High Performance Analytics through Data Appliances

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1 High Performance Analytics through Data Appliances Deriving more from data Sankar Natarajan Practice Lead (Netezza & Vertica Data Warehouse Appliance) at Wipro Technologies

2 Table of contents 01 Does your data need more attention? 01 Limitations of traditional database systems 02 Data Appliances to the rescue 02 Manage the future with Data Appliances 03 About the Author 03 About Wipro Ltd.

3 Does your data need more attention? Limitations of traditional database systems Today, businesses are expecting answers to questions that were previously considered as impossible to answer. Credit card companies want to offer customers best rates based on their existing banking patterns and their long-standing relationships. Insurance companies want to identify fraudulent claims before they surface. How can a retail white goods store automatically identify a customer s need for a loan in order to complete a purchase? Can marketing campaign tactics be adjusted dynamically based on current performance and end goals? How can an airline identify customers who are about to move to a competitor? How can a stock broker move closer to predicting the movement of certain scrips? Can hospitals predict readmissions and prevent them? All this is being made possible by in-database analytics a powerful new method of examining data using an emerging generation of sophisticated data appliances. The data appliances or purpose build devices are pre-loaded with Hardware (processor, memory and storage) and software (server and database) designed to address specific workload. In-database analytics, as the name suggests, has the logic to shift through the data and extract intelligence from the data storage location itself. This eliminates the traditional issues associated with extracting, preparing and shipping large data sets to analytical engines. Extracting intelligence from traditional databases is a time-intensive process. The problem is that organizations today want answers to their business problems at Google speed, customers want lightning-fast personalized attention and enterprise users want zero-latency in their applications. No one has the time to wait. Taking a hit are traditional databases. They are unable to keep pace with the demand from data-hungry analytics engines that make the magic happen in the background. The fall out is that the capability of the traditional enterprise data warehouse (EDW) is being questioned. While being reliable, the inherent nature of traditional databases is not suitable for current business demands. Preparing the data for model development often involves a slow and laborious process of integrating data assets from a variety of sources. Value is seeping out because of: Poor speed and agility of traditional systems Unnecessary data movement, costing time and making the data vulnerable to security threats Data duplication due to data being available in EDW as well as analytic server, resulting in high costs and management implications Data samples, instead of complete data sets, used to speed up the process, with less than optimal output Inordinate amount of human effort involved in data management whereas the same effort could have been made more productive when directed to analytics Inability of the system to scale up as the data volume and diversity grows, potentially crippling operations 01

4 Data Appliances to the rescue The solution lies in a new breed of appliances designed for high data volumes. These data appliances are loaded with OS, database management systems (DBMS), memory, storage, fail-over systems and critically, the analytical models themselves. The data in these appliances is available in close proximity to the analytical engine, eliminating the need to move data. The most immediate impact of this is on the access to parallel processing power and increasing data security. But equally important the shared-nothing architecture reduces the cost of managing the system and radically reduces the analytical cycle time. Processes that earlier took days to complete can now be done in minutes. Identify market trends and factors that can help shape future business decisions (such as investments and product launches) Lower costs by optimizing resources based on business forecasts The age of Big Data and analytics is here to stay. Leveraging data appliances with their powerful analytical capabilities allows organizations to build new capabilities, make accurate and timely decisions, free human resources and optimize costs. Organizations of the future cannot afford to ignore these advantages any longer. Database appliances can initially be more expensive than traditional relational database management systems (RDBMS). But over a period of time the ROI justifies the investment. We need look no further than one simple fact to realize why ROI is guaranteed: In the past, every industry used data sampling to draw intelligence, make predictions and develop strategies. Today, high performance database appliances are making it possible to use every data point to train their analytical models. The accuracy and reliability delivered by such systems is unparalleled. Manage the future with Data Appliances The advantage of using database appliances is that they eliminate the errors induced by antiquated methodologies. They also free up people resources to focus more on analytics activities rather than data extraction and preparation. The benefits of in database analytics are wide ranging: Gain insights into risk and thereby decrease regulatory penalties, cost of legal action, reputational loss, impact on profitability Understand customer needs and behavior to customize offers and improve sales outcomes Maximize the value of data through revenue-generating insights (such as new product initiatives, productive partner programs, channel optimization) 02

5 About The Author Sankar Natarajan Sankar is Practice Lead (Netezza & Vertica Data Warehouse Appliance) at Wipro Technologies. Sankar has more than 14 experienced in DW & BI Professional with incredible Technical experience acquired over the years in diverse areas such as Practice Lead, Pre-sales, Architecting, Designing, Development, Implementation of large Data Warehouse involving Data Migration of large Data centric projects for Insurance & Securities and Capital Markets. Sankar Natarajan holds a Master of Computer Application from Madurai Kamaraj University. About Wipro Ltd. Wipro Ltd. (NYSE:WIT) is a leading Information Technology, Consulting and Business Process Management company that delivers solutions to enable its clients do business better. Wipro delivers winning business outcomes through its deep industry experience and a 360 degree view of "Business through Technology" - helping clients create successful and adaptive businesses. A company recognized globally for its comprehensive portfolio of services, a practitioner's approach to delivering innovation, and an organization wide commitment to sustainability, Wipro has a workforce of over 150,000 serving clients in 175+ cities across 6 continents. For more information, please visit or write to us at 03

6 DO BUSINESS BETTER CONSULTING SYSTEM INTEGRATION BUSINESS PROCESS SERVICES WIPRO LIMITED, DODDAKANNELLI, SARJAPUR ROAD, BANGALORE , INDIA TEL : +91 (80) , FAX : +91 (80) , North America Canada Brazil Mexico Argentina United Kingdom Germany France Switzerland Nordic Region Poland Austria Benelux Portugal Romania Africa Middle East India China Japan Philippines Singapore Malaysia South Korea Australia New Zealand WIPRO LTD No part of this document may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, and printing) without permission in writing from the publisher, except for reading and browsing via the world wide web. Users are not permitted to mount this booklet on any network server. Wipro Limited 2015 IND/B&T/JUL-AUG 2015

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