Business Intelligence (BI) has been successfully deployed by modern businesses to serve their customers and stakeholders. However, organizations increasingly look at BI to be all pervasive and realize its higher level of potential, instead of following it conventionally. The book covers the techniques, technologies and frameworks that can be used to build next generation BI.

Business intelligence (BI) software is the code and tools that allow you to view different components of a business using a single visual platform, making comprehending mountains of data easier. Applications that include reports, analytics, statistics, and historical and predictive modeling are all examples of BI applications. Currently, we are in the second generation of BI software, called BI 2.0. This generation is focused on writing BI software that is predictive, adaptive, simple, and interactive. As computers and software have evolved, more data can be presented to end users with increasingly visually rich techniques. Rich Internet application (RIA) technologies such as Microsoft Silverlight can be used to transform traditional user interfaces filled with boring data into fully interactive analytical applications to deliver insight from large data sets quickly. Furthermore, RIAs include 3D spatial design capabilities that allow for interesting layouts of aggregated data beyond a simple list or grid. BI 2.0 implemented via RIA technology can truly bring out the power of BI and deliver it to an average user via the Web. Next-Generation Business Intelligence Software with Rich Internet Applications provides developers, designers, and architects a solid foundation of BI design and architecture concepts with Microsoft Silverlight. This book covers key BI design concepts and how they can be applied without requiring an existing BI infrastructure. The author, Bart Czernicki, will show you how to build small BI applications by example that are interactive, highly visual, statistical, predictive, and most importantly, intuitive to the user. BI isn't just for the executive branch of a Fortune 500 company; it is for the masses. Let Next-Generation Business Intelligence Software with Rich Internet Applications show you how to unlock the rich intelligence you already have.

Utilize this practical and easy-to-follow guide to modernize traditional enterprise data warehouse and business intelligence environments with next-generation big data technologies. Next-Generation Big Data takes a holistic approach, covering the most important aspects of modern enterprise big data. The book covers not only the main technology stack but also the next-generation tools and applications used for big data warehousing, data warehouse optimization, real-time and batch data ingestion and processing, real-time data visualization, big data governance, data wrangling, big data cloud deployments, and distributed in-memory big data computing. Finally, the book has an extensive and detailed coverage of big data case studies from Navistar, Cerner, British Telecom, Shopzilla, Thomson Reuters, and Mastercard. What You’ll Learn Install Apache Kudu, Impala, and Spark to modernize enterprise data warehouse and business intelligence environments, complete with real-world, easy-to-follow examples, and practical advice Integrate HBase, Solr, Oracle, SQL Server, MySQL, Flume, Kafka, HDFS, and Amazon S3 with Apache Kudu, Impala, and Spark Use StreamSets, Talend, Pentaho, and CDAP for real-time and batch data ingestion and processing Utilize Trifacta, Alteryx, and Datameer for data wrangling and interactive data processing Turbocharge Spark with Alluxio, a distributed in-memory storage platform Deploy big data in the cloud using Cloudera Director Perform real-time data visualization and time series analysis using Zoomdata, Apache Kudu, Impala, and Spark Understand enterprise big data topics such as big data governance, metadata management, data lineage, impact analysis, and policy enforcement, and how to use Cloudera Navigator to perform common data governance tasks Implement big data use cases such as big data warehousing, data warehouse optimization, Internet of Things, real-time data ingestion and analytics, complex event processing, and scalable predictive modeling Study real-world big data case studies from innovative companies, including Navistar, Cerner, British Telecom, Shopzilla, Thomson Reuters, and Mastercard Who This Book Is For BI and big data warehouse professionals interested in gaining practical and real-world insight into next-generation big data processing and analytics using Apache Kudu, Impala, and Spark; and those who want to learn more about other advanced enterprise topics

How does a CEO, manager, or entrepreneur begin to sort out what defines and drives a good customer experience and how it can be measured and made actionable? If you know how well the customer experience is satisfying your customers and you know how to increase their satisfaction, you can then increase sales, return visits, recommendations, loyalty, and brand engagement across all channels. More reliable and more useful data leads to better decisions and better results. Innovating Analytics is also about the need for a comprehensive measurement ecosystem to accurately assess and improve the other elements of customer experience. This is a time of great change and great opportunity. The companies that use the right tools and make the right assessments of how to satisfy their customers will have the competitive advantage. Innovating Analytics introduces an index that measures a customer’s likelihood to recommend and the likelihood to detract. The current concept of the Net Promoter Score (NPS) that has been adopted by many companies during the last decade—is no longer accurate, precise or actionable. This new metric called the Word of Mouth Index (WoMI) has been tested on hundreds of companies and with over 1.5 million consumers over the last two years. Author Larry Freed details the improvement that WoMI provides within what he calls the Measurement Ecosystem. He then goes on to look at three other drivers of customer satisfaction along with word of mouth: customer acquisition, customer loyalty, and customer conversion.

Presentation slides from the year 2019 in the subject Computer Science - General, grade: 20.0, , course: Business Intelligence, language: English, abstract: Business Intelligence and Analytics (BI&A) is the process of extracting and predicting business-critical insights from raw data. Traditional BI focused on data collection, extraction, and organization to enable efficient query processing to derive insights from historical data. With sources of data sources growing steadily, traditional BI&A are evolving to provide intelligence at different scales and perspectives: operational BI, situational BI, self-service BI. In these slides we digested an article that talking about next generation of BI.

Learn all you need to know about seven key innovations disrupting business analytics today. These innovations—the open source business model, cloud analytics, the Hadoop ecosystem, Spark and in-memory analytics, streaming analytics, Deep Learning, and self-service analytics—are radically changing how businesses use data for competitive advantage. Taken together, they are disrupting the business analytics value chain, creating new opportunities. Enterprises who seize the opportunity will thrive and prosper, while others struggle and decline: disrupt or be disrupted. Disruptive Business Analytics provides strategies to profit from disruption. It shows you how to organize for insight, build and provision an open source stack, how to practice lean data warehousing, and how to assimilate disruptive innovations into an organization. Through a short history of business analytics and a detailed survey of products and services, analytics authority Thomas W. Dinsmore provides a practical explanation of the most compelling innovations available today. What You'll Learn Discover how the open source business model works and how to make it work for you See how cloud computing completely changes the economics of analytics Harness the power of Hadoop and its ecosystem Find out why Apache Spark is everywhere Discover the potential of streaming and real-time analytics Learn what Deep Learning can do and why it matters See how self-service analytics can change the way organizations do business Who This Book Is For Corporate actors at all levels of responsibility for analytics: analysts, CIOs, CTOs, strategic decision makers, managers, systems architects, technical marketers, product developers, IT personnel, and consultants.

The Internet is making our daily lives as digital as possible, and this new era is called the Internet of Everything (IoE). The key force behind the rapid growth of the Internet is the technological advancement of enterprises. The digital world we live in is facilitated by these enterprises’ advances and business intelligence. These enterprises need to deal with gazillions of bytes of data, and in today’s age of General Data Protection Regulation, enterprises are required to ensure privacy and security of large-scale data collections. However, the increased connectivity and devices used to facilitate IoE are continually creating more room for cybercriminals to find vulnerabilities in enterprise systems and flaws in their corporate governance. Ensuring cybersecurity and corporate governance for enterprises should not be an afterthought or present a huge challenge. In recent times, the complex diversity of cyber-attacks has been skyrocketing, and zero-day attacks, such as ransomware, botnet, and telecommunication attacks, are happening more frequently than before. New hacking strategies would easily bypass existing enterprise security and governance platforms using advanced, persistent threats. For example, in 2020, the Toll Group firm was exploited by a new crypto-attack family for violating its data privacy, where an advanced ransomware technique was launched to exploit the corporation and request a huge figure of monetary ransom. Even after applying rational governance hygiene, cybersecurity configuration and software updates are often overlooked when they are most needed to fight cyber-crime and ensure data privacy. Therefore, the threat landscape in the context of enterprises has become wider and far more challenging. There is a clear need for collaborative work throughout the entire value chain of this network. In this context, this book addresses the cybersecurity and cooperate governance challenges associated with enterprises, which will provide a bigger picture of the concepts, intelligent techniques, practices, and open research directions in this area. This book serves as a single source of reference for acquiring the knowledge on the technology, process, and people involved in next-generation privacy and security.