Big Data Analytics FOR. by Steve Piper

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3 Big Data Analytics FOR DUMmIES CENTRIFUGE SPECIAL EDITION by Steve Piper

4 Big Data Analytics For Dummies, Centrifuge Special Edition Published by John Wiley & Sons, Inc. 111 River St. Hoboken, NJ Copyright 2012 by John Wiley & Sons, Inc., Hoboken, New Jersey Published by John Wiley & Sons, Inc., Hoboken, New Jersey No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except as permitted under Sections 107 or 108 of the 1976 United States Copyright Act, without the prior written permission of the Publisher. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) , fax (201) , or online at Trademarks: Wiley, the Wiley logo, For Dummies, the Dummies Man logo, A Reference for the Rest of Us!, The Dummies Way, Dummies.com, Making Everything Easier, and related trade dress are trademarks or registered trademarks of John Wiley & Sons, Inc. and/or its affiliates in the United States and other countries, and may not be used without written permission. Centrifuge and the Centrifuge logo are trademarks or registered trademarks of Centrifuge Systems, Inc. All other trademarks are the property of their respective owners. John Wiley & Sons, Inc., is not associated with any product or vendor mentioned in this book. LIMIT OF LIABILITY/DISCLAIMER OF WARRANTY: THE PUBLISHER AND THE AUTHOR MAKE NO REPRESENTATIONS OR WARRANTIES WITH RESPECT TO THE ACCURACY OR COMPLETENESS OF THE CONTENTS OF THIS WORK AND SPECIFICALLY DISCLAIM ALL WARRANTIES, INCLUDING WITHOUT LIMITATION WARRANTIES OF FITNESS FOR A PARTICULAR PURPOSE. NO WARRANTY MAY BE CREATED OR EXTENDED BY SALES OR PROMOTIONAL MATERIALS. THE ADVICE AND STRATEGIES CONTAINED HEREIN MAY NOT BE SUITABLE FOR EVERY SITUATION. THIS WORK IS SOLD WITH THE UNDERSTANDING THAT THE PUBLISHER IS NOT ENGAGED IN RENDERING LEGAL, ACCOUNTING, OR OTHER PROFESSIONAL SERVICES. IF PROFESSIONAL ASSISTANCE IS REQUIRED, THE SERVICES OF A COMPETENT PROFESSIONAL PERSON SHOULD BE SOUGHT. NEITHER THE PUBLISHER NOR THE AUTHOR SHALL BE LIABLE FOR DAMAGES ARISING HEREFROM. THE FACT THAT AN ORGANIZATION OR WEBSITE IS REFERRED TO IN THIS WORK AS A CITATION AND/OR A POTENTIAL SOURCE OF FURTHER INFORMATION DOES NOT MEAN THAT THE AUTHOR OR THE PUBLISHER ENDORSES THE INFORMATION THE ORGANIZATION OR WEBSITE MAY PROVIDE OR RECOMMENDATIONS IT MAY MAKE. FURTHER, READERS SHOULD BE AWARE THAT INTERNET WEBSITES LISTED IN THIS WORK MAY HAVE CHANGED OR DISAPPEARED BETWEEN WHEN THIS WORK WAS WRITTEN AND WHEN IT IS READ. For general information on our other products and services, please contact our Business Development Department in the U.S. at For details on how to create a custom For Dummies book for your business or organization, contact [email protected]. For information about licensing the For Dummies brand for products or services, contact BrandedRights& [email protected]. ISBN (pbk); ISBN (ebk) Manufactured in the United States of America Publisher s Acknowledgments Some of the people who helped bring this book to market include the following: Acquisitions, Editorial, and Vertical Websites Development Editor: Kathy Simpson Project Editor: Jennifer Bingham Editorial Manager: Rev Mengle Business Development Representative: Sue Blessing Custom Publishing Project Specialist: Michael Sullivan Composition Services Senior Project Coordinator: Kristie Rees Layout and Graphics: Claudia Bell, Jennifer Creasey Proofreader: Susan Moritz Special Help: Susan Kane, Renee Lorton, Navin Ganeshan, Scott Ferrebee, Tony Ayaz Business Development Lisa Coleman, Director, New Market and Brand Development

5 Table of Contents Introduction... 1 How This Book Is Organized... 1 Icons Used in This Book... 2 Chapter 1: Understanding Big Data Analytics... 3 What Is Big Data?... 3 Putting the Big in Big Data... 4 Seeing where the data comes from... 4 Figuring out what to do with the data... 5 Evaluating Big Data for analysis: The Four Vs... 7 What Is Big Data Analytics?... 7 Traditional analytics approach... 8 Visual analytics approach... 9 Chapter 2: Getting Started Planning Your Work Step 1: Review the Four Vs Step 2: Define your objectives Step 3: Evaluate potential vendors Step 4: Make your choice Working with Link Analysis and Data Visualizations Link analysis Simple charts Matrix Bubble Drill charts Timelines Relationship maps Geospatial maps Interactive visualizations Chapter 3: Uses in Fraud and Risk Management Counting the Cost of Fraud Fighting Fraud with Big Data Analytics Disabling disability fraud using social media Lending a hand to mortgage fraud detection Ringing out retail fraud Diagnosing prescription fraud... 25

6 iv Big Data Analytics For Dummies, Centrifuge Special Edition Chapter 4: Uses in Anti-counterfeiting Counting the Costs of Counterfeiting Pharmaceuticals Luxury goods Digital media Stopping Counterfeiters with Big Data Analytics Chapter 5: Uses in Homeland Defense Cases for Homeland Defense Cybersecurity Counterterrorism Protection of critical infrastructure Interdiction of drug trafficking Interdiction of human trafficking Border security Big Data Analytics Case Study Step 1: Follow the suspicious money Step 2: Track area crime with data visualization Step 3: Identify and defend against the risk Step 4: Conduct surveillance Step 5: Arrest the suspects Chapter 6: Ten Buying Criteria for Big Data Analytics Quick Implementation Bring Your Own Data Interactive Visualization Collaborative Analysis Integrated-Discovery Experience Support for Advanced Analysis Techniques Ease of Use Web Browser Based Client Designed for Scalability Responsive Customer Support... 44

7 Introduction The amount of data in our world is growing exponentially, and analyzing large, amorphous data sets so-called Big Data generates big information currency for those who are savvy enough to harvest it. With this book, you get the knowledge you need to use Big Data Analytics to uncover hidden meanings within your organization s data. Whether you re tasked with detecting fraud, identifying counterfeit-product operations, protecting your organization s IT infrastructure, or securing the nation, today s Big Data Analytics solutions can help you work smarter to extract tremendous value from your ever-expanding data sources. How This Book Is Organized This book is organized so that you don t have to read it cover to cover, front to back though you re welcome to do that. If you prefer, you can skip around, reading just the chapters that interest you. Here are your choices: Chapter 1, Understanding Big Data Analytics : This chapter defines Big Data, explores the Four Vs for evaluating Big Data, lists popular infrastructure solutions, and describes common approaches and applications for Big Data Analytics. Chapter 2, Getting Started : Chapter 2 provides insights into how to select an appropriate Big Data Analytics solution. This chapter also provides an overview of link analysis and presents graphical depictions and explanations of some common visualizations you re likely to encounter in using today s leading Big Data Analytics solutions. Chapter 3, Uses in Fraud and Risk Management : This chapter discusses the most common types of fraud

8 2 Big Data Analytics For Dummies, Centrifuge Special Edition and assesses the annual cost of fraud to American businesses. Then it explores typical fraud and risk management use cases, describing how Big Data Analytics (and social media) can reduce the time to detection. Chapter 4, Uses in Anti-counterfeiting : This chapter itemizes the product categories that are most susceptible to counterfeiting and estimates the annual cost to the retail industry. It also explains how Big Data Analytics can help identify counterfeiters and describes how a topten pharmaceuticals manufacturer reduced its Big Data analysis costs by 99 percent. Chapter 5, Uses in Homeland Defense : Chapter 5 presents common use cases for Big Data Analytics in the cause of homeland defense, including cybersecurity, counterterrorism, protection of critical infrastructure, interdiction of drug and human trafficking, and border security. Chapter 6, Ten Buying Criteria for Big Data Analytics : The final chapter describes the pitfalls of traditional analytic solutions and tells you exactly what to look for when you evaluate Big Data Analytics solutions on behalf of your organization. Icons Used in This Book This book uses the following icons to indicate special content. You won t want to forget the information in these paragraphs. These paragraphs provide practical advice that can help you craft a better strategy, whether you re planning a purchase or setting up your software. Look out! When you see this icon, it s time to pay attention. The Warning icon flags important cautionary information that you won t want to miss. Maybe you re one of those highly detailed people and really need to grasp all the nuts and bolts, even the most techie parts. If so, these tidbits are right up your alley.

9 Chapter 1 Understanding Big Data Analytics In This Chapter Defining Big Data Understanding Big Data Analytics Contrasting traditional and visual analytics approaches The era of Big Data is upon us. The race is on to extract insight and value from this abundant resource. The opportunities are enormous and so are the challenges. Organizations that master the emerging discipline of Big Data Analytics can reap significant rewards and separate themselves from their competitors; those that fail to do so will be left in the dust. Big Data is here to stay. And you re part of it! In this chapter, I define Big Data and Big Data Analytics and explore the challenges of harvesting value from an evergrowing sea of digital information. What Is Big Data? Big Data is a term applied to data sets so large that common software tools aren t capable of capturing, managing, and processing their data within a tolerable period. Big Data is colossal, unstructured (or loosely structured), distributed, fluid, and often unconnected. The amount of Big Data varies by organization, but its volume (and variety) tends to increase astonishingly quickly and exponentially.

10 4 Big Data Analytics For Dummies, Centrifuge Special Edition In the following sections, I give you some basic background on Big Data: exactly how big it is, where it comes from, how to evaluate it, and how to use it. Putting the Big in Big Data Analysts estimate that approximately 300 million terabytes (TB) of data exist in the world today. But what s staggering is that 90 percent of this data was created in the last two years! In a recent study titled The Digital Decade Are You Ready? market research company IDC projected that by 2020 the digital universe will encompass a staggering 35 zettabytes (ZB). Step aside, petabytes and exabytes! The word is now zettabytes! And with 1ZB being equivalent to 1 billion terabytes, that s a whole lot of data. To put this into perspective, from its founding in April 1800 to April 2011, the U.S. Library of Congress had amassed about 235TB of data. Currently, it s adding about 5TB of new data each month. So if IDC is correct, by 2020, computers will collectively store 400 million times more data than is archived in the entire Library of Congress today! Seeing where the data comes from You may wonder where all this data comes from. It comes from almost everywhere. Enterprises and government agencies aggregate data from myriad private and/or public data sources. Private data is information that your organization specifically collects that is available only to your organization, such as employee data, customer data, and machine data (such as user transactions, customer behavior, computer system health, and cybersecurity threats). Commercial-specific examples include credit-card, pharmacy, and mortgage transactions. Government-specific examples include Social Security data, Medicare transactions, and passport paperwork. Public data is information that s generally available to the public for a fee or at no charge. Examples include stock prices, company and individual credit ratings, social media content (such as Facebook and Twitter), and computer IP

11 Chapter 1: Understanding Big Data Analytics 5 blacklists (such as known hacking sites) along with all other content found on the public Internet. When you stop and think about it, it s no wonder the world is drowning in data. If an organization can record something, it usually does environmental data, financial data, medical data, surveillance data, and on and on. Figuring out what to do with the data The most significant challenges of Big Data no longer involve aggregation and storage but rather what to do with all the accumulated data. Today, common concerns for commercial enterprises and government agencies include the following: Deriving actionable value from Big Data, due to information overload. Analyzing the connections between structured, semistructured, and unstructured data sets. Structured data is stored in relational databases in columns and rows. Semistructured data doesn t conform to the formal structure of tables and rows but contains tags or other markers to separate semantic elements and enforce hierarchies of records and fields. Examples include web pages and XML (extensible markup language). Unstructured data refers to information that doesn t have a predefined data model and thus can t be stored in a relational database. Unstructured data can be textual or nontextual. Examples of textual unstructured data include messages, PowerPoint presentations, and Word documents. Examples of nontextual unstructured data include JPEG images, MP3 audio files, and Flash video files. According to the market-research firm IDC, semistructured and unstructured data accounts for more than 90 percent of the data in today s organizations. Uncovering patterns of useful data when you don t know what questions to ask in the first place. As you discover throughout this book, a Big Data Analytics solution can help your organization meet all these challenges.

12 6 Big Data Analytics For Dummies, Centrifuge Special Edition Popular Big Data infrastructure solutions The following is a list of popular Big Data infrastructure solutions that you re likely to encounter when using Big Data Analytics applications. Apache Hadoop is an opensource software framework that supports data-intensive distributed applications working with thousands of computers and petabytes of data. Cloudera offers Apache Hadoopbased software and services that make it easier to run Hadoop in a production environment. EMC Greenplum is a commercial data warehouse based on the open-source database PostgreSQL and intended for large-scale enterprise and cloud deployments. HP Vertica is a commercial database software platform that uses a column-oriented analytic database to process large amounts of data for quick analysis. IBM Netezza is a commercial data-warehouse solution based on proprietary technology, scaling to more than 10 petabytes (PB) of data. NetApp specializes in enterpriseclass data-warehouse solutions and is a thought leader in Big Data. NetApp s highest-end platform can accommodate up to 4PB of raw data. Oracle is one of the most successful enterprise database warehouse providers today, with all of the Fortune 100 as customers. Oracle offers a line of Big Data Appliances that can accommodate up to 648TB of raw storage in a single rack and up to 5PB within an eight-rack cluster. Splunk is a software application that enables users to search, monitor, and analyze machinegenerated data by applications, systems, and IT infrastructure via a web-based interface. Sybase, an SAP company, is an enterprise software and services company offering software to manage, analyze, and mobilize information using relational databases, analytics, and data warehousing solutions and mobile applications development platforms. Teradata offers commercial relational database management system (RDBMS) hardware and software. The company launched the Petabyte Power Players club to include customers with petabyte-plus data warehouses, including Dell (1PB), Bank of America (1.5PB), Wal-Mart Stores (2.5PB), and ebay (5PB).

13 Chapter 1: Understanding Big Data Analytics 7 Evaluating Big Data for analysis: The Four Vs Diamonds are evaluated on what are commonly known as the Four Cs: color, cut, clarity, and carat weight. Similarly, Big Data is commonly evaluated on the Four Vs: Volume describes the relative size of data typically, in terabytes or petabytes. Velocity describes the frequency at which data is generated, captured, and shared. Variety describes the types of data in a data set, such as transactional, social, content, geospatial, location-based, log, and radio-frequency identification (RFID). Value describes the business benefits reaped by the organization, such as fraud detection, loan risk analysis, and customer-behavioral analytics. All four of these Big Data characteristics are important to consider when you re evaluating solutions for Big Data Analytics which I introduce next. See Chapter 2 for a lot more information about the Four Vs. What Is Big Data Analytics? Big Data Analytics is the process whether manual or automated of analyzing Big Data to extract meaning and actionable intelligence. Put another way, it makes Big Data useful. Only a short time ago, companies used to spend considerable time and resources to identify and procure useful data. Today, most companies have the opposite problem. Aggregating useful data is relatively easy; analyzing that data is the challenge. In the following sections, I explore two approaches to Big Data Analytics: the traditional approach and the visual analytics approach.

14 8 Big Data Analytics For Dummies, Centrifuge Special Edition Traditional analytics approach You may be surprised that many organizations still employ data analysts who use manual techniques to extract useful information from large data warehouses. Such techniques typically include ad hoc database queries followed by a series of univariate (analysis of single-variable distributions), bivariate, and, more often, multivariate analyses. These analysts often have advanced degrees in mathematics and/or statistics and pride themselves on their ability to perform advanced regression analyses. They often view data in columns and rows and then periodically create charts and graphs manually, using spreadsheets or basic business intelligence reporting tools (see Figure 1-1). Figure 1-1: Manual data analysis. Even with automation, this type of analytical approach is limited in its ability to detect unknown or undiscovered patterns (link analysis). Assumptions are often hard-coded, leading to false outcomes. It s like trying to find a needle in a haystack! Ultimately, the results are too little and too late.

15 Chapter 1: Understanding Big Data Analytics 9 Visual analytics approach Today s data analysts take an entirely different approach. They prefer to work smarter not harder to uncover hidden meanings in Big Data, leveraging visual analytics tools to integrate, visualize, and collaborate with data in ways that old-school data analysts have never seen. Visual analytics applications extract value from Big Data through advanced analytics and interactive visualization. Advanced analytics, such as link analysis, enable the integration of complex information in simple visualizations for pattern discovery (for example, seeing the forest through the trees). Interactive visualization refers to the ability to do it yourself through prebuilt charts, graphs, and timelines that tell the complete story. You ve often heard that a picture is worth a thousand words. Would you rather try to extract useful information from the table of data shown in Figure 1-1 or through interactive visualizations displayed in Figure 1-2? Figure 1-2: Data analysis with visual analytics software. For centuries, visualization has been used to support the understanding of complex information. Better understanding of relationships and context is key to visual analytics.

16 10 Big Data Analytics For Dummies, Centrifuge Special Edition Visual analytics software can improve time-to-discovery by more than 50 percent and make data analysts 10 to 20 times more productive than analysts who use traditional manual methods. Organizations typically recoup their investments in visual analytics tools in a matter of months. They also find it easier to fill data-analyst positions because advanced degrees in mathematics and statistics are no longer required. Analysts who leverage visual analytics applications instantly become data scientists because they now have the ability to test new hypotheses and experiment with data in ways never before possible. Visual representation of the data sharpens focus on what s important (so you can see clearly). If you re excited by the prospects of visual analytics, read on. Chapter 2 describes how to get started.

17 Chapter 2 Getting Started In This Chapter Organizing your thoughts Understanding the power of link analysis Exploring common types of data visualizations You re probably tired of combing through endless tables and static reports, trying to make sense of a tidal wave of data. And you re undoubtedly excited about the prospect of using visual analytics to improve your insight. If you ve read Chapter 1, you sense that there s a better way. But where do you start? This chapter can help. Planning Your Work With Big Data Analytics, as with any other complex task, getting yourself oriented properly at the start is essential to success. Here s a four-step process that can help you plan the work ahead: 1. Check your data against the Four Vs. Reviewing the Four Vs (see Chapter 1) helps you assess the availability and accessibility of your data. 2. Consider your data-analysis goals. You should know up front what you ll be trying to accomplish when you analyze your data.

18 12 Big Data Analytics For Dummies, Centrifuge Special Edition 3. Begin identifying and evaluating visual analytics solutions and providers. You want a solution that works with any source of data and can help you uncover hidden meaning within your data sets. 4. Finally, choose the best solution. The following sections explore these concepts in a bit more detail. Step 1: Review the Four Vs It s important to consider each of these key factors when you evaluate Big Data Analytics solutions: Volume: Volume matters because not all visual analytics packages scale the same. Some products are designed for individual or personal analysis, accommodating a few hundred gigabytes of data. Other products are designed to support the largest enterprise and government deployments. If your organization manages 1TB or more of data, be sure to select a package that s truly designed to support Big Data volumes. Velocity: This factor describes the frequency at which data is generated, captured, and shared, which for most organizations is all the time! You need a Big Data Analytics solution that can analyze and visualize all that incremental data on demand without having to constantly contact your database administrator(s) for access. Alternatively, if you have data that s available on a one-time or ad hoc basis, you want to make sure that you can integrate and visualize this data yourself quickly and easily. Variety: The variety of data is also important to consider, especially in terms of the way the data is structured. If you have a mix of structured data (such as databases and spreadsheets) and unstructured data (such as files and Twitter tweets), you need a Big Data Analytics solution that can handle both types. Value: The value of the data is ultimately created from the analysis and visualizations of connections between

19 Chapter 2: Getting Started 13 the data sets through different dimensions. The value is the hardest thing to harness with Big Data. A solution that helps you determine the priority of the data and provides multiple visualization methods can help. I discuss visualizations in detail later in this chapter. Step 2: Define your objectives Do you know exactly what you re looking for? If your answer is Yes, you may be selling yourself short. The best Big Data Analytics solutions allow you to discover things that you weren t even searching for. Don t limit your analytics solutions to pretty visual dashboards. Suppose that you re a data analyst working in the frauddetection division of a major bank. You may be tasked with investigating credit-card fraud, but through a relationship graph (discussed later in this chapter), you could discover that a customer suspected of credit-card fraud may also be involved in an elaborate check-fraud scheme. Itemizing the types of questions you want to ask of Big Data is an excellent way to create a list of objectives. For example, questions involving known metrics or predefined patterns are only the beginning. What about dimensions of time, space, or location? What about other connections between data elements? Most Big Data Analytics methods include testing hypotheses, interacting collaboratively with other analysts, and leveraging your own past experience into the analysis. You want to make sure you have accommodated all these scenarios into your objectives. What types of skills will be required of the analyst doing the investigations? Will you be incorporating other statistical models or predictive models into the analysis? How will you share these analyses with other groups? You should discuss your objectives in great detail with potential Big Data Analytics providers so they can show you how advanced analytics and interactive visualizations map to your objectives.

20 14 Big Data Analytics For Dummies, Centrifuge Special Edition Step 3: Evaluate potential vendors After you ve characterized your data and defined your objectives, construct a short list of adequate Big Data Analytics providers. Make sure that each vendor on your short list can support your data, as defined by the Four Vs (discussed earlier in this chapter), and can accommodate the types of analyses you need to perform. Don t just take a vendor s word for its capabilities. Ask for an onsite evaluation performed on a subset of your actual data against the questions you want to answer. Step 4: Make your choice When you ve completed your vendor evaluations, you should be ready to select one. Before you sign any agreement, be sure to ask the provider for an implementation timeline to ensure that your solution will be deployed in a matter of weeks, rather than months. Many vendors require extensive services and lengthy implementations. Time is money, and the faster you can derive value from your solution, the better for you and your organization. Working with Link Analysis and Data Visualizations Once you ve selected a Big Data Analytics solution, you need to work with your vendor to construct an initial set of data visualizations that you ll use on a day-to-day basis. Almost all visual analytics solutions offer tables and charts that summarize simple data. But Big Data Analytics requires more sophisticated visualizations that support advanced analytical techniques, including temporal analysis, geospatial analysis, and in particular, link analysis.

21 Link analysis Chapter 2: Getting Started 15 Finding patterns and connections between data elements through link analysis, in particular, is a critical part of the Big Data Analytics process. Data visualizations use relationship strength between the entities to scale or change the picture to provide better insight. Through an interactive process, these visualizations can dynamically change based on context. Simple charts Beyond the traditional bar, pie, and line charts found in virtually all Big Data Analytics applications, preferred solutions offer more sophisticated chart options. Matrix A matrix chart (see Figure 2-1) permits users to see the presence and scale of correlated values (with value ranges illustrated by color) and to observe areas of noncorrelation. Figure 2-1: Matrix chart. Bubble Bubble charts (see Figure 2-2) are similar to matrix charts in that they display intersections between fields of data, but rather than color-coding the intersections to communicate value range, the range is displayed in bubbles of varying sizes.

22 16 Big Data Analytics For Dummies, Centrifuge Special Edition Figure 2-2: Bubble chart. Drill charts Drill charts (see Figure 2-3) are among the most common visualizations within any Big Data Analytics application because they re both graphical and interactive. By double-clicking a bar in a drill chart s bar graph, the analyst can drill down into the underlying data over multiple dimensions. Figure 2-3: Drill chart.

23 Timelines Chapter 2: Getting Started 17 A timeline (see Figure 2-4) displays events in temporal order in such a way as to present sequence, duration, and overlap. Figure 2-4: Timeline. Relationship maps A relationship map (see Figure 2-5) displays data in a nodeand-link format so you can easily visualize the relationships among objects of interest within the data. The nodes represent real-world data elements, such as customers, loans, IP addresses, and so on. The links represent the relationships between the elements. The larger the node or map, the more complex the relationship. More sophisticated visual analytics applications incorporate a time player feature into their relationship maps (and other data visualizations) that can visually depict how relationships have changed over a predetermined period of time.

24 18 Big Data Analytics For Dummies, Centrifuge Special Edition Figure 2-5: Relationship map. Relationship maps are particularly useful for discovering connections between data elements that you never knew existed, such as doctors and pharmacists, mortgage lenders and convicted felons, hackers and IT systems, and more. Geospatial maps A geospatial map (see Figure 2-6) places key information at a location on a map so it is possible to observe things in a geocontext that might otherwise not be highlighted. Figure 2-6: Geospatial map.

25 Interactive visualizations Chapter 2: Getting Started 19 Very few visual analytics solutions enable interaction between all these data visualizations within a single dataview, or screen. Interactive visualizations (see Figure 2-7) enable users to intuitively relate multiple visualizations to more efficiently uncover hidden meaning within Big Data. Figure 2-7: Single dataview incorporating multiple interactive visualizations. Discovering insights, patterns, and relationships hidden in Big Data is the key to Big Data Analytics especially if you don t know what questions to ask of your data in the first place! Important stories are buried in Big Data. Through interactive data visualizations and contextual intelligence, these stories can come alive. Interactive visualizations move beyond simple visual dashboards to give you the ability to interpret data within the context of your business. Now that you re grounded in the basic theories of Big Data Analytics, you re ready to see how it works in the real world. In the next three chapters, I use fictional examples to illustrate the benefits of using Big Data Analytics in fraud and risk analysis (Chapter 3), anti-counterfeiting measures (Chapter 4), and homeland defense (Chapter 5).

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27 Chapter 3 Uses in Fraud and Risk Management In This Chapter Calculating how much fraud costs business and government Exploring typical fraud and risk management use cases Understanding how Big Data Analytics (and social media) can help Arguably, the most compelling use cases for Big Data Analytics are tied to detecting and preventing fraud. In this chapter, I show you the real cost of fraud and then use fictional scenarios to illustrate how commercial organizations and government agencies can leverage visual-analytics applications for fraud and risk analysis. Counting the Cost of Fraud The cost of fraud to the U.S. government and American businesses is simply staggering. Following are recent estimates of the annual costs of fraud, derived from various industry research sources: Tax evasion: $500 billion Retail fraud: $139 billion Insurance fraud: $115 billion Mortgage fraud: $10 billion Credit-card fraud: $8.6 billion Embezzlement: $6 billion

28 22 Big Data Analytics For Dummies, Centrifuge Special Edition Big Data Analytics can dramatically improve an organization s ability to detect fraud and reduce risk. It is estimated that for every $1 spent on Big Data Analytics, the average organization will recoup anywhere from $10 to $100 in the first year. Fighting Fraud with Big Data Analytics Big Data Analytics can benefit fraud and risk examiners in many ways. Here are a few of the most typical use cases: Health insurance fraud entails intentional deception or misrepresentation intended to result in unauthorized benefit. False claim schemes are the most common type of health insurance fraud. In the United States alone, more than $100 billion is lost each year to health insurance fraudsters and scammers. Property insurance fraud is fraud committed with the intent to destroy insured property and collect insurance on that property. A classic example is the arsonist who burns down an insured building because the insurance payout is higher than the building s market value. Mortgage fraud is a crime in which a loan applicant intends to materially misrepresent or omit information on a mortgage loan application, either to obtain the loan in the first place or to get a larger loan than he could have obtained by telling the truth. Credit-card fraud is a wide-ranging category that involves theft committed with a credit card as a fraudulent source of funds. Most often, the purpose is to obtain goods without paying for them. Credit-card fraud is often related to identity theft. Retail fraud covers a broad range of crimes against retail stores, including self-checkout fraud, counterfeit coupons, refund fraud, cash-register skimming, and oldfashioned product theft. Retail-fraud perpetrators aren t always would-be customers; they often include dishonest employees.

29 Chapter 3: Uses in Fraud and Risk Management 23 Tax evasion is a general term for efforts by individual taxpayers, corporations, and other entities to deliberately avoid paying taxes by illegal means. Tax evasion usually involves taxpayers deliberately misrepresenting or concealing the true state of their affairs to reduce their tax liability. Prescription fraud is the illegal acquisition of prescription drugs for personal use or profit. This category of fraud includes theft, burglary, unlicensed (backdoor) pharmacies, prescription forgery (which includes altering a legitimate prescription), and illegal importation or distribution of prescription drugs. Embezzlement is a kind of financial fraud in which one or more people to whom assets have been entrusted dishonestly appropriate that is, steal those assets. More often than not, embezzlement is premeditated, systematic, and/or methodical, and the person or people involved explicitly intend to conceal the activities. Money laundering refers to the process of concealing the source of money obtained illegally, such as through organized crime, drugs, or prostitution. The methods by which money may be laundered are varied, and so are the degrees of sophistication of those methods. More than 80 percent of occupational-fraud incidents are committed by workers in one of six departments: accounting, operations, sales, executive/upper management, customer services, and purchasing. A recent global study conducted by the Association of Certified Fraud Examiners ( found that a typical organization loses 5 percent of its annual revenue to fraud. For a company with $1 billion in annual revenue, that is a whopping $50 million per year! The industries most commonly affected are insurance and financial services, manufacturing, and government. Further, the average fraudulent case goes undetected for 18 to 24 months. Today s leading Big Data Analytics software can increase both the efficiency and effectiveness of fraud and risk management for each of these use cases and many more. In the following sections, I present just a few hypothetical scenarios for you to consider.

30 24 Big Data Analytics For Dummies, Centrifuge Special Edition Disabling disability fraud using social media John, a 22-year-old elevator repairman, was recently struck by a 2012 Mercedes-Benz SLK while crossing the street in an affluent neighborhood. He was taken to the hospital by ambulance and was discharged a few hours later after being treated for bruised ribs, a sprained ankle, a concussion, and a case of severe whiplash. The next day, John filed a short-term disability claim with his insurance company and hired an attorney to sue the driver of the Mercedes-Benz. Unbeknownst to John, his insurance company had recently implemented a Big Data Analytics solution that incorporated social-media data feeds and a third-party data feed of known fraud offenders. One week following the accident, John posted a Facebook check-in at an Aspen ski resort along with a photo of two friends he tagged, Chris and Marc. It turns out that Chris is the doctor who treated him at the hospital and Marc is another patient of Chris s who was involved in a similar pedestrian-automobile accident two months prior. After further timeline and link analysis, the insurance company discovered a pattern of six of these similar accidents over the last four months involving the same individuals and other known fraud offenders. Working with law enforcement, they arrested the criminal ring and suspended Chris s license to practice medicine. Lending a hand to mortgage fraud detection Mike, a 44-year-old veteran loan officer at a large national bank, suddenly had an unusually large amount of loan money at risk. But as Mike had been with the bank for more than ten years, his branch manager didn t pay much attention. However, a diligent fraud analyst at headquarters named Carrie leveraged her Big Data Analytics application to delve a little deeper. Within minutes, Carrie discovered that several of Mike s recently approved mortgages entailed unusually high loan-to-value ratios, and some were linked to known white collar criminals.

31 Chapter 3: Uses in Fraud and Risk Management 25 Carrie forwarded this information to Mike s branch manager, who immediately brought this to Mike s attention. Mike acknowledged personal financial difficulties related to his recent divorce and confessed to his wrongdoings. He was fired on the spot, and the bank is now considering pressing charges. Ringing out retail fraud Sally, age 19 and unemployed, was a frequent customer of a national clothing chain, averaging three transactions every week across a half-dozen stores in her state. Strangely enough, the stores had no records of her actually purchasing anything, although they had dozens of records of returns. This discrepancy intrigued a fraud analyst employed by the company, who alerted her stores to be on the lookout for Sally the next time she attempted to return a product. Sure enough, a few days later, Sally returned a designer dress that she said was two sizes too large. Unfortunately, the police arrived soon after with handcuffs that fit her just right. Diagnosing prescription fraud Bill, a 32-year-old auto mechanic, had been suffering from severe back pain for more than a year. At least, that s what his insurance company assumed from the claims that his physician s office submitted for doctor visits. At each visit, the doctor wrote Bill a 30-day prescription for a powerful yet highly addictive pain medication. A fraud analyst at Bill s insurance company started to notice the large volume of doctor and pharmacy claims coming in for the same person. By using a visual-analytics software package equipped with link-analysis visualizations, the analyst clearly saw that Bill had submitted claims from seven doctors and seven pharmacies with great frequency over the past year. The insurance company alerted all of Bill s doctors to the suspected prescription fraud. Shortly thereafter, Bill checked himself into a local drug rehab facility a claim that the insurance company was more than happy to pay.

32 26 Big Data Analytics For Dummies, Centrifuge Special Edition Data visualization and link analysis save health insurer $1 million in first year Recently, one of the largest health insurance providers in America implemented Visual Network Analytics (VNA) from Centrifuge ( systems.com). Like all health insurance providers, the company experiences fraud on an almost daily basis. Tired of using static business intelligence tools, it selected VNA to analyze a complex web of relationships involving doctors, patients, pharmacies, prescriptions, and medical procedures. The company estimates that each instance of insurance fraud costs an average of $16,000. Using VNA, the company expects to save $1 million in the first year alone by detecting fraud schemes that would otherwise go unnoticed. Following deployment of Centrifuge VNA, the solution paid for itself in less than 45 days. And this is only the beginning as the company expects to save even more by detecting collusive fraud rings involving networks of doctors, pharmacists, and known fraud offenders. These typically cost insurers millions of dollars and go unnoticed for up to two years. According to a National Health Care Anti-Fraud Association study, every $2 million invested in fighting health-care fraud returns $17.3 million in returns of bogus claims, court-ordered judgments, and other recoveries.

33 Chapter 4 Uses in Anti-counterfeiting In This Chapter Gauging the costs of counterfeiting and online piracy Identifying the most commonly counterfeited products Putting Big Data Analytics on the case If it s being made, it s being faked somewhere. From handbags, jewelry, and shoes to brake pads, electronics, and pharmaceuticals, counterfeiters leave no product category untouched. The International AntiCounterfeiting Coalition (IACC; estimates that counterfeiting is a $600 billion a year problem: Consumer goods: $200 billion Pharmaceutical drugs: $75 billion Online movie piracy: $58 billion Online music piracy: $12.5 billion Online software piracy: $9.5 billion Today s Big Data Analytics applications, however, provide new capabilities to detect counterfeiting and online piracy that analysts never dreamed possible even a decade ago. In this chapter, I gauge the costs of counterfeiting to businesses in the United States and around the world, identify the industries most adversely affected by counterfeiting, and discuss how Big Data Analytics can identify counterfeiting operations.

34 28 Big Data Analytics For Dummies, Centrifuge Special Edition Counting the Costs of Counterfeiting The IACC estimates that counterfeiting has grown at an alarming rate of more than 10,000 percent in the past two decades and it continues to grow by 30 percent each year. For further insight into the scope of global counterfeiting, here are a few mind-blowing statistics to consider from a variety of industry sources: Since 1982, world trade in illegitimate goods has increased from $5.5 billion to approximately $600 billion annually. Approximately 5 to 7 percent of world trade is in counterfeit goods. Two-thirds of counterfeit goods come from China. China produces enough counterfeit cigarettes to supply every smoker in the U.S. with 460 packs per year. The top three intellectual property rights offenders are China ($205 billion/year), Hong Kong ($27 billion/year), and India ($3 billion/year). There are more than 4 million suspected counterfeit listings on ebay in any given year. In the United States, 1.5 million auto accidents and 36,000 fatalities per year are linked to defective counterfeit automobile parts. Although practically every industry is affected in some way by counterfeiting, three in particular stand out: pharmaceuticals, luxury consumer products, and digital media (movies, music, and software). I discuss these industries in detail in the following sections. Pharmaceuticals Industry experts estimate that 10 percent of all pharmaceuticals in the global supply chain and up to 70 percent in some developing countries are counterfeit. In the United States alone, counterfeiting costs the pharmaceuticals industry $75 billion each year.

35 Chapter 4: Uses in Anti-counterfeiting 29 Blocking bogus goods at the borders In 2011, U.S. Customs and Border Protection seized more than 25,000 shipments of counterfeit goods from would-be smugglers. Here s the retail-value breakdown of those shipments: Clothing: $126.3 million Watches: $112.7 million Electronics: $101.2 million Shoes: $97 million Perfume: $51 million CDs and DVDs: $35 million Toys and games: $26.9 million Pharmaceuticals: $25.2 million Computer hardware: $22.6 million Cigarettes: $10.9 million Fake pharmaceuticals harm not only legitimate drug manufacturers but also consumers. Hundreds of thousands of people around the world die each year from counterfeit drugs, with 200,000 of those deaths being caused by phony malaria medications, according to the World Health Organization. Approximately 16 percent of all counterfeit drugs contain the wrong ingredients, 17 percent contain incorrect amounts of the proper ingredients, and 60 percent have no therapeutically active ingredients at all. The most-counterfeited drugs in the world today include some of the most commonly prescribed brand-name pharmaceuticals, such as Vicodin, Lipitor, Plavix, Zyprexa, and Viagra. Luxury goods Consumers who purchase counterfeit merchandise risk funding nefarious activities, contributing to unemployment (approximately 750,000 U.S. jobs are lost each year due to counterfeiting), and compromising the future of the global economy. Counterfeiters exploit consumers, businesses both large and small, inventors and artists, and children laboring in sweatshops in third-world countries. All types of consumer goods are counterfeited, but luxury goods are particular targets. Some of the most commonly counterfeited luxury brands are Louis Vuitton, Hermès, Gucci, Chanel, Rolex, Cartier, Fendi, and Prada.

36 30 Big Data Analytics For Dummies, Centrifuge Special Edition Digital media A decade ago, piracy of digital media music, movies, and software was limited to physical CDs and DVDs. Today, with the wide availability of high-speed Internet connections, piracy is much more likely to be virtual than physical. One industry study indicates that almost a quarter of the world s available Internet bandwidth is used to download pirated media. Additional statistics include: Since the peer-to-peer (P2P) file-sharing site, Napster, emerged in 1999, music sales in the U.S. have dropped 53 percent, from $14.6 billion to $6.9 billion in Only 37 percent of music acquired by U.S. consumers is paid for. 41 percent of software installed on PCs is pirated. Over 50 percent of Americans feel that sharing music and movies with friends is okay. Over 70 percent of 18- to 29-year-olds have downloaded illegal content. Online piracy costs the entertainment and software industries enormous amounts of lost revenue annual losses of $58 billion for movies, $12.5 billion for music, and $9.5 billion for software in the United States alone. Six strikes and you re out To crack down on online copyright infringers, several major American Internet service providers including AT&T, Verizon, Comcast, Cablevision, and Time Warner Cable have entered into a voluntary agreement with the movie and music industries. This program, called the Six Strike System, will be administered by a new entity called the Center for Copyright Infringement starting in July If a participating ISP detects that one of its customers is illegally downloading copyrighted material, it takes a series of six increasingly severe measures, beginning with warnings and escalating to service slowdowns. If these measures have no effect, the subscriber may lose his Internet access and also face lawsuits from the copyright holders.

37 Chapter 4: Uses in Anti-counterfeiting 31 Big Pharma manufacturer prescribes solution to defeat drug counterfeiting Recently, one of the world s largest pharmaceutical companies evaluated leading Big Data Analytics solutions to help identify online pharmaceuticals distributors engaged in the sale of counterfeit versions of its prescription drugs. The company sought a solution that would link suspected distributors to the counterfeit drugs they sold and the countries they operated in. After a rigorous evaluation process, the company selected Visual Network Analytics (VNA) from Centrifuge ( systems.com) because of its ability to scale to large data sets and its support of advanced data analysis techniques, including link and temporal analysis. VNA helped the company sift through more than 23,000 online pharmaceutical websites and derive the country of origin through Whois data. Each website was geotagged so the company could display the locations of fraudulent online pharmacies against a world map. It then used sophisticated relationship maps and link analysis to derive which of its prescription drugs were counterfeited most, by which online pharmaceutical distributors, and in which countries. By leveraging Centrifuge Systems VNA, the company increased the amount of information analyzed annually by more than 500 percent while reducing its cost per case from $2,000 down to $20 a 99 percent reduction in analysis costs! Recognizing the power and flexibility of Centrifuge VNA, the company is now exploring its potential use in other areas, including analyzing patient data related to clinical trials of prescription drugs. Stopping Counterfeiters with Big Data Analytics Whether it s tasked with detecting counterfeiting related to pharmaceutical drugs, luxury goods, consumer electronics, entertainment media, software, or other industries, a Big Data Analytics program can help. By implementing a modern visual-analytics application, analysts can quickly visualize illicit websites, connect the sites to their owners through relationship graphs and link analysis, and profile counterfeit cases.

38 32 Big Data Analytics For Dummies, Centrifuge Special Edition Internet service providers that voluntarily participate in the Six Strike System, for example (refer to the sidebar Six strikes and you re out, earlier in this chapter), can leverage Big Data Analytics to identify customers who are suspected of copyright infringement and link cases of online piracy to popular file-sharing sites. Through Big Data Analytics, analysts can uncover new cases of counterfeiting and online piracy in a fraction of the time that manual processes take, and with far less human effort. The Anti-Counterfeiting Trade Agreement (ACTA) is a multi national treaty establishing international standards for enforcement of intellectual-property rights, signed by Australia, Canada, Japan, Morocco, New Zealand, Singapore, South Korea, the United States, the European Union, and 22 of the EU s member nations. To find out more about ACTA, visit

39 Chapter 5 Uses in Homeland Defense In This Chapter Identifying common use cases for homeland defense Seeing an example of Big Data Analytics in action Exploring U.S. DHS information-sharing programs Whether the mission is fighting terrorism, stopping drug trafficking, or defending a nation s infrastructure, Big Data Analytics can bridge the gap between Big Data and the highly complex analysis required to leverage it. Big Data Analytics increases both the efficiency and effectiveness of data analysis in the cause of homeland defense. Federal, state, and even local governments can leverage data visualization tools to uncover criminal and terrorist plots that simply could not be detected through manual processes alone. In this chapter, I explore common use cases for Big Data Analytics in the context of homeland defense and present an example scenario. Although the examples provided in this chapter pertain to the U.S. government, the concepts related to homeland defense apply to virtually all nations.

40 34 Big Data Analytics For Dummies, Centrifuge Special Edition Cases for Homeland Defense Governments can leverage the data visualization capabilities of modern Big Data Analytics solutions in many ways. Here are a few of the most common use cases related to the cause of homeland defense. Cybersecurity The growing number of attacks on U.S. cybernetworks represents a serious economic and national security threat. The U.S. Office of Management and Budget (OMB) reports that cyberattacks against federal government systems and networks are rising by an average of 40 percent per year. According to OMB s annual reports on federal cybersecurity efforts, these networks suffered 41,776 attacks in 2010 a marked increase from the 30,000 attacks executed in The U.S. Department of Homeland Security (DHS) counters these threats by working across all branches of the federal government, partnering with the private sector, and empowering the general public to create a safe, secure, and resilient cyberenvironment. Counterterrorism Ever since the terrorist attacks of 9/11, the United States and many of its allies have dramatically increased funding for homeland defense and implemented new programs to help defeat terrorism at home and abroad. Protecting the American people from terrorist threats is the founding principle of the DHS. The department s counterterrorism responsibilities focus on three main goals: Preventing terrorist attacks Preventing the unauthorized acquisition, importation, movement, or use of chemical, biological, radiological, and nuclear materials and capabilities within the United States Reducing the vulnerability of critical infrastructure and key resources, essential leadership, and major events to terrorist attacks and other hazards

41 Chapter 5: Uses in Homeland Defense 35 Surveilling social media In 2012, the U.S. Federal Bureau of Investigation (FBI) submitted a request for information regarding social-media monitoring solutions that could enhance the agency s intelligence-analysis initiatives. In the request, the FBI specifically requested technology that would facilitate continuous monitoring of activities on Facebook, Twitter, and other popular social-media platforms used around the world. The FBI hopes to use information posted on social networks to detect specific and credible threats, locate people who are organizing and taking part in dangerous gatherings, and predict upcoming events. Protection of critical infrastructure U.S. federal law defines critical infrastructure as systems and assets, whether physical or virtual, so vital to the United States that the incapacity or destruction of such systems and assets would have a debilitating impact on security, national economic security, national public health or safety, or any combination of those matters. DHS has identified 18 critical-infrastructure sectors that it is tasked with protecting: Agriculture and food Energy Banking and finance Government facilities Chemical Health care and public health Commercial facilities Information technology Communications National monuments and icons Critical manufacturing Nuclear reactors, materials, and waste Dams Postal and shipping Defense industrial base Transportation systems Emergency services Water

42 36 Big Data Analytics For Dummies, Centrifuge Special Edition Interdiction of drug trafficking Federal agencies are directed to increase coordination and information sharing with state and local law enforcement agencies, intensify national efforts to interdict the southbound flow of weapons and bulk currency, and continue close collaboration with the government of Mexico in its efforts against the drug cartels. DHS prevents and investigates illegal movements across U.S. borders, including the smuggling of drugs, cash, weapons, and people (see the next section). The agency is adding manpower and technology on the southwestern border to disrupt the drug, cash, and weapon smuggling that fuels cartel violence in Mexico. In 2011, the National Southwest Border Counternarcotics Strategy was announced to help stem the flow of illegal drugs and their illicit proceeds across the southwestern border of the United States and to reduce associated crime and violence in the region. You can find more information about this program at Interdiction of human trafficking Hundreds of thousands of men, women, and children are illegally transported across international borders each year. Many of these victims are lured from their homes with false promises of well-paying jobs, only to be forced or coerced into prostitution, domestic servitude, unpaid farm or factory labor, or other types of forced labor. U.S. Immigration and Customs Enforcement (ICE) works with its law enforcement partners to dismantle the global criminal infrastructure engaged in human trafficking. You can read more about its mission at Border security DHS secures the nation s air, land, and sea borders to prevent illegal activity while facilitating lawful travel and trade. The department s border-security and management efforts focus on three goals:

43 Chapter 5: Uses in Homeland Defense 37 Effectively securing air, land, and sea points of entry to the United States Safeguarding and streamlining lawful trade and travel Disrupting and dismantling transnational criminal and terrorist organizations Big Data Analytics Case Study The FBI and the U.S. Central Intelligence Agency (CIA) share a mantra: Our successes are private, but our failures are public. Although Big Data Analytics has helped avert many would-be terrorist attacks, the actual processes involved are rarely disclosed to the public. To give you insight into how Big Data Analytics could be used to foil a terrorist attack, the following hypothetical scenario incorporates both commercial and government use of data visualization solutions. Step 1: Follow the suspicious money Meet Susan Johnson, a fraud analyst working for Sunshine Bank at its headquarters in San Francisco. Susan is responsible for analyzing SARS (Suspicious Activity Reporting System) reports filed by branches up and down the West Coast. Specifically, she looks for spikes in large cash transactions (more than $10,000) using the Big Data Analytics solution that the bank implemented last year. In February, Susan noticed a 300 percent increase in large cash transactions among five of Sunshine s branches in the Los Angeles area. More than 90 percent of these transactions involved three machine-shop business customers located within a 20-block area. (Her Big Data Analytics solution combined temporal and geographic proximity views of the data.) As this activity seemed rather suspicious to her, Susan reported it to her local FBI office.

44 38 Big Data Analytics For Dummies, Centrifuge Special Edition DHS information-sharing programs Following are descriptions of current DHS information-sharing programs that can serve as valuable data sources for Big Data Analytics applications: Automated Critical Asset Management System (ACAMS): A web-enabled information services portal that enables state and local governments to collect asset data and protection information for use in response and recovery plans. Critical Infrastructure and Key Resources (CIKR) Asset Protection Technical Assistance Program (CAPTAP): A program designed to assist state and local first responders, emergency managers, and Homeland Security officials. Homeland Security Information Network: A computer-based counterterrorism communications system connecting all 50 states, the five U.S. territories, Washington, D.C., and 50 major urban areas. MegaCenters: Four centers (located in Michigan, Colorado, Pennsylvania, and Maryland) that monitor multiple alarm systems and closed-circuit television networks and wirelessly dispatch communications within federal facilities throughout the nation. National Network of Fusion Centers: A program that sets up fusion centers in states and major urban areas to blend relevant law-enforcement and intelligence information analysis and to coordinate security measures against threats in local communities. National Terrorism Advisory System (NTAS): A program that communicates information about terrorist threats by providing timely, detailed information to the public, government agencies, first responders, airports and other transportation hubs, and the private sector. Protected Critical Infrastructure Information (PCII): An informationprotection program that enhances information sharing between the private sector and the government. US-VISIT Biometric Identification Services: A program that uses biometrics to simplify travel identification for legitimate visitors and block those who intend to do harm or violate U.S. laws. To learn more about these and other DHS information-sharing programs, connect to files/programs/sharinginformation.shtm.

45 Chapter 5: Uses in Homeland Defense 39 Step 2: Track area crime with data visualization John Smith, a veteran FBI counterterrorism analyst, investigated Susan s report. He correlated the information provided to him with criminal activity in that area, using a data visualization system. John noticed that in the two months preceding the increase in large cash deposits at the Sunshine branches, property-theft cases rose in the same geographic area as the machine shops. Among those cases was the reported theft of 300 gallons of ammonia-based fertilizer. Step 3: Identify and defend against the risk John alerted his supervisor, which resulted in FBI field agents being assigned to investigate the three machine shops. The agents learned that each of the three shops had been awarded a large custom order for parts to strengthen the cargo area of a large white conversion van. Step 4: Conduct surveillance FBI surveillance videos from each of the three shops showed the same person picking up the machine parts in a white conversion van. The van shown in those videos matched the description of a van stolen two months earlier in another part of Los Angeles. Analysis of the three machine shops phone records showed two numbers in common. By monitoring those phone lines, FBI agents discovered an Al Qaeda plot against the Academy Awards. The conspirators were planning a suicide/murder mission: Driving the stolen van through the police barricade outside the Hollywood and Highland Center (formerly known as the Kodak Theatre) during the Academy Awards and then detonating a fertilizer-based bomb inside the van.

46 40 Big Data Analytics For Dummies, Centrifuge Special Edition Step 5: Arrest the suspects The FBI quickly obtained arrest warrants for the conspirators, thus preventing the crime. In the hypothetical example in this section, no individual piece of information could have prevented the horrific attack that was being planned. By using Big Data Analytics and sharing information, however (see the nearby sidebar DHS information-sharing programs ), businesses and government agencies have new capabilities to visualize anomalies in vast amounts of data and then link those anomalies to people, places, accounts, locations, and more.

47 Chapter 6 Ten Buying Criteria for Big Data Analytics In This Chapter Avoiding the pitfalls of traditional analytic solutions Creating a checklist of buying criteria Understanding what to look for when evaluating solutions Not all Big Data Analytics solutions are alike. Many firstgeneration visual analytics packages: Take too long to get up and running: Many solutions require heavy customization, costly services, and data transformation that can take months to deliver. Aren t compatible with all your data: Some solutions require staging your data in a proprietary format and can t ingest new data sources on the fly. Are limited to static visualizations: Harnessing the value of Big Data requires flexibility to ask questions of the data without restriction. Lack collaboration capabilities: Many Big Data Analytics solutions have no mechanism to let analysts share insights or discoveries with colleagues. Are too hard to use: The single-greatest obstacle to adoption of a Big Data Analytics solution is that the application is simply too hard to use, often requiring special skills. Luckily, I can help you steer clear of these pitfalls. Following are ten buying criteria that you should look for when evaluating Big Data Analytics solutions.

48 42 Big Data Analytics For Dummies, Centrifuge Special Edition Quick Implementation Your Big Data Analytics solution should be up and running in a matter of weeks not months. Avoid solutions that require your data to be restructured before being analyzed. Restructuring data can delay the time when you can begin reaping the benefits of your visual-analytics solution. Bring Your Own Data The Big Data Analytics solution you select must work seamlessly with your data sources. Look for solutions that natively support all of your source-data formats (such as Hadoop, HDFS, Oracle, Microsoft SQL Server, Microsoft Excel, PostgreSQL, XML, text, and so on) without the need for special programming or complex configurations. Interactive Visualization The most efficient and effective Big Data analysis requires interactive pictures and dynamic analytics. Users should be able to pose questions through direct interaction with the visualizations rendered. Then they should be able to drill down into charts and filter, partition, subset, and augment data of interest. (For details on visualizations, see Chapter 2.) Collaborative Analysis Leading Big Data Analytics solutions provide a framework for easy collaboration, allowing users to share knowledge as they make discoveries. Analysts should be able to publish live assets to peers who are authorized to collaborate or view their insights for auditing. Integrated-Discovery Experience Today s visual-analytics applications incorporate a wide variety of interactive visualizations (see Chapter 2 for more

49 Chapter 6: Ten Buying Criteria for Big Data Analytics 43 on this topic). Users should be able to create interactive visualizations and relate these analyses intuitively to each other as they discover patterns, positioning complementary visualizations on the same screen. This helps facilitate integrated discovery the ability to discover answers to questions that you never thought to ask. Support for Advanced Analysis Techniques Your chosen solution should support a variety of advanced analysis techniques, including link analysis (displaying relationships among entities visually), temporal analysis (viewing events over the course of time), and geospatial analysis (viewing data in the context of geography). Some Big Data Analytics solutions incorporate one or two of these elements; look for a solution that addresses them all. Ease of Use Experience has shown that if end users can t get productive with a new application quickly, that application simply won t get adopted. Big Data Analytics products should require no special programming and little setup and should be easy, intuitive, and enjoyable to use. No special skills should be required to operate Big Data Analytics software. Web Browser Based Client Some analytics software packages require the installation of proprietary client desktop software. Look for solutions that incorporate a browser-based client to reduce deployment costs and avoid the security risks inherent in desktop applications. Designed for Scalability Be sure to select a solution that can efficiently render largescale visualizations from disparate data sources that are

50 44 Big Data Analytics For Dummies, Centrifuge Special Edition hundreds of terabytes or even petabytes in size without causing your visual-analytics server to drop to its knees. Responsive Customer Support An important yet often-overlooked buying criterion is the vendor s responsiveness. Look for a vendor that is committed to support you every step of the way before, during, and after your deployment is up and running. Seek a long-term partner that is truly committed to your success.

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52 Find out how Big Data Analytics can help you extract value from your data quickly and easily Drowning in a sea of data? Well, don t worry. Big Data Analytics can help. Whether you re tasked with detecting fraud, identifying counterfeit product operations, protecting IT infrastructure, or securing homeland defenses, today s Big Data Analytics solutions can help you work smarter to extract the information you need. Understanding the basics define Big Data Analytics and contrast common approaches Getting started discover the power of link analysis and explore common everyday data visualizations Uses in fraud and risk management review typical fraud use cases and hypothetical usage scenarios Uses in anti-counterfeiting uncover counterfeit product operations more quickly than ever before Uses in homeland defense explore uses for counterterrorism, protecting critical infrastructure, border security, cybersecurity, and more Important buying criteria know exactly what to look for when evaluating solutions Learn more at Steve Piper is a high-tech marketing and product management veteran with nearly 20 years of experience. A freelance writer and consultant, Steve is the author of Intrusion Prevention Systems For Dummies and has achieved a CISSP security certification from ISC 2 and BS and MBA degrees from George Mason University. Find out more about Steve Piper at Open the book and find: Attributes of top-tier Big Data Analytics applications Descriptions and samples of common data visualizations The power of link, temporal, and geospatial analyses Usage scenarios for both private and public sectors Buying criteria for Big Data Analytics solutions Go to Dummies.com for videos, step-by-step examples, how-to articles, or to shop! ISBN: Not for resale