX


Book Description

This comprehensive and authoritative guide will teach you the DAX language for business intelligence, data modeling, and analytics. Leading Microsoft BI consultants Marco Russo and Alberto Ferrari help you master everything from table functions through advanced code and model optimization. You’ll learn exactly what happens under the hood when you run a DAX expression, how DAX behaves differently from other languages, and how to use this knowledge to write fast, robust code. If you want to leverage all of DAX’s remarkable power and flexibility, this no-compromise “deep dive” is exactly what you need. Perform powerful data analysis with DAX for Microsoft SQL Server Analysis Services, Excel, and Power BI Master core DAX concepts, including calculated columns, measures, and error handling Understand evaluation contexts and the CALCULATE and CALCULATETABLE functions Perform time-based calculations: YTD, MTD, previous year, working days, and more Work with expanded tables, complex functions, and elaborate DAX expressions Perform calculations over hierarchies, including parent/child hierarchies Use DAX to express diverse and unusual relationships Measure DAX query performance with SQL Server Profiler and DAX Studio




Splunk Operational Intelligence Cookbook


Book Description

This book is intended for users of all levels who are looking to leverage the Splunk Enterprise platform as a valuable operational intelligence tool. The recipes provided in this book will appeal to individuals from all facets of a business – IT, Security, Product, Marketing, and many more!




Business Intelligence


Book Description

Business intelligence is a broad category of applications and technologies for gathering, providing access to, and analyzing data for the purpose of helping enterprise users make better business decisions. The term implies having a comprehensive knowledge of all factors that affect a business, such as customers, competitors, business partners, economic environment, and internal operations, therefore enabling optimal decisions to be made. Business Intelligence provides readers with an introduction and practical guide to the mathematical models and analysis methodologies vital to business intelligence. This book: Combines detailed coverage with a practical guide to the mathematical models and analysis methodologies of business intelligence. Covers all the hot topics such as data warehousing, data mining and its applications, machine learning, classification, supply optimization models, decision support systems, and analytical methods for performance evaluation. Is made accessible to readers through the careful definition and introduction of each concept, followed by the extensive use of examples and numerous real-life case studies. Explains how to utilise mathematical models and analysis models to make effective and good quality business decisions. This book is aimed at postgraduate students following data analysis and data mining courses. Researchers looking for a systematic and broad coverage of topics in operations research and mathematical models for decision-making will find this an invaluable guide.




Data Governance


Book Description

Managing data continues to grow as a necessity for modern organizations. There are seemingly infinite opportunities for organic growth, reduction of costs, and creation of new products and services. It has become apparent that none of these opportunities can happen smoothly without data governance. The cost of exponential data growth and privacy / security concerns are becoming burdensome. Organizations will encounter unexpected consequences in new sources of risk. The solution to these challenges is also data governance; ensuring balance between risk and opportunity. Data Governance, Second Edition, is for any executive, manager or data professional who needs to understand or implement a data governance program. It is required to ensure consistent, accurate and reliable data across their organization. This book offers an overview of why data governance is needed, how to design, initiate, and execute a program and how to keep the program sustainable. This valuable resource provides comprehensive guidance to beginning professionals, managers or analysts looking to improve their processes, and advanced students in Data Management and related courses. With the provided framework and case studies all professionals in the data governance field will gain key insights into launching successful and money-saving data governance program. - Incorporates industry changes, lessons learned and new approaches - Explores various ways in which data analysts and managers can ensure consistent, accurate and reliable data across their organizations - Includes new case studies which detail real-world situations - Explores all of the capabilities an organization must adopt to become data driven - Provides guidance on various approaches to data governance, to determine whether an organization should be low profile, central controlled, agile, or traditional - Provides guidance on using technology and separating vendor hype from sincere delivery of necessary capabilities - Offers readers insights into how their organizations can improve the value of their data, through data quality, data strategy and data literacy - Provides up to 75% brand-new content compared to the first edition




A Human's Guide to Machine Intelligence


Book Description

A Wharton professor and tech entrepreneur examines how algorithms and artificial intelligence are starting to run every aspect of our lives, and how we can shape the way they impact us Through the technology embedded in almost every major tech platform and every web-enabled device, algorithms and the artificial intelligence that underlies them make a staggering number of everyday decisions for us, from what products we buy, to where we decide to eat, to how we consume our news, to whom we date, and how we find a job. We've even delegated life-and-death decisions to algorithms--decisions once made by doctors, pilots, and judges. In his new book, Kartik Hosanagar surveys the brave new world of algorithmic decision-making and reveals the potentially dangerous biases they can give rise to as they increasingly run our lives. He makes the compelling case that we need to arm ourselves with a better, deeper, more nuanced understanding of the phenomenon of algorithmic thinking. And he gives us a route in, pointing out that algorithms often think a lot like their creators--that is, like you and me. Hosanagar draws on his experiences designing algorithms professionally--as well as on history, computer science, and psychology--to explore how algorithms work and why they occasionally go rogue, what drives our trust in them, and the many ramifications of algorithmic decision-making. He examines episodes like Microsoft's chatbot Tay, which was designed to converse on social media like a teenage girl, but instead turned sexist and racist; the fatal accidents of self-driving cars; and even our own common, and often frustrating, experiences on services like Netflix and Amazon. A Human's Guide to Machine Intelligence is an entertaining and provocative look at one of the most important developments of our time and a practical user's guide to this first wave of practical artificial intelligence.




Intelligence Operations


Book Description

Intelligence Operations: Understanding Data, Tools, People, and Processes helps readers understand the various issues and considerations an intelligence professional must tackle when reviewing, planning, and managing intelligence operations, regardless of level or environment. The book opens by introducing the reader to the many defining concepts associated with intelligence, as well as the main subject of intelligence: the threat. Additional chapters examine the community of intelligence, revealing where intelligence is actually practiced, as well as what defines and characterizes intelligence operations. Readers learn about the four critical components to every intelligence operation--data, tools, people, and processes--and then explore the various operational and analytic processes involved in greater detail. Throughout, the text encourages discovery and discussion, urging readers to first understand the material, then break it down, adapt it, and apply it in a way that supports their particular operations or requirements. Unique in approach and designed to assist professionals at all levels, Intelligence Operations is an excellent resource for both academic courses in the subject and practical application by intelligence personnel. Erik Kleinsmith is Associate Vice President for Strategic Relations in Intelligence, National, Homeland and Cyber Security for American Military University. Culminating his military career as Chief of Intelligence for the U.S. Army's Land Information Warfare Activity, Erik pioneered the development of asymmetric threat analysis using data mining technology. In this capacity, he gained national prestige related to his involvement in the Able Danger program as the military lead of a team of analysts profiling and mapping Al Qaeda prior to 9/11. Erik continued his career in intelligence as a defense contractor, managing intelligence training with the U.S. Army for over a decade. His areas of expertise include intelligence, security-related training and analysis, counterintelligence, and information operations.




Improving Your Splunk Skills


Book Description

Transform machine-generated data into valuable business insights using the powers of Splunk Key FeaturesExplore the all-new machine learning toolkit in Splunk 7.xTackle any problems related to searching and analyzing your data with SplunkGet the latest information and business insights on Splunk 7.xBook Description Splunk makes it easy for you to take control of your data and drive your business with the cutting edge of operational intelligence and business analytics. Through this Learning Path, you'll implement new services and utilize them to quickly and efficiently process machine-generated big data. You'll begin with an introduction to the new features, improvements, and offerings of Splunk 7. You'll learn to efficiently use wildcards and modify your search to make it faster. You'll learn how to enhance your applications by using XML dashboards and configuring and extending Splunk. You'll also find step-by-step demonstrations that'll walk you through building an operational intelligence application. As you progress, you'll explore data models and pivots to extend your intelligence capabilities. By the end of this Learning Path, you'll have the skills and confidence to implement various Splunk services in your projects. This Learning Path includes content from the following Packt products: Implementing Splunk 7 - Third Edition by James MillerSplunk Operational Intelligence Cookbook - Third Edition by Paul R Johnson, Josh Diakun, et alWhat you will learnMaster the new offerings in Splunk: Splunk Cloud and the Machine Learning ToolkitCreate efficient and effective searchesMaster the use of Splunk tables, charts, and graph enhancementsUse Splunk data models and pivots with faster data model accelerationMaster all aspects of Splunk XML dashboards with hands-on applicationsApply ML algorithms for forecasting and anomaly detectionIntegrate advanced JavaScript charts and leverage Splunk's APIWho this book is for This Learning Path is for data analysts, business analysts, and IT administrators who want to leverage the Splunk enterprise platform as a valuable operational intelligence tool. Existing Splunk users who want to upgrade and get up and running with Splunk 7.x will also find this book useful. Some knowledge of Splunk services will help you get the most out of this Learning Path.




AFIO's Guide to the Study of Intelligence


Book Description

The goal of the Guide to the Study of Intelligence is to help instructors teach about the field of intelligence. This includes... undergraduate and graduate professors of History, Political Science, International Relations, Security Studies, and related topics, especially those with no or limited professional experience in the field. The assumption is that none of the... instructors is an expert in the topic of intelligence. Even those who are former practitioners are likely to have only a limited knowledge of the very broad field of intelligence, as most spend their careers in one or two agencies at most and may have focused only on collection or analysis of intelligence or support to those activities."In each of the articles the intent is to identify the important learning points for students and the materials that an instructor can use to teach. This includes books, articles, and websites..."




Business Intelligence Guidebook


Book Description

Between the high-level concepts of business intelligence and the nitty-gritty instructions for using vendors' tools lies the essential, yet poorly-understood layer of architecture, design and process. Without this knowledge, Big Data is belittled – projects flounder, are late and go over budget. Business Intelligence Guidebook: From Data Integration to Analytics shines a bright light on an often neglected topic, arming you with the knowledge you need to design rock-solid business intelligence and data integration processes. Practicing consultant and adjunct BI professor Rick Sherman takes the guesswork out of creating systems that are cost-effective, reusable and essential for transforming raw data into valuable information for business decision-makers. After reading this book, you will be able to design the overall architecture for functioning business intelligence systems with the supporting data warehousing and data-integration applications. You will have the information you need to get a project launched, developed, managed and delivered on time and on budget – turning the deluge of data into actionable information that fuels business knowledge. Finally, you'll give your career a boost by demonstrating an essential knowledge that puts corporate BI projects on a fast-track to success. - Provides practical guidelines for building successful BI, DW and data integration solutions. - Explains underlying BI, DW and data integration design, architecture and processes in clear, accessible language. - Includes the complete project development lifecycle that can be applied at large enterprises as well as at small to medium-sized businesses - Describes best practices and pragmatic approaches so readers can put them into action. - Companion website includes templates and examples, further discussion of key topics, instructor materials, and references to trusted industry sources.




Integration Challenges for Analytics, Business Intelligence, and Data Mining


Book Description

As technology continues to advance, it is critical for businesses to implement systems that can support the transformation of data into information that is crucial for the success of the company. Without the integration of data (both structured and unstructured) mining in business intelligence systems, invaluable knowledge is lost. However, there are currently many different models and approaches that must be explored to determine the best method of integration. Integration Challenges for Analytics, Business Intelligence, and Data Mining is a relevant academic book that provides empirical research findings on increasing the understanding of using data mining in the context of business intelligence and analytics systems. Covering topics that include big data, artificial intelligence, and decision making, this book is an ideal reference source for professionals working in the areas of data mining, business intelligence, and analytics; data scientists; IT specialists; managers; researchers; academicians; practitioners; and graduate students.