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T DW I R E S E A R C HT DW I BE S T P R AC T ICE S RE P O R TBIG DATAANALYTICSBy Philip RussomCo-sponsored bytdwi.orgFOURTH QUARTER 2011

FOURTH QUARTER 2011TDWI BEST PRACTICES REPORTBIG DATA A N A LY T IC SBy Philip RussomTable of ContentsResearch Methodology and Demographics . . . . . . . . . . . . . . . 3Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4Introduction to Big Data Analytics . . . . . . . . . . . . . . . . . . . 5Defining Advanced Analytics as a Discovery Mission . . . . . . . . . 5Defining Big Data Via the Three Vs. . . . . . . . . . . . . . . . . . . 6Defining Big Data Analytics . . . . . . . . . . . . . . . . . . . . . . 8Why Put Big Data and Analytics Together Now? . . . . . . . . . . . . 9The State of Big Data Analytics . . . . . . . . . . . . . . . . . . . . . 10Big Data Analytics Adoption . . . . . . . . . . . . . . . . . . . . . . 10Benefits of Big Data Analytics . . . . . . . . . . . . . . . . . . . . . 10Barriers to Big Data Analytics . . . . . . . . . . . . . . . . . . . . . 11Big Data: Problem or Opportunity? . . . . . . . . . . . . . . . . . . 12Organizational Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . 13Ownership and Control of Big Data Analytics . . . . . . . . . . . . . 13Big Data Analytics Can Have a Departmental Focus . . . . . . . . . . 14Job Titles for Big Data Analytics . . . . . . . . . . . . . . . . . . . . 14Best Practices in Big Data Analytics . . . . . . . . . . . . . . . . . . 15Volume Growth of Analytic Big Data . . . . . . . . . . . . . . . . . . 15Managing Analytic Big Data . . . . . . . . . . . . . . . . . . . . . . 16Data Types for Big Data . . . . . . . . . . . . . . . . . . . . . . . . 17Refresh Rates for Analytic Data . . . . . . . . . . . . . . . . . . . . 19Replacing Analytics Platforms . . . . . . . . . . . . . . . . . . . . . 20Tools, Techniques, and Trends for Big Data Analytics . . . . . . . . . 22Potential Growth versus Commitment for Big Data Analytics Options . . 24Trends for Big Data Analytics Options . . . . . . . . . . . . . . . . . 26Vendor Products for Big Data Analytics. . . . . . . . . . . . . . . . . 31Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 2011 by TDWI (The Data Warehousing InstituteTM), a division of 1105 Media, Inc. All rights reserved. Reproductions in wholeor in part are prohibited except by written permission. E-mail requests or feedback to Product and company namesmentioned herein may be trademarks and/or registered trademarks of their respective companies.tdwi.org1

B I G DATA A N A LY T I C SAbout the AuthorPHILIP RUSSOM is director of TDWI Research for data management and oversees many of TDWI’sresearch-oriented publications, services, and events. He is a well-known figure in data warehousingand business intelligence, having published over five hundred research reports, magazine articles,opinion columns, speeches, Webinars, and more. Before joining TDWI in 2005, Russom was anindustry analyst covering BI at Forrester Research and Giga Information Group. He also ran his ownbusiness as an independent industry analyst and BI consultant and was a contributing editor withleading IT magazines. Before that, Russom worked in technical and marketing positions for variousdatabase vendors. You can reach him at, @prussom on Twitter, and on LinkedInat TDWITDWI, a division of 1105 Media, Inc., is the premier provider of in-depth, high-quality educationand research in the business intelligence and data warehousing industry. TDWI is dedicated toeducating business and information technology professionals about the best practices, strategies,techniques, and tools required to successfully design, build, maintain, and enhance businessintelligence and data warehousing solutions. TDWI also fosters the advancement of businessintelligence and data warehousing research and contributes to knowledge transfer and theprofessional development of its members. TDWI offers a worldwide membership program, fivemajor educational conferences, topical educational seminars, role-based training, onsite courses,certification, solution provider partnerships, an awards program for best practices, live Webinars,resourceful publications, an in-depth research program, and a comprehensive Web site: the TDWI Best Practices Reports SeriesThis series is designed to educate technical and business professionals about new business intelligencetechnologies, concepts, or approaches that address a significant problem or issue. Research for thereports is conducted via interviews with industry experts and leading-edge user companies and issupplemented by surveys of business intelligence professionals.To support the program, TDWI seeks vendors that collectively wish to evangelize a new approachto solving business intelligence problems or an emerging technology discipline. By banding together,sponsors can validate a new market niche and educate organizations about alternative solutionsto critical business intelligence issues. Please contact TDWI Research Director Philip Russom( to suggest a topic that meets these requirements.AcknowledgmentsTDWI would like to thank many people who contributed to this report. First, we appreciate themany users who responded to our survey, especially those who responded to our requests for phoneinterviews. Second, our report sponsors, who diligently reviewed outlines, survey questions, andreport drafts. Finally, we would like to recognize TDWI’s production team: Jennifer Agee, BillGrimmer, and Denelle Hanlon.SponsorsCloudera, EMC Greenplum, IBM, Impetus Technologies, Kognitio, ParAccel, SAND Technology,SAP, SAS, Tableau Software, and Teradata sponsored the research for this report.2TDWI RESE A RCH

Research Methodology and DemographicsResearch Methodology andDemographicsReport Scope. According to TDWI survey data, a new floodof user organizations is currently commencing or expandingsolutions for analytics with big data. To supply the demand,vendors have recently released numerous new productsand functions, specifically for advanced forms of analytics(beyond OLAP and reporting) and analytic databases thatcan manage big data. While it’s good to have options, it’shard to track them and determine in which situations theyare ready for use. The purpose of this report is to accelerateusers’ understanding of the many new tools and techniquesthat have emerged for analytics with big data in recent years.It will also help readers map newly available options to realworld use cases.Survey Methodology. In May 2011, TDWI sent an invitation viae-mail to the data management professionals in its database,asking them to complete an Internet-based survey. Theinvitation was also distributed via Web sites, newsletters, andpublications from TDWI and other firms. The survey drewresponses from almost 360 survey respondents. From these,we excluded incomplete responses and respondents whoidentified themselves as academics or vendor employees. Theresulting completed responses of 325 respondents form thecore data sample for this report.Survey Demographics. The majority of survey respondentsare corporate IT professionals (58%), whereas the others arebusiness sponsors or users (22%) and consultants (20%).We asked consultants to fill out the survey with a recent clientin mind.The consulting (15%) and financial services (15%) industriesdominate the respondent population, followed by software(10%), healthcare (7%), insurance (7%), and other industries.Most survey respondents reside in the U.S. (56%) or Europe(17%). Respondents are fairly evenly distributed across allsizes of companies and other organizations.Other Research Methods. In addition to the survey, TDWIResearch conducted many telephone interviews withtechnical users, business sponsors, and recognized datamanagement experts. TDWI also received product briefingsfrom vendors that offer products and services related to thebest practices under discussion.PositionCorporate IT professional58%Business professional services15%Financial ce7%Manufacturing (non-computers)5%Telecommunications5%Government: ng/marketing/PR3%Computer ther” consists of multiple industries, eachrepresented by 2% or less of respondents.)GeographyUnited States56%Europe17%Asia7%Canada6%Australia4%Central or South America3%Middle East3%Africa2%Other2%Company Size by RevenueLess than 100 million26% 100–500 million11% 500 million– 1 billion12% 1–5 billion 5–10 billionMore than 10 billionDon’t know18%7%18%8%Based on 325 survey respondents.tdwi.org3

B I G DATA A N A LY T I C SExecutive SummaryBig data used to be atechnical problem. Now it’s abusiness opportunity.Oddly enough, big data was a serious problem just a few years ago. When data volumes startedskyrocketing in the early 2000s, storage and CPU technologies were overwhelmed by the numerousterabytes of big data—to the point that IT faced a data scalability crisis. Then we were once againsnatched from the jaws of defeat by Moore’s law. Storage and CPUs not only developed greatercapacity, speed, and intelligence; they also fell in price. Enterprises went from being unable to affordor manage big data to lavishing budgets on its collection and analysis.Today, enterprises are exploring big data to discover facts they didn’t know before. This is animportant task right now because the recent economic recession forced deep changes into mostbusinesses, especially those that depend on mass consumers. Using advanced analytics, businessescan study big data to understand the current state of the business and track still-evolving aspects suchas customer behavior.Big data is not just big. It’salso diverse data types andstreaming data.If you really want the lowdown on what’s happening in your business, you need large volumes ofhighly detailed data. If you truly want to see something you’ve never seen before, it helps to tap intodata that’s never been tapped for business intelligence (BI) or analytics. Some of the untapped datawill be foreign to you, coming from sensors, devices, third parties, Web applications, and socialmedia. Some big data sources feed data unceasingly in real time. Put all that together, and you seethat big data is not just about giant data volumes; it’s also about an extraordinary diversity of datatypes, delivered at various speeds and frequencies.Big data analytics is theapplication of advancedanalytic techniques to verybig data sets.Note that two technical entities have come together. First, there’s big data for massive amountsof detailed information. Second, there’s advanced analytics, which is actually a collection ofdifferent tool types, including those based on predictive analytics, data mining, statistics, artificialintelligence, natural language processing, and so on. Put them together and you get big dataanalytics, the hottest new practice in BI today.Of course, businesspeople can learn a lot about the business and their customers from BI programsand data warehouses. But big data analytics explores granular details of business operations andcustomer interactions that seldom find their way into a data warehouse or standard report. Someorganizations are already managing big data in their enterprise data warehouses (EDWs), whileothers have designed their DWs for the well-understood, auditable, and squeaky clean data that theaverage business report demands. The former tend to manage big data in the EDW and execute mostanalytic processing there, whereas the latter tend to distribute their efforts onto secondary analyticplatforms. There are also hybrid approaches.There are many types ofvendor products to considerfor big data analytics.This report discussesthe types.4TDWI RESE A RCHRegardless of approach, user organizations are currently reevaluating their analytic portfolios. Inresponse to the demand for platforms suited to big data analytics, vendors have released a slew of newproduct types including analytic databases, data warehouse appliances, columnar databases, no-SQLdatabases, distributed file systems, and so on. There is also a new slew of analytic tools.This report drills into all the aspects of big data analytics mentioned here to give users and theirbusiness sponsors a solid background for big data analytics, including business and technologydrivers, successful business use cases, and common technology enablers. The report also uses surveydata to project the future of the most common tool types, features, and functions associated with bigdata analytics, so users can apply this information to planning their own programs and technologystacks for big data analytics.

IntroductionIntroduction to Big Data AnalyticsBig data analytics is where advanced analytic techniques operate on big data sets. Hence, big dataanalytics is really about two things—big data and analytics—plus how the two have teamed up tocreate one of the most profound trends in business intelligence (BI) today. Let’s start by definingadvanced analytics, then move on to big data and the combination of the two.Defining Advanced Analytics as a Discovery MissionAccording to a 2009 TDWI survey, 38% of organizations surveyed reported practicing advancedanalytics, whereas 85% said they would be practicing it within three years.1 Why the rush toadvanced analytics? First, change is rampant in business, as seen in the multiple “economies” we’vegone through in recent years. Analytics helps us discover what has changed and how we should react.Second, as we crawl out of the recession and into the recovery, there are more and more businessopportunities that should be seized. To that end, advanced analytics is the best way to discovernew customer segments, identify the best suppliers, associate products of affinity, understand salesseasonality, and so on. For these reasons, TDWI has seen a steady stream of user organizationsimplementing analytics in recent years.In the last three years orso, many organizations havedeployed analytics for thefirst time.The rush to analytics means that many organizations are embracing advanced analytics for the firsttime, and hence are confused about how to go about it. Even if you have related experience in datawarehousing, reporting, and online analytic processing (OLAP), you’ll find that the business andtechnical requirements are different for advanced forms of analytics. To help user organizations selectthe right form of analytics and prepare big data for analysis, this report will discuss new options foradvanced analytics and analytic databases for big data so that users can make intelligent decisions asthey embrace analytics.Note that user organizations are implementing specific forms of analytics, particularly what issometimes called advanced analytics. This is a collection of related techniques and tool types, usuallyincluding predictive analytics, data mining, statistical analysis, and complex SQL. We might alsoextend the list to cover data visualization, artificial intelligence, natural language processing, anddatabase capabilities that support analytics (such as MapReduce, in-database analytics, in-memorydatabases, columnar data stores).Instead of “advanced analytics,” a better term would be “discovery analytics,” because that’s whatusers are trying to accomplish. (Some people call it “exploratory analytics.”) In other words, with bigdata analytics, the user is typically a business analyst who is trying to discover new business facts thatno one in the enterprise knew before. To do that, the analyst needs large volumes of data with plentyof detail. This is often data that the enterprise has not yet tapped for analytics.“Discovery analytics” is amore descriptive term than“advanced analytics.”For example, in the middle of the recent economic recession, companies were constantly being hitby new forms of customer churn. To discover the root cause of the newest form of churn, a businessanalyst would grab several terabytes of detailed data drawn from operational applications to get aview of recent customer behaviors. The analyst might mix that data with historic data from a datawarehouse. Dozens of queries later, the analyst would discover a new churn behavior in a subset of thecustomer base. With any luck, that discovery would lead to a metric, report, analytic model, or someother product of BI, through which the company could track and predict the new form of churn.Discovery analytics against big data can be enabled by different types of analytic tools, includingthose based on SQL queries, data mining, statistical analysis, fact clustering, data visualization,1 See the TDWI Best Practices Report Next Generation Data Warehouse Platforms (Q4 2009), available on

B I G DATA A N A LY T I C Snatural language processing, text analytics, artificial intelligence, and so on. It’s quite an arsenal oftool types, and savvy users get to know their analytic requirements before deciding which tool type isappropriate to their needs.All these techniques have been around for years, many of them appearing in the 1990s. Thedifference today is that far more user organizations are actually using them. That’s because most ofthese techniques adapt well to very large, multi-terabyte data sets with minimal data preparation.That brings us to big data.Defining Big Data Via the Three VsBig data isn’t just aboutdata volume.Most definitions of big data focus on the size of data in storage. Size matters, but there are otherimportant attributes of big data, namely data variety and data velocity. The three Vs of big data(volume, variety, and velocity) constitute a comprehensive definition, and they bust the myth thatbig data is only about data volume. In addition, each of the three Vs has its own ramifications foranalytics.2 (See Figure 1.)VOLUMETerabytesRecordst Transactionst Tables, filestt3 Vs ofBig DataBatchNear timet Real timet StreamsStructuredUnstructuredt Semistructuredt All the abovettttVELOCITYVARIETYFigure 1. The three Vs of big dataData volume as a defining attribute of big data.It’s obvious that data volume is the primary attribute of big data. With that in mind, most peopledefine big data in terabytes—sometimes petabytes. For example, a number of users interviewed byTDWI are managing 3 to 10 terabytes (TB) of data for analytics. Yet, big data can also be quantifiedby counting records, transactions, tables, or files. Some organizations find it more useful to quantifybig data in terms of time. For example, due to the seven-year statute of limitations in the U.S., manyfirms prefer to keep seven years of data available for risk, compliance, and legal analysis.The scope of big datavaries widely.6TDWI RESE A RCHThe scope of big data affects its quantification, too. For example, in many organizations, thedata collected for general data warehousing differs from data collected specifically for analytics.Different forms of analytics may have different data sets. Some analytic practices lead a businessanalyst or similar user to create ad hoc analytic data sets per analytic project. Then, there’s theentire enterprise, which in toto has its own, even larger scope of big data. Furthermore, each of these2 These definitions of big data were originally developed in TDWI blog posts, available at

Introductionquantifications of big data grows continuously. All this makes big data for analytics a moving targetthat’s tough to quantify.USER STORY THERE ARE VARIOUS WAYS TO QUANTIFY BIG DATA.TDWI asked a user how many terabytes he’s managing for analytics, and he said: “I don’t know, because I don’thave to worry about storage. IT provides it generously, and I tap it like crazy.” Another user said: “We don’t countterabytes. We count records. My analytic database for quality assurance alone has 3 billion records. There’sanother 3 billion in other analytic databases.”Data type variety as a defining attribute of big data.One of the things that makes big data really big is that it’s coming from a greater variety of sourcesthan ever before. Many of the newer ones are Web sources, including logs, clickstreams, and socialmedia. Sure, user organizations have been collecting Web data for years. But, for most organizations,it’s been a kind of hoarding. We’ve seen similar untapped big data collected and hoarded, such asRFID data from supply chain applications, text data from call center applications, semistructureddata from various business-to-business processes, and geospatial data in logistics. What’s changed isthat far more users are now analyzing big data instead of merely hoarding it. The few organizationsthat have been analyzing this data now do so at a more complex and sophisticated level. Big data isn’tnew, but the effective analytical leveraging of big data is.Big data is remarkablydiverse in terms of sources,data types, and entitiesrepresented.The recent tapping of these sources for analytics means that so-called structured data (whichpreviously held unchallenged hegemony in analytics) is now joined by unstructured data (textand human language) and semistructured data (XML, RSS feeds). There’s also data that’s hard tocategorize, as it comes from audio, video, and other devices. Plus, multidimensional data can bedrawn from a data warehouse to add historic context to big data. That’s a far more eclectic mix ofdata types than analytics has ever seen. So, with big data, variety is just as big as volume. In addition,variety and volume tend to fuel each other.USER STORY HADOOP IS ABOUT DATA VARIETY, NOT JUST DATA VOLUME.TDWI found a couple of users who have employed Hadoop as an analytic platform. Both said the same thing:Hadoop’s scalability for big data volumes is impressive, but the real reason they’re working with Hadoop is itsability to manage a very broad range of data types in its file system, plus process analytic queries via MapReduceacross numerous eccentric data types. It’s not just Hadoop; TDWI has heard users make similar comments aboutother analytic platforms.Data feed velocity as a defining attribute of big data.Big data can be described by its velocity or speed. You may prefer to think of it as the frequency ofdata generation or the frequency of data delivery. For example, think of the stream of data comingoff of any kind of device or sensor, say robotic manufacturing machines, thermometers sensingtemperature, microphones listening for movement in a secure area, or video cameras scanningfor a specific face in a crowd. The collection of big data in real time isn’t new; many firms havebeen collecting clickstream data from Web sites for years, using streaming data to make purchaserecommendations to Web visitors. With sensor and Web data flying at you relentlessly in real time,data volumes get big in a hurry. Even more challenging, the analytics that go with streaming datahave to make sense of the data and possibly take action—all in real time.The leading edge of bigdata is streaming data.tdwi.org7

B I G DATA A N A LY T I C SUSER STORY PROCESSING STREAMING BIG DATA ENABLES NEW ANALYTIC APPLICATIONS.A consultant who specializes in streaming data told TDWI about the video and audio analytic applications he’slooking into: “Think about the algorithms that enable us to parse text and perform sentiment analysis, sometimesin real time. Very similar algorithms can parse video images to document and analyze changes in the thing that’sbeing imaged. For example, satellite images could monitor and analyze troop movements, a flood plane, cloudpatterns, or grass fires. Or a video analysis system could monitor a sensitive or valuable facility, watching forpossible intruders, then alert authorities in real time.“You can implement similar applications with sound monitoring. One of my analytic applications involves 2,000underground microphones that listen for movement in geologic formations. I hope that the big data the applicationis collecting can eventually help predict earthquakes.”Defining Big Data AnalyticsMost users are familiarwith big data analyticsbut don’t use the term.Again, big data analytics is where advanced analytic techniques operate on big data. The definition iseasy to understand, but do users actually use the term? To quantify this question, the survey for thisreport asked: “Which of the following best characterizes your familiarity with big data analytics andhow you name it?” (See Figure 2.) The survey results show that most users understand the concept ofbig data analytics, whether they have a name for it or not:Few respondents are unfamiliar with the concept. Only 7% report that they “haven’t seen or heard ofanything resembling big data analytics.”Most users surveyed don’t have a name for big data analytics. Even so, they understand the definition(65% of respondents).Roughly a quarter of respondents have a name for big data analytics. Twenty-eight percent bothunderstand the concept and have named it.Which of the following best characterizes your familiarity with big data analytics and how you name it?I haven’t seen or heard of anything resembling big data analytics.7%I know what you mean, but I don’t have a formal name for it.I know what you mean, and I have a name for it.65%28%Figure 2. Based on 325 respondents.When users have aterm, it’s most often“big data analytics.”Most of the survey respondents who report having a name for big data analytics typed the name theyuse into the survey software. The name entered most often is the term used in this report: “big dataanalytics” (18% in Figure 3). Similar terms appeared, such as large-volume or large-data-set analytics(7%). Many use the popular term advanced analytics (12%), or they simply call it analytics (12%). Afew common terms were entered, such as data warehousing (4%), data mining (2%), and predictiveanalytics (2%). A whopping 43% entered a unique name, showing that names for analytic methodsare amazingly diverse.Finally, a few survey respondents entered humorous but revealing terms such as honking big data, myday job, pain in the neck, and we-need-to-buy-more-hardware analytics.8TDWI RESE A RCH

IntroductionEnter the term you use for big data analytics.Big data analyticsAdvanced analyticsAnalyticsLarge-volume or large-data-set analytics18%12%12%7%Data warehousing4%Data mining2%Predictive analytics2%Other (miscellaneous unique terms)43%Figure 3. Based on 92 respondents who report having a name for big data analytics.Why Put Big Data and Analytics Together Now?Big data provides gigantic statistical samples, which enhance analytic tool results. Most tools designedfor data mining or statistical analysis tend to be optimized for large data sets. In fact, the generalrule is that the larger the data sample, the more accurate are the statistics and other products of theanalysis. Instead of using mining and statistical tools, many users generate or hand-code complexSQL, which parses big data in search of just the right customer segment, churn profile, or excessiveoperational cost. The newest generation of data visualization tools and in-database analytic functionslikewise operate on big data.Analytic platforms todayhandle big data betterthan ever.Analytic tools and databases can now handle big data. They can also execute big queries and parsetables in record time. Recent generations of vendor tools and platforms have lifted us onto a newplateau of performance that is very compelling for applications involving big data.The economics of analytics is now more embraceable than ever. This is due to a precipitous dropin the cost of data storage and processing bandwidth. The fact that tools and platforms for bigdata analytics are relatively affordable is significant because big data is not just for big business.Many small-to-midsize businesses (especially those deep into digital processes for sales, customerinteractions, or supply chain) also need to manage and leverage big data.There’s a lot to learn from messy data, as long as it’s big. Most modern tools and techniques foradvanced analytics and big data are very tolerant of raw source data, with its transactional schema,non-standard data, and poor-quality data. That’s a good thing, because discovery and predictiveanalytics depend on lots of details—even questionable data. For example, analytic applications forfraud detection often depend on outliers and non-standard data as indications of fraud. So, be careful:If you apply ETL and data quality processes to big data as you do for a data warehouse, you run therisk of stripping out the very nuggets that make big data a treasure trove for advanced analytics.3Big data is an enterpriseasset that yields actionablebusiness insights.Big data is a special asset that merits leverage. That’s the real point of big data analytics. The newtechnologies and new best practices are fascinating, even mesmerizing, and there’s a certain machocoolness to working with dozens of terabytes. But don’t do it for the technology. Put big data anddiscovery analytics together for the new insights they give the business.Analytics based on large data samples reveals and leverages business change. The recession hasaccelerated the already quickening pace of business. The recovery, though welcome, brings evenmore change. In fact, the average business has changed beyond all recognition because of the recenteconomic recession and recovery. The change has not gone unnoticed. Businesspeople now share awholesale recognition that they must explore change just to understand the new state of the business.3 The preparation of big data for advanced analytics rarely follows the same best practices we associate with mainstream data warehousing,reporting, and OLAP. To understand the differences, see the TDWI Checkli 5 Introduction 1 See the TDWI Best Practices Report Next Generation Data Warehouse Platforms (Q4 2009), available on Introduction to Big Data Analytics Big data analytics is where advanced analytic techniques operate on big data sets. Hence, big data analytics is really about two things—big data and analytics—plus how the two have teamed up to

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