Hvordan kan analyser forbedre virksomheten? - Teknisk transkripsjon av episode 2

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Eric Kavanagh: Mine damer og herrer, hei og velkommen igjen til Episode 2 av TechWise. Ja, det er på tide å få kloke mennesker! Jeg har en gjeng veldig smarte mennesker på linjen i dag for å hjelpe oss i den bestrebelsen. Jeg heter selvfølgelig Eric Kavanagh. Jeg vil være din vert, din moderator, for denne lynrunden. Vi har mye innhold her, folkens. Vi har noen store navn i bransjen, som har vært analytikere i vår plass og fire av de mest interessante leverandørene. Så vi kommer til å ha mye god action på samtalen i dag. Og selvfølgelig spiller du der ute i publikum en betydelig rolle i å stille spørsmål.

Så nok en gang er showet TechWise og temaet i dag er "Hvordan kan Analytics forbedre virksomheten?" Det er klart det er et varmt tema der det skal prøve å forstå de forskjellige typer analyser du kan gjøre, og hvordan det kan forbedre driften din, for det er det det handler om på slutten av dagen.

Så du kan se meg selv der oppe, det er virkelig ditt. Dr. Kirk Borne, en god venn fra George Mason University. Han er en dataforsker med en enorm mengde erfaring, veldig dyp kompetanse på dette rommet og data mining og big data og alt det slags morsomme ting. Og selvfølgelig har vi vår helt egen Dr. Robin Bloor, sjefanalytiker her i Bloor Group. Som trente som aktuar for mange, mange år siden. Og han har virkelig vært fokusert på hele dette store datarommet og det analytiske rommet ganske intenst det siste halve tiåret. Det er fem år nesten siden vi lanserte Bloor Group per se. Så tiden flyr når du har det moro.

Vi kommer også til å høre fra Will Gorman, sjefsarkitekt i Pentaho; Steve Wilkes, administrerende direktør for WebAction; Frank Sanders, teknisk direktør i MarkLogic; og Hannah Smalltree, direktør hos Treasure Data. Så som jeg har sagt, det er mye innhold.

Så hvordan kan analytics hjelpe bedriften din? Hvordan kan det ikke hjelpe virksomheten din, helt ærlig? Det er alle slags måter analytics kan brukes til å gjøre ting som forbedrer organisasjonen din.

Så effektiviser driften. Det er det du ikke hører så mye om som om markedsføring eller inntekt eller til og med å identifisere muligheter. Men å effektivisere operasjonene dine er dette virkelig, virkelig kraftige ting du kan gjøre for organisasjonen din fordi du kan identifisere steder hvor du enten kan outsource noe eller du kan legge til data til en bestemt prosess, for eksempel. Og det kan strømlinjeforme det ved ikke å kreve at noen tar opp telefonen for å ringe eller noen til å sende e-post. Det er så mange forskjellige måter du kan effektivisere driftene dine. Og alt dette hjelper virkelig med å få ned kostnadene, ikke sant? Det er nøkkelen, det reduserer kostnadene. Men det lar deg også betjene kundene dine bedre.

Og hvis du tenker på hvor utålmodige mennesker har blitt, og jeg ser dette hver eneste dag når det gjelder hvordan folk samhandler på nettet, selv med showene våre, tjenesteleverandørene vi bruker. Tålmodigheten som folk har, oppmerksomhetsspennet, blir kortere og kortere med dagen. Og hva det betyr er at du som en organisasjon må svare på raskere og raskere tidsperioder for å kunne tilfredsstille kundene dine.

Så hvis for eksempel noen er på nettstedet ditt eller surfer rundt og prøver å finne noe, hvis de blir frustrerte og forlater det, kan du ha mistet en kunde. Og avhengig av hvor mye du betaler for produktet eller tjenesten din, og det er kanskje en stor sak. Så poenget er at strømlinjeformingsoperasjoner, tror jeg, er et av de hotteste områdene for bruk av analyser. Og du gjør det ved å se på tallene, ved å knuse dataene, ved å finne ut for eksempel: "Hei, hvorfor mister vi så mange mennesker på denne siden av nettstedet vårt?" "Hvorfor får vi noen av disse telefonsamtalene akkurat nå?"

Og jo mer sanntid du kan svare på den typen ting, jo større sjanser er det for at du kommer til å komme på toppen av situasjonen og gjøre noe med det før det er for sent. Fordi det er det tidsvinduet når noen blir opprørt over noe, de er misfornøyde eller prøver å finne noe, men de er frustrerte; du fikk et vindu med muligheter der for å nå ut til dem, ta tak i dem, omgås den kunden. Og hvis du gjør det på riktig måte med riktige data eller fine kundebilder - å forstå hvem som er denne kunden, hva er lønnsomheten deres, hva er deres preferanser - hvis du virkelig kan takle det, skal du gjøre en god jobb med å holde på kundene dine og få nye kunder. Og det er det det handler om.

Så med det skal jeg faktisk overlevere det til Kirk Borne, en av våre dataforskere på samtalen i dag. Og de er ganske sjeldne i disse dager, folkens. Vi har minst to av dem på samtalen, så det er veldig bra. Med det, Kirk, skal jeg overlevere det til deg for å snakke om analyse og hvordan det hjelper virksomheten. Gå for det.

Dr. Kirk Borne: Tusen takk, Eric. Kan du høre meg?

Eric: Det er bra, fortsett.

Dr. Kirk: Ok, bra. Jeg vil bare dele hvis jeg snakker i fem minutter, og folk vifter med hendene på meg. Så åpningsmerknadene, Eric, at du virkelig ble knyttet til dette emnet jeg kommer til å snakke kort om i løpet av de neste minuttene, som er denne bruken av big data og analyse for data til beslutninger om å støtte, der. Kommentaren du kom med om effektivisering av operasjonen, for meg, faller den slags inn i dette konseptet med operasjonell analyse der du kan se omtrent i alle applikasjoner over hele verden om det er en vitenskapsapplikasjon, en virksomhet, cybersikkerhet og rettshåndhevelser og myndighet, helsevesen. Et hvilket som helst antall steder der vi har en datastrøm og vi tar en slags respons eller avgjørelse som reaksjon på hendelser og varsler og atferd som vi ser i den datastrømmen.

Og en av tingene jeg vil snakke om i dag, er hvordan du trekker ut kunnskapen og innsikten fra big data for å komme til det punktet hvor vi faktisk kan ta beslutninger om å ta grep. Og ofte snakker vi om dette i en automatiseringssammenheng. Og i dag vil jeg blande automatiseringen med den menneskelige analytikeren i løkken. Så med dette mener jeg, mens forretningsanalytikeren spiller en viktig rolle her når det gjelder spill, kvalifisering, validering av spesifikke handlinger eller maskinlæringsregler som vi trekker ut fra dataene. Men hvis vi kommer til et punkt hvor vi er ganske overbevist om at forretningsreglene vi har trukket ut og mekanismene for å varsle oss er gyldige, kan vi ganske mye overføre dette til en automatisert prosess. Vi gjør faktisk den operasjonelle effektiviseringen som Eric snakket om.

Så jeg har litt spill på ord her, men jeg håper at hvis det fungerer for deg, snakket jeg om D2D-utfordringen. Og D2D, ikke bare data om beslutningene i alle sammenhenger, vi ser på dette i den nederste delen av dette lysbildet, forhåpentligvis kan du se det, gjøre funn og øke inntektsdollar fra analyserørene.

Så i denne sammenhengen har jeg faktisk denne rollen som markedsfører for meg selv her nå som jeg jobber med og det er; det første du vil gjøre er å karakterisere dataene dine, trekke ut funksjonene, trekke ut egenskapene til kundene dine eller hvilken enhet det er du sporer på din plass. Kanskje det er en pasient i et helseanalytisk miljø. Kanskje det er en nettbruker hvis du ser på en slags cybersikkerhetsproblem. Men karakteriser og trekk ut kjennetegn og trekk så ut noen kontekst om den personen, om den enheten. Og så samler du de brikkene du nettopp har laget, og legger dem i en slags samling som du deretter kan bruke maskinlæringsalgoritmer fra.

Grunnen til at jeg sier det på denne måten er at, la oss bare si, at du har et overvåkningskamera på en flyplass. Selve videoen er et enormt stort volum, og den er også veldig ustrukturert. Men du kan trekke ut fra videoovervåkning, ansiktsbiometri og identifisere enkeltpersoner i overvåkningskameraene. Så for eksempel på en flyplass, kan du identifisere bestemte individer, du kan spore dem gjennom flyplassen ved å krysse for å identifisere den samme personen i flere overvåkningskameraer. Slik at de ekstraherte biometriske funksjonene du virkelig gruver og sporer ikke er selve den detaljerte videoen i seg selv. Men når du har tatt utdragene, kan du bruke maskinlæringsregler og analyser for å ta avgjørelser om du trenger å iverksette tiltak i et bestemt tilfelle eller noe skjedde feil eller noe som du har en mulighet til å gi et tilbud. Hvis du for eksempel har en butikk på flyplassen og ser den kunden komme din vei og du vet fra annen informasjon om den kunden, at han kanskje virkelig var interessert i å kjøpe ting i taxfree-butikken eller noe sånt, gi det tilbudet.

Så hva slags ting vil jeg mene med karakterisering og potensialisering? Med karakterisering mener jeg igjen å trekke ut funksjonene og egenskapene i dataene. Og dette kan enten være maskingenerert, da kan dets algoritmer faktisk trekke ut for eksempel biometriske signaturer fra video- eller sentimentanalyse. Du kan hente ut kundens stemning gjennom online anmeldelser eller sosiale medier. Noen av disse tingene kan være generert av mennesker, slik at mennesket, forretningsanalytikeren, kan trekke ut flere funksjoner som jeg vil vise i neste lysbilde.

Noen av disse kan være publikum. Og av folkemengdene, det er mange forskjellige måter du kan tenke på det. Men ganske enkelt, for eksempel, brukerne dine kommer til nettstedet ditt, og de legger inn søkeord, nøkkelord, og de havner på en bestemt side og bruker faktisk tid der på den siden. At de faktisk i det minste forstår at de enten ser på, bla gjennom, klikker på ting på den siden. Det som sier til deg er at nøkkelordet som de skrev inn helt i begynnelsen, er deskriptoren for den siden fordi det landet kunden på siden de forventet. Og slik at du kan legge til den ekstra informasjonen, det vil si kunder som bruker dette nøkkelordet, som faktisk identifiserte denne nettsiden i informasjonsarkitekturen som stedet der innholdet samsvarer med det søkeordet.

Og så er folkemengdene et annet aspekt som noen ganger glemmer, den slags sporing av kundenes brødsmuler, så å si; hvordan beveger de seg gjennom plassen sin, enten det er en online eiendom eller en fast eiendom. Og bruk så den typen bane de, som kunden tar som tilleggsinformasjon om tingene vi ser på.

Så jeg vil si at menneskegenererte ting, eller maskingenererte, endte opp med å ha en kontekst i slags annotering eller tagging av spesifikke datagranuler eller enheter. Enten disse enhetene er pasienter i sykehusinnstillinger, kunder eller hva som helst. Og så er det forskjellige typer tagging og merknader. Noe av det handler om selve dataene. Det er en av tingene, hvilken type informasjon, hva slags informasjon, hva er funksjonene, formene, kanskje teksturer og mønstre, anomali, ikke-anomali oppførsel. Og så trekke ut noen semantikk, det vil si hvordan forholder dette seg til andre ting som jeg vet, eller at denne kunden er en elektronikkunde. Denne kunden er en kleskunde. Eller denne kunden liker å kjøpe musikk.

Så å identifisere noen semantikk om det, disse kundene som liker musikk har en tendens til å like underholdning. Kanskje vi kunne tilby dem noe annet underholdningseiendom. Så å forstå semantikken og også en viss herkomst, som i utgangspunktet sier: hvor kom dette fra, hvem ga denne påstanden, hvilken tid, hvilken dato, under hvilke omstendigheter?

Så når du har alle disse merknadene og karakteristikkene, legger du til det, så er neste trinn, som er konteksten, sortering av hvem, hva, når, hvor og hvorfor av det. Hvem er brukeren? Hva var kanalen de kom inn på? Hva var kilden til informasjonen? Hva slags gjenbruk har vi sett i dette bestemte informasjons- eller dataproduktet? Og hva er, det er liksom verdi i forretningsprosessen? Og deretter samle du tingene og administrer dem, og faktisk hjelper deg med å opprette database, hvis du vil tenke på det på den måten. Gjør dem søkbare, gjenbrukbare av andre forretningsanalytikere eller av en automatisert prosess som vil neste gang jeg ser disse settene med funksjoner, systemet kunne utføre denne automatiske handlingen. Og slik får vi til den typen driftsanalytisk effektivitet, men desto mer samler vi nyttig, omfattende informasjon og deretter sammenligner den for disse brukstilfellene.

Vi kommer til virksomheten. Vi gjør dataanalysene. Vi ser etter interessante mønstre, overraskelser, nyhetsutviklere, avvik. Vi ser etter de nye klassene og segmentene i befolkningen. Vi ser etter assosiasjoner og sammenhenger og koblinger mellom de ulike enhetene. Og så bruker vi alt dette for å drive vår oppdagelses-, beslutnings- og dollarprosess.

Så der igjen, her har vi den siste datasiden jeg har, er bare i utgangspunktet å oppsummere, holde forretningsanalytikeren i loopen, igjen, du henter ikke ut det menneskelige, og det er viktig å holde den menneskelige der inne.

Så disse funksjonene, de er alle levert av maskiner eller menneskelige analytikere eller til og med crowddsourcing. Vi bruker den kombinasjonen av ting for å forbedre våre treningssett for modellene våre og ender opp med mer nøyaktige prediktive modeller, færre falske positiver og negativer, mer effektiv oppførsel, mer effektive inngrep med våre kunder eller hvem som helst.

Så på slutten av dagen kombinerer vi egentlig bare maskinlæring og big data med denne kraften til menneskelig erkjennelse, og det er der den slags merkingsnotatbrikken kommer inn. Og det kan føre til visualisering og visuell analyse-type verktøy eller fordypende datamiljøer eller crowddsourcing. Og på slutten av dagen, hva dette virkelig gjør er å generere vår oppdagelse, innsikt og D2D. Og det er kommentarene mine, så takk for at du hørte på.

Eric: Hei, det høres bra ut og la meg gå foran og overlate nøklene til Dr. Robin Bloor for også å gi sitt perspektiv. Ja, jeg liker å høre deg kommentere om den effektiviseringen av driftskonseptet, og du snakker om operasjonsanalyse. Jeg tror det er et stort område som må utforskes ganske grundig. Og jeg antar, veldig raskt før Robin, jeg vil ta deg tilbake, Kirk. Det krever at du har et ganske betydelig samarbeid mellom forskjellige aktører i selskapet, ikke sant? Du må snakke med operasjonsfolk; må du skaffe deg tekniske folk. Noen ganger får du markedsføringsfolket eller folkene dine på webgrensesnittet. Dette er typisk forskjellige grupper. Har du noen gode fremgangsmåter eller forslag til hvordan alle kan få huden sin i spillet?

Dr. Kirk: Vel, jeg tror dette kommer med forretningskulturen for samarbeid. Faktisk snakker jeg om de tre C-ene for en slags analysekultur. Den ene er kreativitet; en annen er nysgjerrighet og den tredje er samarbeid. Så du vil ha kreative, seriøse mennesker, men du må også få disse menneskene til å samarbeide. Og det starter virkelig fra toppen, den slags å bygge den kulturen med mennesker som åpent skal dele og samarbeide mot de felles målene for virksomheten.

Eric: Det er fornuftig. Og du må virkelig få et godt lederskap i toppen for å få det til. Så la oss gå videre og overlate det til Dr. Bloor. Robin, gulvet er ditt.

Dr. Robin Bloor: OK. Takk for introen, Eric. OK, slik disse viser seg, fordi vi har to analytikere; Jeg får se analytikerens presentasjon som de andre gutta ikke gjør. Jeg visste hva Kirk hadde tenkt å si, og jeg går bare en helt annen vinkel slik at vi ikke går for mye overlapp.

Så det jeg faktisk snakker om eller har tenkt å snakke om her, er rollen som dataanalytiker kontra rollen som forretningsanalytiker. Og måten jeg karakteriserer det på, tunge-i-kinnet til en viss grad, er slags Jekyll og Hyde-ting. Forskjellen er spesielt dataforskerne, i det minste i teorien, vet hva de gjør. Mens forretningsanalytikerne ikke er det, er det greit med matematikkens funksjoner, hva man kan stole på og hva man ikke kan stole på.

Så la oss bare komme til grunnen til at vi gjør dette, grunnen til at dataanalyse plutselig har blitt en stor del bortsett fra det faktum at vi faktisk kan analysere veldig store datamengder og hente inn data fra utenfor organisasjonen; er det lønner seg. Måten jeg ser på dette - og jeg tror dette bare blir en sak, men jeg tror definitivt at det er en sak - dataanalyse er virkelig FoU. Det du faktisk gjør på en eller annen måte med dataanalyse, er at du ser på en forretningsprosess på en eller annen måte, eller om det er samspillet med en kunde, enten det er slik som detaljhandelen din, måten du distribuerer butikkene dine. Det spiller ingen rolle hva problemet er. Du ser på en gitt forretningsprosess og prøver å forbedre den.

Utfallet av vellykket forskning og utvikling er en endringsprosess. Og du kan tenke deg å produsere, hvis du vil, som et vanlig eksempel på dette. Fordi i produksjonen samler folk informasjon om alt for å prøve å forbedre produksjonsprosessen. Men jeg tror at hva som har skjedd eller hva som skjer ved big data, alt dette blir nå brukt til alle virksomheter av noe slag på noen måte som noen kan tenke på. Så stort sett alle forretningsprosesser er til eksamen hvis du kan samle inn data om den.

Så det er en ting. Hvis du vil, kommer det til spørsmålet om dataanalyse. Hva kan dataanalyse gjøre for virksomheten? Vel, det kan endre virksomheten fullstendig.

Dette spesielle diagrammet som jeg ikke skal beskrive nærmere, men dette er et diagram som vi kom frem til som kulminasjonen på forskningsprosjektet vi gjorde de første seks månedene av året. Dette er en måte å representere en big data-arkitektur på. Og en rekke ting som er verdt å påpeke før jeg går videre til neste lysbilde. Det er to datastrømmer her. Den ene er en datastrøm i sanntid, som går på toppen av diagrammet. Den andre er en langsommere datastrøm som går langs bunnen av diagrammet.

Se nederst i diagrammet. Vi har Hadoop som et datareservoar. Vi har forskjellige databaser. Vi har en hel data der med en hel haug aktivitet som skjer på det, det meste er analytisk aktivitet.

Poenget jeg gjør her, og det eneste poenget jeg virkelig vil gjøre her, er at teknologien er hard. Det er ikke enkelt. Det er ikke lett. Det er ikke noe som alle som er nye i spillet, faktisk bare kan sette sammen. Dette er ganske sammensatt. Og hvis du skal instrumentere en virksomhet for å gjøre pålitelig analyse i alle disse prosessene, er det ikke noe som kommer til å skje spesielt raskt. Det kommer til å kreve mye teknologi for å bli lagt til blandingen.

Greit. Spørsmålet om hva som er en dataforsker, kan jeg påstå å være dataforsker fordi jeg faktisk ble opplært i statistikk før jeg noen gang ble trent i databehandling. Og jeg gjorde en aktuariell jobb i en periode, så jeg vet hvordan en virksomhet organiserer, statistisk analyse, også for å drive seg selv. Dette er ikke en bagatellmessig ting. Og det er veldig mange beste praksis involvert både på den menneskelige siden og på teknologisiden.

Så når jeg stiller spørsmålet "hva er en dataforsker", så har jeg satt Frankenstein-bildet bare fordi det er en kombinasjon av ting som må strikkes sammen. Det er prosjektledelse involvert. Det er dyp forståelse i statistikk. Det er domenevirksomhetskompetanse, som nødvendigvis er mer et problem for en forretningsanalytiker enn dataforskeren. Det er erfaring eller behov for å forstå dataarkitektur og å kunne bygge dataarkitekt, og det er programvareutvikling involvert. Med andre ord, det er sannsynligvis et team. Det er sannsynligvis ikke et individ. Og det betyr at det sannsynligvis er en avdeling som må organiseres, og at organisasjonen må tenkes ganske omfattende.

Kaster du inn blandingen faktumet av maskinlæring. Vi kunne ikke gjøre, jeg mener, maskinlæring er ikke nytt i den forstand at de fleste av de statistiske teknikkene som brukes i maskinlæring har vært kjent om i flere tiår. Det er noen få nye ting, jeg mener nevrale nettverk er relativt nye, jeg tror at de bare er rundt 20 år gamle, så noe av det er relativt nytt. Men problemet med maskinlæring var at vi egentlig ikke hadde datakraft til å gjøre det. Og det som skjedde, bortsett fra alt annet, er at datamaskinens strøm nå er på plass. Og det betyr utrolig mye av det vi, sier, forskere har gjort tidligere når det gjelder modelleringssituasjoner, prøvetaking av data og deretter fremført dette for å produsere en dypere analyse av dataene. Egentlig kan vi bare kaste datamaskinkraft på det i noen tilfeller. Bare velg maskinlæringsalgoritmer, kast dem på dataene og se hva som kommer ut. Og det er noe en forretningsanalytiker kan gjøre, ikke sant? Men forretningsanalytikeren må forstå hva de gjør. Jeg mener, det er problemet egentlig, mer enn noe annet.

Vel, dette er bare for å vite mer om virksomheten fra dataene sine enn på noen annen måte. Einstein sa ikke det, det sa jeg. Jeg la bildet hans opp for troverdighet. Men situasjonen begynner faktisk å utvikle seg, er teknologien, hvis den brukes riktig, og matematikken, hvis den brukes riktig, vil være i stand til å drive en virksomhet som enhver person. Vi har sett dette med IBM. Først av alt, det kunne slå de beste gutta ved sjakk, og så kunne den slå de beste gutta på Jeopardy; men etter hvert vil vi kunne slå de beste gutta ved å drive et selskap. Statistikken vil til slutt seire. Og det er vanskelig å se hvordan det ikke vil skje, det har bare ikke skjedd ennå.

Så det jeg sier, og dette er en slags fullstendig melding fra presentasjonen min, er disse to sakene av virksomheten. Den første er, kan du få teknologien riktig? Kan du få teknologien til å fungere for teamet som faktisk vil være i stand til å lede det og få fordeler for bedriften? Og så for det andre, kan du få folket rett? Og begge disse spørsmålene. Og det er problemer som ikke er løst, til nå.

Ok Eric, jeg vil gi det tilbake til deg. Eller jeg burde kanskje gi den videre til Will.

Eric: Egentlig, ja. Takk, Will Gorman. Ja, der går du, Will. Så la oss se. La meg gi deg nøkkelen til WebEx. Så hva har du skjedd? Pentaho, tydeligvis, dere har eksistert en stund og åpen kildekode BIs hvor du startet. Men du har mye mer enn du pleide å ha, så la oss se hva du har i disse dager for analyse.

Will Gorman: Absolutt. Hei alle sammen! Jeg heter Will Gorman. Jeg er sjefsarkitekt på Pentaho. For de av dere som ikke har hørt om oss, nevnte jeg nettopp Pentaho er et big data integrasjons- og analyseselskap. Vi har vært i bransjen i ti år. Produktene våre har utviklet seg side om side med big data community, og startet som en åpen kildekode-plattform for dataintegrering og analyse, og innovert med teknologi som Hadoop og NoSQL selv før kommersielle enheter ble dannet rundt disse teknologiene. Og nå har vi over 1500 kommersielle kunder og mange flere produksjonsavtaler som et resultat av innovasjonen vår rundt open source.

Arkitekturen vår er svært innebygd og utvidbar, spesialbygget for å være fleksibel ettersom big data-teknologi i særdeleshet utvikler seg i veldig raskt tempo. Pentaho tilbyr tre hovedproduktområder som jobber sammen for å adressere bruk av store dataanalyser.

Det første produktet i omfanget av vår arkitektur er Pentaho Data Integration som er rettet mot datateknolog og dataingeniører. Dette produktet tilbyr en visuell dra-og-slipp-opplevelse for å definere datapipelines og prosesser for orkestrering av data i big data miljøer og tradisjonelle miljøer også. Dette produktet er en lett, metadatabase, dataintegrasjonsplattform bygd på Java og kan distribueres som en prosess i MapReduce eller YARN eller Storm og mange andre batch- og sanntidsplattformer.

Det andre produktområdet vårt handler om visuell analyse. Med denne teknologien kan organisasjoner og OEM-er tilby en rik dra-og-slipp-visualiserings- og analyseopplevelse for forretningsanalytikere og forretningsbrukere av moderne nettlesere og nettbrett, slik at ad hoc-oppretting av rapporter og dashboards kan skje. Samt presentasjon av pixel-perfekt dashboarding og rapporter.

Vårt tredje produktområde fokuserer på prediktiv analyse målrettet for dataforskere, maskinlæringsalgoritmer. Som nevnt før, som nevrale nettverk og slikt, kan integreres i et datatransformasjonsmiljø, slik at dataforskere kan gå fra modellering til produksjonsmiljø, gi tilgang til å forutsi, og det kan påvirke forretningsprosesser veldig umiddelbart, veldig raskt.

Alle disse produktene er tett integrert i en enkel smidig opplevelse og gir våre bedriftskunder den fleksibiliteten de trenger for å håndtere sine forretningsproblemer. Vi ser et raskt utviklende landskap med store data i tradisjonelle teknologier. Alt vi hører fra noen selskaper i big data-rommet at EDW er nær en slutt. Det vi ser hos våre bedriftskunder er faktisk at de trenger å introdusere big data i eksisterende forretnings- og IT-prosesser og ikke erstatte disse prosessene.

Dette enkle diagrammet viser poenget i arkitektur som vi ofte ser, som er en type EDW-distribusjonsarkitektur med dataintegrasjon og BI-brukssaker. Nå er dette diagrammet likt Robins lysbilde om big data-arkitektur, det inkluderer sanntids- og historiske data. Når nye datakilder og sanntidskrav dukker opp, ser vi big data som en ekstra del av den generelle IT-arkitekturen. Disse nye datakildene inkluderer maskingenererte data, ustrukturerte data, standardvolum og hastighet og forskjellige krav som vi hører om i big data; de passer ikke inn i tradisjonelle EDW-prosesser. Pentaho samarbeider tett med Hadoop og NoSQL for å forenkle inntak, databehandling og visualisering av disse dataene, samt blande disse dataene med tradisjonelle kilder for å gi kundene full oversikt over datamiljøet. Vi gjør dette på en styrt måte slik at IT kan tilby en komplett analyseløsning på deres bransje.

Avslutningsvis vil jeg trekke frem vår filosofi rundt analyser og integrering av big data; vi tror at disse teknologiene fungerer bedre sammen med en enhetlig arkitektur, noe som muliggjør en rekke brukstilfeller som ellers ikke ville være mulig. Våre kunders datamiljøer er mer enn bare big data, Hadoop og NoSQL. Eventuelle data er rettferdige spill. Og store datakilder må være tilgjengelige og samarbeide for å påvirke forretningsverdien.

Til slutt tror vi at for å løse disse forretningsproblemene i bedrifter veldig effektivt gjennom data, trenger IT og bransjer å samarbeide om en styrt, blandet tilnærming til big data-analyse. Tusen takk for at du ga oss tid til å snakke, Eric.

Eric: Du vedder. Nei, det er gode ting. Jeg vil komme tilbake til den siden av arkitekturen din når vi kommer til spørsmål og svar. Så la oss gå gjennom resten av presentasjonen og takke veldig mye for det. Dere har definitivt kommet raskt de siste par årene, det må jeg si helt sikkert.

Så Steve, la meg gå videre og overlate det til deg. Og bare klikk der på pil ned og gå etter den. Så Steve, jeg gir deg nøklene. Steve Wilkes, bare klikk på den fjerneste pilen der på tastaturet.

Steve Wilkes: Der går vi.

Eric: Der drar du.

Steve: Det er en flott introduksjon du har gitt meg.

Eric: Ja.

Steve: Så jeg er Steve Wilkes. Jeg er CCO på WebAction. Vi har bare eksistert de siste par årene, og vi har definitivt gått raskt også siden den gang. WebAction er en sanntids big data-analyseplattform. Eric nevnte tidligere, slags, hvor viktig sanntid er og hvor sanntid applikasjonene dine blir. Plattformen vår er designet for å bygge sanntidsapper. Og for å aktivere neste generasjon av datadrevne apper som kan bygges trinnvis på og slik at folk kan bygge dashbord fra data generert fra disse appene, men som fokuserer på sanntid.

Plattformen vår er faktisk en fullstendig ende-til-ende-plattform, og gjør alt fra datainnsamling, databehandling, helt gjennom til datavisualisering. Og gjør det mulig for flere forskjellige typer mennesker i vårt selskap å samarbeide om å lage ekte sanntidsapper, og gi dem innsikt i ting som skjer i bedriften når de skjedde.

Og dette er litt annerledes enn hva folk flest har sett i big data, slik at den tradisjonelle tilnærmingen - vel, tradisjonell de siste par årene - har vært å fange den fra en hel haug med forskjellige kilder og deretter haug det opp i et stort reservoar eller innsjø eller hva du vil kalle det. Og deretter behandle det når du trenger å kjøre en spørring på den; å kjøre storstilt historisk analyse eller til og med bare ad hoc-spørring av store datamengder. Nå som fungerer for visse brukstilfeller. Men hvis du vil være proaktiv i bedriften din, hvis du faktisk vil bli fortalt hva som skjer fremfor å finne ut når noe gikk galt på slutten av dagen eller ukens slutt, må du virkelig flytte til sanntid.

Og det bytter litt rundt. Den flytter behandlingen til midten. Så effektivt tar du strømmer av store datamengder som blir generert kontinuerlig i bedriften, og du behandler den etter hvert som du får den. Og fordi du behandler det slik du får det, trenger du ikke å lagre alt. Du kan bare lagre viktig informasjon eller tingene du trenger å huske som faktisk skjedde. Så hvis du sporer GPS-plasseringen til kjøretøyer som beveger seg nedover veien, bryr du deg ikke hvor de er hvert sekund, trenger du ikke å lagre der de er hvert sekund. Du trenger bare å bry deg om, har de forlatt dette stedet? Har de kommet til dette stedet? Har de kjørt, eller ikke, motorveien?

Så det er veldig viktig å tenke på at etter hvert som flere og flere data blir generert, så vil de tre Vs. Hastighet bestemmer i utgangspunktet hvor mye data som genereres hver dag. Jo mer data som genereres, jo mer må du lagre. Og jo mer du må lagre, jo lenger tid tar det å behandle. Men hvis du kan behandle det slik du får det, får du en virkelig stor fordel, og du kan reagere på det. Du kan bli fortalt at ting skjer i stedet for å måtte søke etter dem senere.

Så plattformen vår er designet for å være svært skalerbar. Den har tre store brikker - anskaffelsesstykket, behandlingsstykket og deretter leveringsvisualiseringsdelene til plattformen. På anskaffelsessiden ser vi ikke bare på maskingenererte loggdata som nettlogger eller applikasjoner som har alle de andre loggene som blir generert. We can also go in and do change data capture from databases. So that basically enables us to, we've seen the ETL side that Will presented and traditional ETL you have to run queries against the databases. We can be told when things happen in the database. We change it and we capture it and receive those events. And then there's obviously the social feeds and live device data that's being pumped to you over TCP or ACDP sockets.

There's tons of different ways of getting data. And talking of volume and velocity, we're seeing volumes that are billions of events per day, right? So it's large, large amounts of data that is coming in and needs to be processed.

That is processed by a cluster of our servers. The servers all have the same architecture and are all capable of doing the same things. But you can configure them to, sort of, do different things. And within the servers we have a high-speed query processing layer that enables you to do some real-time analytics on the data, to do enrichments of the data, to do event correlation, to track things happening within time windows, to do predictive analytics based on patterns that are being seen in the data. And that data can then be stored in a variety places - the traditional RDBMS, enterprise data warehouse, Hadoop, big data infrastructure.

And the same live data can also be used to power real-time data-driven apps. Those apps can have a real-time view of what's going on and people can also be alerted when important things happen. So rather than having to go in at the end of the day and find out that something bad really happened earlier on the day, you could be alerted about it the second we spot it and it goes straight to the page draw down to find out what's going on.

So it changes the paradigm completely from having to analyze data after the fact to being told when interesting things are happening. And our platform can then be used to build data-driven applications. And this is really where we're focusing, is building out these applications. For customers, with customers, with a variety of different partners to show true value in real-time data analysis. So that allows people that, or companies that do site applications, for example, to be able track customer usage over time and ensure that the quality of service is being met, to spot real-time fraud or money laundering, to spot multiple logins or hack attempts and those kind of security events, to manage things like set-top boxes or other devices, ATM machines to monitor them in real time for faults, failures that have happened, could happen, will happen in the future based on predictive analysis. And that goes back to the point of streamlining operations that Eric mentioned earlier, to be able to spot when something's going to happen and organize your business to fix those things rather than having to call someone out to actually do something after the fact, which is a lot more expensive.

Consumer analytics is another piece to be able to know when a customer is doing something while they're still there in your store. Data sent to management to be able to in real time monitor resource usage and change where things are running and to be able to know about when things are going to fail in a much more timely fashion.

So that's our products in a nutshell and I'm sure we'll come back to some of these things in the Q&A session. Takk skal du ha.

Eric: Yes, indeed. Great job. Okay good. And now next stop in our lightning round, we've got Frank Sanders calling in from MarkLogic. I've known about these guys for a number of years, a very, very interesting database technology. So Frank, I'm turning it over to you. Just click anywhere in that. Use the down arrow on your keyboard and you're off to the races. Der går du.

Frank Sanders: Thank you very much, Eric. So as Eric mentioned, I'm with a company called MarkLogic. And what MarkLogic does is we provide an enterprise NoSQL database. And perhaps, the most important capability that we bring to the table with regards to that is the ability to actually bring all of these disparate sources of information together in order to analyze, search and utilize that information in a system similar to what you're used to with traditional relational systems, right?

And some of the key features that we bring to the table in that regard are all of the enterprise features that you'd expect from a traditional database management system, your security, your HA, your DR, your backup are in store, your asset transactions. As well as the design that allows you to scale out either on the cloud or in the commodity hardware so that you can handle the volume and the velocity of the information that you're going to have to handle in order to build and analyze this sort of information.

And perhaps, the most important capability is that fact that we're scheme agnostic. What that means, practically, is that you don't have to decide what your data is going to look like when you start building your applications or when you start pulling those informations together. But over time, you can incorporate new data sources, pull additional information in and then use leverage and query and analyze that information just as you would with anything that was there from the time that you started the design. Okay?

So how do we do that? How do we actually enable you to load different sorts of information, whether it be text, RDF triples, geospatial data, temporal data, structured data and values, or binaries. And the answer is that we've actually built our server from the ground up to incorporate search technology which allows you to put information in and that information self describes and it allows you to query, retrieve and search that information regardless of its source or format.

And what that means practically is that - and why this is important when you're doing analysis - is that analytics and information is most important ones when it's properly contextualized and targeted, right? So a very important key part of any sort of analytics is search, and the key part is search analytics. You can't really have one without the other and successfully achieve what you set out to achieve. Right?

And I'm going to talk briefly about three and a half different use cases of customers that we have at production that are using MarkLogic to power this sort of analytics. Greit. So the first such customer is Fairfax County. And Fairfax County has actually built two separate applications. One is based around permitting and property management. And the other, which is probably a bit more interesting, is the Fairfax County police events application. What the police events application actually does is it pulls information together like police reports, citizen reports and complaints, Tweets, other information they have such as sex offenders and whatever other information that they have access to from other agencies and sources. Then they allow them to visualize that and present this to the citizens so they can do searches and look at various crime activity, police activity, all through one unified geospatial index, right? So you can ask questions like, "what is the crime rate within five miles" or "what crimes occurred within five miles of my location?" Greit.

Another user that we've got, another customer that we have is OECD. Why OECD is important to this conversation is because in addition to everything that we've enabled for Fairfax County in terms of pulling together information, right; all the information that you would get from all various countries that are members of the OECD that they report on from an economic perspective. We actually laid a target drill into that, right. So you can see on the left-hand side we're taking the view of Denmark specifically and you can kind of see a flower petal above it that rates it on different axes. Right? And that's all well and good. But what the OECD has done is they've gone a step further.

In addition to these beautiful visualizations and pulling all these information together, they're actually allowing you in real time to create your own better life index, right, which you can see on the right-hand side. So what you have there is you have a set of sliders that actually allow you to do things like rank how important housing is to you or income, jobs, community, education, environment, civic engagement, health, life satisfaction, safety and your work/life balance. And dynamically based on how you are actually inputting that information and weighting those things, MarkLogic's using its real-time indexing capability and query capability to actually then change how each and every one of these countries is ranked to give you an idea of how well your country or your lifestyle maps through a given country. Okay?

And the final example that I'm going to share is MarkMail. And what MarkMail really tries to demonstrate is that we can provide these capabilities and you can do the sort of analysis not only on structured information or information that's coming in that's numerical but actually on more loosely structured, unstructured information, right? Things like emails. And what we've seen here is we're actually pulling information like geolocation, sender, company, stacks and concepts like Hadoop being mentioned within the context of an email and then visualizing it on the map as well as looking at who those individuals and what list across that, a sent and a date. This where you're looking at things that are traditionally not structured, that may be loosely structured, but are still able to derive some structured analysis from that information without having to go to a great length to actually try and structure it or process it at a time. And that's it.

Eric: Hey, okay good. And we got one more. We've got Hannah Smalltree from Treasure Data, a very interesting company. And this is a lot of great content, folks. Thank you so much for all of you for bringing such good slides and such good detail. So Hannah, I just gave the keys to you, click anywhere and use the down arrow on your keyboard. You got it. Ta den bort.

Hannah Smalltree: Thank you so much, Eric. This is Hannah Smalltree from Treasure Data. I'm a director with Treasure Data but I have a past as a tech journalist, which means that I appreciate two things. First of all, these can be long to sit through a lot of different descriptions of technology, and it can all sound like it runs together so I really want to focus on our differentiator. And the real-world applications are really important so I appreciate that all of my peers have been great about providing those.

Treasure Data is a new kind of big data service. We're delivered entirely on the cloud in a software as a service or managed-service model. So to Dr. Bloor's point earlier, this technology can be really hard and it can be very time consuming to get up and running. With Treasure Data, you can get all of these kinds of capabilities that you might get in a Hadoop environment or a complicated on-premise environment in the cloud very quickly, which is really helpful for these new big data initiatives.

Now we talk about our service in a few different phases. We offer some very unique collection capabilities for collecting streaming data so particularly event data, other kinds of real-time data. We'll talk a little bit more about those data types. That is a big differentiator for our service. As you get into big data or if you are already in it then you know that collecting this data is not trivial. When you think about a car with 100 sensors sending data every minute, even those 100 sensors sending data every ten minutes, that adds up really quickly as you start to multiply the amount of products that you have out there with sensors and it quickly becomes very difficult to manage. So we are talking with customers who have millions, we have customers who have billions of rows of data a day that they're sending us. And they're doing that as an alternative to try and to manage that themselves in a complicated Amazon infrastructure or even try to bring it into their own environment.

We have our own cloud storage environment. We manage it. We monitor it. We have a team of people that's doing all that tuning for you. And so the data flows in, it goes into our managed storage environment.

Then we have embedded query engines so that your analyst can go in and run queries and do some initial data discovery and exploration against the data. We have a couple of different query engines for it actually now. You can use SQL syntax, which your analysts probably know and love, to do some basic data discovery, to do some more complex analytics that are user-defined functions or even to do things as simple as aggregate that data and make it smaller so that you can bring it into your existing data warehouse environment.

You can also connect your existing BI tools, your Tableau, is a big partner of ours; but really most BIs, visualization or analytics tools can connect via our industry standard JDBC and ODBC drivers. So it gives you this complete set of big data capabilities. You're allowed to export your queries results or data sets anytime for free, so you can easily integrate that data. Treat this as a data refinery. I like to think of it more as a refinery than a lake because you can actually do stuff with it. You can go through, find the valuable information and then bring it into your enterprise processes.

The next slide, we talk about the three Vs of big data - some people say four or five. Our customers tend to struggle with the volume and velocity of the data coming at them. And so to get specific about the data types - Clickstream, Web access logs, mobile data is a big area for us, mobile application logs, application logs from custom Web apps or other applications, event logs. And increasingly, we have a lot of customers dealing with sensor data, so from wearable devices, from products, from automotive, and other types of machine data. So when I say big data, that's the type of big data that I'm talking about.

Now, a few use cases in perspective for you - we work with a retailer, a large retailer. They are very well known in Asia. They're expanding here in the US. You'll start to see stores; they're often called Asian IKEA, so, simple design. They have a loyalty app and a website. And in fact, using Treasure Data, they were able to deploy that loyalty app very quickly. Our customers get up and running within days or weeks because of our software and our service architecture and because we have all of the people doing all of that hard work behind the scenes to give you all of those capabilities as a service.

So they use our service for mobile application analytics looking at the behavior, what people are clicking on in their mobile loyalty application. They look at the website clicks and they combine that with our e-commerce and POS data to design more efficient promotions. They actually wanted to drive people into stores because they found that people, when they go into stores spend more money and I'm like that; to pick up things, you spend more money.

Another use case that we're seeing in digital video games, incredible agility. They want to see exactly what is happening in their game, and make changes to that game even within hours of its release. So for them, that real-time view is incredibly important. We just released a game but we noticed in the first hour that everyone is dropping off at Level 2; how are we going to change that? They might change that within the same day. So real time is very important. They're sending us billions of event logs per day. But that could be any kind of mobile application where you want some kind of real-time view into how somebody's using that.

And finally, a big area for us is our product behavior and sensor analytics. So with sensor data that's in cars, that's in other kinds of machines, utilities, that's another area for us, in wearable devices. We have research and development teams that want to quickly know what the impact of a change to a product is or people interested in the behavior of how people are interacting with the product. And we have a lot more use cases which, of course, we're happy to share with you.

And then finally, just show you how this can fit into your environment, we offer again the capability to collect that data. We have very unique collection technology. So again, if real-time collection is something that you're struggling with or you anticipate struggling with, please come look at the Treasure Data service. We have really made capabilities for collecting streaming data. You can also bulk load your data, store it, analyze it with our embedded query engines and then, as I mentioned, you can export it right to your data warehouse. I think Will mentioned the need to introduce big data into your existing processes. So not go around or create a new silo, but how do you make that data smaller and then move it into your data warehouse and you can connect to your BI, visualization and advanced analytics tools.

But perhaps, the key points I want to leave you with are that we are managed service, that's software as a service; it's very cost effective. A monthly subscription service starting at a few thousand dollars a month and we'll get you up and running in a matter of days or weeks. So compare that with the cost of months and months of building your own infrastructure and hiring those people and finding it and spending all that time on infrastructure. If you're experimenting or if you need something yesterday, you can get up and running really quickly with Treasure Data.

And I'm just pointing you to our website and to our starter service. If you're a hands-on person who likes to play, please check out our starter service. You can get on, no credit card required, just name and email, and you can play with our sample data, load up your own data and really get a sense of what we're talking about. So thanks so much. Also, check our website. We were named the Gartner Cool Vendor in Big Data this year, very proud of that. And you can also get a copy of that report for free on our website as well as many other analyst white papers. So thanks so much.

Eric: Okay, thank you very much. We've got some time for questions here, folks. We'll go a little bit long too because we've got a bunch of folks still on the line here. And I know I've got some questions myself, so let me go ahead and take back control and then I'm going to ask a couple of questions. Robin and Kirk, feel free to dive in as you see fit.

So let me go ahead and jump right to one of these first slides that I checked out from Pentaho. So here, I love this evolving big data architecture, can you kind of talk about how it is that this kind of fits together at a company? Because obviously, you go into some fairly large organization, even a mid-size company, and you're going to have some people who already have some of this stuff; how do you piece this all together? Like what does the application look like that helps you stitch all this stuff together and then what does the interface look like?

Will: Great question. The interfaces are a variety depending on the personas involved. But as an example, we like to tell the story of - one of the panelists mentioned the data refinery use case - we see that a lot in customers.

One of our customer examples that we talk about is Paytronix, where they have that traditional EDW data mart environment. They are also introducing Hadoop, Cloudera in particular, and with various user experiences in that. So first there's an engineering experience, so how do you wire all these things up together? How do you create the glue between the Hadoop environment and EDW?

And then you have the business user experience which we talked about, a number of BI tools out there, right? Pentaho has a more embeddable OEM BI tool but there are great ones out there like Tableau and Excel, for instance, where folks want to explore the data. But usually, we want to make sure that the data is governed, right? One of the questions in the discussions, what about single-version experience, how do you manage that, and without the technology like Pentaho data integration to blend that data together not on the glass but in the IT environments. So it really protects and governs the data and allows for a single experience for the business analyst and business users.

Eric: Okay, good. That's a good answer to a difficult question, quite frankly. And let me just ask the question to each of the presenters and then maybe Robin and Kirk if you guys want to jump in too. So I'd like to go ahead and push this slide for WebAction which I do think is really a very interesting company. Actually, I know Sami Akbay who is one of the co-founders, as well. I remember talking to him a couple years ago and saying, "Hey man, what are you doing? What are you up to? I know you've got to be working on something." And of course, he was. He was working on WebAction, under the covers here.

A question came in for you, Steve, so I'll throw it over to you, of data cleansing, right? Can you talk about these components of this real-time capability? How do you deal with issues like data cleansing or data quality or how does that even work?

Steve: So it really depends on where you're getting your feeds from. Typically, if you're getting your feeds from a database as you change data capture then, again, it depends there on how the data was entered. Data cleansing really becomes a problem when you're getting your data from multiple sources or people are entering it manually or you kind of have arbitrary texts that you have to try and pull things out of. And that could certainly be part of the process, although that type simply doesn't lend itself to true, kind of, high-speed real-time processing. Data cleansing, typically, is an expensive process.

So it may well be that that could be done after the fact in the store site. But the other thing that the platform is really, really good at is correlation, so in correlation and enrichment of data. You can, in real time, correlate the incoming data and check to see whether it matches a certain pattern or it matches data that's being retrieved from a database or Hadoop or some other store. So you can correlate it with historical data, is one thing you could do.

The other thing that you can do is basically do analysis on that data and see whether it kind of matches certain required patterns. And that's something that you can also do in real time. But the traditional kind of data cleansing, where you're correcting company names or you're correcting addresses and all those types of things, those should probably be done in the source or kind of after the fact, which is very expensive and you pray that they won't do those in real time.

Eric: Yeah. And you guys are really trying to address the, of course, the real-time nature of things but also get the people in time. And we talked about, right, I mentioned at the top of the hour, this whole window of opportunity and you're really targeting specific applications at companies where you can pull together data not going the usual route, going this alternate route and do so in such a low latency that you can keep customers. For example, you can keep people satisfied and it's interesting, when I talked to Sami at length about what you guys are doing, he made a really good point. He said, if you look at a lot of the new Web-based applications; let's look at things like Twitter, Bitly or some of these other apps; they're very different than the old applications that we looked at from, say, Microsoft like Microsoft Word.

I often use Microsoft as sort of a whipping boy and specifically Word to talk about the evolution of software. Because Microsoft Word started out as, of course, a word processing program. I'm one of those people who remember Word Perfect. I loved being able to do the reveal keys or the reveal code, basically, which is where you could see the actual code in there. You could clean something up if your bulleted list was wrong, you can clean it up. Well, Word doesn't let you do that. And I can tell you that Word embeds a mountain of code inside every page that you do. If anyone doesn't believe me, then go to Microsoft Word, type "Hello World" and then do "Export as" or "Save as" .html. Then open that document in a text editor and that will be about four pages long of codes just for two words.

So you guys, I thought it was very interesting and it's time we talked about that. And that's where you guys focus on, right, is identifying what you might call cross-platform or cross-enterprise or cross-domain opportunities to pull data together in such quick time that you can change the game, right?

Steve: Yeah, absolutely. And one of the keys that, I think, you did elude to, anyway, is you really want to know about things happening before your customers do or before they really, really become a problem. As an example are the set-top boxes. Cable boxes, they emit telemetry all the time, loads and loads of telemetry. And not just kind of the health of the box but it's what you're watching and all that kind of stuff, right? The typical pattern is you wait till the box fails and then you call your cable provider and they'll say, "Well, we will get to you sometime between the hours of 6am and 11pm in the entire month of November." That isn't a really good customer experience.

But if they could analyze that telemetry in real time then they could start to do things like that we know these boxes are likely to fail in the next week based historical patterns. Therefore we'll schedule our cable repair guy to turn up at this person's house prior to it failing. And we'll do that in a way that suits us rather than having to send him from Santa Cruz up to Sunnyvale. We'll schedule everything in a nice order, traveling salesman pattern, etc., so that we can optimize our business. And so the customer is happy because they don't have a failing cable box. And the cable provider is happy because they have just streamlined things and they don't have to send people all over the place. That's just a very quick example. But there are tons and tons of examples where knowing about things as they happen, before they happen, can save companies a fortune and really, really improve their customer relations.

Eric: Yeah, right. No doubt about it. Let's go ahead and move right on to MarkLogic. As I mentioned before, I've known about these guys for quite some time and so I'll bring you into this, Frank. You guys were far ahead of the whole big data movement in terms of building out your application, it's really database. But building it out and you talked about the importance of search.

So a lot of people who followed the space know that a lot of the NoSQL tools out there are now bolting on search capabilities whether through third parties or they try to do their own. But to have that search already embedded in that, baked-in so to speak, really is a big deal. Because if you think about it, if you don't have SQL, well then how do you go in and search the data? How do you pull from that data resource? And the answer is to typically use search to get to the data that you're looking for, right?

So I think that's one of the key differentiators for you guys aside being able to pull data from all these different sources and store that data and really facilitate this sort of hybrid environment. I'm thinking that search capability is a big deal for you, right?

Frank: Yeah, absolutely. In fact, that's the only way to solve the problem consistently when you don't know what all the data is going to look like, right? If you cannot possibly imagine all the possibilities then the only way to make sure that you can locate all the information that you want, that you can locate it consistently and you can locate it regardless of how you evolve your data model and your data sets is to make sure you give people generic tools that allow them to interrogate that data. And the easiest, most intuitive way to do that is through a search paradigm, right? And through the same approach in search takes where we created an inverted index. You have entries where you can actually look into those and then find records and documents and rows that actually contain the information you're looking for to then return it to the customer and allow them to process it as they see fit.

Eric: Yeah and we talked about this a lot, but you're giving me a really good opportunity to kind of dig into it - the whole search and discovery side of this equation. But first of all, it's a lot of fun. For anyone who likes that stuff, this is the fun part, right? But the other side of the equation or the other side of the coin, I should say, is that it really is an iterative process. And you got to be able to - here I'll be using some of the marketing language - have that conversation with the data, right? In other words, you need to be able to test the hypothesis, play around with it and see how that works. Maybe that's not there, test something else and constantly change things and iterate and search and research and just think about stuff. And that's a process. And if you have big hurdles, meaning long latencies or a difficult user interface or you got to go ask IT; that just kills the whole analytical experience, right?

So it's important to have this kind of flexibility and to be able to use searches. And I like the way that you depicted it here because if we're looking at searching around different, sort of, concepts or keys, if you will, key values and they're different dimensions. You want to be able to mix and match that stuff in order to enable your analyst to find useful stuff, right?

Frank: Yeah, absolutely. I mean, hierarchy is an important thing as well, right? So that when you include something like a title, right, or a specific term or value, that you can actually point to the correct one. So if you're looking for a title of an article, you're not getting titles of books, right? Or you're not getting titles of blog posts. The ability to distinguish between those and through the hierarchy of the information is important as well.

You pointed out earlier the development, absolutely, right? The ability for our customers to actually pull in new data sources in a matter of hours, start to work with them, evaluate whether or not they're useful and then either continue to integrate them or leave them by the wayside is extremely valuable. When you compare it to a more traditional application development approach where what you end up doing is you have to figure out what data you want to ingest, source the data, figure out how you're going to fit it in your existing data model or model that in, change that data model to incorporate it and then actually begin the development, right? Where we kind of turn that on our head and say just bring it to us, allow you to start doing the development with it and then decide later whether or not you want to keep it or almost immediately whether or not it's of value.

Eric: Yeah, it's a really good point. That's a good point. So let me go ahead and bring in our fourth presenter here, Treasure Data. I love these guys. I didn't know much about them so I'm kind of kicking myself. And then Hannah came to us and told us what they were doing. And Hannah mentioned, she was a media person and she went over to the dark side.

Hannah: I did, I defected.

Eric: That's okay, though, because you know what we like in the media world. So it's always nice when a media person goes over to the vendor side because you understand, hey, this stuff is not that easy to articulate and it can be difficult to ascertain from a website exactly what this product does versus what that product does. And what you guys are talking about is really quite interesting. Now, you are a cloud-managed service. So any data that someone wants to use they upload to your cloud, is that right? And then you will ETL or CDC, additional data up to the cloud, is that how that works?

Hannah: Well, yeah. So let me make an important distinction. Most of the data, the big data, that our customers are sending us is already outside the firewall - mobile data, sensor data that's in products. And so we're often used as an interim staging area. So data is not often coming from somebody's enterprise into our service so much as it's flowing from a website, a mobile application, a product with lots of sensors in it - into our cloud environment.

Now if you'd like to enrich that big data in our environment, you can definitely bulk upload some application data or some customer data to enrich that and do more of the analytics directly in the cloud. But a lot of our value is around collecting that data that's already outside the firewall, bringing together into one place. So even if you do intend to bring this up sort of behind your firewall and do more of your advanced analytics or bring it into your existing BI or analytics environment, it's a really good staging point. Because you don't want to bring a billion rows of day into your data warehouse, it's not cost effective. It's even difficult if you're planning to store that somewhere and then batch upload.

So we're often the first point where data is getting collected that's already outside firewall.

Eric: Yeah, that's a really good point, too. Because a lot of companies are going to be nervous about taking their proprietary customer data, putting it up in the cloud and to manage the whole process.

Hannah: Yeah.

Eric: And what you're talking about is really getting people a resource for crunching those heavy duty numbers of, as you suggest, data that's third party like mobile data and the social data and all that kind of fun stuff. That's pretty interesting.

Hannah: Yeah, absolutely. And probably they are nervous about the products because the data are already outside. And so yeah, before bringing it in, and I really like that refinery term, as I mentioned, versus the lake. So can you do some basic refinery? Get the good stuff out and then bring it behind the firewall into your other systems and processes for deeper analysis. So it's really all data scientists can do, real-time data exploration of this new big data that's flowing in.

Eric: Yeah, that's right. Well, let me go ahead and bring in our analysts and we'll kind of go back in reverse order. I'll start with you, Robin, with respect to Treasure Data and then we'll go to Kirk for some of the others. And then back to Robin and back to Kirk just to kind of get some more assessment of this.

And you know the data refinery, Robin, that Hannah is talking about here. I love that concept. I've heard only a few people talking about it that way but I do think that you certainly mentioned that before. And it really does speak to what is actually happening to your data. Because, of course, a refinery, it basically distills stuff down to its root level, if you think about oil refineries. I actually studied this for a while and it's pretty basic, but the engineering that goes into it needs to be exactly correct or you don't get the stuff that you want. So I think it's a great analogy. What do you think about this whole concept of the Treasure Data Cloud Service helping you tackle some of those very specific analytical needs without having to bring stuff in-house?

Robin: Well, I mean, obviously depending on the circumstances to how convenient that is. But anybody that's actually got already made process is already going to put you ahead of the game if you haven't got one yourself. This is the first takeaway for something like that. If somebody assembled something, they've done it, it's proven in the marketplace and therefore there's some kind of value in effect, well, the work is already gone into it. And there's also the very general fact that refining of data is going to be a much bigger issue than it ever was before. I mean, it is not talked about, in my opinion anyway, it's not talked about as much as it should be. Simply apart from the fact that size of the data has grown and the number of sources and the variety of those sources has grown quite considerably. And the reliability of the data in terms of whether it's clean, they need to disambiguate the data, all sorts of issues that rise just in terms of the governance of the data.

So before you actually get around to being able to do reliable analysis on it, you know, if your data's dirty, then your results will be skewed in some way or another. So that is something that has to be addressed, that has to be known about. And the triangulator of providing, as far as I can see, a very viable service to assist in that.

Eric: Yes, indeed. Well, let me go ahead and bring Kirk back into the equation here just real quickly. I wanted to take a look at one of these other slides and just kind of get your impression of things, Kirk. So maybe let's go back to this MarkLogic slide. And by the way, Kirk provided the link, if you didn't see it folks, to some of his class discovery slides because that's a very interesting concept. And I think this is kind of brewing at the back of my mind, Kirk, as I was talking about this a moment ago. This whole question that one of the attendees posed about how do you go about finding new classes. I love this topic because it really does speak to the sort of, the difficult side of categorizing things because I've always had a hard time categorizing stuff. I'm like, "Oh, god, I can fit in five categories, where do I put it?" So I just don't want to categorize anything, right?

And that's why I love search, because you don't have to categorize it, you don't have to put it in the folder. Just search for it and you'll find it if you know how to search. But if you're in that process of trying to segment, because that's basically what categorization is, it's segmenting; finding new classes, that's kind of an interesting thing. Can you kind of speak to the power of search and semantics and hierarchies, for example, as Frank was talking about with respect to MarkLogic and the role that plays in finding new classes, what do you think about that?

Kirk: Well, first of all, I'd say you are reading my mind. Because that was what I was thinking of a question even before you were talking, this whole semantic piece here that MarkLogic presented. And if you come back to my slide, you don't have to do this, but back on the slide five on what I presented this afternoon; I talked about this semantics that the data needs to be captured.

So this whole idea of search, there you go. I firmly believe in that and I've always believed in that with big data, sort of take the analogy of Internet, I mean, just the Web, I mean having the world knowledge and information and data on a Web browser is one thing. But to have it searchable and retrievable efficiently as one of the big search engine companies provide for us, then that's where the real power of discovery is. Because connecting the search terms, sort of the user interests areas to the particular data granule, the particular webpage, if you want to think the Web example or the particular document if you're talking about document library. Or a particular customer type of segment if that's your space.

And semantics gives you that sort of knowledge layering on top of just a word search. If you're searching for a particular type of thing, understanding that a member of a class of such things can have a certain relationship to other things. Even include that sort of relationship information and that's a class hierarchy information to find things that are similar to what you're looking for. Or sometimes even the exact opposite of what you're looking for, because that in a way gives you sort of additional core of understanding. Well, probably something that's opposite of this.

Eric: Yeah.

Kirk: So actually understand this. I can see something that's opposite of this. And so the semantic layer is a valuable component that's frequently missing and it's interesting now that this would come up here in this context. Because I've taught a graduate course in database, data mining, learning from data, data science, whatever you want to call it for over a decade; and one of my units in this semester-long course is on semantics and ontology. And frequently my students would look at me like, what does this have to do with what we're talking about? And of course at the end, I think we do understand that putting that data in some kind of a knowledge framework. So that, just for example, I'm looking for information about a particular customer behavior, understanding that that behavior occurs, that's what the people buy at a sporting event. What kind of products do I offer to my customers when I notice on their social media - on Twitter or Facebook - that they say they're going to a sporting event like football, baseball, hockey, World Cup, whatever it might be.

Okay, so sporting event. So they say they're going to, let's say, a baseball game. Okay, I understand that baseball is a sporting event. I understand that's usually a social and you go with people. I understand that it's usually in an outdoor space. I mean, understanding all those contextual features, it enables sort of, more powerful, sort of, segmentation of the customer involved and your sort of personalization of the experience that you're giving them when, for example, they're interacting with your space through a mobile app while they're sitting in a stadium.

So all that kind of stuff just brings so much more power and discovery potential to the data in that sort of indexing idea of indexing data granules by their semantic place and the knowledge space is really pretty significant. And I was really impressed that came out today. I think it's sort of a fundamental thing to talk.

Eric: Yeah, it sure is. It's very important in the discovery process, it's very important in the classification process. And if you think about it, Java works in classes. It's an object oriented, I guess, more or less, you could say form of programming and Java works in classes. So if you're actually designing software, this whole concept of trying to find new classes is actually pretty important stuff in terms of the functionality you're trying to deliver. Because especially in this new wild, wooly world of big data where you have so much Java out there running so many of these different applications, you know there are 87, 000 ways or more to get anything done with a computer, to get any kind of bit of functionality done.

One of my running jokes when people say, "Oh, you can build a data warehouse using NoSQL." I'm like, "well, you could, yeah, that's true. You could also build a data warehouse using Microsoft Word." It's not the best idea, it's not going to perform very well but you can actually do it. So the key is you have to find the best way to do something.

Go ahead.

Kirk: Let me just respond to that. It's interesting you mentioned the Java class example which didn't come into my mind until you said it. One of the aspects of Java and classes and that sort of object orientation is that there are methods that bind to specific classes. And this is really the sort of a message that I was trying to send in my presentation and that once you understand some of these data granules - these knowledge nuggets, these tags, these annotations and these semantic labels - then you can bind a method to that. They basically have this reaction or this response and have your system provide this sort of automated, proactive response to this thing the next time that we see it in the data stream.

So that concept of binding actions and methods to specific class is really one of the powers of automated real-time analytics. And I think that you sort of hit on something.

Eric: Good, good, good. Well, this is good stuff. So let's see, Will, I want to hand it back to you and actually throw a question to you from the audience. We got a few of those in here too. And folks, we're going long because we want to get some of these great concepts in these good questions.

So let me throw a question over to you from one of the audience numbers who's saying, "I'm not really seeing how business intelligence is distinguishing cause and effect." In other words, as the systems are making decisions based on observable information, how do they develop new models to learn more about the world? It's an interesting point so I'm hearing a cause-and-effect correlation here, root cause analysis, and that's some of that sort of higher-end stuff in the analytics that you guys talk about as opposed to traditional BI, which is really just kind of reporting and kind of understanding what happened. And of course, your whole direction, just looking at your slide here, is moving toward that predictive capability toward making those decisions or at least making those recommendations, right? So the idea is that you guys are trying to service the whole range of what's going on and you're understanding that the key, the real magic, is in the analytical goal component there on the right.

Will: Absolutely. I think that question is somewhat peering into the future, in the sense that data science, as I mentioned before, we saw the slide with the requirements of the data scientist; it's a pretty challenging role for someone to be in. They have to have that rich knowledge of statistics and science. You need to have the domain knowledge to apply your mathematical knowledge to the domains. So what we're seeing today is there aren't these out-of-the-box predictive tools that a business user, like, could pull up in Excel and automatically predict their future, right?

It does require that advanced knowledge in technology at this stage. Now someday in the future, it may be that some of these systems, these scale-out systems become sentient and start doing some wild stuff. But I would say at this stage, you still have to have a data scientist in the middle to continue to build models, not these models. These predictive models around data mining and such are highly tuned in and built by the data scientist. They're not generated on their own, if you know what I mean.

Eric: Yeah, exactly. That's exactly right. And one of my lines is "Machines don't lie, at least not yet."

Will: Not yet, exactly.

Eric: I did read an article - I have to write something about this - about some experiment that was done at a university where they said that these computer programs learned to lie, but I got to tell you, I don't really believe it. We'll do some research on that, folks.

And for the last comment, so Robin I'll bring you back in to take a look at this WebAction platform, because this is very interesting. This is what I love about a whole space is that you get such different perspectives and different angles taken by the various vendors to serve very specific needs. And I love this format for our show because we got four really interesting vendors that are, frankly, not really stepping on each others' toes at all. Because we're all doing different bits and pieces of the same overall need which is to use analytics, to get stuff done.

But I just want to get your perspective on this specific platform and their architecture. How they're going about doing things. I find it pretty compelling. Hva tror du?

Robin: Well, I mean, it's pointed at extremely fast results from streaming data and as search, you have to architect for that. I mean, you're not going to get away with doing anything, amateurish, as we got any of that stuff. I hear this is extremely interesting and I think that one of the things that we witnessed over the past; I mean I think you and I, our jaw has been dropping more and more over the past couple of years as we saw more and more stuff emerge that was just like extraordinarily fast, extraordinarily smart and pretty much unprecedented.

This is obviously, WebAction, this isn't its first rodeo, so to speak. It's actually it's been out there taking names to a certain extent. So I don't see but supposed we should be surprised that the architecture is fairly switched but it surely is.

Eric: Well, I'll tell you what, folks. We burned through a solid 82 minutes here. I mean, thank you to all those folks who have been listening the whole time. If you have any questions that were not answered, don't be shy, send an email to yours truly. We should have an email from me lying around somewhere. And a big, big thank you to both our presenters today, to Dr. Kirk Borne and to Dr. Robin Bloor.

Kirk, I'd like to further explore some of that semantic stuff with you, perhaps in a future webcast. Because I do think that we're at the beginning of a very new and interesting stage now. What we're going to be able to leverage a lot of the ideas that the people have and make them happen much more easily because, guess what, the software is getting less expensive, I should say. It's getting more usable and we're just getting all this data from all these different sources. And I think it's going to be a very interesting and fascinating journey over the next few years as we really dig into what this stuff can do and how can it improve our businesses.

So big thank you to Techopedia as well and, of course, to our sponsors - Pentaho, WebAction, MarkLogic and Treasure Data. And folks, wow, with that we're going to conclude, but thank you so much for your time and attention. We'll catch you in about a month and a half for the next show. And of course, the briefing room keeps on going; radio keeps on going; all our other webcast series keep on rocking and rolling, folks. Tusen takk. We'll catch you next time. Ha det.