1 SOLUTIONS BROCHURE Sybase IQ Supercharges Predictive Analytics Deliver smarter predictions with Sybase IQ for SAP BusinessObjects users Optional Photos Here (fill space)
2 SOLUTION FEATURES AND BENEFITS AT A GLANCE Optimize performance and enable more SAP BusinessObjects users Perform analysis with all leading data mining/predictive analytics tools, including SAS, IBM SPSS, KXEN, Fuzzy Logix, and Visual Numerics Ensure greater accuracy of predictive models by using full data sets rather than smaller samples Achieve performance gains of X via in-database analytics and query optimizer Obtain superior price/performance through extreme columnar data compression and associated physical storage cost savingsthan 3200 installs worldwide In an age of more demanding customer expectations and increasingly aggressive and adaptable competitors, organizations are rapidly moving from reliance on business intelligence (BI) tools that provide a snapshot of the past to those that provide an accurate picture of the present and a prediction of future trends. This branch of data mining known as predictive analytics is the latest front in the battle for the advancement of BI tool capabilities, as customers demand not only an understanding of what happened in the past, and why, but also want to be able to accurately predict what is going to happen in the future. Predictive analytics is a subset of advanced analytics and data mining that is concerned with predicting future events via mathematical models. The central element of predictive analytics is the predictor, a variable that can be measured to predict future behavior. For example, an insurance company is likely to take into account potential driving safety predictors such as age, gender, and driving record when issuing car insurance policies. A collection of such predictors is combined into a predictive model, which, when subjected to analysis, can be used to forecast future probabilities with an acceptable level of reliability. Businesses that rely on predictive modeling follow a process that involves collecting data, developing a statistical model, and then making predictions which enable validation or revision of the model. PREDICTIVE ANALYTICS DRIVES COMPETITIVE ADVANTAGE ACROSS INDUSTRIES AND FUNCTIONS Predictive Analytics is used in multiple industries and business functions, including verticals such as financial services, insurance, telecommunications, healthcare, retail and consumer packaged goods and functions such as sales and marketing, supply chain, and engineering. One of the most well-known applications is credit scoring, which is used throughout the financial services industry. Scoring models process a customer s credit history, loan application, customer data, etc., in order to rank-order individuals by their likelihood of making future credit payments on time. In telecommunications, customer analytics is performed by building models for segmentation and prediction that deliver key insights into which customers are most profitable, which are most likely to leave and ways to prevent that from happening, and determining which new subscription packages will be most likely to retain customers. In healthcare, predictive analytics promises the potential for combining data from physicians, individuals, labs, payers and beyond to transform the quality of healthcare. The move to electronic health records and the deployment of advanced clinical data systems drives an explosion in the amount of data that can be mined for intelligence. Providers can analyze a wide variety of variables including past medical history, medications, treatment plans, and environmental factors such as air quality or ozone level and ensure the right levels of care are in place to meet predicted demand. 2
3 In business processes, one area that benefits greatly from predictive analytics is pricing analysis done by sales or marketing teams. When products and services are priced sub-optimally, the consequences can include such problems as poor margins, low volume, or unacceptably low average sales. These problems raise the risk of lost opportunities to boost sales and profits and the inability to extract revenue from existing customers. Predictive analytics can be used to build models which successfully predict the optimal prices that customers will be willing to pay for goods and services that maximize revenue over a period of time. PREDICTIVE ANALYTICS DEMANDS EXTREMELY ROBUST BI INFRASTRUCTURES The value of predictive analytics is substantial and can be measured in terms of competitive advantage, significant cost savings, and greater revenues. Yet the very large data sets often required (sometimes hundreds of terabytes of raw data) and the query-intensive and predictive-scoring workloads involved place additional pressures on information technology infrastructures. "THE DIVISIONS NEED TO BE ABLE TO RUN ANALYSES ON THEIR OWN AND CUSTOMIZE THEM AS NEEDED. TO ACCOMPLISH THIS, WE NEED AN IT SYSTEM THAT IS EASY TO UNDERSTAND, FLEXIBLE, AND ABOVE ALL, VERY FAST." ANDREAS SEIBERT, HEAD OF IT BUSINESS DEPARTMENT, AOK HESSEN To provide accurate analysis, some models require a large base of historical information. As collections of data continue to grow, organizations face challenges both in mining such large quantities of data and in managing costs associated with storage and management of it. For example, comparing customer attributes or specifics of a marketing campaign within a region, a location, or for a particular product or service over three years provides a significantly clearer picture showing longer term trends and cyclical or seasonal patterns much better than can be gleaned from six months (or less) of data. The more data that is accessible for analysis, the more accurate predicted outcomes are likely to be. Implementing a predictive analytics solution also requires real-time delivery of complex answers across the organization. High-speed query processing ensures answers when they are needed, enabling organizations to monitor and respond to multiple predictors, synthesizing historical data as well as up-tothe- minute live data interactions. The need for models to address multiple products and services, customer types, risk factors, and predicates (e.g. sex, age, income, location, etc.) to correlate cause and effect makes high-performance analytics all the more crucial even as it makes it more challenging. All of these demands of predictive analytics consume significant computing resources and are placing added pressures upon already overworked IT departments and the systems established to support data warehousing or business intelligence. When using traditional databases or data warehouses, these workloads can significantly slow down system performance and result in higher costs due to extensive efforts to tune and optimize the data warehouse, or to add hardware resources. As a result, this slow down in system performance leads to lower BI application performance and limitations on the number of users, all of which spells disaster for making forward-looking decisions. "THIS SYSTEM GOES WELL BEYOND REPORTING. THE ABILITY TO DELVE INTO VERY LARGE VOLUMES OF DATA TO GAIN INSIGHTS INTO USAGE PATTERNS AND TO BEGIN TO IDENTIFY AND UNDERSTAND INDUSTRY TRENDS IS INVALUABLE." DUANE GREEN, VP OF SYSTEMS OPERATIONS, HEALTH TRANS Traditional enterprise data warehouses (EDWs) or online transaction processing (OLTP) systems consume large amounts of cpu cycles to read every byte of every row of large database tables and deliver the query result. They also require complex, space-consuming indexing and summary tables to perform queryintensive workloads well (which actually explode data sizes and slow down performance). In order to keep performance at target levels, more hardware must be added to the system and more database administrator (DBA) time must be used to tune queries. To solve these problems and enable predictive analytics to deliver real competitive advantage, an analytics server is needed which is architected and optimized from the ground-up for the massive data volumes and complex models required by this analysis. 3
4 "OUR RESPONSE TIMES ARE NOW 10 PERCENT OF THE PREVIOUS LEVELS. IN A RECENT PROJECT, WE UPDATED 10 MILLION RECORDS IN 10 MINUTES WE COULDN T DO IT AT ALL BEFORE! THE IMPACT ON OUR USERS HAS BEEN TREMENDOUS. BJORN BENTZEN, IT MANAGER, LINDORFF ANALYTIC SERVERS ACCELERATE PERFORMANCE FOR SAP BUSINESSOBJECTS USERS In cases where advanced and predictive analytics workloads are affecting the performance of OLTP systems or EDWs, many IT organizations offload critical data to a separate analytics server system to support the analyst or decision-making community of SAP BusinessObject users. Analytics servers are often a low-risk way to preserve the performance of operational systems or EDWs by separating distinct workloads and optimizing each system for its particular task. Relevant data is copied and placed on a separate server and storage repository, and refreshed at designated intervals depending on how current the data must be to serve the analytical needs of the business. Sybase IQ is a market-leading analytics server which enables organizations to perform deep analysis of massive amounts of data, accessed by multiple users requiring answers in real time. It was positioned in the leader s quadrant of the 2011 Gartner Data Warehouse Database Management System (DBMS) Magic Quadrant Report, and is the #1 column-store in the market with over 2,000 customers worldwide. SYBASE IQ SUPERCHARGES PREDICTIVE ANALYTICS Sybase IQ is a high-performance, scalable column-store database engine which has been repeatedly proven to meet the predictive analytics needs for a wide variety of businesses. It is an analytics server designed specifically for mission-critical analytics applications, and enables development and implementation of predictive models in forecasting, optimization, and simulation to support critical business processes. These include real-time credit scoring, inventory management, risk mitigation, customer churn management, insurance fraud detection, and sales optimization, regardless of the number of concurrent users, amount of data being searched, or query complexity. "WITH THE PERFORMANCE NOW AVAILABLE, THE NUMBER OF AD-HOC QUERIES HAS SHOT UP AND IT GIVES A DIFFERENT FLAVOR TO THE BUSINESS WHEN USERS CAN ANALYZE WHATEVER THEY DREAM UP." VARUNDEEP KAUR, MANAGER-IT, SPICE TELECOM Sybase IQ analytics technologies are specifically designed for speed, scalability, flexibility, cost-efficiency, and ease of deployment removing the barriers currently associated with immediate insight into unprecedented amounts of data. Sybase IQ has key architectural and technical capabilities that make it ideal for predictive analytics environments. These include: Column-Based Architecture for Extreme Performance The orientation of data on disk contributes significantly to the performance of predictive analytic database applications. The traditional row layout may work well for transactional systems, but an alternate approach is better suited to the demands of analytical processing. Most analytical queries only need to access a subset of record attributes, usually to satisfy join or aggregation conditions, which is why storing data values (record attributes) as separately accessible columns is the optimal structure for analytics. And this is how Sybase IQ produces its superior query performance results through a unique architecture combining a column-based data structure with patented indexing and a scalable grid. Scalability and Flexibility Sybase IQ s architecture allows for massive scalability of data, queries, or users, which in turn also provides greater analytics flexibility. The Sybase IQ multiplex architecture is a highly scalable shared disk grid technology that allows concurrent data loads and queries via independent data processing nodes connected to a shared data source. This provides the ability to add nodes as demands on the predictive analytics environment grow. Nodes can be designated as reader nodes (that can run readonly operations) or writer nodes (that can run both read-only and readwrite operations) to provide the scalability and flexibility needed to adapt the environment to rapidly changing requirements. Companies can scale their analytic environments to support tens of thousands of users, hundreds of terabytes of data, and concurrent mixed workloads without any reduction in data loading and query performance. 4
5 Cost Effectiveness via Data Compression Unfortunately, most traditional environments have difficulty handling even six months of historical data, and typically require massively expanding the footprint of that data with index tables in order to optimize it for query performance. Sybase IQ has sophisticated compression algorithms that reduce storage needs from 30 to 85 percent. Independently audited tests have confirmed that to store one petabyte of raw input data, Sybase IQ only required 160 terabytes of physical storage. Conversely, rather than compress data for storage, row-based databases explode the storage requirement to at least 3-4 times the raw data. With data compression, Sybase IQ brings a much clearer picture of an organization s business into view while providing tremendous cost savings. Advanced Data Management Sybase IQ has numerous data management features that are also key to predictive analytics including high speed data access (via selective traversal of the required columns for increased data access speed), rapid joins and aggregations (to quickly evaluate join conditions and incrementally compute aggregate function results), data compression (for significant decreases in storage needs/costs while maintaining high performance), and rapid data loading (by loading columns in parallel using multiple threads). SUPPORT FOR PREDICTIVE ANALYTICS METHODS AND TOOLS The purpose-built features and capabilities of Sybase IQ along with its market-leading position provide critical business and competitive differentiation benefits to companies wanting to advance their predictive analytics environments to optimize decision-making. In addition to the architectural and technical benefits already mentioned, Sybase IQ provides extremely strong support for specific complex predictive analytic methods and tools. These include in-database analytics, massive data sets for better modeling accuracy, and a growing partner ecosystem of leading analytics and visualization tools. "THE SUCCESS OF SYBASE IQ HAS PROMPTED US TO EXAMINE OTHER TOOLS WE USE TO MANAGE AND PROCESS DATA. WE VE ADDED AN INCREDIBLY POWERFUL ENGINE TO OUR SYSTEM, AND NOW WE WANT TO EXPLORE WAYS THAT WE CAN BETTER UTILIZE OUR SYSTEM TO LEVERAGE THE POWER SYBASE PROVIDES." EMMETT ZAHN, GROUP VICE PRESIDENT, U.S. INFORMATION TECHNOLOGY, TRANSUNION Complex Analytics Support Sybase IQ supports complex predictive analytics methods through important features including advanced query optimization, distributed queries, and in-database analytics. For query optimization, the query engine executes the best, most selective predicates and leverages the data column indices. For distributed queries, the latest version of Sybase IQ supports a parallel architecture that provides better concurrency, self-service ad hoc queries, and independent scale out of compute and storage resources. The Sybase IQ In-Database Analytics Option provides access to an extensive library of built-in numerical, statistical, and predictive analytics functions, so that query results can be immediately analyzed within the database, and then sent directly to a visualization tool. Additional libraries of pluggable analytical algorithms certified from third-party software vendors can also be used in this process. Modeling Accuracy Accuracy of results is critical for predictive modeling. For example, when attempting to pinpoint the reasons a particular offer is not receiving strong demand, or the likelihood that a particular customer may leave in favor of a competitor, even a slight inaccuracy can be quickly multiplied by thousands or even millions of instances. The combination of Sybase IQ s ability to return results quickly and operate on large datasets enables analysts and quantitative staff to use full datasets when scoring and tuning their models, instead of using smaller samples of data. This leads to higher accuracy in the results of the analysis, meaning that firms multiply the benefits of correct answers rather than the decisions that are almost correct. Partner Ecosystem Sybase IQ has a growing partner ecosystem that enables customers to create comprehensive predictive analytics solutions that are easy to acquire, integrate, and implement. Key third party offerings that integrate with Sybase IQ for predictive and advanced analytics solutions include Tableau Software for fast analytics and data visualization, SAS, a leader in business analytics software and services, which has introduced the SAS/ACCESS interface to Sybase IQ for out-of-the-box integration, Kapow Technologies for integrating unstructured data, and Fuzzy Logix which provides a rich collection of in-database analytics functions via its DB Lytix certified analytics library. 5
6 "WE NEED AN ENTERPRISE- WIDE ANALYTICS SYSTEM THAT DELIVERS RAPID ANSWERS TO JUST ABOUT ANY QUESTION A CLAIMS ADJUSTOR, UNDERWRITER, OR EXECUTIVE MAY HAVE. THE SYBASE IQ TECHNOLOGY IS FAR MORE ROBUST AND SCALABLE THAN OUR PREVIOUS SOLUTION, PROVIDING ACCURATE INFORMATION ON DEMAND, ALLOWING US TO IDENTIFY BOTH POOR PERFORMANCE AND BEST PRACTICES, SET BENCHMARKS AND ASSIST WITH INDIVIDUAL PERFORMANCE REVIEWS. SYBASE IQ AND SAP BUSINESSOBJECTS A CLEAR WINNER FOR OPTIMIZING YOUR BI ENVIRONMENT Sybase IQ infuses organizations with fast, flexible access to information and analytics. Coupled with SAP BusinessObjects BI applications, you can quickly visualize answers and generate reports for real insight, in real time. Whether you need to predict customer trends, forecast supply chain inventories more accurately, detect and prevent fraud, or optimize marketing results, Sybase IQ can enable more users and supercharge decision making. For more information, contact us today at or visit DAVID BROTHERTON, VICE PRESIDENT ANALYTICS, INNOVATION GROUP For information on our comprehensive Consulting and Education Services to support your Sybase technology initiatives, visit us at Sybase, Inc. Worldwide Headquarters One Sybase Drive Dublin, CA U.S.A sybase Copyright 2011 Sybase, Inc. All rights reserved. Unpublished rights reserved under U.S. copyright laws. Sybase and the Sybase logo are trademarks of Sybase, Inc. or its subsidiaries. indicates registration in the United States of America. SAP, the SAP logo, and SAP BusinessObjects are the trademarks or registered trademarks of SAP AG in Germany and in several other countries. All other trademarks are the property of their respective owners. 04/11
SAP BusinessObjects Business Intelligence SAP BusinessObjects Business Intelligence 4.0 Solutions Empowering the Real-Time, Mobile, Social, and Global Enterprise SAP BusinessObjects Business Intelligence
SAP Solutions for Analytics Big Data Analytics Guide Better technology, more insight for the next generation of business applications Big Data Analytics Guide 2012 Big Data Analytics Guide 2012 Big Data
An Oracle White Paper March 2013 Big Data Analytics Advanced Analytics in Oracle Database Advanced Analytics in Oracle Database Disclaimer The following is intended to outline our general product direction.
BIG DATA ANALYTICS Gain competitive advantage from the combination of big data and advanced analytics E M C P E R S P E C T I V E TABLE OF CONTENTS Back to the Future: the Advent of Big Data 1 EXPLOTING
Global Headquarters: 5 Speen Street Framingham, MA 01701 USA P.508.872.8200 F.508.935.4015 www.idc.com W H I T E P A P E R B i g D a t a : W h a t I t I s a n d W h y Y o u S h o u l d C a r e Sponsored
For Big Data Analytics There s No Such Thing as Too Big The Compelling Economics and Technology of Big Data Computing March 2012 By: 4syth.com Emerging big data thought leaders Forsyth Communications 2012.
IBM Software Big Data & Analytics Thought Leadership White Paper Better business outcomes with IBM Big Data & Analytics The insights to transform your business with speed and conviction 2 Better business
February 2009 Seeding the Clouds: Key Infrastructure Elements for Cloud Computing Page 2 Table of Contents Executive summary... 3 Introduction... 4 Business value of cloud computing... 4 Evolution of cloud
QLIKVIEW FOR LIFE SCIENCES A Clinical and Operational Breakthrough for the Life Sciences Industry TABLE OF CONTENTS Running on Insight 3 The Answer to Complexity 3 Doing More, Doing it Better 4 The Integrated
Plug Into The Cloud with Oracle Database 12c ORACLE WHITE PAPER DECEMBER 2014 Disclaimer The following is intended to outline our general product direction. It is intended for information purposes only,
QLIKVIEW AND BIG DATA: HAVE IT YOUR WAY A QlikView White Paper November 2012 qlikview.com Table of Contents Executive Summary 3 Introduction 3 The Two Sides of Big Data Analytics 3 How Big Data Flows from
J U L Y 2 0 1 2 OpenText Enterprise Information Management CIOs are under siege Do more with less is no longer an ideal, it s a mandate. With growing volumes and a host of information formats to manage
INTELLIGENT BUSINESS STRATEGIES W H I T E P A P E R Architecting A Big Data Platform for Analytics By Mike Ferguson Intelligent Business Strategies October 2012 Prepared for: Table of Contents Introduction...
Retail Banking Business Review Industry Trends and Case Studies U.S. Bank Scotiabank Pershing LLC Saudi Credit Bureau Major International Bank Information Builders has been helping customers to transform
Microsoft Dynamics NAV 2009 Business Intelligence Driving insight for more confident results White Paper November 2008 www.microsoft.com/dynamics/nav Table of Contents Overview... 3 What Is Business Intelligence?...
Big Data Mining with SAP HANA Reinventing Businesses through Innovation, Value & Simplicity Dr Asadul Islam Senior Researcher Strategic Customer Engagement, Product & Innovation Innovate with speed SAP
Microsoft Dynamics NAV 2009 Business Intelligence Driving insight for more confident results White Paper November 2008 www.microsoft.com/dynamics/nav Table of Contents Overview... 3 What Is Business Intelligence?...
How to embrace Big Data A methodology to look at the new technology Contents 2 Big Data in a nutshell 3 Big data in Italy 3 Data volume is not an issue 4 Italian firms embrace Big Data 4 Big Data strategies
An Oracle White Paper June 2009 An Overview of Oracle Business Intelligence Applications Executive Overview... 1 Introduction... 1 The Build Versus Buy Decision... 3 Solving the Data Access Challenge...
The Infrastructure for Information Management: A Brave New World for the CIO WHITE PAPER SAS White Paper Table of Contents Trends and Drivers for Information Infrastructure.... 1 Objectives for Organizational
white paper Boosting Retail Revenue and Efficiency with Big Data Analytics A Simplified, Automated Approach to Big Data Applications: StackIQ Enterprise Data Management and Monitoring Abstract Contents
CRISP-DM 1.0 Step-by-step data mining guide Pete Chapman (NCR), Julian Clinton (SPSS), Randy Kerber (NCR), Thomas Khabaza (SPSS), Thomas Reinartz (DaimlerChrysler), Colin Shearer (SPSS) and Rüdiger Wirth
Internet of Things Next-Generation Business and the Internet of Things Opportunities and Challenges Created by a Connected and Real-Time World Table of Contents 3 The Internet of Things Is Redefining Enterprise
1 Contents Introduction. 1 View Point Phil Shelley, CTO, Sears Holdings Making it Real Industry Use Cases Retail Extreme Personalization. 6 Airlines Smart Pricing. 9 Auto Warranty and Insurance Efficiency.
CHAPTER9 BUSINESS INTELLIGENCE THE VALUE OF DATA MINING Data mining tools are very good for classification purposes, for trying to understand why one group of people is different from another. What makes