The new challenge of integrated solutions is to get more knowledge from data in order to build the most valuable solutions. This IBM Redbooks publication is a solution guide to address the business issues in telecommunications by real usage experience and to position the value of DB2 Intelligent Miner for Data in a Business Intelligence architecture as an integrated solution. Typical telecoms issues are addressed in this book, such as: What are the characteristics of your customers? Can you predict the customers who are likely to leave? How do you discover the true value of your customers? This book also describes a data mining method to ensure that the optimum results are obtained. It details for each business issue: - What common data model to use - How to source the data - How to evaluate the model - What data mining technique to use - How to interpret the results - How to deploy the model Business users who want to know the payback on their organization when using the DB2 Intelligent Miner for Data solution should read the sections about the business issues, how to interpret the results, and how to deploy the model in the enterprise. Implementers who want to start using mining techniques should read the sections about how to define the common data model to use, how to source the data, and how to choose the data mining techniques.

The new challenge of integrated solutions is to get more knowledge from data in order to build the most valuable solutions. This IBM Redbooks publication is a solution guide to address the business issues in banking by real usage experience and to position the value of DB2 Intelligent Miner For Data in a Business Intelligence architecture as an integrated solution. Typical banking issues are addressed in this book, such as: How can you discover the characteristics of your customers? Which products can you sell to which customers and how? This book also describes a data mining method to ensure that the optimum results are obtained. It details for each business issue: - What common data model to use - How to source the data - How to evaluate the model - What data mining technique to use - How to interpret the results - How to deploy the model Business users who want to know the payback on their organization when using the DB2 Intelligent Miner For Data solution should read the sections about the business issues, how to interpret the results, and how to deploy the model in the enterprise. Implementers who want to start using mining techniques should read the sections about how to define the common data model to use, how to source the data, and how to choose the data mining techniques.

The new challenge of integrated solutions is to get more knowledge from data in order to build the most valuable solutions. This IBM Redbooks publication is a solution guide to address the business issues in retail by real usage experience and to position the value of DB2 Intelligent Miner For Data in a Business Intelligence architecture. Typical retail issues are addressed in this book, such as: How can I characterize my customers from the mix of products that they purchase? How can I decide which products to recommend to my customers? How can I categorize my customers and identify new potential customers? This book also describes a data mining method to ensure that the optimum results are obtained. It details for each business issue: - What common data model to use - How to source the data - How to evaluate the model - What data mining technique to use - How to interpret the results - How to deploy the model Business users who want to know the payback on their organization when using the DB2 Intelligent Miner For Data solution should read the sections about the business issues, how to interpret the results, and how to deploy the model in the enterprise. Implementers who want to start using mining techniques should read the sections about how to define the common data model to use, how to source the data, and how to choose the data mining techniques.

The second edition of O'Reilly's bestselling Oracle Essentials has been updated to include the latest Oracle release, Oracle9 i. Oracle Essentials distills an enormous amount of information about Oracle's myriad technologies and releases into a compact, easy-to-read volume filled with focused text, illustrations, and helpful hints. Oracle9 i promises to be an even more significant upgrade than Oracle8 i, offering such major features as Real Application Clusters, flashback queries, Oracle personalization, clickstream intelligence, and Oracle Database Cache and Web Cache; it also promises significant improvements in Oracle's business intelligence, XML integration, high availability, and management capabilities. The book includes overviews of these features, as well as the new Oracle9 I Application Server (Oracle9 iAS) and Oracle9 i Portal. The book contains chapters on: Oracle products, options, and overall architecture for Oracle9 i and other recent releases Installing and running Oracle: creating databases, configuring Net8 (known as Oracle Net in Oracle9 i), starting up and shutting down Oracle Oracle data structures, datatypes, and ways of extending datatypes Managing Oracle: security, the Oracle Enterprise Manager, fragmentation and reorganization, and backup and recovery Oracle networking, monitoring, and tuning Multi-user concurrency, online transaction processing (OLTP), and high availability Hardware architectures (e.g., SMP, MPP, NUMA) and their impact on Oracle Data warehousing and distributed databases Oracle9 i, Oracle8 i, and the Web, including the latest Java, web, and XML technologies, interMedia, Oracle9 i Application Server, and Oracle9 i Portal For new Oracle users, DBAs, developers, and managers, Oracle Essentials is an all-in-one introduction to the full range of Oracle features and technologies, including the just-released Oracle9 i features. But even if you already have a library full of Oracle documentation, this compact book is the one you'll turn to, again and again, as your one-stop, truly essential reference.

You go online to buy a digital camera. Soon, you realize you've bought a more expensive camera than intended, along with extra batteries, charger, and graphics software-all at the prompting of the retailer. Happy with your purchases? The retailer certainly is, and if you are too, you both can be said to be the beneficiaries of "customer intimacy" achieved through the transformation of data collected during this visit or stored from previous visits into real business intelligence that can be exercised in real time. Data Warehousing and Business Intelligence for e-Commerce is a practical exploration of the technological innovations through which traditional data warehousing is brought to bear on this and other less modest e-commerce applications, such as those at work in B2B, G2C, B2G, and B2E models. The authors examine the core technologies and commercial products in use today, providing a nuts-and-bolts understanding of how you can deploy customer and product data in ways that meet the unique requirements of the online marketplace-particularly if you are part of a brick-and-mortar company with specific online aspirations. In so doing, they build a powerful case for investment in and aggressive development of these approaches, which are likely to separate winners from losers as e-commerce grows and matures. * Includes the latest from successful data warehousing consultants whose work has encouraged the field's new focus on e-commerce. * Presents information that is written for both consultants and practitioners in companies of all sizes. * Emphasizes the special needs and opportunities of traditional brick-and-mortar businesses that are going online or participating in B2B supply chains or e-marketplaces. * Explains how long-standing assumptions about data warehousing have to be rethought in light of emerging business models that depend on customer intimacy. * Provides advice on maintaining data quality and integrity in environments marked by extensive customer self-input. * Advocates careful planning that will help both old economy and new economy companies develop long-lived and successful e-commerce strategies. * Focuses on data warehousing for emerging e-commerce areas such as e-government and B2E environments.

Enterprise leaders are driving continuous transformation to stay at the forefront of real-time intelligent growth. Join us to learn how Forza Steel collaborated with Kyndryl and Microsoft to build a unified AI platform with Microsoft Fabric, Power BI, and Azure IoT to drive real-time insights, automation, and predictive analytics across manufacturing, logistics, and finance, as well as migrate their on-prem SAP to RISE on Azure.

This is a talk about how we thought we had Big Data, and we built everything planning for Big Data, but then it turns out we didn't have Big Data, and while that's nice and fun and seems more chill, it's actually ruining everything, and I am here asking you to please help us figure out what we are supposed to do now.

📓 Resources Big Data is Dead: https://motherduck.com/blog/big-data-... Small Data Manifesto: https://motherduck.com/blog/small-dat... Is Excel Immortal?: https://benn.substack.com/p/is-excel-immortal Small Data SF: https://www.smalldatasf.com/

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Mode founder David Wheeler challenges the data industry's obsession with "big data," arguing that most companies are actually working with "small data," and our tools are failing us. This talk deconstructs the common sales narrative for BI tools, exposing why the promise of finding game-changing insights through data exploration often falls flat. If you've ever built dashboards nobody uses or wondered why your analytics platform doesn't deliver on its promises, this is a must-watch reality check on the modern data stack.

We explore the standard BI demo, where an analyst uncovers a critical insight by drilling into event data. This story sells tools like Tableau and Power BI, but it rarely reflects reality, leading to a "revolving door of BI" as companies swap tools every few years. Discover why the narrative of the intrepid analyst finding a needle in the haystack only works in movies and how this disconnect creates a cycle of failed data initiatives and unused "trashboards."

The presentation traces our belief that "data is the new oil" back to the early 2010s, with examples from Target's predictive analytics and Facebook's growth hacking. However, these successes were built on truly massive datasets. For most businesses, analyzing small data results in noisy charts that offer vague "directional vibes" rather than clear, actionable insights. We contrast the promise of big data analytics with the practical challenges of small data interpretation.

Finally, learn actionable strategies for extracting real value from the data you actually have. We argue that BI tools should shift focus from data exploration to data interpretation, helping users understand what their charts actually mean. Learn why "doing things that don't scale," like manually analyzing individual customer journeys, can be more effective than complex models for small datasets. This talk offers a new perspective for data scientists, analysts, and developers looking for better data analysis techniques beyond the big data hype.

Power BI Desktop bietet Datenaktualisierung bis in den Bereich von Sekunden an. Dazu dient in der Datenquellenkonfiguration die DirectQuery.

Die Session beginnt mit dem Anbinden von Azure SQL-DBs und zeigt anschließend die Unterschiede und Feinheiten zu Databricks als Datenquelle.

Dies beginnt beim Erstellen von Databricks DBs, geht weiter zur Notebook Konfiguration und beinhaltet auch die Authentication zu Databricks-Clustern.

Sascha Götz zeigt die Synergie von Power BI und Power Apps, demonstriert nahtloses Wechseln zwischen Visualisierungen und interaktiven Benutzeroberflächen sowie bewährte Methoden und praktische Anwendungen. Die Session ist Demo-Heavy und Hands-On.

In this game you will learn to build a BI dashboard with Looker Studio as the front end, powered by BigQuery on the back end, learn to use BigQuery to find data, build a time series model to forecast demand of multiple products using BigQuery ML, and create a basic report in Google Data Studio.

Click the blue “Learn more” button above to tap into special offers designed to help you implement what you are learning at Google Cloud Next 25.

With the surge of new generative AI capabilities, companies and their customers can now interact with systems and data in new ways. To activate AI organizations require a data foundation with the scale and efficiency to bring business data together with AI models and ground them in customer reality. Join this session to learn the latest innovations for data analytics and BI, and why tens of thousands of organizations are fueling their journey with BigQuery and Looker.

Click the blue “Learn more” button above to tap into special offers designed to help you implement what you are learning at Google Cloud Next 25.