1 Page 1 Data Mining for Everyone Christoph Sieb Senior Software Engineer, Data Mining Development Dr. Andreas Zekl Manager, Data Mining Development
2 Page 2 Executive Summary Contents 2 Data mining in the context of business intelligence and dynamic warehousing 5 Creating a data mining application with InfoSphere Warehouse 9 Business scenarios Business intelligence systems are key tools for providing organizations with important business insights, allowing for fast and reliable business decisions. Data warehousing is an integral part of the business intelligence systems used by many organizations. Data mining, by contrast, is not as widespread as data warehousing, even though it is a mature methodology and technology that can be used to help discover hidden patterns in data and to enable predictive capabilities. Data mining is not only an analytic tool for knowledge discovery; it is also an important part of analytical and operational business processes that operate in real time and thus contributes directly to a successful business. In this paper, we show data mining in the context of IBM s dynamic warehousing strategy and outline the functionality that comes with IBM InfoSphere Warehouse. We then use a set of real-world business scenarios to illustrate the business value of those data mining solutions. Data mining in the context of business intelligence and dynamic warehousing Business intelligence comprises software, hardware, methods, applications, and best practices that provide fast and reliable insights into the current business and thus allow for fast and reliable business decisions. Business intelligence technologies have been used for decades. Data mining is the most advanced business intelligence analysis component: it produces more business insights than other analytical components such as online analytical processing (OLAP)  and standard reporting tools. Data mining originated in the late 1980s and has grown in importance since then, but many people still believe that using data mining techniques is difficult and, therefore, do not exploit its inherent business potential.
3 Page 3 Dynamic warehousing is a new approach that addresses the primary business challenges that organizations face today. Organizations need to deliver the right information to the right people at the right time to more effectively leverage business data and make more effective business decisions. Dynamic warehousing is about providing information on demand to optimize real-time processes. Dynamic warehousing provides four key features: Support for real-time access to aggregated, cleansed information representing a single version of the truth that can be delivered in the context of the activities and processes being performed The ability to extract knowledge from unstructured information 1 Embedded analytics that can be leveraged as part of a business process A complete set of integrated capabilities that extends beyond the data warehouse to enable the use of information on demand Data mining plays an integral role in the first two of these key features. In the following pages, we will show that you can embed data mining into a business process, providing real-time data access. Also, we will point out how you can use data mining technologies to extract additional business insight from unstructured information, which traditionally has been difficult to do. But what exactly is data mining, how can it be used, and who can use it? Data mining is defined as the nontrivial extraction of implicit, previously unknown and potentially useful information from data.  Data mining methods can be categorized into two groups: Discovery methods. These find patterns and associations in data that can be used to make business decisions. For example, for cross-selling purposes, a company can find out which products are bought together, or using customer segmentation, a company can decide how to advertise. Prediction methods. These use historical data to create models that help to predict currently unknown values of new data: for example, sales forecasts, stock price predictions, medical prognoses, and potential churners 2. 1 In contrast to structured data, which is atomic data like a name or a person s age, unstructured data is most often continuous text such as s, comments of call center agents, or textual reports. 2 Churners are customers who moved to a competitor.
4 Page 4 The creation of models from data is called modeling, and using these models to perform predictions or segmentations is called scoring. Creating a model often requires analyzing a lot of data and find out relationships, which is a computing-intensive task. By contrast, scoring involves matching other data (each record can be matched independently) against a model. In the case of real-time applications, the data to match is often just a single record. Thus, you can easily embed scoring into business processes, such as by using SOA-based Web services . Data mining is an iterative process that is described in the CRoss Industry Standard Process for Data Mining (CRISP-DM) . CRISP-DM comprises five steps, as shown in Figure 1: 1. Business understanding. A business analyst defines what he or she wants to investigate and which information might help in making business decisions and business improvements: that is, which information might help to achieve a business goal. 2. Data understanding. After the business goal is defined, the data necessary to reach that goal must be identified. First samples, statistics, and visualizations are used to get a feeling for the data and identify quality problems within the data. For this step, a business analyst, a data mining specialist, and a data warehouse administrator must work together. 3. Data preparation. This is often the most time-consuming step. Based on the outcome of step 2, the existing data needs to be merged and transformed into the right format for the data mining methods. This task is usually performed by a data warehouse administrator or a data warehouse application developer. 4. Modeling. The data mining model is created. This task is performed by the data mining specialist and the data warehouse application developer. 5. Evaluation. Results are evaluated against the business goals; if the results are unsatisfactory, one or more steps in the iterative process are repeated. 6. Deployment. After the results are satisfactory, the data mining flow can be deployed as a data warehouse application and made available to users. For the users, most of the complexity is hidden; only a few people outside the user groups must understand the whole data mining process in detail.
5 Page 5 Figure 1. CRISP-DM process model. All these steps are supported by functions available in InfoSphere Warehouse, as described in the next section. Creating a data mining application with InfoSphere Warehouse InfoSphere Warehouse includes an Eclipse-based  tool called Design Studio (see Figure 2), which enables you to perform all data warehouse design tasks. You can define and deploy data models, compose SQL queries using wizards that simplify their creation, and create and deploy whole OLAP cubes. You can even design and deploy dashboards without writing a single line of code. You can also perform data mining within Design Studio. You can gather and transform data with minimal SQL knowledge, and you can easily create and visualize mining models. Furthermore, you can deploy mining flows that you intend to use in a business process from
6 Page 6 the Design Studio workbench, which greatly simplifies life cycle management. Statistical functions and integrated visualizers such as multivariate distribution views help during the data-understanding step. Figure 2. Design Studio All the pre-processing and mining functionality in Design Studio is encapsulated in operators that you can drag onto a flow editor. You can then connect the operators according to the specific business scenario. Figure 3 shows a simple data mining flow for customer segmentation in the banking industry. The flow consists of five operators connected from left to right.
7 Page 7 Figure 3. Simple data mining flow The upper table source processes customer bank transactions from the corporate transaction table, while the lower table source processes the customer master data. Both tables are joined to a single table that is used to feed the clustering operator. Finally, the cluster model is propagated to the visualizer, which displays the learned customer segmentation model shown in Figure 4. The visualizer shows the discovered segments and the characteristic data distribution of each cluster. Figure 4. Visualized clustering model
8 Page 8 InfoSphere Warehouse provides many more operators for preprocessing and data mining. Table 1 lists the data mining methods available in InfoSphere Warehouse 3. The right column shows some typical problem scenarios that can be solved by the corresponding mining method. Table 1. Data mining methods of InfoSphere Warehouse and typical scenarios Data mining Methods Typical scenarios Clustering Perform customer segmentation for selective marketing Group similar types of houses, and use the data in city planning Detect fraudulent users Classification Understand which customers are valuable Predict which customers are valuable Perform churn analysis Predict heart attacks Value prediction - regression Predict claim amounts of insurance customers Predict cholesterol values Association rule mining Perform market basket analysis Support cross-selling Sequential pattern mining Perform market basket analysis Market selectively Plan purchases Note: Design Studio is not a full-blown statistics workbench. Design Studio is a tool for data warehouse application developers with data mining expertise, not for statisticians. In most cases, the Design Studio functionality is sufficient; however, in case you need a full-blown statistics workbench, InfoSphere Warehouse offers integration points with workbenches from non-ibm vendors. For this purpose, InfoSphere Warehouse Data Mining supports the standardized Predictive Model Markup Language (PMML)  to exchange models. Thus, you can 3 Each category provides one or more algorithms, depending on the specific analysis problem.
9 Page 9 easily integrate models created by other tools, such as into a scoring flow that itself is used inside a business process. Although Design Studio is mainly a tool for data warehouse application developers, data mining experts, and IT-savvy business analysts, you can make the data mining flows that you deploy from Design Studio accessible to a variety of users. You can make the data mining flows accessible to managers, financial analysts, marketing staff, sales staff, consultants in a banking call center, or even cashiers using a cash desk computer (see the Retail business scenario in the next section). After you deploy a data mining flow to an application server, you can use that data mining flow to visualize data mining results, such as by using a Web application using Alphablox  and Miningblox. You can also use an SOA architecture that includes Web services to perform real-time scoring. Another option is to use other front-end applications such as Cognos to access deployed data mining applications and incorporate those applications into the front-end application s dashboards. The following section describes some business scenarios in more detail. However, the possibilities of data mining in the context of dynamic warehousing are much broader than those described in these scenarios. Business scenarios In this section, we describe scenarios for four types of businesses (retail, banking, insurance, and mobile network operator), representing parts of real-world business processes. Each scenario is introduced with information about the business requirement. Next, a data mining approach is described that can improve the business process in terms of customer satisfaction, revenue, and profit. Finally, there is a description of a scenario that uses the data mining approach. Retail Support product selling creating discount coupons on the fly Business requirement A consumer electronics retailer is planning to support sales of specific products by providing coupons for special offers at the cash desk. The coupons must be created on the fly, taking into consideration the customer s buying behavior. If the customer can be identified with a credit or customer card, the system should consider historical
10 Page 10 shopping patterns. If the customer cannot be identified, only the justbought products should be considered. Data mining approach The retailer records the purchase transactions in its data warehouse. Using Design Studio, the retailer creates a data mining flow that extracts association rules and sequential patterns from those transactions (that is, the retailer performs market basket analysis; for details, refer to ). The retailer deploys the flow on an application server that runs the analysis regularly so that results are up-to-date. Furthermore, the retailer installs a scoring Web service that predicts the products that a customer is most likely to buy according to the current products in his or her market basket and according to his or her previous shopping history. Scenario using the data mining approach The sales manager obtains a monthly report on sales figures through e- mail from the corporate business intelligence system. He recognizes that a newly introduced product is not selling as well as expected, even though it was advertised the week before its introduction. He logs on to the business intelligence system using a Web browser. He selects the product of concern and has a closer look at its sales figures using the integrated OLAP capabilities. He realizes that the product is especially poorly sold in the western region of the company s sales area. To support sales of this specific product, the system provides the ability to automatically create coupons for selected regions and products. The manager selects the effected region and product and the system automatically takes all those rules from the regular market basket analysis that contains the poorly selling product in the rule s consequence (for more details on rules, refer to ). Using those rules, the system will issue a coupon for those customers who are most likely to be interested in the poor selling product. For example, an association rule might state that customers who bought products X and Y also bought the poorly selling product in 40% of the cases. According to this rule, a coupon promoting the poorly selling product will be created whenever a customer buys products X and Y. After the manager confirms his selection, the corresponding rules are deployed in real time. This deployment sets up a scoring service that can be accessed using an SOA-based Web service. Inputs for the Web service are information about just-bought products and, if available, the customer card or credit card ID that is necessary to
11 Page 11 refer to previously bought products. The service returns information about the products for cross-selling according to previously deployed rules. Now, the rules can be automatically accessed by the cash desk computer using a call to the Web service in the central data warehouse. Whenever a customer with the promising buying behavior is paying, the cash desk automatically creates a coupon with a price reduction on the poorly selling product. This process allows for a selective, cost-effective promotion in real time using an underlying Web service that has been deployed in real time. Thus, the time to react is drastically reduced, which avoids large opportunity costs increasing customer retention and the sales figures. Banking Detect reliable loan customers Business requirement A bank decides to start providing a new consumer loan product. To avoid profit losses due to non-payments by customers, the bank wants to learn about the reliability of its customers by using records of their other loans, thus improving decision-making. The bank also wants to record new consumer loan contracts. Information about loan decisions must be comprehensible both for the customer and for the customer consultant. Data mining approach The sales manager and the internal data analyst use Design Studio to create a data mining flow that produces a classification model from the historical data. The historical data comprises not only demographic data about the customer (such as age, gender, and family status) but also transactional data (such as account balances and transfers). Additionally, after each loan process is closed, a customer consultant classifies each loan record as to whether the customer was reliable. This classification data is used to create a decision tree model (for details, refer to ) to classify new customers as reliable or unreliable. Additionally, the decision tree model provides confidence levels for each decision (0 100%). If a loan request is refused because there is a high degree of confidence that a customer is unreliable, the decision tree provides a comprehensible explanation that can be given to the customer.
12 Page 12 The mining flow is deployed to an application server that creates a new, updated decision tree every month to incorporate newly available customer classification data that improves the decisionmaking process. Additionally, the decision tree model is made available as a Web service to score new customers. A customer ID from a previously created customer record is used as input to the Web service to retrieve customer-related data. The service classifies the reliability of a customer with a certain degree of confidence. Scenario using the data mining approach A customer wants to take out a loan. He enters the bank and asks for the new consumer loan product. First, the consultant collects general information about the customer and enters it into the banking software system. The system automatically invokes the Web service running on the bank s application server using the customers data as input. The Web service immediately classifies the customer and the consultant can review the classification with the corresponding confidence value and the reason for the classification. This information supports the consultant to make better decisions about which customers are reliable and, thus, to reduce profit losses. Insurance Determine insurance rates in real time Business requirement An insurance company offers car insurance that it distributes using the Internet and call centers. The company wants to give a customer using the company s Web site or a call center agent the possibility of determining an individual insurance rate by providing data such as age, gender, and car type. Data mining approach The company s car insurance manager uses Design Studio to create a prediction model. The prediction model incorporates customer demographic data, data about his car(s) and data about former claims. Additionally, the cost and profit margin for a single insurance contract is incorporated into the model, allowing the individual insurance rate to be predicted. The manager creates a prediction model according to the customer characteristics which is able to predict the necessary insurance rates. After the manager has created the model, he deploys it to an application server, using Web services to make the results available to other applications. Call center agents and customers using the Internet can access the prediction model in real time.
13 Page 13 Scenario using the data mining approach A customer calls the call center and asks for a car insurance quotation. The call center agent asks for the customer s necessary data and uses it as input for the previously deployed Web service to get a precise rate for the insurance, without the need for detailed knowledge of the insurance industry. Call center Improve churn analysis Business requirement The call center of a mobile network operator is responsible for new customer contracts, customer questions, complaints, and general service support. An important focus is customers who are considering canceling their contracts and switching to another operator. The call center agents must recognize those customers and avoid the cancellations by making them special offers. Data mining approach Often, it is not easy to detect customers who are likely to switch to another company. Data mining can provide ways to create models that explain why and predict which customers tend to cancel their contracts. In the data mining context, this type of analysis is called churn analysis. As in the previous banking scenario, customers in the insurance scenario can be classified, using a classification model, into likely to switch and likely to stay customers. The insurance company also uses existing automated segmentation of customers into similar groups to create special offers related to the characteristics of those groups. The company also has a large potential source of information about customers: namely, the unstructured textual information recorded by the call center agents during calls. The information is stored in a database, but ordinary methods cannot access the data, so the company hadn t been using that data to improve automatic identification of customers who might switch to a different operator. However, using InfoSphere Warehouse, the company can now exploit the textual information. InfoSphere Warehouse provides analysis methods for unstructured data in a call center application context, allowing the unstructured data to be automatically converted into structured data. To implement this solution, a data analyst creates domain-specific dictionaries to extract structured information from the
14 Page 14 unstructured information. InfoSphere Warehouse provides support for creating the dictionaries and revising them over time. The additional information from the unstructured text can significantly improve the prediction accuracy of the classification model and thus increases the probability of preventing customers from switching operators. The created dictionaries, the classification model, and the customer group model are deployed as a Web service on the company s application server to make them accessible from other business applications in real time. Scenario using the data mining approach A call center agent talks to a customer asking for price information. During the call, the agent retrieves the customer s record and adds a comment to the record that states Customer asked for fixed-price offers. After committing the comment, the system automatically submits the information to the deployed Web service. The Web service extracts fixed-price offers from the comment automatically and requests a prediction about whether the customer is likely to switch. To make the prediction, the system uses the extracted information and structured data about the customer, such as city, age, current contract details, and call history. The system identifies the customer as likely to switch and retrieves the special offer for the corresponding customer group (here, persons often performing foreign calls). In this case, the system immediately suggests a fixed-price contract with an additional initial credit for foreign calls, which the agent offers to the customer. The customer is attracted by this offer and requests a contract change. The reason that the customer asked for information about fixed-price options was that he was unsatisfied with his current contract and planned to compare different offers from different competitors. By analyzing the classification model, it is possible to understand the underlying intention of the customer. In this case, the customer lives in the southern part of the company s territory, where a local competitor had attracted other customers from the company by using a special offer for fixed-price contracts. This fact was detected while building the classification model. Thus, the special offer with an initial credit convinced the customer to stay.
15 Page 15 Summary and conclusions This paper provided a short introduction to data mining in the context of dynamic warehousing and business intelligence. It also showed how InfoSphere Warehouse supports the development of data mining applications. A selection of business scenarios gave an idea of the enormous potential of data mining with real-time access. Using the powerful data warehouse technology of InfoSphere Warehouse, including tooling (Design Studio) and in-line analytics (such as those provided by Alphablox and Miningblox), you can create many solutions with just a small amount of custom development effort. As a result, you can reduce both the time and cost needed to install business intelligence solutions, such as those described in the business scenarios. Further reading 1. IBM Data Warehousing and Business Intelligence 2. InfoSphere Warehouse documentation p?topic=/com.ibm.dwe.welcome.doc/dwev9welcome.html 3. DB2 9.5 for Linux, UNIX, and Windows manuals support/docview.wss?rs=71&uid=swg W. Frawley and G. Piatetsky-Shapiro and C. Matheus, "Knowledge Discovery in Databases: An Overview". AI Magazine: pp , ISSN , C. Ballard, J. Rollins, J. Ramos, A. Perkins, R. Hale, A. Dorneich, E. C. Milner and J. Chodagam, Dynamic Warehousing: Data Mining Made Easy. International Technical Support Organization Redbooks publication (IBM), ISBN , September Available at last accessed date: 03/17/ M. Alcorn, M. Flasza, OLAP and Cubing Services., to be published 2008.
16 Page C. Ballard, A. Beaton, D. Chiou, J. Chodagam, M. Lowry, A. Perkins, R. Phillips and J. Rollins, Leveraging DB2 Data Warehouse Edition for Business Intelligence. International Technical Support Organization Redbooks publication (IBM), ISBN , published November 2006, last updated September Available at last accessed date: 03/17/ M. Endrei, J. Ang, A. Arsanjani, S. Chua, P. Comte, P. Krogdahl, M. Luo and T. Newling, Patterns: Service-Oriented Architecture and Web Services. International Technical Support Organization Redbooks publication (IBM), ISBN , published April 2004, last updated July Available at last accessed date: 03/17/ CRoss Industry Standard Process for Data Mining, last accessed date: 03/17/ The Eclipse Foundation, The Eclipse Project, available at last accessed date: 03/17/ Data Mining Group, PMML Standard, available at last accessed date: 03/17/2008.
17 Page 17 7 Copyright IBM Corporation, All Rights Reserved IBM, the IBM logo, Alphablox, DB2, InfoSphere, and Redbooks are registered trademarks or trademarks of International Business Machines Corporation in the United States, other countries, or both. Windows is a trademark of Microsoft Corporation in the United States, other countries, or both. UNIX is a registered trademark of The Open Group in the United States and other countries. Linux is a registered trademark of Linus Torvalds in the United States, other countries, or both. Other company, product, or service names may be trademarks or service marks of others. References in this publication to IBM products and services do not imply that IBM intends to make them available in all countries in which IBM operates. Neither this documentation nor any part of it may be copied or reproduced in any form or by any means or translated into another language, without the prior consent of all of the above mentioned copyright owners. IBM makes no warranties or representations with respect to the content hereof and specifically disclaims any implied warranties of merchantability or fitness for any particular purpose. IBM assumes no responsibility for any errors that may appear in this document. The information contained in this document is subject to change without any notice. IBM reserves the right to make any such changes without obligation to notify any person of such revision or changes. IBM makes no commitment to keep the information contained herein up to date. The information in this document concerning non-ibm products was obtained from the supplier(s) of those products. IBM has not tested such products and cannot confirm the accuracy of the performance, compatibility or any other claims related to non-ibm products. Questions about the capabilities of non-ibm products should be addressed to the supplier(s) of those products.
Fast and Easy Delivery of Data Mining Insights to Reporting Systems Ruben Pulido, Christoph Sieb email@example.com, firstname.lastname@example.org Abstract: During the last decade data mining and predictive
CHAPTER 1 DATA MINING AND WAREHOUSING CONCEPTS 1.1 INTRODUCTION The past couple of decades have seen a dramatic increase in the amount of information or data being stored in electronic format. This accumulation
Database Systems Journal vol. IV, no. 4/2013 21 Data Mining Solutions for the Business Environment Ruxandra PETRE University of Economic Studies, Bucharest, Romania email@example.com Over
IBM Software IBM SPSS Modeler Solve your toughest challenges with data mining Use predictive intelligence to make good decisions faster Solve your toughest challenges with data mining Imagine if you could
Name: Srinivasan Govindaraj Title: Big Data Predictive Analytics Please note the following IBM s statements regarding its plans, directions, and intent are subject to change or withdrawal without notice
Data Mining Techniques forcrm Data Mining The non-trivial extraction of novel, implicit, and actionable knowledge from large datasets. Extremely large datasets Discovery of the non-obvious Useful knowledge
IBM Cognos Performance Management Solutions for Oracle Gain more value from your Oracle technology investments Highlights Deliver the power of predictive analytics across the organization Address diverse
IBM Software Business Analytics IBM SPSS Modeler Solve Your Toughest Challenges with Data Mining Use predictive intelligence to make good decisions faster Solve Your Toughest Challenges with Data Mining
Frequently Asked Questions November 2015, Version 1 EXTERNAL SAP S/4HANA Embedded Analytics The purpose of this document is to provide an external audience with a selection of frequently asked questions
Foundations of Business Intelligence: Databases and Information Management Problem: HP s numerous systems unable to deliver the information needed for a complete picture of business operations, lack of
IBM Software Information Management White Paper Harnessing the power of advanced analytics with IBM Netezza How an appliance approach simplifies the use of advanced analytics Harnessing the power of advanced
Predictive analytics with System z Faster, broader, more cost effective access to critical insights Highlights Optimizes high-velocity decisions that can consistently generate real business results Integrates
BUSINESS INTELLIGENCE Microsoft Dynamics NAV BUSINESS INTELLIGENCE Driving better business performance for companies with changing needs White Paper Date: January 2007 www.microsoft.com/dynamics/nav Table
205 A STUDY OF DATA MINING ACTIVITIES FOR MARKET RESEARCH ABSTRACT MR. HEMANT KUMAR*; DR. SARMISTHA SARMA** *Assistant Professor, Department of Information Technology (IT), Institute of Innovation in Technology
IBM Software Business Analytics IBM SPSS Modeler Solve your toughest challenges with data mining Use predictive intelligence to make good decisions faster 2 Solve your toughest challenges with data mining
A SAS White Paper: Implementing the Customer Relationship Management Foundation Analytical CRM Table of Contents Introduction.......................................................................... 1
Hexaware E-book on Predictive Analytics Business Intelligence & Analytics Actionable Intelligence Enabled Published on : Feb 7, 2012 Hexaware E-book on Predictive Analytics What is Data mining? Data mining,
IBM Analytical Decision Management Deliver better outcomes in real time, every time Highlights Organizations of all types can maximize outcomes with IBM Analytical Decision Management, which enables you
IBM SPSS Modeler Three proven methods to achieve a higher ROI from data mining Take your business results to the next level Highlights: Incorporate additional types of data in your predictive models By
IBM Software White Paper Consumer Products Delivering new insights and value to consumer products companies through big data 2 Delivering new insights and value to consumer products companies through big
Moving Large Data at a Blinding Speed for Critical Business Intelligence A competitive advantage Intelligent Data In Real Time How do you detect and stop a Money Laundering transaction just about to take
WHITEPAPER Today, leading companies are looking to improve business performance via faster, better decision making by applying advanced predictive modeling to their vast and growing volumes of data. Business
IBM Software Business Analytics Customer Analytics Achieving customer loyalty with customer analytics 2 Achieving customer loyalty with customer analytics Contents 2 Overview 3 Using satisfaction to drive
Business Intelligence for Everyone Business Intelligence for Everyone Introducing timextender The relevance of a good Business Intelligence (BI) solution has become obvious to most companies. Using information
IBM Global Business Services Microsoft Dynamics CRM solutions from IBM Power your productivity 2 Microsoft Dynamics CRM solutions from IBM Highlights Win more deals by spending more time on selling and
WHITE PAPER: THE BENEFITS OF DATA MODELING IN BUSINESS INTELLIGENCE The Benefits of Data Modeling in Business Intelligence DECEMBER 2008 Table of Contents Executive Summary 1 SECTION 1 2 Introduction 2
INSIGHT Microsoft DynamicsTM NAV Business Intelligence Driving business performance for companies with changing needs White Paper January 2008 www.microsoft.com/dynamics/nav/ Table of Contents 1. Introduction...
ISSN: 2321-7782 (Online) Volume 3, Issue 4, April 2015 International Journal of Advance Research in Computer Science and Management Studies Research Article / Survey Paper / Case Study Available online
ORACLE BUSINESS INTELLIGENCE, ORACLE DATABASE, AND EXADATA INTEGRATION EXECUTIVE SUMMARY Oracle business intelligence solutions are complete, open, and integrated. Key components of Oracle business intelligence
HELSINKI UNIVERSITY OF TECHNOLOGY 26.1.2005 T-86.141 Enterprise Systems Integration, 2001. Data warehousing and Data mining: an Introduction Federico Facca, Alessandro Gallo, firstname.lastname@example.org email@example.com
ETPL Extract, Transform, Predict and Load An Oracle White Paper March 2006 ETPL Extract, Transform, Predict and Load. Executive summary... 2 Why Extract, transform, predict and load?... 4 Basic requirements
IBM Software Business Analytics IBM SPSS Predictive Analytics Predictive Analytics for Donor Management Predictive Analytics for Donor Management Contents 2 Overview 3 The challenges of donor management
PRODUCTS BUSINESSOBJECTS PREDICTIVE WORKBENCH XI 3.0 Transform Your Future with Insight Today Key Features As part of the BusinessObjects XI platform, BusinessObjects Predictive Workbench: Provides robust
Focus on the business, not the business of data warehousing! Adam M. Ronthal Technical Product Marketing and Strategy Big Data, Cloud, and Appliances @ARonthal 1 Disclaimer Copyright IBM Corporation 2014.
Database Marketing simplified through Data Mining Author*: Dr. Ing. Arnfried Ossen, Head of the Data Mining/Marketing Analysis Competence Center, Private Banking Division, Deutsche Bank, Frankfurt, Germany
Stella-Jones takes pole position with IBM Faster, more accurate reports, budgets and forecasts support a rapidly growing business Overview The need Following several key strategic acquisitions, Stella-Jones
WHITE PAPER: THE BENEFITS OF DATA MODELING IN BUSINESS INTELLIGENCE The Benefits of Data Modeling in Business Intelligence DECEMBER 2008 Table of Contents Executive Summary 1 SECTION 1 2 Introduction 2
IBM Software Business Analytics Analysis Making confident decisions with the full spectrum of analysis capabilities Making confident decisions with the full spectrum of analysis capabilities Contents 2
ORACLE SUPPLY CHAIN AND ORDER MANAGEMENT ANALYTICS KEY FEATURES & BENEFITS FOR BUSINESS USERS Provide actionable information to conduct intelligent analysis of orders related to regions, products, periods
Portal solutions for the retail industry Executive brief October 2005 Improving customer satisfaction and operational efficiencies with a proven portal solution. Page 2 Contents 2 Executive summary 2 Retail
The IBM Cognos Platform Deliver complete, consistent, timely information to all your users, with cost-effective scale Highlights Reach all your information reliably and quickly Deliver a complete, consistent
IBM InfoSphere Optim Test Data Management Highlights Create referentially intact, right-sized test databases or data warehouses Automate test result comparisons to identify hidden errors and correct defects
Big Data Analytics with IBM Cognos BI Dynamic Query IBM Redbooks Solution Guide IBM Cognos Business Intelligence (BI) helps you make better and smarter business decisions faster. Advanced visualization
The Top 10 Secrets to Using Data Mining to Succeed at CRM Discover proven strategies and best practices Highlights: Plan and execute successful data mining projects. Understand the roles and responsibilities
THE INTELLIGENT BUSINESS INTELLIGENCE SOLUTIONS ADRIAN COJOCARIU, CRISTINA OFELIA STANCIU TIBISCUS UNIVERSITY OF TIMIŞOARA, FACULTY OF ECONOMIC SCIENCE, DALIEI STR, 1/A, TIMIŞOARA, 300558, ROMANIA firstname.lastname@example.org,
September 10-13, 2012 Orlando, Florida SAP Predictive Analysis: Strategy, Value Proposition Thomas B Kuruvilla, Solution Management, SAP Business Intelligence Scott Leaver, Solution Management, SAP Business
The top 10 secrets to using data mining to succeed at CRM Discover proven strategies and best practices Highlights: Plan and execute successful data mining projects using IBM SPSS Modeler. Understand the
Embedding Analytics in Decision Management Systems In-database analytics offer a powerful tool for embedding advanced analytics in a critical component of IT infrastructure. James Taylor CEO CONTENTS Introducing
1 September 2013 Industry Models and Information Server Data Models, Metadata Management and Data Governance Gary Thompson (email@example.com ) Information Management Disclaimer. All rights reserved.
The Future of Business Analytics is Now! 1 The pressures on organizations are at a point where analytics has evolved from a business initiative to a BUSINESS IMPERATIVE More organization are using analytics
IBM Software A Journey to Adaptive MDM What is Master Data? Why is it Important? A Journey to Adaptive MDM Contents 2 MDM Business Drivers and Business Value 4 MDM is a Journey 7 IBM MDM Portfolio An Adaptive
Datalogix Using IBM Netezza data warehouse appliances to drive online sales with offline data Overview The need Infrastructure could not support the growing online data volumes and analysis required The
The Benefits of Data Modeling in Business Intelligence Table of Contents Executive Summary...... 3 Introduction.... 3 Why Data Modeling for BI Is Unique...... 4 Understanding the Meaning of Information.....
Frameworx 10 Business Process Framework R8.0 Product Conformance Certification Report Microsoft Business Analytics Accelerator for Telecommunications Release 1.0 November 2011 TM Forum 2011 Table of Contents
An Oracle White Paper October 2013 Oracle Data Integrator 12c (ODI12c) - Powering Big Data and Real-Time Business Analytics Introduction: The value of analytics is so widely recognized today that all mid
High-Performance Business Analytics: SAS and IBM Netezza Data Warehouse Appliances Highlights IBM Netezza and SAS together provide appliances and analytic software solutions that help organizations improve
Predixion VICTORY INDEX CHALLENGER Marcia Kaufman COO and Principal Analyst Daniel Kirsch Principal Analyst The Hurwitz Victory Index Report Predixion is one of 10 advanced analytics vendors included in
Ventana Research: Predictive Analytics Enters the Mainstream Predictive Analytics Enters the Mainstream Taking Advantage of Trends to Gain Competitive Advantage White Paper Sponsored by 1 Ventana Research
IBM SPSS Modeler Professional Make better decisions through predictive intelligence Highlights Create more effective strategies by evaluating trends and likely outcomes. Easily access, prepare and model
15.564 Information Technology I Business Intelligence Outline Operational vs. Decision Support Systems What is Data Mining? Overview of Data Mining Techniques Overview of Data Mining Process Data Warehouses
Big Data Analytics Assessing the Revolution in Big Data and Business Analytics 10 Best Practice Recommendations Sponsored by Copyright Ventana Research 2013 Do Not Redistribute Without Permission February
IBM SPSS Statistics Easily Identify Your Best Customers Use IBM SPSS predictive analytics software to gain insight from your customer database Contents: 1 Introduction 2 Exploring customer data Where do
Top 5 Transformative Analytics Applications in Retail Learn how you can boost your bottom line and acquire engaged, happy customers with actionable insight from the world s most comprehensive analytics
Analytics Solutions from SAP The Edge Editions of SAP InfiniteInsight Overview Enabling Predictive Insights with Mouse Clicks, Not Computer Code Table of Contents 3 The Case for Predictive Analysis 5 Fast
IBM Software Solution Brief IBM InfoSphere: Solutions for retail Build a single view of customer information and a trusted source for product information with data integration and master data management
IBM Software MDM-Powered Solutions for Salesforce CRM Customer data you can trust for sales and marketing success MDM-Powered Solutions for Salesforce CRM Contents 2 Introduction 2 Empower Sales and Marketing
Digging for Gold: Business Usage for Data Mining Kim Foster, CoreTech Consulting Group, Inc., King of Prussia, PA ABSTRACT Current trends in data mining allow the business community to take advantage of
Predictive Analytics: Too Important to Ignore The six secrets to success with predictive analytics Webinar December 18, 2013 Sponsored by: Tony Cosentino VP & Research Director, Business Analytics Ventana
Business Intelligence Solutions Cognos BI 8 by Adis Terzić Fairfax, Virginia August, 2008 Table of Content Table of Content... 2 Introduction... 3 Cognos BI 8 Solutions... 3 Cognos 8 Components... 3 Cognos
Chapter 5 Foundations of Business Intelligence: Databases and Information Management 5.1 Copyright 2011 Pearson Education, Inc. Student Learning Objectives How does a relational database organize data,
ADVANTAGES OF IMPLEMENTING A DATA WAREHOUSE DURING AN ERP UPGRADE Advantages of Implementing a Data Warehouse During an ERP Upgrade Upgrading an ERP system presents a number of challenges to many organizations.
ORACLE BUSINESS INTELLIGENCE SUITE ENTERPRISE EDITION PLUS PRODUCT FACTS & FEATURES KEY FEATURES Comprehensive, best-of-breed capabilities 100 percent thin client interface Intelligence across multiple
Predictive Analytics: Turn Information into Insights Pallav Nuwal Business Manager; Predictive Analytics, India-South Asia firstname.lastname@example.org +91.9820330224 Agenda IBM Predictive Analytics portfolio
Using Predictions to Power the Business Wayne Eckerson Director of Research and Services, TDWI February 18, 2009 Sponsor 2 Speakers Wayne Eckerson Director, TDWI Research Caryn A. Bloom Data Mining Specialist,
Sample Essay on Data Analysis and Design Introduction Organizations aim at making profits and as such they must come up with effective strategies for attracting customers. The use of database technology
SAP Brief SAP HANA Objectives Transform Your Future with Better Business Insight Using Predictive Analytics Dealing with the new reality Dealing with the new reality Organizations like yours can identify
IBM Software White paper Information Management Continuing the MDM journey Extending from a virtual style to a physical style for master data management 2 Continuing the MDM journey Organizations implement
ORACLE UTILITIES ANALYTICS TRANSFORMING COMPLEX DATA INTO BUSINESS VALUE UTILITIES FOCUS ON ANALYTICS Aging infrastructure. Escalating customer expectations. Demand growth. The challenges are many. And
Grow Revenues and Reduce Risk with Powerful Analytics Software Overview Gaining knowledge through data selection, data exploration, model creation and predictive action is the key to increasing revenues,
IBM InfoSphere Optim Test Data Management Solution Highlights Create referentially intact, right-sized test databases Automate test result comparisons to identify hidden errors Easily refresh and maintain
Next Generation Business Performance Management Solution Why Existing Business Intelligence (BI) Products are Inadequate Changing Business Environment In the face of increased competition, complex customer
SPSS Modeler Integration with IBM DB2 Analytics Accelerator Markus Nentwig August 31, 2012 Markus Nentwig SPSS Modeler Integration with IDAA 1 / 12 Agenda 1 Motivation 2 Basics IBM SPSS Modeler IBM DB2
Big Data Analytics DAMA NY DAMA Day October 17, 2013 IBM 590 Madison Avenue 12th floor New York, NY Tom Haughey InfoModel, LLC 868 Woodfield Road Franklin Lakes, NJ 07417 201 755 3350 email@example.com