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Visual Discovery Tools: Market Segmentation and Product Positioning 1

Visual Discovery Tools: Market SegmentatinnannnPrinuctnPisitining BI Leadership Technology Guide

By Wayne Eckerson Director, BI Leadership Research 2013


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About QlikTech QlikTech (NASDAQ: QLIK) is a leader in Business Discovery—user-driven Business Intelligence (BI). Its QlikView Business Discovery soluton riiges tte gap etween traiitonal BI solutons ani inaieduate spreaisteet applicatons. The in-memory associatve searct tectnology Qlikkect pioneerei createi tte self-service BI category, allowing users to explore informaton freely ratter ttan eing confinei to a preiefinei patt of duestons. ppropriate from SB to tte largest glo al enterprise, QlikView’s self-service analysis can e ieployei witt iata governance in iays or weeks. kte QlikView Business Discovery platormms app-iriven moiel works witt existng BI solutons, o ering an immersive mo ile ani social, colla oratve experience. Heaiduarterei in Rainor, Pennsylvania, Qlikkect tas offices arouni tte worli serving approximately 29,000 customers in over 100 countries. For more informaton, please visit www.qlikview.com.

http://www.qlikview.com/


B CKGROUND

Background WHAT ARE VISUAL DISCOVERY TOOLS? They are self-service, in-memory analysis tools ttat ena le usiness users to access ani analyze iata visually at the speed of thought with minimal or no IT assistance and then share the results of their discoveries with colleagues, usually in tte form of an interactve iast oari. WHO ARE THE TARGET USERS? Visual discovery tools are usei y (1) power users to explore ani analyze iata in a variety of systems, (2) superusers and BI specialists to create interactve iast oaris for colleagues ani (() casual users to view ani work witt ttose iast oaris.

MarketnPisitining AS DEPICTED IN FIGURE 1, visual discovery tools straddle top-iown ani otom-up approaches to usiness intelligence (BI) and are typically ieployei more duickly and at a lower cost than enterprise-centric BI tools. Visual discovery tools ofen replace desktop analysis tools like Excel and stare most ctaracteristcs witt multiimensional online analytcal processing (OL P) tools except write- ack capa ilites. Visual iiscovery tools are now startng to leei into otter BI segments, partcularly ai toc ani operatonal reportng, relatonal OL P ani ig iata analytcs platorms. Today, visual discovery tools are primarily geared toward iepartmental applicatons, not enterprise ones. But as functonality evolves in tte next year or two, visual iiscovery tools will compete more iirectly witt relatonal OL P ani ig iata analytcs platorms for iominance in enterprise deployments (see Appendix for a ietailei reakiown of tte BI market).

DEFINITIONS Power Users Business users who are paid to crunch data on a iaily asis, incluiing usiness analysts, statisticians and data scientists Superusers Business users who master the use of a BI tool ani duickly ecome tte go-to people in each department to create ad hoc reports and iast oaris Casual Users Business users who use information to do their jo s, incluiing executives, managers, front-line workers, customers and suppliers


B CKGROUND

Figure 1. BI Tools Framework

ource: Wayne Eckerson, BI Leaierstip Researct

BI F

UN

CTIO

NAL

ITY

EnterpriseDepartment

SCOPE OF DEPLOYMENT

Top-downBottom-up

Repo

rting

Dast o

aris

Anal

ysis

Sining

Pixel Perfect ReportingAd hoc Reports/

Dast oaris

Analyst

Data Sining Work enct

OperationalReports/

Dast oaris

Big Data Analytics Platforms

Relational OL P

Sulti-dimensional

OL P

Desktop Analysis (e.g. Excel)

Visual Discovery

Power Users

Casual Users

TYPES OF INTELLIGENCE Top-down intelligence consists of reports and iast oaris ttat answer preiefinei duestions or monitor usiness processes using metrics alignei witt strategic o jectives Bottom-up intelligence consists of ad hoc analysis and mining tools that answer unanticipatei duestions arising from new ani ctanging usiness coniitions See Appendix for more detail.

KEY CH R CkERI kIC


KeynCharacteristcs VISUAL DISCOVERY TOOLS stare tte following ctaracteristcs, wtict iistnguist ttem from otter categories of BI tools:

1. Desktop tools with in-memirynpricessingnannnanniptinaanserrer. Sost visual discovery tools run on Windows desktops and contain an em eiiei in-memory iata ase or cacte. ome of ttese iesktop tools are We “fat clients” suct as Flast, ilverligtt or synctronous Java cript ani XSL ttat connect to server- asei cu es or in-memory data ases. ni some rely less on in-memory processing than on duerying iata sources iirectly—for example, ka leau. Sost visual iiscovery tools o er optonal server sofware ttat ena les analysts ani iast oari auttors to uploai content, apply security, manage data connectons ani pu list tteir output for auttorizei users to view. The server also taniles communicatons witt We rowsers ttat renier tte pu listei iast oaris.

2. Anaaysis,nnitnrepirtngg Visual discovery tools are designed for visual iata exploraton, analysis and lightweight data mining. Although they can e usei to create interactve iast oaris for iepartmental users ani riefing ooks for executves, ttey io not generate pixel-perfect or page-orientei reports ani iast oaris.

(. Universal data connectritygnko ensure ttat usiness analysts can get the data they need, visual discovery tools support connectors to almost any iata source, incluiing files—Excel and comma-separated values—ani relatonal iata ases, largely ttrougt Open Data ase Connectvity or Java Data ase Connectvity irivers. Sany also connect iirectly to applicatons—for example, P Business uite ani alesforce.com, XSL and Hadoop—usually ttrougt separately pricei aiapters. Once connected to a data source, visual discovery tools downloai preiefinei sets of iata into local memory, alttougt some also let users duery tte data directly in source systems.

4. In-memory processing. To deliver speed-of-thought analysis, most visual discovery tools store data in an in-memory iata ase or cacte to avoii tte performance otleneck associatei witt accessing iata from a rotatng iisk. kte tools aggregate or calculate ase-level data iynamically ani scale linearly as customers aii more R S ani CPU. But since R S is duickly consumei y iata, users ani visual controls, most visual discovery tools are designed to manage workgroup and iepartmental applicatons, not enterprise ones—for example, data marts instead of data warehouses. But the memory footprint of servers

50 MILLION ROWS

A visual discovery tool running on a laptop with a 64-bit operatin system and 16GB of RAM can store 50 million rows of data, which is the equivalent of most small data marts.

KEY CH R CkERI kIC


is expaniing duickly, making it possi le for companies to store ever-larger volumes of iata in memory, even tera yte-scale data waretouses in tte near future. Soreover, visual iiscovery tools are following tte leai of ka leau ani augmentng tteir in-memory iata ases witt iirect duery connectvity, so ttere is no longer a finite limit to the amount of data the tools can access.

5. Simpaenannnflexibaennatannesigng As self-service tools, most visual iiscovery proiucts moiel iata on tte fly, minimizing tte amount of up-front iesign reduirei to implement ani use tte sofware. Unlike top-down BI tools, visual discovery tools ionmt reduire a specific iata moiel like a star sctema or semantc layer of user-frienily iata o jects. Sost visual iiscovery proiucts focus on one iata set at a tme ani automatcally link ta les using starei keys, making a est guess at iata relatonstips, wtict users can overriie if iesirei. Of course, tte traieo for ttis iesign simplicity ani flexi ility is complexity. Sost visual discovery tools don’t deal well with complex enterprise applicatons witt ttousanis of ta les ani iirty iata. Like most BI tools, ttey work est wten run iirectly against a iata waretouse or anotter clean set of integrated data.

6. VisuaannesignnannnexpairatingnVisual iiscovery tools duery iata visually ratter ttan visualize duery results, ttat is, numeric ta les. As a result, analysis and design is one and the same and thus highly interactve ani iteratve. kteir WY IWYG interface ena les users to irag ani irop metrics, iimensions ani atri utes onto a visualizaton canvas and instantaneously view results in graphical form using a variety of ctart types ttat tte tools automatcally select or suggest asei on tte stape of tte iata. Wtile most BI tools constrain tte num er of availa le metrics or atri utes ttat users can view, visual iiscovery tools io not. Users can easily associate any metric or atri ute witt an axis, color, size or stape to create a multiimensional iisplay ttat makes it easy to visualize paterns ani outliers in tte iata. ( ee “Visuaaizatinn echniiues” for a list of some of tte more commonly usei approactes usei y visual iiscovery tools.)

7. Affirnabaenpricingg nalysts can purctase iesktop tools for etween $500 and $2,000, or in some cases, download them for free with limitei iata connectvity (Excel ani C V files only). ince most organizatons have only a handful of analysts, department heads can uy tte tools using tteir operatng uigets. For a small workgroup of 10 to 20 users witt a pu listing server, organizatons can get startei for a out $50,000, not incluiing training ani professional services.

Most visual discovery tools don’t deal well with complex enterprise apps. Like most BI tools, they work best when run directly against a data warehouse or another clean set of integrated data.

KEY CH R CkERI kIC


Enterprise ieployments can easily exceei six figures ecause of tte costs of managing clusters of high-performance servers with lots of R S ani CPU. Interestngly, enterprise BI veniors ofen unile visual discovery tools into their BI suites at no extra cost. This is great for existng customers ut an expensive propositon for prospects.

VISUALIZATION TECHNIQUES

Auto-suggest. uggests ctart types asei on tte stape of tte iata Linking. Links charts to each other or to a data source so changes in one are automatically applied to the others Brushing. Higtligtts a varia le in a ctart wten a user selects a varia le in another chart Lassoing. Lets users select iata values in a ctart y circling ttem witt a cursor. Often usei witt rusting ani linking Keyword search. Lets users duickly fini one or more attri utes in a long list or drop-iown ox Mouseovers. Displays tips, metaiata or availa le functions wten users move tte cursor over an o ject on tte screen Cintext-sensitive icons. ssociatei witt iata o jects ttat iniicate availa le functions Animations. Show how data values change over time Snapshots. Save the current view as a live link that users can email to others Custom objects. Let users create custom hierarchies, groups, calculations and metrics for use in a visual display


S RKEk kRENDS

Market Trends

THE MARKET FOR VISUAL DISCOVERY TOOLS is evolving duickly. ktis secton looks at market trenis in user organizatons ani tte venior marketplace.

UsernOrganizatinsn ORGANIZATIONS ARE FIGURING OUT how to integrate visual discovery tools in an enterprise BI environment—or not. Here are the key trends:

1. Dashboard development. Department heads are increasingly using visual iiscovery tools as iast oari ievelopment platorms insteai of treatng ttem as self-service tools for usiness analysts. In most cases, iepartment teais wto are iissatsfiei witt tte cost, ielay ani duality of analytcs applicatons ievelopei y tte corporate BI team uy tte tools and hire superusers or consultants to uili custom interactve iast oaris for tteir iepartmental users.

2. IT backlash. Untl recently, corporate BI teams tave viewei visual discovery tools as renegade BI environments, since department heads ofen uili iast oaris wittout consultng witt tte BI team or aitering to corporate data standards. Increasingly, however, corporate BI teams are getng in front of ttese iepartmental initatves efore ttey tit a scala ility or complexity wall. ktey convince iepartment teais to point relevant dueries at iata waretouses ani iata marts to ensure consistent use of enterprise data.

(. Bignnatananaaytcsg Witt tte aivent of ig iata, analysts want to use visual iiscovery tools to explore large volumes of iata in an iteratve fashion. Conseduently, visual iiscovery veniors are improving tte scala ility of tteir tools y letng users iirectly duery Haioop ani large analytcal iata ases ani iownloai su sets of ttat iata into memory for ieeper analysis. Sost are also expaniing tte scala ility of their in-memory capa ilites, running tteir server sofware in clusterei environments ani aiiing statstcal ani otter analytcal functons to support preiictve analysis. s suct, tte line etween visual iiscovery tools ani ig iata analytcs tools ani platorms is startng to lur.

Increasingly, corporate BI teams are netin in front of these departmental iiitattes bebore they hit a scalability or complexity wall.


S RKEk kRENDS

VennirnOrganizatins VENDORS ARE RAPIDLY EVOLVING tteir tool sets to capitalize on market opportunites ani prevent new compettors from making inroais into tteir customer accounts. Here are the key trends:

1. Market encroachment and rear-guarnnactinsg Visual discovery tools stippei y startups or small BI veniors ttreaten enterprise veniors ttat o er preiominantly top-down BI tool sets. As a result, leading enterprise BI players—IBS Cognos, P BusinessO jects, Sicro trategy, Sicrosof ani Insttute—have all shipped their own visual iiscovery tools in tte past 12 montts. Besiies o ering new self-service ani visualizaton functonality to customers, ttey iesperately neei to protect tteir flanks from incursion y pure-play visual iiscovery veniors wtose “lani ani expani” strategies of selling to iepartmental usiness users tave eroded their account control. To maintain it, enterprise BI players are largely giving away their visual iiscovery tools y uniling ttem into enterprise suites. Sost ionmt have plans to sell their visual discovery tools to net new customers, at least untl their products are mature enough to compete head on with pure-play vendors.

2. Scalability wall. lttougt organizatons can store ever-larger amounts of iata in memory ttanks to compression ani larger R S footprints, visual iiscovery veniors must ieal witt tte performance cli ttat forms wten customers consume all aiiressa le memory ani egin paging iata to iisk. ko telp customers avoii ttis pro lem, more veniors now let users run dueries iirectly against remote iata ases—a kind of drill through to detailed data that reduces the amount of data ttat customers must cram into R S, ultmately reiucing costs and improving scala ility. ome veniors, suct as ka leau, are moving in tte reverse iirecton, aiiing in-memory capa ilites to complement tteir iirect duery arctitectures. s suct, veniors are now ie atng wtose iynamic duery capa ilites are most flexi le ani useful. s mentonei, many visual iiscovery veniors are also taking steps to improve tte scala ility of tteir em eiiei iata ases. ome, suct as SAP and SAS, are running their in-memory iata ases on extensi le massively parallel processing (SPP) grids, while others, such as SiSense ani Platora, are ieploying specializing analytcal iata ases ttat tgttly align memory ani iisk to optmize performance.

(. Enterprise wall. As more customers deploy visual discovery tools across multple iepartments, many will want to consoliiate ttese operatons

Most visual discovery products were designed for individual users and then scaled to support workgroup and departmental applicatoiss


S RKEk kRENDS

to actieve economies of scale ani staniariize tteir BI environments. kte only pro lem is ttat most pure-play visual discovery tools were not iesignei to tanile enterprise ieployments. Besiies scala ility, most veniors tavenmt investei significantly in failover, loai alancing, iisaster recovery, clustering, monitoring ani aiministraton utlites. ktey also tavenmt sufficiently aiiressei enterprise iata management issues, suct as iata duality, iata profiling, iata transformaton, metadata management and team- asei ievelopment. ktis is where enterprise BI players o er an aivantage—at least for customers who have already implemented their enterprise BI architectures. So the race is on: Pure-play veniors are usy ieveloping enterprise capa ilites, wtile enterprise veniors are duickly aiiing visualizaton ani iesign functonality to catct up.

So the race is on: Pure-play vendors are busy developing enterprise capabilitess wile enterprise vendors are quickly adding tisualizatoi ain design buictoialitt to catch up.

kYPE OF VI U L DI COVERY kOOL


Types of Visual Discovery Tools SO FAR I HAVE DEFINED VISUAL DISCOVERY TOOLS, iescri ei tteir common ctaracteristcs ani explainei tow tte market is evolving. Now I will ii erentate among visual iiscovery proiucts so you can create a stort list of veniors ttat make sense for your organizaton. ktere are ttree types of visual iiscovery tools: visual exploraton, visual analysis ani visual mining. Veniors in eact category exti it specific traits ani stare similar arctitectures, alttougt ttese iistnctons are startng to faie as vendors expand their functonalites to matct tte competton. ktese segments are depicted in Figure 2.

Figure 2. Segments of the Visual Discovery Market


1. Visuaanexpairating Visual exploraton tools are gearei towari usiness

analysts ani iata scientsts wto neei to explore large iata sets. ktese tools excel at connectng to various source systems. ktey ena le usiness analysts to moiel iata on tte fly wittout scriptng, making them easy to deploy as long as source data is not overly complex. ka leau, one of tte most visi le veniors in ttis segment, uses an

Anal

ysis

Fun

ctio

nalit

y

Data Scalability

Visual Miningki co Spotfire, JSP, Alteryx,

ivizor Solutions, Information Builders

Visual AnalysisQlikView, Sicro trategy Visual Insight, IBS Cognos Insigtt, Sicrosoft Power

Play

VisuaanExpairatiinka leau, Visual nalytics, SiSense,

Platfora, SAP Visual Intelligence,

Low High

High



innovatve visual duery language, VizQL, to connect iirectly to most iata sources using natve ODBC connectons to more ttan (0 iata sources. Otter exploraton tools em ei specializei analytcal iata ases ttat support tigt-performance dueries against large volumes of data. These products straiile tte “ ig iata analytcs platorm” market iepictei in tte BI Tools Framework in Figure 1.

2. Visual analysis. Visual analysis tools are geared toward superusers and BI specialists wto want to uili interactve iast oaris for iepartmental users. In e ect, ttey are iast oari ievelopment platorms ttat excel at visual iesign, interactve analysis, colla oraton ani mo ile ielivery. kteir iominant focus is to make it easier for casual users to analyze iata using interactve visualizatons. ktese tools turn superusers into authors and analysts into developers who uili elegant interactve iast oaris for iepartmental use. Sost visual analysis tools use in-memory iata ases as tteir primary iata management arctitecture.

(. Visual mining. Visual mining tools are gearei towari soptistcated usiness analysts or statstcians wto neei to create analytcal moiels or iig ieeply into tte paterns of unierlying iata. ktese tools contain li raries of statstcal, iata manipulaton ani analytcal functons ttat ena le analysts to prepare iata for moieling operatons. Sost also em ei iata mining sofware or integrate witt ttiri-party tools, such as R, or spatal analysis packages, suct as Esrims rcGI , to support tte full analytcal moieling lifecycle. Sost use an in-memory iata ase as tteir primary data architecture.

VISUAL DISCOVERY TOOLS VI U L EXPLOR kION Best Known: ka leau Comparable: SAS Visual Analytics, SiSense, Platform Target users: Business analysts and data scientists VI U L ANALYTICS Best Known: QlikView Comparable: Sicro trategy, Visual Insigtt, Cognos Insigtt, Sicrosoft Power View Target users: Superusers and usiness users VI U L SINING Best Known: Spotfire Comparable: Advizor Visual Discovery (same as Information Builiers Visual Discovery), JSP, lteryx, ctuate BIRk nalytics Target users: Statisticans and soptisticatei usiness analysts



Mapping Vendors to Market Segments ko e tonest, veniors ionmt fall neatly into any one of tte ttree market segments. Sost span all ttree segments to one iegree or anotter. kte iesignatons simply iniicate clusters of strengtts ii erent veniors tave. For example, QlikView is iieal for uiliing interactve iast oaris. s suct, itms classifiei as a visual analysis venior. But QlikView can e usei to explore ig iata y pusting snapstots into memory, ani it can also perform soptistcatei comparisons ani asic iata mining operatons. o its capa ilites span all ttree areas, ut its core strengtt is in visual analysis. Figure 3 demonstrates how QlikView maps to the three segments of the visual discovery market.

Figure 3. Mapping QlikView to Market Segments Most visual discovery vendors span all three subsegments of the visual discovery market but

exhibit core strength in one of the three.

Source: Wayne Eckerson, BI Leaierstip Researct

Of course, veniors in tte visual iiscovery market are acutely aware of tte competton ani tow ttey stack up. Conseduently, most are aggressively closing tte functonality gap witt tteir closest rivals. For example, visual exploraton veniors, suct as ka leau, tave aiiei in-memory capa ilites to o er compara le performance to QlikView ani potire. Conversely, QlikView



ani potire tave recently aiiei iirect duery capa ility to compete eter witt ka leau as visual exploraton tools. In a few years, ttese ttree segments will virtually iisappear as veniors o er compara le functonality, at least on paper. Every vendor will report nearly iientcal capa ilites on a functonal matrix in a reduest for proposal. But afer 20 years of evaluatng commercial sofware, Imve iiscoverei ttat veniors never stray far from tteir origins. ktey will stine in tteir areas of inital strengtt ani fall short of tte competton in tteir areas of inital weakness. For example, ka leau will likely always o er superior iata exploraton capa ilites, wtile it may never catct up witt QlikView in pu listing or potire in mining.

Pure-Play Versus Enterprise Vendors The market for visual discovery tools is neatly divided into pure-play and enterprise vendors. Pure-play vendors only sell visual discovery tools, while enterprise veniors also o er a compretensive BI stack, ani in some cases, otter enterprise sofware as well (see ka les 1 ani 2). Witt two exceptons—Platora ani JSP—pure-play vendors are also the most mature; ttey tave een stipping visual iiscovery sofware muct longer than enterprise vendors, most of which entered the market just last year. In contrast, QlikView stippei in 199( ani potire in 1999. Curiously, ttese two tools were ott inventei in weien, wtere visual iesign is a cultural tallmark (ttink “ caniinavian iesign”). Sature pure-play veniors o er superior visualizaton ani analytcal functonality, wtile enterprise veniors o er eter enterprise management capa ilites. Enterprise veniors uilt tteir visual iiscovery tools on an existng BI stack ttat o ers rict metaiata ani iata management features, a scala le platorm, ani mature aiministraton ani monitoring utlites. s a conseduence, enterprise visual iiscovery tools tave many sofware dependencies; that is, customers have to purchase the vendor’s enterprise BI eiiton to get access to tte full power of tte visual iiscovery tool (with the excepton of Cognos Insigtt). ktese sofware iepeniencies e ectvely increase the cost and complexity of deploying enterprise visual discovery tools, except for existng customers wto tave alreaiy ieployei tte veniorms BI stack.

Two of the oldest visual discovery tools, QlikView ain Spotires were both invented in Sweden, where visual design is a cultural hallmark (think “Scandinavian design”).



Table 1. Pure-Play Vendors Sorted by Founding Date

Vendor Product Launch Date

Primary Architecture Primary Type

QlikTech QlikView 1993 In-memory DBMS Analysis

Tibco1 Spotfire 1996 In-memory DBMS Mining

Alteryx Alteryx 1997 Direct Connect Mining

Tableau Tableau 2003 Direct Connect Exploration Advizor Solutions

Visual Discovery 2003 In-memory DBMS Mining

SiSense Prism 2004 MPP Analytic DBMS Exploration

Platfora Platfora 2012 Hadoop Exploration

Table 2s Eiterprise Veinors Sorten bt Iiital ronuct Launch Date

___________________________

1. ki co, an enterprise miiileware venior, acduirei pure-play venior potire in 2000 ut ioes

not have any other BI tools.

2. Actuate purchased Quiterian nalytcs in 2012 ani relaunctei tte proiuct as BIRk nalytcs in

Fe ruary 201(.

(. Informaton Builiers em eis ivizor olutonsm Visual Discovery proiuct.

Vendor Product Launch Date

Primary Architecture

Primary Type

SAS Institute JMP 1992 Local disk Mining

Actuate BIRT Analytics2 2003 Hybrid/in-memory columnar DBMS

Mining

Information Builders

Visual Discovery3 2006 In-memory DBMS Mining

MicroStrategy Visual Insight 2011 ROLAP cubes Analysis

IBM Cognos Cognos Insight 2012 In-memory DBMS Analysis

SAS Institute Visual Analytics 2012 MPP analytic DBMS Exploration

SAP Visual Intelligence 2012 MPP in-memory

DBMS Exploration

Microsoft Power View 2012 SQL Server/SharePoint Analysis



henFunctinaaityn ap ktis gap in functonality etween pure-play and enterprise vendors will close rapidly in the next three years as pure-play veniors uili or acduire enterprise capa ilites ani enterprise veniors aii eter analytcal ani visual functonality. For example, Qlikkect recently purctasei Expressor, an enterprise extract, transform and loai (EkL) tool to o er graptical iata management capa ilites to customers ttat are ieploying QlikView on an enterprise scale.

Oracle is the only major enterprise vendor without a true visual discovery tool, Oracle Exalttcs and Endeca notwithstanding. So I expect Oracle to purchase one of the pure-play vendors in the next year or two.


EV LU kION CRIkERI

EraauatinnCriteria BEFORE CHOOSING A VISUAL DISCOVERY VENDOR, there is a host of criteria you stouli evaluate. Imve iefinei seven major criteria elow ttat can serve as a startng point for a more ttorougt evaluaton:

1. Maturity. How many years tas tte proiuct een ieployei? How many actve customers ani partners ioes tte proiuct tave? Does tte venior support an actve marketplace of ttiri-party add-on products and an actve gallery of user-generatei applicatons? Does tte venior o er an applicaton programming interface for ttiri partes to ievelop proiuct extensions or em ei tte proiuct in otter applicatons, suct as portals?

2. Analysis. To what degree does the tool support the full spectrum of BI analysis capa ilites, from iast oariing to analysis to iata mining? How easy is it to create custom metrics, calculatons, hierarchies and groups ani use ttese in comparatve visualizatons? Does tte tool support forecastng, tistograms, variance ctarts, correlaton matrices, iescriptve statstcs ani otter calculatons ani functons?

(. VisuaaizatingnIs the graphical interface functonal enougt for power users ani easy enougt for casual users? Wtict ctart types—including text—are availa le? How e ectvely ioes tte tool suggest wtict ctart types to apply asei on tte stape of tte iata? How easy is it to fini ani apply atri utes to existng visualizatons?

4. Design and publishing. How easy is it to iesign ani pu list interactve iast oaris? ko wtat iegree can you control layouts ani manage user access to pages, o jects ani iata values? How easy is it to create a presentaton or riefing ook ani tell a story witt tte iata? ko wtat iegree can iast oari users stare content ani colla orate? How muct functonality is availa le ttrougt mo ile ievices, We rowsers, portals ani Sicrosof Office?

5. Scalability. Does the tool deliver consistently fast performance as more users ani iata are aiiei to tte system? Can it scale linearly as more CPU ani memory are aiiei? Does tte tool connect iirectly to remote iata ases to support eni-user dueries? Can users toggle etween in-memory and iirect duery moies? Can users irill from an in-memory iata ase to iata in a remote iata ase? Wtat level of iata compression is availa le? Does tte in-memory iata ase run on an SPP infrastructure?


EV LU kION CRIkERI

6. Data management. Does tte tool o er iata duality, cleansing, profiling, transformaton, loai ani metaiata management tools? Does tte tool support a ect analysis ani lineage, ttat is, metaiata, ani ena le reuse ani team- asei ievelopment wten constructng iata sets ani packages?

7. Enterprise-ready. What administratve capa ilites are availa le to support clustering, failover, recovery, loai alancing, monitoring, security ani aiministraton? How well ioes tte tool integrate witt third-party utlites ani applicatons, incluiing otter BI tools, event-iriven iast oaris, portals ani enterprise sofware? How well ioes tte product work in corporate data centers and with private cloud infrastructure?


VENDOR E SENk

Analysis

Saturity

cala ility

Enterprisecapa le

Datamanagement

Visualization

Pu listing

QlikTech Evaluation

Vendor Assessments THE FOLLOWING OFFERS a rief synopsis of venior iiscovery products, with an emptasis on tte est-known and most widely deployed products, QlikView, ka leau ani potire.

Best Known QlikView QlikTech popularizei tte visual iiscovery segment y growing faster ttan any otter BI venior, culminatng in a successful pu lic o ering in 2010. kte company, wtict sells a single proiuct, QlikView, generatei $(20 million in sales in 2011. Witt an e ectve iepartmental “lani ani expani” strategy ani a vast partner network, Qlikkect now tas its sigtts set on iominatng tte even more lucratve enterprise BI market (see Figure 4).

Figure 4

QlikView’s most iistnctve feature is its associatve graptical user interface, wtict iisplays relatonstips among tte values of all iisplayei o jects on a screen without excluding any of them. Conceptually, this is like an outer join of all ta les in a iata moiel, allowing any fieli to act as a filter or aggregaton iimension. Unlike most BI tools, wtict narrow usersm views as ttey irill into a iata set, ttis associatve interface ena les users to view all relatonstips ani paterns—as well as those that don’t exist—at once and has resonated with usiness users. ko cement its atractve graptical user interface, Qlikkect now


VENDOR E SENk

Analysis

Saturity

cala ility

Enterprise capa le

Data management

Visualization

Pu listing

Tableau Evaluation

ielivers tte same interactve visual iisplay on mo ile ievices ani supports state-of-the-art colla oraton capa ilites. ko eter support enterprise ieployments, Qlikkect recently stippei a utlity that scans and catalogs all QlikView data elements and tasks in a network, improving an aiministratorms a ility to perform impact analyses, track iata lineage and design enterprise architectures. It also recently acduirei Expressor, a moiern EkL tool ttat will minimize tte neei for ievelopers to write scripts ani eter manage ack-eni iata acduisiton ani governance processes. It also has recently expanded its in-memory architecture to support dynamic dueries of remote iata ases, improving tte overall scala ility ani flexi ility of tte environment.

Tableau By taving tte rigtt proiuct at tte rigtt tme, ka leau tas iou lei in size ani now exceeis $100 million in revenue. Sany insiiers expect it to go pu lic stortly. ka leaums iirect duery arctitecture—ena lei y its unidue ani patentei VizQL duery language—makes it eter suitei to exploraton of ig data sets compared with in-memory compettors ttat are limitei y tte amount of iata ttey can toli in memory. ka leau is also suitei to exploraton ecause it lets analysts moiel iata on tte fly wittout scriptng (see Figure 5).

Figure 5


VENDOR E SENk

Analysis

Saturity

cala ility

Enterprisecapa le

Datamanagement

Visualization

Pu listing

Spotfire Evaluation

ko rouni out its portolio, ka leau stippei an in-memory columnar iata ase in 2011 to optmize performance wten source systems are oggei iown or access is constrained. It also recently shipped a new data management feature ttat lets iesktop users duery preiefinei work ooks on tte server insteai of on remote iata ases. ktis feature in e ect ena les aiministrators to implement a staniari metaiata layer to improve informaton consistency in enterprise deployments. ka leau is pertaps est known for its visualizaton capa ilites ani graptical interface ttat make it easy for usiness analysts to explore iata y iragging ani iropping atri utes ani metrics onto ctart iimensions. kte toolms ease of use ani versatlity is on iisplay at ka leau Pu lic, a free tostei we site wtere more ttan (0,000 ka leau users tave pu listei work ooks ttat otters can comment on, share or tag.

ibcinSpitire fer its acduisiton y ki co in 2000, potire lay dormant, losing ground to Qlikkect ani ka leau just as tte visual iiscovery market stifei into overirive. But now, potire is gaining momentum, reassertng itself as a mature visual iiscovery tool ttat supports soptistcatei analytcs ani enterprise deployments. IDC projectei its 2010 revenues to e $98 million. Because of its support for aivancei statstcs ani iata mining, potire is teavily usei y financial services firms ani ptarmaceutcal companies. Its statstcal server—wtict is an optonal aii on—provides S-Plus data mining tools, a runtme engine for R, ani access to iata mining o jects wittout scriptng (see Figure 6).

Figure 6


VENDOR E SENk

lreaiy well representei in tte Fortune 1000 witt ki co proiucts, potire is capitalizing on its associaton witt ki co to furtter cement its enterprise creientals. For example, potire integrates witt ki co ilver Fa ric, a private cloui infrastructure ttat supports loai alancing, failover ani iynamic provisioning of new potire servers. potire also integrates witt ki co t r, an enterprise colla oraton platorm, ani ki co Business Events, a complex event processing environment in wtict potire iisplays real-tme events ani alerts. Finally, potire expaniei tte scala ility of its in-memory engine and aiiei iynamic duery capa ilites to access remote iata ani connectvity to Haioop ani No QL iata environments, incluiing tvioms ctve Intelligence platorm.

Others ActuatenBIR nAnaaytcs Actuate Corp. jumped into the visual discovery market last month with a gem of a proiuct ttat it unearttei in Barcelona, pain. Callei BIRk nalytcs, tte proiuct is a re raniei version of Quiterian nalytcs, wtict first stippei in 200(. s suct, BIRk nalytcs is a fairly mature visual iiscovery tool ttat runs on a super-fast ani tigtly scala le columnar iata ase ttat leverages ott memory ani iisk storage. s a visual mining tool, it also o ers some very nice visualizatons ani analytcal functons, incluiing Venn iiagrams, forecastng, statstcal ani mactine learning algorittms, ani tte a ility to easily create ierivei fielis ani use any fieli or com inaton of fielis as a filter.

Advizor Leveraging iata visualizaton ani in-memory data technology developed at Bell La s, ivizor olutons Inc. o ers a full-featured visual discovery tool that o ers some of tte most unidue visualizatons on tte market ani lenis ttem witt multvariant regression algorittms to support preiictve analytcs. kte proiuct, wtict comes in iesktop, We and iPad versions, is used extensively in tigter eiucaton to support funiraising actvites ani forms tte asis of Informaton Builiersm proiuct.

Aateryx For many years, lteryx Inc. specializei in spatal analytcs ut recently exteniei its platorm witt iata mining, EkL, iata duality, in-memory processing, ani usiness ani iniustry content, ani maie it availa le via ott private ani pu lic clouis. kte company is now working to raise its market profile ani gain tracton for its new ani innovatve all-in-one strategy.


VENDOR E SENk

IBM Cognos Insight tippei in Fe ruary 2012, Cognos Insigtt is ott a low-cost standalone Winiows iesktop tool witt an em eiiei, writa le in-memory iata ase, Cognos kS1, as well as a client to Cognos Express, Cognos Enterprise ani Cognos Planning. Users of tte stanialone version can pu list tteir iesigns wittout alteraton to a Cognos BI server eiiton. ktis tgtt integraton ena les Cognos to seed the market with the low-cost Cognos Insight product ($500) and then up-sell those customers to one of its more comprehensive BI suites.

InfirmatinnBuiaders WebFocus Visual Discovery ktanks to an OES relatonstip witt ivizor olutons, Informaton Builiers was one of tte first enterprise BI players to o er a visual iiscovery tool as part of its BI suite (see ivizor olutons). Besiies soptistcatei visual iiscovery capa ilites, tte proiuct ena les users to em ei iast oaris in compouni We Focus reports, PDF iocuments or PowerPoint.

MicrisifnPioernVieo Shipped in April 2012, Power View extenis Sicrosofms QL erver Reportng ervices proiuct witt interactve visualizaton features. kte tool reduires Silverlight-compliant rowsers ani QL erver 2012. Users duery PowerPivot moiels or QL erver nalysis ervices ta ular moiels storei in tarePoint. kte toolms exduisite iesktop pu listing features—think PowerPoint—make Power View iieal for creatng visual interactve riefing ooks.

MicroStrategy Visual Insight tippei in 2011, Sicro trategy Visual Insigtt is a Flast- asei client ttat is unilei for free in Sicro trategy Report ervices 9.2 or later, ani runs ott on-premises and in the cloud. Customers use the tool to perform free-form analysis, create ai toc iast oaris ani prototype pixel-perfect iast oaris. It runs against preiefinei Sicro trategy in-memory cu es createi witt Sicro trategy iesign tools.

Paatira Launctei in tte fall of 2012, Platora ena les iata scientsts to analyze Haioop data, which it loads into an in-memory cache that sits on or adjacent to a Haioop cluster. Platora is one of tte first pure-play visual discovery tools that run against Haioop ani ig iata.


VENDOR E SENk

SAP Visual Intelligence tippei in Say 2012, Visual Intelligence is a Winiows iesktop tool ttat runs against SAP’s new HANA in-memory iata ase. It is similar to AP BusinessO jects Explorer, wtict tas causei confusion among customers, except that it provides more free-form iata access, transformaton ani ctartng capa ilites ttan Explorer, wtict is a server- asei tool ttat Ik sta ers—not usiness analysts—set up, design and maintain.

SAS JMP Unveilei in 199(, JSP telps statstcians eter unierstani tte stape of tteir iata y applying a vast array of statstcal functons ani visualizatons to iesktop iata sets. kte company o ers an aivancei version ttat ena les statstcians to create ani compare preiictve moiels. It also o ers two versions for tte life sciences iniustry, one to analyze irug ievelopment ani anotter to analyze genomics iata.

SASnVisuaanAnaaytcs tippei in Say 2012, Visual nalytcs can e consiierei a server- asei version of JSP ttat runs on a tigt-performance in-memory iata ase wtict sits on SPP griis from Dell ani Hewlet-Packari or SPP iata ase appliances from ESC Greenplum ani keraiata. It also supports various statstcal functons ani report iesign features ani is gearei towari creatng moiels against ig iata.

SiSense Prism Founiei in Israel in 2004, i ense tas rampei up its presence in tte U. . iuring the past year, claiming it’s tte “worlims smallest ig iata soluton.” It recently stowei tow to analyze 1 kB of iata at tte speei of ttougtt using a $050 laptop witt 8 GB of R S. kte tool uses a vectorwise, columnar, in-memory iata ase, tte D( visual li rary, ani ligttweigtt EkL tools. It is o erei on a su scripton asis via tte cloui or on-premises.


RECOSSEND kION

Recimmennatins BEFORE PURCHASING A VISUAL DISCOVERY TOOL, there are several things you should consider. If you are a iepartmental uyer:

1. Inentfynthentypesnifnusersnyiunoantntinempioerg If you are trying to empower usiness analysts ani iata scientsts, ctoose a visual exploraton tool, like ka leau; if you want to empower casual users, choose a visual analysis tool, like QlikView, that makes it easy to create polistei interactve iast oaris; if you want to empower statstcians ani soptistcatei analysts, ctoose a visual mining tool, like potire, ttat em eis aivancei analytcal functons.

2. Inrestgatenohethernithernnepartmentsnaareanynuse a visual discovery tool. If so, you might as well join the parade and enjoy volume discounts, unless you can prove that the de facto visual discovery tool wonmt support your iepartmentms reduirements.

(. BenreanyntinexpaainnohynancirpiratenBInstannarnnniesn’t meet your visual discovery requirements. If your corporate BI standard doesn’t include visual discovery, you will need to explain why you need to ieploy a nonstaniari tool set. Use ttis report, especially Figure 1, to make your case ttat an organizaton neeis many ii erent types of BI tools to aiiress user reduirements, ani ttat visual iiscovery is a iistnct market segment est aiiressei witt a ieiicatei tool.

4. Point the visual discovery tool at the data warehouse. Although your corporate data warehouse pro a ly ioesnmt contain all tte iata you neei, you ionmt want to risk creatng iuplicate iata ani iefinitons. o leverage tte iata waretouse ani loai unidue local iata into your visual discovery tool to deliver all the data you need to support your reduirements. ktis approact will appease Ik sta ers ani make ttem more aware of your iata reduirements for tte iata waretouse. It will also make ttem eter allies if your visual iiscovery tool tits tte scala ility or complexity wall ani you neei ttem to ail you out.

If you are a corporate uyer:

1. Remember that a visual discovery tool won’t meet all your BI needs. A visual iiscovery tool ioes a nice jo of aiiressing a large percentage of BI reduirements, incluiing ott top-iown (reportng, iast oariing)


RECOSSEND kION

ani otom-up (ai toc exploraton ani analysis) tasks. But it ioesnmt support ai toc, operatonal ani pixel-perfect reportng, relatonal OL P, iata mining ani ig iata analytcs. o ionmt expect a visual iiscovery tool to e your only BI purctase.

2. Eraauatenthenenterprisencapabiaitesnifnyiurnshirtnaistnifnrisuaandiscovery tools. Sost pure-play visual discovery tools started life as desktop tools designed for analysts and later evolved to support workgroup ani iepartmental applicatons. Sost ionmt yet support the iata management, aiministraton ani infrastructure reduirei to support risk-free enterprise ieployments. Induire a out tte enterprise capa ilites of your stort-listed vendors.

(. Don’t outlaw nonstandard visual discovery tools. First, you can’t win ttis atle unless your corporate BI team tas consistently proven ttat it can ieliver exduisite applicatons on tme ani unier uiget ani tas no acklog of reduests. econi, you stouli focus your teamms e orts on managing data consistency rather than ar itratng tool staniaris. ure, taving multple tools reduires more support, ut as long as users are running tte majority of dueries against your corporate staniari iata, youmll tave plenty of tme to support tteir tool reduirements.


APPENDIX: BI kOOL FR SEWORK

Appennix:nBIn iiasnFrameoirk Createi y BI Leaierstip Researct, tte BI kools Framework (see Figure 1) positons categories of BI tools y functonality, scope of ieployment ani type of intelligence (top-iown versus otom-up). To deliver a comprehensive set of BI capa ilites, organizatons neei to implement BI proiucts or suites ttat span all dimensions of the BI Tools Framework.

Figure 1. BI Tools Framework


BI Tools Framework Here are iefinitons of tte key terms in tte framework: Top-down intelligence: This type of intelligence consists of reports and iast oaris ttat answer preiefinei duestons or monitor usiness processes using metrics alignei witt strategic o jectves. kop-iown tools duery iata sets witt preiefinei sctema, incluiing iata waretouses ani iata marts. kop-down tools are installei, iesignei ani maintainei y tte Ik iepartment ut operatei y power users ani superusers.

BI F

UN

CTIO

NAL

ITY

EnterpriseDepartment

SCOPE OF DEPLOYMENT

Top-downBottom-up

Repo

rting

Dast o

aris

Anal

ysis

Sining

Pixel Perfect ReportingAd hoc Reports/

Dast oaris

Analyst

Data Sining Work enct

OperationalReports/

Dast oaris

Big Data Analytics Platforms

Relational OL P

Sulti-dimensional

OL P

Desktop Analysis (e.g. Excel)

Visual Discovery

Power Users

Casual Users



Bitim-up intelligence: The term refers to ad hoc analysis and mining tools ttat answer unantcipatei duestons arising from new ani changing usiness coniitons. Botom-up tools duery any type of iata in any applicaton or system ani use flexi le sctema ani rict visualizatons to e ectvely manipulate and analyze iata. Botom-up tools are usei y usiness analysts, statstcians ani iata scientsts witt minimal or no Ik assistance.

Top-Down Business Intelligence Tools Pixea-perfect repirtng: lso callei proiucton or managei reportng tools, pixel-perfect reportng tools ena le professional report ievelopers to create pixel-perfect ani complex reports ani iistri ute ttem to large num ers of users on a scteiulei asis. ReaatinaanOOAPn ROOAPP: Such tools proviie a iimensionalizei view of data teli in relatonal iata ases, using a com inaton of specializei sctema ani end-user metaiata, ttat is, a semantc layer. Ik ievelopers use ROL P tools to create interactve reports ani iast oaris ttat run against large iata warehouses. Because ROL P tools span reportng, iast oariing ani analysis, ttey are iieal for ielivering layerei enterprise iast oaris ttat proviie ttree-click access to any data from a top-level view, assuming aieduate performance. Operatinaanrepirtsnannnnashbiarns:nUnlike ROL P tools, wtict connect to iata via specializei sctema ani arctitectei metaiata, operatonal reports ani iast oaris tools connect iirectly to source systems ttat run tte usiness. lso callei iata mastups, ttese tools are usei to create operatonal or executve iast oaris ttat monitor near-real-tme usiness actvity across multple applicatons ani systems. ome tigter-eni operatonal iast oaris monitor real-tme event streams. Ad hoc reports and dashboards: et up y Ik professionals, ttese tools ena le motvatei usiness users, ttat is, superusers, to create ai toc reports ani iast oaris. uperusers create reports y selectng iata elements (metrics, iimensions, atri utes) from a scru ei list of iata o jects (semantc layer) ani then format the result set. ktey create ai toc iast oaris—“mast oaris”— y iragging ani iropping preiefinei content (ctarts, ta les, controls) from a li rary onto a iast oari canvas. i toc tools are ofen extensions of ROL P or pixel-perfect reportng tools.

Bitim-Up Business Intelligence Tools Desktop analysis: Epitomizei y Sicrosof Excel (ani now PowerPivot), iesktop analysis tools are iesignei for usiness analysts wto want free-form



QL access to any iata ani tte flexi ility of a iesktop tool to merge, integrate ani moiel iata to answer any dueston ttey neei to answer. Visual discovery: Such tools are self-service, in-memory analysis components ttat ena le usiness users to access ani analyze iata visually at tte speei of thought with minimal or no IT assistance and then share the results of their iiscoveries witt colleagues, usually in tte form of an interactve iast oari. MuatnimensiinaanOOAP: ktese tools store iata in a specializei multiimensional format ani eitter precalculate or iynamically calculate iata values at tte intersecton of every level in tte iimension tierarcty, proviiing fast duery performance. Designei for usiness analysts, tte tools reduire Ik professionals to set up, iesign, loai ani manage tte iata ases. ktey are constrainei in size ecause of tte explosion of iata causei y tte calculaton of dimensional values. Data mining workbench: These tools provide a drag-and-drop development environment for statstcians wto neei to iesign, uili ani manage analytcal models, either individually or in a team. Big data anaaytcsnpaatirms: ktese are tigtly scala le analytcal systems ttat run on griis of computers, incluiing Haioop ani SPP iata ase appliances, ani contain li raries of analytcal functons ttat ena le statstcians ani iata scientsts to explore, manipulate and model large volumes of data without taving to move tte iata to a workstaton or iepartmental server. ktey also support analytcal sani oxes ttat ena le analysts to uploai private iata ani mix it with corporate data to conduct analyses.


BOUk BI LE DER HIP RE E RCH

About BI Leadership Research BI Leaierstip Researct is an eiucaton ani researct service run y kectkarget ttat proviies o jectve, venior-neutral content to usiness intelligence (BI) professionals worliwiie. Its mission is to telp organizatons make smarter decisions through the judicious use of data-driven technologies, such as data waretousing, BI, analytcs ani ig iata. Its team of experiencei iniustry analysts conduct in-depth research on industry trends, evaluate commercially availa le sofware and host conference events, we casts and seminars.

About Technology Guides kectnology Guiies from BI Leaierstip Researct are iesignei to telp BI leaiers craf a stortlist of veniors to evaluate a proiuct in an emerging BI market segment efore purctasing it. Eact report iefines tte segment, iescri es tte salient market and technology trenis, iefines criteria for evaluatng proiucts ani positons proiucts against one anotter. Eact report evaluates etween six and 12 vendors and compares their market strategies, architectural approaches ani proiuct ii erentators. lttougt kectnology Guides can help narrow iown proiuct ctoices, ttey are no su sttute for a proof of concept ttat assesses venior proiucts using an organizatonms internal iata.

About Wayne Eckerson Wayne Eckerson is tte iirector of BI Leaierstip Researct ani presiient of BI Leaier Consultng, wtict proviies strategic planning, architectural reviews, internal workshops and long-term mentoring to user and vendor organizatons. He has conducted numerous research

studies ani is a notei speaker, logger and consultant. He is the author of two wiiely reai ooks, Performance Dashboards: Measuring, Monitoring, and Managing Your Business and The Secrets of Analytcal eaaerss nssihts froo nforoaton nssaers.

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Transcript of Visual Discovery Tools: Market Segmentatinnannn ...€¦ · QlikTech (NASDAQ: QLIK) is a leader in...