SAP Solutions for Analytics. Big Data Analytics Guide Better technology, more insight for the next generation of business applications

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1 SAP Solutions for Analytics Big Data Analytics Guide Better technology, more insight for the next generation of business applications

2 Big Data Analytics Guide 2012 Big Data Analytics Guide 2012 Big Data Analytics Guide 1

3 Big Data Analytics Guide: 2012 Published by SAP 2012 SAP AG. All rights reserved. SAP, R/3, SAP NetWeaver, Duet, PartnerEdge, ByDesign, SAP BusinessObjects Explorer, StreamWork, SAP HANA, and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP AG in Germany and other countries. Business Objects and the Business Objects logo, BusinessObjects, Crystal Reports, Crystal Decisions, Web Intelligence, Xcelsius, and other Business Objects products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of Business Objects Software Ltd. Business Objects is an SAP company. Sybase and Adaptive Server, ianywhere, Sybase 365, SQL Anywhere, and other Sybase products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of Sybase Inc. Sybase is an SAP company. Crossgate, EDDY, B2B 360, and B2B 360 Services are registered trademarks of Crossgate AG in Germany and other countries. Crossgate is an SAP company. All other product and service names mentioned are the trademarks of their respective companies. Data contained in this document serves informational purposes only. National product specifications may vary. These materials are subject to change without notice. These materials are provided by SAP AG and its affiliated companies ( SAP Group ) for informational purposes only, without representation or warranty of any kind, and SAP Group shall not be liable for errors or omissions with respect to the materials. The only warranties for SAP Group products and services are those that are set forth in the express warranty statements accompanying such products and services, if any. Nothing herein should be construed as constituting an additional warranty. Library of Congress Cataloging-in-Publication Data SAP Big Data Analytics Guide 2012: Better technology, more insight for the next generation of business applications Edited by Don Marzetta. p. cm. ISBN Big data. 2. Analytics. 3. Databases 2 Big Data Analytics Guide

4 Welcome to Big Data Analytics Guide 2012 Big Data Means Big Business By Steve Lucas, Executive Vice President and General Manager, Database and Technology, SAP With more and more people spending much of their existence in the digital world whether it s for work, play, learning, or to socialize the amount of data being generated is truly astounding. Just think about the number of SMS messages and s sent, phone calls placed, and Facebook updates made every minute, and it boggles the mind how much data is traversing networks around the world. At SAP, we believe this confluence of events is a golden opportunity for enterprises to rethink how they do business, and our goal is to help them do their jobs better than ever. As co-chair of the Big Data commission sponsored by TechAmerica, I m hearing firsthand how government and private enterprise is evaluating how to embrace a Big Data world. But this requires organizations to take a new approach to data. Innovations in in-memory computing are turning the whole idea of data management on its head by allowing enterprises to get rid of the complexity that s been encroaching on their systems. That s why we ve built the real-time data platform with SAP HANA at its core, giving enterprises the foundation they need to embrace Big Data. But we recognize a platform is not enough. Together, we need to dream new ways to do business by leveraging Big Data insights. And at SAP we re serious about making this a reality by investing in new businesses through the $155 million venture start-up fund dubbed the SAP HANA Real-Time Fund, through the HANA Start-up program, through innovation labs around the world, and most importantly by co-innovating with our customers. To further our path toward better, smarter ways of working with data, we ve put together a series of articles in the Big Data Analytics Guide. Within these pages you ll find real solutions, real ways of operating, real results and perhaps most importantly real technology that can be used in your business today. The guide outlines the opportunity and business case for Big Data in the first chapter, and subsequent chapters look at SAP technology innovations, real-world examples, and insights from analytic leaders who are on the forefront of the Big Data market. In the last chapter, you ll find a set of interesting market research statistics that highlight how C-level executives are using Big Data now and their plans for using it to their advantage in the future. So join in the conversation and co-innovate with us to re-invent business. n Big Data Analytics Guide 3

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6 Table of Contents 03 Welcome to Big Data Analytics Guide 2012 Big Data Means Big Business By Steve Lucas, Executive Vice President and General Manager, Database and Technology, SAP 06 Big Data Opportunity 06: Measuring the Value and Potential Yield of Big Data Projects By Dan Lahl, Senior Director, Product Marketing, SAP 08: The Numbers are In: Early Stage ROI and Proof of Concept By David Jonker, Product Marketing Director, SAP 30 Analytics Advantage 30: Data Variety Is the Spice of Analytics By Amr Awadallah, CTO, Cloudera 33: Text Analytics for Speed Reading Do You Mean What You Say? By Seth Grimes, Strategy Consultant and Industry Analyst, Alta Plana 36: Image Recognition, Pattern Identification, and the New Memory Game By Joydeep Das, Director, Data Warehousing and Analytics Product Management, SAP 10: Analytics in the Cloud: Traversing a Legal Minefield By Dr. Brian Bandey, Principal, Patronus 38: Technology Alone is Not the Answer By Byron Banks, Vice President of Business Analytics Marketing, SAP 13: Big Data Analytics Earns High Scores in the Field 016: Big Data Is Only a Small Part of the Opportunity By Mike Upchurch, Chief Operating Officer, Fuzzy Logix 18 Business Analytics Roadmap 18: Business Value through Operational BI By Claudia Imhoff, President of Intelligent Solutions, Inc. and Founder of the Boulder BI Brain Trust 21: Real-time Data Platform for a Real-time World By Amit Sinha, Head, Database and Technology Innovation, SAP 23: How HANA Changes the Database Market By Ken Tsai, Vice President HANA Solution Marketing, SAP 25: DBTA: Data Marts Can t Dance to Data s New Groove By John Schitka, Senior Product Marketing Manager, SAP Sybase IQ 40 Analytics Innovations 40: What s All the Hadoop-la About? By Wayne Eckerson, Principal, BI Leader Consulting 43: Fast Flowing Decisions Through Streams of Data By Irfan Khan, Senior Vice President and Chief Technology Officer, SAP Database and Technology 45: Age of Influence: Making the Most of Social Networks By Bruno Delahaye, Senior Vice President Worldwide Business Development, KXEN 48: Embracing a Standard for Predictive Analytics By Michael Zeller, Ph.D., CEO, Zementis 51: How Modern Analytics R Done By Dave Rich, Chief Executive Officer, Revolution Analytics 54: Navigating a 4G World By Greg Dunn, Vice President, Sybase : In-Database Analytics: Reducing Travel Time By Courtney Claussen, SAP Sybase IQ Product Manager, SAP 57: Increasing the IQ of Everyone with Analytics By Jürgen Hirsch, CEO, Qyte GmbH 60 Market Data 60: The Big Deal with Big Data 68 Company Index Big Data Analytics Guide 5

7 Big Data Opportunity Measuring the Value and Potential Yield of Big Data Projects Where should companies look for the return on investment that determines whether, or how much, Big Data projects pay off? By Dan Lahl, Senior Director, Product Marketing, SAP Data analytics has traditionally been expensive and inefficient, but new analytical platforms optimized for Big Data are heralding a brave new world. Hadoop, an open-source Apache product, and Not Only SQL (NoSQL) databases don t require the significant upfront license costs of traditional systems, and that s making setting up an analytics platform and seeing a return on the investment (ROI) more accessible than ever before. Costs are coming down, but this is no free lunch. Crunching Big Data analytics still requires hardware, database administrators, developers to build the models, business intelligence tools, and training for the people who will use it to make decisions. Where to Look for ROI Companies can look for ROI in these three ways: do what they re already doing better, do more of what they re already doing, and do things they ve never thought about before. Big Data provides new solutions for current problems, converts incomprehensible data to actionable business recommendations, and makes previously impossible business models possible. The first, and often most compelling, benefits of upgrading to a Big Data platform are in speed and cost savings gains. The new systems allow organizations to do what they are already doing faster, better, and cheaper. What used to take hours or days suddenly only takes minutes. As data volume, velocity, and variety have grown, legacy data warehouse systems have been bogging down unable to handle bigger data, more users, and increasingly complex queries. Results take longer. Users get frustrated and stop using the data because it takes too long. Many data warehouses are in this predicament today, having hit a performance and scalability 6 Big Data Analytics Guide

8 wall. Big Data technologies break through these roadblocks, delivering faster performance and higher availability. The second benefit is the ability to do more. Not only will a Big Data system not bog down at current usage rates, but it can also handle more users, more data, and more complex queries. For example, instead of storing a year s worth of loan default records, a lender can store 30 years worth and perform more detailed analyses, with more accurate results. Additionally, a Big Data system can handle a mixed workload. Instead of processing a few queries from a handful of power users, it can handle a bunch of short technical queries by a large front-line service team, such as a customer lookup that identifies cross-sell and up-sell opportunities in real time. The third piece of Big Data ROI is that it opens up new opportunities. Once the data has been liberated and employees can get at it, they find all kinds of new ways to use Big Data. For example, if a utility company had previously outsourced the analytics from its self-service Web pages and analyzed that data separately from its other customer service channels, Big Data technology can bring all the analysis together, in-house. That opens the possibility to track customer behavior in multiple channels at the same time. This kind of 360-degree visibility provides a more accurate picture of the customer experience and customer satisfaction, providing new and deeper insight into any business. Show Me The Metrics When establishing metrics for any new analytics project, focus in two areas: employee usage of the resulting intelligence and key performance indicators for the processes where analytics will be used. First, create metrics and track data for the project itself. Think of employees as the data team s customers, and establish measures that show the total amount of information consumed in the organization: who s using the data, how much are they using it, and what are they using it for? The higher the usage rates, the better. Second, wherever data becomes part of a business process, such as in customer support or sales, companies can measure customer satisfaction, sales figures, and other established metrics. Comparing numbers before and after the Big Data implementation should clearly show what the organization has gained as a result as well as other opportunities to collect data and put it to work. Used this way, analytics often deliver a huge ROI, helping a company identify problems early on (or before they happen) and take steps to fix (or prevent) them. Predictive analytics Wherever data becomes part of a business process, such as in customer support or sales, companies can measure customer satisfaction, sales figures, and other established metrics. can recognize customer care or sales opportunities at the right moment, offering discounts or related products that increase satisfaction and boost sales. Success for the Long Term Once an enterprise decides that its analytics project has legs, there are some well-established tactics to help assure its long-term success. Foremost is communication. Keep everyone in the loop from strategy to deployment and beyond. Key individuals should never be blindsided by hiccups or new developments in the process. New technology built to handle today s veritable deluge of data is bringing down the cost of analytics, delivering better performance, and helping companies put data to work in new ways. Measuring the ROI of a Big Data project can be accomplished by establishing company metrics that highlight analytics usage, encouraging data teams and employees to define actionable reports, and incorporating analytics into more decision making throughout the enterprise. n Dan Lahl has been in high tech for almost 30 years. In addition to bringing to market SAP Sybase SQL Server, SAP Sybase ASE, and SAP Sybase IQ, Lahl has evaluated multiple emerging technology areas leading to EII, ETL, and GRID technology purchases for the company. Big Data Analytics Guide 7

9 ENTERPRISES, BUSINESSES, AND BUSINESSES AND GOVERNMENTS ARE SEEING SIZABLE RETURNS ON THEIR INVESTMENT IN BIG DATA ANALYTICS PROJECTS. The Numbers are In: Early Stage ROI and Proof of Concept By David Jonker, Product Marketing Director, SAP As new technology helps organizations put today s massive data sets to use, real-world examples and research are proving that analytics helps cut costs, increase revenue, eliminate waste, and otherwise boost the bottom line. Real Returns Recent reports confirm that organizations around the world are finding an edge by incorporating advanced analytics into business processes, leading to informed decisions around customer satisfaction, new service success rates, and competitive analysis (see Table 1.) American Airlines has saved roughly $1 million annually in fraud detection, helping the company identify forms of fraud it never knew existed and eliminating the loopholes that criminals were exploiting. The Sao Paulo State Treasury Department has so far identified $100 million in untaxed earnings, which allowed tax inspectors to adopt a proactive approach to tax evaders, investigating telltale behavior patterns in its data and taking corrective measures early on, rather than punitive ones after the fact. Organizations around the world are finding an edge by incorporating advanced analytics into business processes. AOK Hessen, a public health insurance organization in Germany, uses pattern analysis to detect fraudulent invoicing, and it has reclaimed US $3.2 million of unjustified charges. Lawson HMV Entertainment, a leading retailer in Japan of DVDs, Blu-ray discs, books, and games, integrates in-store and Website data into one massive marketing database that the company uses to fuel its targeted campaigns to customers. The result has been 3 to 15 times higher purchase rates and double digit revenue growth. Table 1. Big Data Analytics ROI Who American Airlines State of Sao Paulo, Brazil Cell AOK Hessen HMV Japan What $1 million annual fraud detection $100 million untaxed earnings $20 million saved on one project $3.2 million fraud detection 3 to 15 times higher purchase rates Research Backs Up the Numbers After studying 179 large public companies that use what they call data-driven decision making, authors from MIT and the Wharton School concluded that organizations using analytics in their decision-making processes derived 5 to 6% better output and productivity than if they had not used analytics. Researchers at the University of Texas studied how analytics affected the finances, customer activities, and operations of 150 Fortune 1000 firms. According to the research, the product development area alone justifies deploying analytics 8 Big Data Analytics Guide

10 According to a University of Texas study, product development alone justifies deploying analytics for a typical Fortune 1000 enterprise. Table 2. Legacy vs. Next Generation Real-Time Analytics in Telecommunications Legacy Analytics Infrastructure Next-Generation Real-Time Analytics Infrastructure Storage cost High Low Analytics Offline Real time Data loading speed Low High Data loading time Long Average 50% faster Administration time Long Average 60% faster Complex query response time Hours/days Minutes Data compression technique Not mature Average 40 to 50% more data compression Support cost High Low for a typical Fortune 1000 enterprise. The study states, Revenue due to a company s ability to innovate new products and services increases with data accessibility and specialty products and services, which, in turn, is positively affected by data intelligence. How positive is that impact? According to the analysis, a $16.8 billion company might see an extra $64 million top-line dollars over five years if analytics are put into the hands of more authorized employees who can make use of information to better spot trends, demand patterns, improve recommendations for decision making and profile match. All of those benefits could contribute to new-product revenue. Such firms could also add $14 million in new customer sales annually. The University of Texas report also discovered that the comprehensive use of analytics inside a company improved results in the operational areas of asset utilization, forecasting and planning, and on-time delivery of products or services. For example, wider use of analytics can lead to an 18.5% improvement in planning and forecasting for a typical firm studied. The sources of these significant ROI metrics vary by company and industry. To examine an example in depth, consider how using analytics for real-time applications has impacted the telecommunications market (see Table 2). You can see how a modern analytics environment leaves legacy decision-support systems in the proverbial dust. n David Jonker is focused on market strategy for the SAP Data Management and Analytics product lines, including SAP Sybase IQ, ASE, Replication Server, and SQL Anywhere. Jonker s career includes more than 10 years in software engineering and product management roles before leading the SAP product marketing teams for data management and analytics. Big Data Analytics Guide 9

11 TO AVOID LEGAL LIABILITY, ORGANIZATIONS THAT WANT TO REAP THE BENEFITS OF CLOUD-BASED BIG DATA ANALYTICS MUST CAREFULLY VET PARTNER TECHNOLOGY. Analytics in the Cloud: Traversing a Legal Minefield By Dr. Brian Bandey, Doctor of Law When a corporation mines the Big Data within its IT infrastructure a number of laws will automatically be in play. However, if that corporation wants to analyze the same Big Data in the cloud a new tier of legal obligations and restrictions arise. Some of them quite foreign to a management previously accustomed to dealing with its own data within its own infrastructure. A corporation holding Big Data will possess different types of data which the Law will automatically classify and attach lawbased obligations. Some of that data may not be owned by the corporation. It may be a third party s data which it holds pursuant to a Confidentiality Agreement. Such agreements may not only produce Often law-based security obligations cannot be delegated to the cloud services provider. Legal responsibility may remain with the data controller. obligations that go to nondisclosure; but may also restrict the uses to which the data can be put and define what level of security is to be employed. Other data might be owned by the corporation, but identifies living individuals (whether directly or indirectly). Data Protection Law (as it s generally known) is concerned with the access, use, movement, and the technological safeguards to prevent disclosure of Personal Identifying Information (PII). A corporation will also own secrets about itself which, if disclosed, might cause irreparable damage. Officers owe stakeholders a legally binding duty of care to take all reasonable precautions to ensure the security of such information. Due to restrictions on processing, both from Data Protection and Confidentiality Laws, care will need to be taken when building the data warehouse to be analyzed. Certain classes of data may need to be excluded. All of these different types of law intersect over the area of Big Data storage, security, and processing. They produce a matrix of law-based obligations which, in many areas, cannot be delegated or avoided only met. Security But what happens when we translate that matrix into the cloud? The first matter is that of security. Breaches occasioning the loss of data can cause an abundance of law-based difficulties: from breach of contract, fines under Data Protection Law, uncapped damages due to the release of third-party secrets and so on. But why is this the first matter? The corporation cedes actual security to its cloud services provider. Instead of the corporation implementing its own 10 Big Data Analytics Guide

12 The cloud computing architecture must be able to identify what data is in which jurisdiction and, if necessary, keep it there. security directly; that role is handed to the cloud services provider. A great deal is said about service level agreements on this subject their utility and importance. Frankly, I don t see it that way. What remedies are available to the corporation under a SLA other than contractual remedies? Usually none! In my opinion, it is highly likely that money damages will not put the corporation back in the position it would have been but for the security/contractual breach. No. What is needed is the choice of a correct cloud security architecture of sufficient robustness. One may ask why is that a legal topic? Surely it is strictly an IT matter? I take the view that one must look to the propensity of the cloud technology itself to cause the corporation legal exposure. The Duty of Care owed by officers to their stakeholders, the corporation s duty to those persons whose PII it holds, and the contractual obligations it owes with respect to third-party confidential information all compel the corporation to exercise expertise, care, and prudence in the selection of a technologically secure cloud computing environment. This means that they must look beyond the cloud services provider per se, and discharge the Duty of Care through due diligence on the technology underpinning it. How capable is the architecture of securing the data? Is the architecture built to be secure and resistant to the correct range of security threats? How robust and secure is it against measurable benchmarks? Secondly, there are significant technological differences between a cloud computing environment and a corporation s owned infrastructure. I am referring especially to the integrity of multi-tenancy architectures. A leaky multi-tenancy system must cause a significant probability that the corporation will be in breach of its obligations to many prospective litigants. Thus real attention will need to be given to the architecture that isolates one data set from another and keeps it isolated. These are not matters of academic technical interest but go to the ability of the corporation to discharge what are often, non-delegable, unavoidable legal duties. Personal Identifying Information Moving on from security; there is the matter that is generally referred to as the trans-border movement of PII. Many countries either restrict or prohibit the exporting of PII. To do so can even be a corporate crime certainly exposing the wrongful exporter to the likelihood of a hefty fine, adverse publicity, and reputational loss. Thus the problem for our conceptual corporation is the nature of cloud computing itself. By that I mean that the advantages of scalability, flexibility, and economies of scale that are accessed through the technological advantage of distributing data across a number of servers which may not all be in the same country. Thus PII may be automatically exported illegally. There are two avenues open to the corporation to obviate this unlawfulness. The first is to choose a Big Data warehousing and analytics architecture which, with certainty, can confine data storage and processing to servers residing in nominated legal jurisdictions. The cloud computing architecture must be able to identify what data is in which jurisdiction and, if necessary, keep it there. The second is to transform the PII so that it no longer constitutes, in law, PII. Data which is not PII cannot be subject to data protection law. For some time now, medical researchers have shared patient information internationally through a process of either anonymize or pseudonymization. Anonymization is a process whereby the identifier sub-data is removed, prior to export, thus enabling any type of processing, anywhere. The data Big Data Analytics Guide 11

13 needs to be of a configuration that can still be effectively processed in the absence of identifier sub-data. Where the presence of a form of identifier sub-data is required for processing (or analysis) pseudonymization is used. The aim of these two forms of de-identification is to obscure the identifier sub-data items within the patient records sufficiently that the risk of potential identification of the subject of a patient record is minimized to acceptable and permissible legal levels. Although the risk of identification cannot be fully removed, it can often be minimized so as to fall below the defining threshold. Analytics There is no reason, in law, why Big Data analytics cannot be performed lawfully in the cloud. However, in order to do so, significant attention needs to be directed to the actual software and hardware programming architectures to be employed and match those to the matrix of laws which operate over the storage, use, processing, and movement of data. It may seem strange that I am advocating an almost technology-centric solution to what is clearly (and perhaps solely) a law-based problem. But as I said before money damages in these scenarios will never, in my opinion, be sufficient compensation for the owners of Big Data. Rather, the requirements of the law need to be soundly and accurately matched and, indeed, mapped onto the cloud computing technology at hand. Only then can the minefield of Big Data analytics in the cloud be successfully traversed without an explosion. n Dr. Brian Bandey is acknowledged as one of the leading experts on Computer Law and the international application of Intellectual Property Law to Computer and Internet Programming Technologies. His experience in the global computer law environment spans more than three decades. He is the author of a definitive legal practitioners textbook and his commentaries on contemporary IT legal issues are regularly published throughout the world. Dr. Bandey is now well-advanced upon the unique route of studying for a Second Doctorate of Law advancing the current state of the art in the Intellectual Property in Internet and Cloud Technologies with St. Peter s College at the University of Oxford in England. In order for Big Data analytics in the cloud to be lawful, the requirements of the law need to be accurately mapped onto the cloud computing technology at hand. 12 Big Data Analytics Guide

14 INTERNET METRICS, TELECOMMUNICATIONS, AND FINANCIAL SERVICES PROVIDERS ARE USING BIG DATA ANALYTICS TO BOOST PROFITS AND ADD CUSTOMERS. Big Data Analytics Earns High Scores in the Field While industries vary greatly in what they need from their data, and even companies within the same industry are unalike, virtually every organization in every market has these two related problems: what to do with all the information pouring into data centers every second, and how to serve the growing number of users who want to analyze that data. Massive data sets, even those including variable data types like unstructured data, can be analyzed. Not only are they In healthcare, the move to electronic medical records and the data analysis of patient information are being spurred by estimated annual savings to providers in the tens of billions of dollars. ready for analysis, modern Big Data analytic software performs faster and readily scales to handle as many users and as much data as needed. By seeking out Big Data inside and outside your organization and using it to push intelligence deep into the enterprise, organizations can be more responsive, more competitive, and more profitable. At the heart of these endeavors are columnarbased databases and in-database analytics. Companies from many industries are already taking advantage of technological advances in storing and analyzing Big Data to gain business insight and provide better service to customers. These real-world examples prove the value of Big Data analytics. comscore Stops Counting Visitors and Starts Counting Profits comscore, a cloud-based provider of analytics services and solutions for the ecommerce marketplace, realized when it began operations that the focus of Internet marketing was shifting from visitor counts to profitability. comscore s Customer Knowledge Platform provides a 360-degree view of Big Data Analytics Guide 13

15 customer behavior and preferences as they visit sites throughout the Internet. The service monitors surfing and buying behavior at every site visited by consumers who have opted in to having their Internet behavior analyzed. With millions of Web users signing up to be monitored, the data collected was enormous. comscore applies its analytics to more than 40 terabytes of compressed data, while adding close to 150 gigabytes every week. Despite this volume of data, query-response time is exceptional. We are able to mine the data and produce results for our customers much more quickly. That helps them market more effectively and generate more business, says Ric Elert, vice president of engineering at comscore. The company achieves 40% compression ratios using column-store technology. Had they used a traditional approach, comscore says its storage costs would have been much higher. The compression is extremely important to us because we have fire hoses of data, says Scott Smith, vice president data warehousing. We have a tremendous amount of data. More data than most people will ever see. Suntel Introduces Customized Service Offerings to Sri Lanka As Sri Lanka s fastest growing telecommunications company, Suntel has 500,000 customers. The company puts the latest technology, innovative thinking, and an unprecedented service commitment into customizing telecommunications solutions for its subscribers. We re exploring ways to exploit this data trove to develop ways to customize the customer experience across different sized customers and to implement programs to cross-sell and upsell our services. Tarik Markar Director of Information Technology and Solutions Delivery Suntel Suntel s traditional relational database was unacceptably sluggish. We reached a point, explains Tariq Marikar, director of information technology and solutions delivery, at which we were seeing a 20% overload on our production database, which was unacceptable to us. Additionally, we wanted to be able to run reports and queries against years of historical data rather than just a few months. We knew we needed to create a separate repository a data warehouse specifically designated and designed for reporting and analytics in order to solve this problem. Suntel achieved its goal by adopting a column-store data warehouse designed for advanced analytics. It s very important to our business to be able to view large volumes of historical data, says Marikar. As with comscore, the compression capability has meant that the data residing on our production database only requires about one-third the space. The new platform can scale as Suntel increases the numbers of users and explores other strategies for tapping into this valuable data. We re exploring ways to exploit this data trove to develop ways to customize the customer experience across different sized customers and to implement programs to cross-sell and upsell our services, says Markar. Airtel Vodafone Makes Better Decisions with Business Intelligence In Spain, Airtel Vodafone created a data warehouse to help it accurately analyze and predict customer activity. The company developed a data warehouse with information generated from multiple departments and organized the data according to the company s business map. The data warehouse allows Airtel Vodafone to convert data into valuable business intelligence. Query demands on the company s data warehouse are intense. More than 1,000 employees use the data warehouse for multidimensional analysis. The information has specifically designed structures for the data concerning customers, infrastructures, and company processes. This structure allows users to extract the data to create modeling and simulation processes. Data-mining techniques extract information on customer behavior patterns. Airtel Vodafone s customer-facing personnel are able to input the information they collect on a daily basis, so that it is integrated with data already stored in 14 Big Data Analytics Guide

16 the warehouse. This data is subsequently combined and converted into information structures for inquiries. The data warehouse environment comprises marketing databases, call systems, customer service, GSM network statistical data, invoicing systems, collections and retrievals, and all logistics information. The marketing team uses the same information as those in finance, although they look at it from different angles and use it for different analyses. Having this structured data allows Airtel Vodafone to provide both detailed and summarized information of the company s activities directly from the data warehouse. These advantages are helping Airtel Vodafone make informed business decisions based on customer activity. Vertical Industries Reap the Rewards of Data Other industries are waking up and taking advantage of Big Data analytics. In healthcare, the move to electronic medical records and the data analysis of patient information are being spurred by estimated annual savings to providers in the tens of billions of dollars. In the manufacturing sector, outsourcing a supply chain may save money, according to McKinsey & Company, but it has made it even more critical for executives, business analysts, and forecasters to acquire, access, retain, and analyze as much information as possible about everything from availability of raw materials to a partner s inventory levels. In these and other industries, users are lining up outside the CIO s door, asking to analyze the incoming flood of data. And when they get access, users want query-response times comparable to what they experience using search engines such as Google and Bing. In some markets, response times that satisfy humans aren t fast enough. These enterprises demand machine-to-machine speeds for analytics. technologies necessary to achieve these feats make it possible for only the largest players in the industry to continuously throw faster hardware and network gear at the problem. For everyone else, says Larry Tabb, CEO of the Tabb Group, a financial services technology consultant, you need to be significantly smarter. To compete, Tabb says, you need to raise the analytical barriers. In the face of these Big Data challenges, some in the analytics industry caution that companies might be smarter to take a more is less approach to analyzing data sets. Sometimes To speed decision making, firms are applying analytics to business processes for financial transactions executed by computers. Humans once were solely responsible for the decisions, but now only computers can work as fast as the data is moving. you might hear arguments that applying analytics to smaller data sets is, in effect, good enough. More often than not, this argument is made by those that can t analyze large data sets. As Google Chief Economist Hal Varian observes, analyzing a small, random slice of data can indeed yield valid results. But to get a truly random data set, that sliver of information needs to come from a massive amount of information. Without a large enough pool of data to draw from, the validity of your analytics processes can be called into question. In other words, Big Data generates the best valid data. n According to the publication Wall Street & Technology, financial services companies are under increasing pressure to accelerate decision making from microseconds to milliseconds to nanoseconds. To speed decision making, firms are applying analytics to business processes for financial transactions executed by computers. Humans once were solely responsible for the decisions, but now only computers can work as fast as the data is moving. The pricey Big Data Analytics Guide 15

17 CONSIDERING BIG DATA ALONE IS INSUFFICIENT; ANALYTICS MUST ALSO BECOME PERVASIVE ACROSS THE ENTERPRISE IN ORDER TO TRULY LEVERAGE THE OPPORTUNITY. Big Data Is Only a Small Part of the Opportunity By Mike Upchurch, Chief Operating Officer, Fuzzy Logix The opportunity Big Data represents goes beyond the data and the related new technologies that capture and store it. The real benefit is that organizations can derive better business intelligence from far more sources than ever before, and make it available to decision makers at every level. The keys to success are designing your Big Data analytics to support business goals and enabling decision makers to take action. It s easy to collect large amounts of data. Knowing what to do with it all and making changes based on what you learn is the challenge. We can liken the task to searching for diamonds in a giant pile of sand. Storing the sand is easy, but sifting through it requires a special set of tools, as well as a sufficient understanding of what you re looking for, why, and what you re going to do when you find it. Historically, data analysis has been a story of complexity, limited capacity, elaborate tools, cryptic results, and poor distribution. Special equipment was required; only a small number of people knew how to use it; and the demand on Today, new, powerful data warehouse systems using in-database analytics can quickly ingest and process Big Data wherever it resides. their time was high. Analysis also required moving data from a database to an analytics server, processing it and pushing it, back. Just moving the data was 80% of the work akin to trucking our pile of sand 10 miles to sift it. Today, new, powerful data warehouse systems using in-database analytics can quickly ingest and process Big Data wherever it resides. What s more, business users can now sift through data using familiar reporting tools, gaining easy access to powerful on-demand analytics and allowing data scientists to focus on building models instead of running reports. Best of all, these new solutions generally cost around 20% less to build than traditional platforms and perform more than ten times faster. Start With Why Data analysis is more accessible than ever, and it can solve many problems but not all of them. The key to identifying which problems to tackle is to start with why. Why are we analyzing Big Data? First, assess your strategic goals. These could be growing market share, controlling cost and risk, or understanding customer behavior. Then, determine if using analytics will deliver value. There are two important questions to answer: Can the company use data models to derive insight, and can it act on the results? Working through this process will help determine where your organization can realize value from Big Data analytics. Changing Company Culture Companies need a focused plan, great execution, the right technical platform, and the ability to operationalize the results of analysis. Without accompanying cultural change, however, those things will only deliver a fraction of the potential value of Big Data analysis. Let s go back to the diamond mine one more time. They have new sifting equipment that tells the miners where the highestvalue diamonds are, but the miners aren t authorized to react to the information. The best equipment can t make up for broken culture. Employees should be able to run analytics and see actionable answers on demand: a forecast of how close the sales team 16 Big Data Analytics Guide

18 It s crucial to create a culture that rewards decisions and encourages analytics innovation, which may require modifying incentive and bonus structures. is to meeting this month s numbers, a customer s credit score, or a report of which advertising keywords to buy today. Armed with information, employees must also be comfortable and confident taking action before the value of the insight diminishes. As a company incorporates the use of analytics, employees will have ideas about how to improve on the original models. Building a culture that encourages constant testing and learning as well as providing access to a flexible platform that can accommodate new ideas will greatly improve the value companies can reap from Big Data. It s crucial to create a culture that rewards decisions and encourages analytics innovation, which may require modifying incentive and bonus structures. Not allowing employees to act is the most common point of failure for analytics projects don t make that mistake. It s rarely mentioned in discussions of Big Data, but it can make or break an analytics initiative. Maximizing Results Many companies are succeeding at their search for value in Big Data. They have the systems and infrastructure to capture and analyze Big Data; they have operational processes in place; and their employees have permission to act on the results. For these companies, the payoff can be dramatic. For example, equity traders may need to buy or sell assets during the trading day to balance their portfolios, but one day s Opera feed can contain data for 500,000 to 1 million trades. If portfolio risk can only be calculated overnight, then institutions are exposed to an unquantifiable amount of risk during each trading day. With Big Data analytics, traders can get real-time pricing and calculate risk throughout the day. The result is that they can rebalance their portfolios at the expense of less agile traders. Millions of dollars can be won and lost by having better information than the institution on the other side of the trade. Other examples of capitalizing on Big Data include modeling loan default risk on demand, and stress-testing entire portfolios in a fraction of the time required by traditional solutions. history and the actions of customers with similar histories, an analytics engine can recommend actions that will reduce churn, or suggest products or services that will be the customer s next likely purchase. One call center leveraged Big Data analytics and saw a 10% reduction in churn, an 8% increase in per call revenue, and a 12% improvement in cross-sale revenue. Health care organizations are using Big Data analytics when evaluating care quality and efficiency. Using traditional methods, analyzing more than 700 million lines of claims data can take six weeks and a dedicated team of analysts, and only produce reports twice a year. With Big Data solutions, risk management teams can now run the models in 22 minutes and take immediate action to improve quality of care, reducing the window during which risk can go unnoticed from six months to less than a week. Big Data analytics are ushering in a new era of predictive insight that is changing how companies operate and engage with their customers, suppliers, and employees. To take advantage of the opportunity, companies must start with the whys, align analytics projects with business needs, and quantify the value that can be created. To realize the value, employees must have access to powerful, innovative, and proven technology, participate in the process, understand the results, and be empowered to act. Get all of this right, and your diamonds will shine bright, creating competitive advantage and financial gain. n Mike Upchurch is responsible for customer acquisition, partnerships, global operations, and corporate culture at Fuzzy Logix. Previously he worked at Bank of America, leveraging trading instruments to create consumer products, mining consumer data to identify trading opportunities, and building and implementing a strategy that grew telephone mortgage lending from $9 billion to $22 billion in four years. He has also held a number of strategy and operational roles at global technology companies. Call centers use analytics to better serve customers, reduce churn, and cross-sell new products. By analyzing a customer s Big Data Analytics Guide 17

19 Business Analytics Roadmap Business Value through Operational BI Align operational and real-time business analytics and analytics technology with true business requirements and capabilities to ensure greater success in reaching business and IT goals. President of Intelligent Solutions, Inc. and Founder of the Boulder BI Brain Trust Excerpted from TDWI Checklist Report, Delivering Higher Business Value with Operational Business Intelligence and Real-Time Information Operational BI (OBI) is a popular topic in most business intelligence (BI) shops these days, and rightfully so. OBI enables more informed business decisions by directly supporting specific business process and activities. OBI has had a dramatic impact on traditional BI environments and on a new audience of BI users. These users now have immediate access to the insights they need when making decisions about customers, products, and even campaigns while these business activities are happening. This Checklist Report helps you determine how to align the implementation of operational and real-time BI and analytics technology with true business requirements and capabilities to ensure greater success in reaching business and IT goals. 1. RECOGNIZE THAT NOT ALL ANALYTICS MUST COME FROM THE DATA warehouse ENvIRONMENT. The data warehouse (DW) is a key supplier of data analytics, but it s not the sole supplier of analytics. Other forms of analytics are needed for a fully functioning OBI environment. Because many analytics used in OBI require low-latency or real-time data, organizations try to speed up the overall processes of the DW trickle-feeding the data, automating analyses, and so on in an effort to make it the sole supplier of analytics. Although this approach works for some lowlatency analytics, at some point the DW team must turn to other analytical techniques to complete the OBI picture. One of these techniques is event analytics. Event data is created by business activities (generated by banking transactions [ATM], retail operations [POS, RFID], market trades, and Web interactions) or by system events (generated by sensors, security devices, or system hardware or software). Event analytics applications often perform their analyses even before the transactional data is stored in an operational 18 Big Data Analytics Guide

20 system. For example, many fraud-detection applications analyze transactions for fraudulent characteristics first and then store them in transactional systems for further processing. Obviously, the DW contributes to the overall OBI environment by generating the fraud models used by the event analytics software. Another technique is to make BI analytics (or its results) available as callable services within an operational workflow. Embedded BI services can be external to the workflow (as a part of a service-oriented architecture) or included within the workflow itself. These services come in two flavors. The first calls a stored analysis or model, uses it dynamically during the workflow, and receives the results before invoking the next activity for example, calling a stored analysis to dynamically determine a loan applicant s credit worthiness. The second type retrieves the static results from an earlier analysis; for example, a customer service representative (CSR) retrieves a customer s lifetime value score or segment ID stored in a DW. Both types are employed by a business process or person to support real-time or near-real-time business decisions and actions. The combination of traditional data analytics, embedded BI services, and event analytics forms the foundation of OBI. All three must come together at appropriate points in the workflow to provide a mature and effective operational decisionmaking environment. 2. MATCH REAL-TIME CAPABILITIES FOR INCREASING BI AGILITY TO ACTUAL BUSINESS NEEDS. There is a lag between the time an event happens and the time a company responds to it. This lag is caused by several factors, such as preparing the data for analysis, running the analysis, and determining the best course of action based on the results for example, taking action when a campaign sells a product that is about to run out of stock. Clearly, the ability to reduce the time to action here (stopping the campaign or changing the featured product) can have significant impact on a company s revenues and reputation. This is BI agility. It requires that the action time match the business need. However, there is a trade-off. Is it timely enough for the business or is it actually too fast? Even if the business requires reduced latency, can the business users correctly process the inputs that quickly? Can the operating procedures handle the time frame appropriately to ensure a correct reaction? There are many moving parts in an OBI environment, and any that are out of sync or incomplete can cause an erroneous decision to be made. In this situation, the cost of creating such a low-latency BI environment may be more than the actual benefit the company receives. Another trade-off is the soundness and flexibility of the architectural infrastructure in terms of allowing for delivery of information in different latency time frames (more on this later). Building an OBI solution that is inflexible or fragile just to meet an arbitrary time frame may spell disaster. If the action time requirement changes (and it almost certainly will) from two hours to one hour, you don t want to have to rebuild the entire architecture. To avoid this situation, the BI implementers must understand how the business community interacts with OBI, from event Embedded BI services can be external to the workflow (as a part of a service-oriented architecture) or included within the workflow itself. occurrence to action taken. Interactions must include the impact of the growing usage of tablets and mobile devices. OBI must reach its audience with the appropriate information formatted for the myriad mobile devices available today. 3. DETERMINE THE PROPER INFRASTRUCTURE FOR BUSINESS-CRITICAL OPERATIONAL BI. Although traditional BI processing is often critical to business operations, a temporary failure of the BI system will not typically affect short-term business operations. Also, given that the BI system is separated from operational processing, it means that BI processing has little effect on operational performance except during the capturing of operational data. The situation with OBI is different from traditional BI because it is closely tied to the daily operations of the business. A failure in an OBI system could severely impact business operations. This risk is especially relevant for OBI applications that support close to real-time decision making, such as fraud detection. There are several approaches to supporting OBI, including embedding BI in operational processes, accessing live operational data, and capturing operational data events and tricklefeeding them to a DW. All of these approaches have the ability to affect the performance of operational systems. It is very important, therefore, that the infrastructure of the BI system, its underlying DW environment, and related operational systems be capable of providing the performance, Big Data Analytics Guide 19

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