Beschreibung The Analytical Puzzle: Profitable Data Warehousing, Business Intelligence and Analytics. Do you enjoy completing puzzles? Perhaps one of the most challenging (yet rewarding) puzzles is delivering a successful data warehouse suitable for data mining and analytics. The Analytical Puzzle describes an unbiased, practical, and comprehensive approach to building a data warehouse which will lead to an increased level of business intelligence within your organization. New technologies continuously impact this approach and therefore this book explains how to leverage big data, cloud computing, data warehouse appliances, data mining, predictive analytics, data visualization and mobile devices. Here are the main objectives for each of the book's 19 chapters: Chapter 1: Develop a foundational knowledge of data warehousing, business intelligence and analytics Chapter 2: Build the business case needed to sell your data warehousing project, and then produce a project plan that avoids common pitfalls Chapter 3: Elicit and organize business intelligence and data warehousing business requirements Chapter 4: Specify the technical architecture of the data warehousing system, including software and infrastructure components, technology stack, and non-functional requirements. Gain an understanding of cloud based data warehousing and data warehouse appliances Chapter 5: Learn about data attributes including metrics and key performance indicators (KPIs), the raw material of data warehousing and business intelligence Chapter 6: Learn about data modeling and how to apply design patterns for each part of the data warehouse Chapter 7: Speak the dimensional modeling language of measures, dimensions, facts, cubes, stars, and snowflakes Chapter 8: Organize a successful data governance program. Learn how to manage metadata for your data warehousing and business intelligence project Chapter 9: Identify useful data sources and implement a data quality program Chapter 10: Use database technology for your data warehousing project, and understand the impact of data warehouse appliances, big data, in memory databases, columnar databases and OnLine Analytical Processing (OLAP) Chapter 11: Apply data integration and understand the role data mapping, data cleansing, data transformation, and loading data play in a successful data warehouse Chapter 12: Use the business intelligence (BI) operations of slice, dice, drill down, roll up, and pivot to analyze and present data Chapter 13: Learn about descriptive and predictive statistics, and calculate mean, median, mode, variance and standard deviation Chapter 14: Harness analytical methods such as regression analysis, data mining, and statistics to make profitable decisions and anticipate the future Chapter 15: Appreciate the components and design patterns that compose a successful analytic application Chapter 16: Gain an understanding of the uses and benefits of scorecards and dashboards including support of mobile device users Chapter 17: Gain insight into applications of business intelligence that could profit your organization, including risk management, finance, marketing, government, healthcare, science and sports Chapter 18: Perform customer analytics to better understand and segment your customers Chapter 19: Test, roll out, and sustain the data warehouse
The Analytical Puzzle: Profitable Data Warehousing ~ Get The Analytical Puzzle: Profitable Data Warehousing, Business Intelligence and Analytics now with O’Reilly online learning.. O’Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers.
The Analytical Puzzle: Profitable Data Warehousing ~ The Analytical Puzzle: Profitable Data Warehousing, Business Intelligence and Analytics [Haertzen, David] on . *FREE* shipping on qualifying offers. The Analytical Puzzle: Profitable Data Warehousing, Business Intelligence and Analytics
The analytical puzzle: profitable data warehousing - CORE ~ The analytical puzzle: profitable data warehousing, business intelligence and analytics
Data Warehousing Tutorial - Infogoal ~ The book, The Analytical Puzzle: Profitable Data Warehousing, Business Intelligence and Analytics provides even more details - plus over 20 helpful templates to accelerate your data warehousing and analytics projects. You will better understand: How to get the right people involved in the project
Behind Every Good Decision: How Anyone Can Use Business ~ There is a costly misconception in business today--that the only data that matters is BIG data, and that complex tools and data scientists are required to extract any practical information. Nothing could be further from the truth. In Behind Every Good Decision, authors and analytics experts Piyanka Jain and Puneet Sharma demonstrate how professionals at any level can take the information at .
Data Warehousing - DZone - Refcardz ~ Data warehousing is a process for collecting, storing, and delivering decision-support data for some or all of an enterprise. Data warehousing is a broad subject that is described point-by-point .
Sept. 15th: Big Data: Advanced structures and technologies ~ David Haertzen is the author of The Analytical Puzzle: Profitable Data Warehousing, Business Intelligence and Analytics. He is an acknowledged trail blazer and thinker in the fields of data warehousing, business intelligence and analytics. In addition, he contributes to industry publications and blogs. He has aided a diverse set of organizations from start-ups to multinationals to utilize data .
Business Intelligence vs. Data Analytics; What’s the ~ Business intelligence is the use of data to help make business decisions. BI as it’s commonly referred to, is a broad umbrella term for the use of data in a predictive environment. Business intelligence encompasses analytics, acting as the non-technical sister term used to define this process. BI often refers to the process that is undertaken by business analysts in order to learn from the .
Data Warehousing Tutorial - Tutorialspoint ~ A data warehouse is constructed by integrating data from multiple heterogeneous sources. It supports analytical reporting, structured and/or ad hoc queries and decision making. This tutorial adopts a step-by-step approach to explain all the necessary concepts of data warehousing.
Data Warehouse / IBM ~ IBM data warehouse solutions are available on premises, on cloud or as an integrated appliance. Infused with AI for deeper, faster analytics, they also share a common SQL engine for streamlining queries.The IBM data warehouse is also available on the IBM Cloud Pak® for Data platform to support hybrid cloud deployments.
Data Warehousing - Concepts - Tutorialspoint ~ Data warehousing is the process of constructing and using a data warehouse. A data warehouse is constructed by integrating data from multiple heterogeneous sources that support analytical reporting, structured and/or ad hoc queries, and decision making. Data warehousing involves data cleaning, data integration, and data consolidations.
Top 10 Popular Data Warehouse Tools and Testing Technologies ~ What is a Data Warehouse? Data warehouse, also known as DWH is a system that is used for reporting and data analysis. It is considered to be the core of business intelligence (BI) as all the analytical sources revolve around the data warehouse.
Industry Data Models / IBM ~ Industry data models from IBM can help accelerate your analytics journey by applying best practices, using predesigned industry-specific content. It can help you manage your enterprise data, whether in your data warehouse or in the data lake, so you can derive insights and make informed decisions.
Business Intelligence – BI Definition ~ Business intelligence (BI) refers to the procedural and technical infrastructure that collects, stores, and analyzes data produced by a company.
Data warehousing in Microsoft Azure - Azure Architecture ~ In either case, the data warehouse becomes a permanent data store for reporting, analysis, and business intelligence (BI). Data warehouse architectures. The following reference architectures show end-to-end data warehouse architectures on Azure: Enterprise BI in Azure with Azure Synapse Analytics. This reference architecture implements an extract, load, and transform (ELT) pipeline that moves .
What is a Data Warehouse? / Key Concepts / Web Services ~ A data warehouse is a central repository of information that can be analyzed to make more informed decisions. Data flows into a data warehouse from transactional systems, relational databases, and other sources, typically on a regular cadence.Business analysts, data engineers, data scientists, and decision makers access the data through business intelligence (BI) tools, SQL clients, and other .
What is Business Intelligence? BI Defined ~ Business intelligence (BI) is a technology-driven process for analyzing data and delivering actionable information that helps executives, managers and workers make informed business decisions. As part of the BI process, organizations collect data from internal IT systems and external sources, prepare it for analysis, run queries against the data and create data visualizations, BI dashboards .
AnalyticsCreator ~ AnalyticsCreator is a software for automating the design, creation and maintenance process of Data Lakes, Data Warehouses and Data Marts (Cubes, PowerBI, Tabular) AnalyticsCreator replaces individual consulting services through automation in a software tool Significant reduction of implementation time & costs Lightning speed time to market (Agile Development Processes)
Advanced Analytics in Theorie und Praxis / [at] Blog ~ Business Intelligence vs. Advanced Analytics. Der Begriff Advanced Analytics lässt sich in Abgrenzung zum klassischen Konzept von Business Intelligence definieren.Aufgrund der prinzipiell anderen Herangehensweise wird anstatt von Advanced Analytics immer wieder auch verallgemeinernd von Predictive Analytics gesprochen. Dadurch wird betont, dass durch die Datenanalysen nicht einfach vergangene .
OLAP and data mining: What’s the difference? ~ A data mine can be implemented only when there is a need to address business queries. On the other hand, OLAP can be easily employed to further the goals of any business that can be satiated by .
Five organizations that are using big data to power ~ Big data can be a great asset in achieving digital transformation. Here are five organizations that have used data science to boost their business.
Big data and analytics in the automotive industry ~ everything from sensors to artificial intelligence to big data analysis; the ecosystem is witnessing a steady influx of new players and the continued evolution of the roles played by key stakeholders and the balance of power among them. Of particular interest is the evolving relationship between automakers and software providers. Analytics allows this data to be merged regardless of the format .
Data-Mart – Wikipedia ~ Ein Data-Mart ist eine Kopie des Teildatenbestandes eines Data-Warehouse (DW), die für einen bestimmten Organisationsbereich oder eine bestimmte Anwendung oder Analyse (siehe unten) erstellt wird. Es kann auch als Teilansicht auf das Data-Warehouse oder nicht-persistenter Zwischenspeicher verstanden werden.In der Praxis wird in einigen Fällen der in einem Data-Mart vorhandene Datenbestand .
SAS Institute – Wikipedia ~ SAS ist ein 1976 gegründeter, weltweit operierender Analytics-Anbieter mit Sitz in Cary, North Carolina, USA mit 3,27 Mrd. US$ Umsatz (2018). Damit ist SAS der größte Softwarehersteller in Privatbesitz. SAS konzentriert sich heute auf die Anwendungsfelder künstliche Intelligenz (KI), Machine Learning und Analytics. Nach eigenen Angaben nutzen 96 der größten 100 Unternehmen der Welt .
SAP IQ / RDBMS for Big Data Analytics / Sybase ~ Deliver speed and power for petabyte-scale data warehousing and Big Data analytics. SAP IQ, edge edition . Support extreme-scale analytics with software that is efficient and cost-effective enough for midsize companies and partners. SAP IQ, public cloud edition. Perform interactive analysis and ad-hoc queries with software deployed on Web Services. Request pricing information Start free .