Hvordan kan analytics forbedre forretningen? - Teknisk transkription af episode 2

Redaktørens note: Dette er en udskrift af en af ​​vores tidligere webcasts. Den næste episode kommer hurtigt, klik her for at registrere.

Eric Kavanagh: Mine damer og herrer, hej og velkommen igen til afsnit 2 af TechWise. Ja, det er faktisk tid til at få kloge mennesker! Jeg har en flok rigtig smarte mennesker på banen i dag for at hjælpe os i denne bestræbelse. Jeg hedder selvfølgelig Eric Kavanagh. Jeg vil være din vært, din moderator, til denne lyn-runde session. Vi har meget indhold her, folkens. Vi har nogle store navne i branchen, der har været analytikere i vores rum og fire af de mest interessante leverandører. Så vi vil have en masse god handling på opkaldet i dag. Og selvfølgelig spiller du derude i publikum en betydelig rolle i at stille spørgsmål.

Så endnu en gang er showet TechWise, og emnet i dag er "Hvordan kan Analytics forbedre forretningen?" Det er klart, det er et varmt emne, hvor det vil prøve at forstå de forskellige slags analyser, du kan gøre, og hvordan det kan forbedre dine operationer, fordi det er det, det handler om i slutningen af ​​dagen.

Så du kan se mig selv der oppe, det er din virkelig. Dr. Kirk Borne, en god ven fra George Mason University. Han er en dataforsker med en enorm mængde erfaring, meget dyb ekspertise inden for dette rum og data mining og big data og al den slags sjove ting. Og selvfølgelig har vi vores helt egen Dr. Robin Bloor, Chief Analyst her i Bloor Group. Hvem træner som aktuar for mange, mange år siden. Og han har virkelig været fokuseret på dette hele big data-rum og det analytiske rum ganske intenst i det sidste halve årti. Det er næsten fem år siden vi lancerede Bloor Group i sig selv. Så tiden flyver, når du har det sjovt.

Vi vil også høre fra Will Gorman, chefarkitekt for Pentaho; Steve Wilkes, CCO for WebAction; Frank Sanders, teknisk direktør hos MarkLogic; og Hannah Smalltree, direktør hos Treasure Data. Så som jeg har sagt, det er meget indhold.

Så hvordan kan analytics hjælpe din virksomhed? Hvordan kan det ikke hjælpe din virksomhed helt ærligt? Der er forskellige måder, hvorpå analytics kan bruges til at gøre ting, der forbedrer din organisation.

Så strømline operationer. Det er en, du ikke hører så meget om, som du gør om ting som markedsføring eller skaffer indtægter eller endda identificere muligheder. Men at strømline dine operationer er dette virkelig, virkelig magtfulde ting, som du kan gøre for din organisation, fordi du kan identificere steder, hvor du enten kan outsource noget, eller du kan tilføje data til en bestemt proces, f.eks. Og det kan strømline det ved ikke at kræve nogen at hente telefonen for at ringe eller nogen til at e-maile. Der er så mange forskellige måder, du kan strømline dine operationer. Og alt dette hjælper virkelig med at nedbringe dine omkostninger, ikke? Det er nøglen, det reducerer omkostningerne. Men det giver dig også mulighed for bedre at betjene dine kunder.

Og hvis du tænker over, hvor utålmodige mennesker er blevet, og jeg ser dette hver eneste dag med hensyn til, hvordan folk interagerer online, selv med vores shows, tjenesteudbydere, som vi bruger. Folk har tålmodighed, opmærksomhedsspændet, bliver kortere og kortere med dagen. Og hvad det betyder er, at du som organisation skal svare hurtigere og hurtigere perioder for at være i stand til at tilfredsstille dine kunder.

Så for eksempel, hvis nogen er på dit webcast-websted eller surfer rundt for at finde noget, hvis de bliver frustrerede og de forlader, ja, har du måske lige mistet en kunde. Og afhængigt af hvor meget du debiterer for dit produkt eller din service, og måske er det en stor aftale. Så bundlinjen er, at strømlineoperationer, synes jeg, er et af de hotteste rum til anvendelse af analyser. Og du gør det ved at se på tallene, ved at knuse dataene, ved at finde ud af for eksempel "Hej, hvorfor mister vi så mange mennesker på denne side af vores hjemmeside?" "Hvorfor får vi nogle af disse telefonopkald lige nu?"

Og jo mere realtid du kan reagere på den slags ting, jo større er chancerne for at du kommer til at komme oven på situationen og gøre noget ved det, før det er for sent. Fordi der er det tidsvindue, hvor nogen bliver oprørt over noget, de er utilfredse, eller de prøver at finde noget, men de er frustrerede; du fik et vindue af muligheder der for at nå ud til dem, at gribe dem, at interagere med denne kunde. Og hvis du gør det på den rigtige måde med de rigtige data eller dejlige kundebillede - at forstå, hvem der er denne kunde, hvad er deres rentabilitet, hvad er deres præferencer - hvis du virkelig kan få et greb om det, skal du gøre et godt stykke arbejde med at holde fast på dine kunder og få nye kunder. Og det er hvad det handler om.

Så med det overleverer jeg det faktisk til Kirk Borne, en af ​​vores datavidenskabsmænd på opkaldet i dag. Og de er temmelig sjældne i disse dage, folkens. Vi har mindst to af dem i det mindste på opkaldet, så det er big deal. Med det, Kirk, overlader jeg det til dig for at tale om analyse og hvordan det hjælper forretningen. Gå efter det.

Dr. Kirk Borne: Nå, tak, Eric. Kan du høre mig?

Eric: Det er fint, gå videre.

Dr. Kirk: Okay, god. Jeg vil bare dele, hvis jeg taler i fem minutter, og folk vinker deres hænder på mig. Så åbningsbemærkningerne, Eric, at du virkelig bundet dig til dette emne, jeg vil tale kort om i de næste par minutter, hvilket er denne brug af big data og analyser til data til beslutninger om at støtte, der. Den kommentar, du kom med om operationel strømlining, for mig, falder den slags ind i dette begreb af driftsanalyse, hvor du næsten kan se i enhver applikation overalt i verden, uanset om det er en videnskabsprogram, en virksomhed, cybersikkerhed og retshåndhævelser og regering, sundhedsydelser. Ethvert antal steder, hvor vi har en strøm af data, og vi tager en slags reaktion eller beslutning som reaktion på begivenheder og alarmer og adfærd, som vi ser i den datastrøm.

Og en af ​​de ting, som jeg gerne vil tale om i dag, er sådan, hvordan man udtrækker viden og indsigt fra big data for at komme til det punkt, hvor vi rent faktisk kan tage beslutninger om at tage handlinger. Og ofte taler vi om dette i en automatiseringskontekst. Og i dag vil jeg blande automatiseringen med den menneskelige analytiker i løkken. Så med dette mener jeg, mens forretningsanalytikeren spiller en vigtig rolle her med hensyn til væddemål, kvalificering, validering af specifikke handlinger eller maskinlæringsregler, som vi uddrager fra dataene. Men hvis vi kommer til et punkt, hvor vi stort set er overbeviste om de forretningsregler, vi har uddraget, og mekanismerne til at advare os, er gyldige, kan vi stort set overføre dette til en automatiseret proces. Vi gør faktisk den operationelle strømline, som Eric talte om.

Så jeg spiller lidt på ord her, men jeg håber, at hvis det fungerer for dig, talte jeg om D2D-udfordringen. Og D2D, ikke bare data beslutningerne i alle sammenhænge, ​​vi ser på dette i form af bunden af ​​dette lysbillede forhåbentlig kan du se det, foretage opdagelser og øge indtægter dollars fra vores analyserørledninger.

Så i denne sammenhæng har jeg faktisk denne rolle som marketingmedarbejder for mig selv nu, hvor jeg arbejder med, og det er; den første ting, du vil gøre, er at karakterisere dine data, udtrække funktionerne, udtrække egenskaberne for dine kunder eller hvilken enhed det er, du sporer i dit rum. Måske er det en patient i et sundhedsanalytisk miljø. Måske er det en webbruger, hvis du ser på en slags cybersikkerhedsspørgsmål. Men karakteriser og udpak karakteristika og træk derefter ud nogle kontekster om den enkelte, om den enhed. Og så samler du de stykker, du lige har oprettet, og placerer dem i en slags samling, hvorfra du derefter kan anvende maskinlæringsalgoritmer.

Årsagen til, at jeg siger det på denne måde, er, at lad os bare sige, at du har et overvågningskamera i en lufthavn. Videoen i sig selv er en enorm, stor lydstyrke, og den er også meget ustruktureret. Men du kan udtrække fra videoovervågning, ansigtsbiometri og identificere enkeltpersoner i overvågningskameraerne. Så for eksempel i en lufthavn kan du identificere bestemte personer, du kan spore dem gennem lufthavnen ved at krydse identifikationen af ​​den samme person i flere overvågningskameraer. I og med at de udtrukne biometriske funktioner, som du virkelig gruver og sporer, ikke er selve den detaljerede video. Men når du først har fået disse udtrækninger, kan du anvende maskinlæringsregler og analyser for at tage beslutninger om, hvorvidt du er nødt til at tage en handling i et bestemt tilfælde, eller der skete noget forkert, eller noget, som du har mulighed for at komme med et tilbud. Hvis du for eksempel er, hvis du har en butik i lufthavnen, og du ser denne kunde komme din vej, og du ved fra andre oplysninger om denne kunde, at han måske virkelig blev interesseret i at købe ting i den toldfri butik eller sådan noget, kom med dette tilbud.

Så hvad slags ting ville jeg mene med karakterisering og potentialisering? Med karakterisering mener jeg igen at udtrække funktioner og egenskaber i dataene. Og dette kan enten være maskingenereret, så kan dets algoritmer faktisk udtrække for eksempel biometriske signaturer fra video- eller sentimentanalyse. Du kan udtrække kundens stemning gennem online anmeldelser eller sociale medier. Nogle af disse ting kan være menneskeskabte, så mennesket, forretningsanalytikeren, kan udtrække yderligere funktioner, som jeg viser i det næste lysbillede.

Nogle af disse kan være crowddsourced. Og ved crowddsourced er der mange forskellige måder, du kan tænke på. Men meget enkelt, for eksempel kommer dine brugere til dit websted, og de sætter søgeord, nøgleord, og de ender på en bestemt side og tilbringer faktisk tid der på den side. At de faktisk i det mindste forstår, at de enten ser, gennemser eller klikker på tingene på den side. Det, der siger til dig, er, at nøgleordet, som de indtastede i starten, er deskriptoren for den side, fordi det landede kunden på den side, de forventede. Og så kan du tilføje det ekstra stykke information, det vil sige kunder, der bruger dette nøgleord, der faktisk identificerede denne webside inden for vores informationsarkitektur som det sted, hvor indholdet matcher dette nøgleord.

Og derfor er crowddsourcing et andet aspekt, som folk undertiden glemmer, den slags sporing af dine kunders brødkrumme, så at sige; hvordan bevæger de sig gennem deres plads, uanset om det er en online-ejendom eller en fast ejendom. Og brug derefter den slags sti de, som kunden tager som yderligere oplysninger om de ting, vi ser på.

Så jeg vil sige, at mennesker-genererede ting, eller maskingenererede, endte med at have en sammenhæng i form af annotering eller mærkning af specifikke datagranuler eller enheder. Uanset om disse enheder er patienter i hospitaler, kunder eller hvad som helst. Og så er der forskellige typer tagging og kommentarer. Noget af det handler om selve dataene. Det er en af ​​tingene, hvilken type information, hvilken type information, hvad er funktionerne, figurerne, måske teksturer og mønstre, afvigelse, ikke-afvigende adfærd. Og så træk nogle semantik ud, det vil sige, hvordan forholder det sig til andre ting, som jeg ved, eller at denne kunde er en elektronik-kunde. Denne kunde er en beklædningskunde. Eller denne kunde kan lide at købe musik.

Så at identificere nogle semantik om det, disse kunder, der kan lide musik, har en tendens til at lide underholdning. Måske kunne vi tilbyde dem noget andet underholdningsejendom. Så forstå semantikken og også en vis oprindelse, som dybest set siger: hvor kom dette fra, hvem leverede denne påstand, hvad tid, hvilken dato, under hvilken omstændighed?

Så når du først har alle disse kommentarer og karakteriseringer, skal du tilføje til det, så er det næste trin, som er konteksten, slags hvem, hvad, hvornår, hvor og hvorfor. Hvem er brugeren? Hvad var den kanal, de kom ind på? Hvad var kilden til informationen? Hvilken genanvendelse har vi set i netop dette informations- eller dataprodukt? Og hvad er, det er slags værdi i forretningsprocessen? Og saml derefter disse ting og administrer dem, og hjælp faktisk med at oprette database, hvis du vil tænke på det på den måde. Gør dem søgbare, genanvendelige af andre forretningsanalytikere eller ved en automatiseret proces, der næste gang jeg ser disse sæt funktioner, kan systemet udføre denne automatiske handling. Og så kommer vi til den slags operationelle analytiske effektivitet, men jo mere vi samler nyttig, omfattende information og derefter sammenlægger den til disse brugssager.

Vi kommer i gang. Vi udfører dataanalyse. Vi ser efter interessante mønstre, overraskelser, nye outliers, anomalier. Vi ser efter de nye klasser og segmenter i befolkningen. Vi ser efter foreninger og sammenhænge og forbindelser mellem de forskellige enheder. Og så bruger vi alt det til at drive vores opdagelse, beslutning og dollar-beslutningsproces.

Så der igen, her har vi den sidste dataslide, jeg har, er bare dybest set at opsummere, holde forretningsanalytikeren i løkken, igen, du trækker ikke det menneskelige ud, og det er alt vigtigt at holde det menneske derinde.

Så disse funktioner, de leveres alle af maskiner eller menneskelige analytikere eller endda crowddsourcing. Vi anvender denne kombination af ting for at forbedre vores træningssæt til vores modeller og ender med mere nøjagtige forudsigelsesmodeller, færre falske positiver og negativer, mere effektiv opførsel, mere effektive indgange med vores kunder eller hvem som helst.

Så til sidst på dagen kombinerer vi virkelig bare maskinlæring og big data med denne kraft af menneskelig kognition, og det er her, den slags tagging-annotationsstykke kommer ind. Og det kan føre gennem visualisering og visuel analyse-type værktøjer eller fordybende datamiljøer eller crowddsourcing. Og til sidst på dagen genererer vores opdagelse, indsigt og D2D hvad dette virkelig gør. Og det er mine kommentarer, så tak for at have hørt.

Eric: Hej, det lyder godt, og lad mig gå foran og overdrage nøglerne til Dr. Robin Bloor for også at give sit perspektiv. Ja, jeg kan godt lide at høre dig kommentere om den strømlining af operationskonceptet, og du taler om operationel analyse. Jeg tror, ​​det er et stort område, der skal udforskes ganske grundigt. Og jeg antager, virkelig hurtigt før Robin, jeg bringer dig tilbage, Kirk. Det kræver, at du har noget ret betydeligt samarbejde mellem forskellige spillere i virksomheden, ikke? Du skal tale med driftsfolk; skal du hente dine tekniske folk. Undertiden får du dine marketingfolk eller dine webgrænsefolk. Disse er typisk forskellige grupper. Har du nogen bedste praksis eller forslag til, hvordan man slags får alle til at sætte deres hud i spillet?

Dr. Kirk: Nå, jeg tror, ​​at dette kommer med forretningskulturen for samarbejde. Faktisk taler jeg om de tre C'er i form af analytisk kultur. Den ene er kreativitet; en anden er nysgerrighed, og den tredje er samarbejde. Så du vil have kreative, seriøse mennesker, men du skal også få disse mennesker til at samarbejde. Og det starter virkelig fra toppen, den slags opbygning af denne kultur med mennesker, der åbent bør dele og arbejde sammen mod de fælles mål for virksomheden.

Eric: Det giver mening. Og du er virkelig nødt til at få et godt lederskab i toppen for at få det til. Så lad os gå videre og overlevere det til Dr. Bloor. Robin, ordet er dit.

Dr. Robin Bloor: Okay. Tak for det intro, Eric. Okay, hvordan disse panorerer, disse viser, fordi vi har to analytikere; Jeg får se analytikerens præsentation, som de andre fyre ikke gør. Jeg vidste, hvad Kirk ville sige, og jeg går bare en helt anden vinkel, så vi ikke går for meget over hinanden.

Så det, jeg faktisk taler om eller har til hensigt at tale om her, er dataanalytikerens rolle kontra forretningsanalytikerens rolle. Og den måde, jeg karakteriserer det på, tunge-i-kind til en vis grad, er slags Jekyll og Hyde-ting. Forskellen er specifikt dataforskerne, i det mindste i teorien, ved hvad de laver. Mens forretningsanalytikere ikke er det, er okay med den måde, matematikken fungerer på, hvad man kan stole på, og hvad man ikke kan stole på.

Så lad os bare komme til grunden til, at vi gør dette, grunden til, at dataanalyse pludselig er blevet en stor del bortset fra det faktum, at vi faktisk kan analysere meget store mængder data og hente data fra uden for organisationen; er det betaler. Den måde, jeg ser på dette - og jeg tror, ​​dette kun bliver en sag, men jeg synes bestemt, at det er en sag - dataanalyse er virkelig F & U-erhverv. Hvad du faktisk gør på en eller anden måde med dataanalyse er, at du ser på en forretningsproces i en slags, eller om det er interaktionen med en kunde, uanset om det er med den måde, din detailhandling har, den måde, du implementerer dine butikker. Det betyder ikke noget, hvad problemet er. Du ser på en given forretningsproces, og du prøver at forbedre den.

Resultatet af vellykket forskning og udvikling er en ændringsproces. Og du kan tænke på fremstilling, hvis du vil, som et sædvanligt eksempel på dette. For i fremstillingen samler folk information om alt for at prøve og forbedre produktionsprocessen. Men jeg tror, ​​hvad der er sket, eller hvad der sker ved big data, alt dette anvendes nu til alle virksomheder af enhver art på enhver måde, som enhver kan tænke på. Så stort set enhver forretningsproces er til undersøgelse, hvis du kan indsamle data om den.

Så det er en ting. Hvis du kan lide det, er det ved spørgsmålet om dataanalyse. Hvad kan dataanalyse gøre for virksomheden? Det kan godt ændre virksomheden fuldstændigt.

Dette særlige diagram, som jeg ikke vil beskrive i nogen dybde, men dette er et diagram, som vi kom frem til som kulminationen på det forskningsprojekt, vi gjorde i de første seks måneder af dette år. Dette er en måde at repræsentere en big data-arkitektur på. Og en række ting, der er værd at påpege, før jeg går videre til det næste lysbillede. Der er to datastrømme her. Den ene er en datastrøm i realtid, der går langs toppen af ​​diagrammet. Den anden er en langsommere datastrøm, der går langs bunden af ​​diagrammet.

Se i bunden af ​​diagrammet. Vi har Hadoop som et datareservoir. Vi har forskellige databaser. Vi har der en hel data der med en hel masse aktiviteter, der foregår på det, hvoraf det meste er analytisk aktivitet.

Det punkt, jeg gør her, og det eneste punkt, jeg virkelig ønsker at gøre her, er, at teknologien er hård. Det er ikke enkelt. Det er ikke nemt. Det er ikke noget, som enhver, der er ny til spillet, bare kan sammensætte. Dette er ret kompliceret. Og hvis du vil instrumentere en virksomhed til at udføre pålidelig analyse på tværs af alle disse processer, er det ikke noget, der vil ske specifikt hurtigt. Det vil kræve en masse teknologi, der skal føjes til blandingen.

Okay. Spørgsmålet, hvad er en datavidenskabsmand, kunne jeg hævde at være datavidenskabsmand, fordi jeg faktisk blev uddannet i statistik, før jeg nogensinde blev uddannet i computing. Og jeg udførte et aktuarmæssigt job i en periode, så jeg kender den måde, en virksomhed organiserer, statistisk analyse, også for at køre selv. Dette er ikke en triviel ting. Og der er en frygtelig masse bedste praksis involveret både på den menneskelige side og på teknologisiden.

Så når jeg stiller spørgsmålet "hvad er en dataforsker", har jeg lagt Frankenstein-billedet simpelthen fordi det er en kombination af ting, der skal strikkes sammen. Der er projektledelse involveret. Der er dyb forståelse i statistikker. Der er domænevirksomhedskompetence, som nødvendigvis mere er et problem for en forretningsanalytiker end dataforskeren. Der er erfaring eller behovet for at forstå dataarkitektur og være i stand til at bygge dataarkitekt, og der er software-engineering involveret. Med andre ord er det sandsynligvis et hold. Det er sandsynligvis ikke et individ. Og det betyder, at det sandsynligvis er en afdeling, der skal organiseres, og dens organisation skal tænkes forholdsvis omfattende.

At smide i blandingen faktumet med maskinlæring. Vi kunne ikke gøre, jeg mener, maskinlæring er ikke nyt i den forstand, at de fleste af de statistiske teknikker, der bruges i maskinlæring, har været kendt i årtier. Der er et par nye ting, jeg mener, at neurale netværk er relativt nye, jeg tror, ​​de er kun omkring 20 år gamle, så nogle af dem er relativt nye. Men problemet med maskinlæring var, at vi faktisk ikke havde computerkraften til at gøre det. Og hvad der er sket bortset fra alt andet, er, at computerens strøm nu er på plads. Og det betyder meget, hvad vi siger, dataforskere har gjort før i form af modelleringssituationer, prøveudtagning af data og derefter styring af dem for at fremstille en dybere analyse af dataene. Faktisk kan vi bare kaste computerkraft på det i nogle tilfælde. Vælg bare maskinlæringsalgoritmer, smid dem på dataene og se hvad der kommer ud. Og det er noget, som en forretningsanalytiker kan gøre, ikke? Men forretningsanalytikeren skal forstå, hvad de laver. Jeg mener, det er virkelig problemet mere end noget andet.

Dette er bare at vide mere om forretning fra dens data end på nogen anden måde. Einstein sagde ikke det, det sagde jeg. Jeg lagde hans billede op for troværdighed. Men situationen begynder faktisk at udvikle sig, hvor teknologien, hvis den anvendes korrekt, og matematikken, hvis den anvendes korrekt, vil være i stand til at drive en virksomhed som ethvert individ. Vi har set dette med IBM. Først og fremmest kunne det slå de bedste fyre ved skak, og derefter kunne det slå de bedste fyre på Jeopardy; men til sidst vil vi være i stand til at slå de bedste fyre ved at drive et firma. Statistikkerne vil til sidst sejre. Og det er svært at se, hvordan det ikke sker, det er bare ikke sket endnu.

Så hvad jeg siger, og dette er en slags fuldstændig besked fra min præsentation, er disse to emner af virksomheden. Den første er, kan du få teknologien rigtigt? Kan du få teknologien til at fungere for det team, der rent faktisk vil være i stand til at præsidere det og få fordele for virksomheden? Og så for det andet, kan du få folket ret? Og begge disse ting er problemer. Og det er problemer, der ikke er, til dette tidspunkt, siger de, er løst.

Okay Eric, jeg giver det tilbage til dig. Eller jeg måske videregive den til Will.

Eric: Faktisk, ja. Tak, Will Gorman. Ja, der går du, Will. Så lad os se. Lad mig give dig nøglen til WebEx. Hvad har du så sket? Pentaho, naturligvis, I har været i et stykke tid og open source BIs slags, hvor du startede. Men du har meget mere, end du plejede at have, så lad os se, hvad du fik i disse dage til analyse.

Will Gorman: Absolut. Hej allesammen! Jeg hedder Will Gorman. Jeg er chefarkitekt i Pentaho. For dem af jer, der ikke har hørt om os, nævnte jeg lige Pentaho er et big dataintegrations- og analysefirma. Vi har været i branchen i ti år. Vores produkter har udviklet sig side om side med big data-samfundet og starter som en open source-platform til dataintegration og -analyse, innovativt med teknologi som Hadoop og NoSQL, selv før kommercielle enheder blev dannet omkring disse tech. Og nu har vi over 1500 kommercielle kunder og mange flere produktionsaftaler som et resultat af vores innovation omkring open source.

Vores arkitektur er yderst integrerbar og udvidelig, specialbygget til at være fleksibel, da big data-teknologi især udvikler sig i et meget hurtigt tempo. Pentaho tilbyder tre vigtigste produktområder er, der arbejder sammen for at tackle big data analytics brugssager.

Det første produkt i omfanget af vores arkitektur er Pentaho Data Integration, der er rettet mod datateknolog og dataingeniører. Dette produkt tilbyder en visuel, træk-og-slip-oplevelse til at definere datapipelelinjer og processer til orkestrering af data i big data-miljøer og traditionelle miljøer også. Dette produkt er en let, metadatabase, dataintegrationsplatform bygget på Java og kan distribueres som en proces inden for MapReduce eller YARN eller Storm og mange andre batch- og real-time platforme.

Vores andet produktområde er omkring visuel analyse. Med denne teknologi kan organisationer og OEM'er tilbyde en rig træk-og-slip-visualiserings- og analyseroplevelse til forretningsanalytikere og forretningsbrugere af moderne browsere og tablets, hvilket tillader ad hoc-oprettelse af rapporter og dashboards. Samt præsentation af pixel-perfekt dashboarding og rapporter.

Vores tredje produktområde fokuserer på forudsigelig analyse målrettet mod dataforskere, maskinlæringsalgoritmer. Som nævnt før, som neurale netværk og sådan, kan indarbejdes i et datatransformationsmiljø, hvilket tillader dataforskere at gå fra modellering til produktionsmiljø, hvilket giver adgang til at forudsige, og det kan påvirke forretningsprocesser meget øjeblikkeligt, meget hurtigt.

Alle disse produkter er tæt integreret i en enkelt smidig oplevelse og giver vores erhvervskunder den fleksibilitet, de har brug for for at løse deres forretningsproblemer. Vi ser et hurtigt voksende landskab af big data i traditionelle teknologier. Alt, hvad vi hører fra nogle virksomheder i big data-rummet, at EDW er tæt på en ende. Det, vi ser hos vores virksomhedskunder, er faktisk, at de er nødt til at introducere big data i eksisterende forretnings- og it-processer og ikke erstatte disse processer.

Dette enkle diagram viser det punkt i arkitekturen, som vi ofte ser, som er en type EDW-implementeringsarkitektur med dataintegration og BI-brugssager. Nu ligner dette diagram Robin's dias om big data-arkitektur, det inkluderer realtids- og historiske data. Når nye datakilder og realtidskrav dukker op, ser vi big data som en yderligere del af den overordnede IT-arkitektur. Disse nye datakilder inkluderer maskingenererede data, ustrukturerede data, standardvolumen og hastighed og forskellige krav, som vi hører om i big data; de passer ikke ind i traditionelle EDW-processer. Pentaho arbejder tæt sammen med Hadoop og NoSQL for at forenkle indtagelse, databehandling og visualisering af disse data samt blanding af disse data med traditionelle kilder for at give kunderne et fuldt overblik over deres datamiljø. Vi gør dette på en styret måde, så IT kan tilbyde en komplet analyseløsning til deres branche.

Afslutningsvis vil jeg gerne fremhæve vores filosofi omkring big data-analyse og integration; vi mener, at disse teknologier fungerer bedre sammen med en samlet arkitektur, hvilket muliggør en række brugssager, som ellers ikke ville være mulige. Vores kunders datamiljøer er mere end bare big data, Hadoop og NoSQL. Alle data er fair spil. Og store datakilder skal være tilgængelige og arbejde sammen for at påvirke forretningsværdien.

Endelig mener vi, at for at løse disse forretningsproblemer i virksomheder meget effektivt gennem data, er IT og forretningsområder nødt til at arbejde sammen om en styret, blandet tilgang til big data-analyse. Nå tak, fordi du har givet os tid til at tale, Eric.

Eric: Du satser. Nej, det er gode ting. Jeg vil vende tilbage til den side af din arkitektur, når vi kommer til spørgsmål og spørgsmål. Så lad os gå gennem resten af ​​præsentationen og tak meget for det. I fyre har bestemt bevæget sig hurtigt de sidste par år, det må jeg sige helt sikkert.

Så Steve, lad mig gå foran og overgive det til dig. Og bare klik der på pil ned og gå efter det. Så Steve, jeg giver dig nøglerne. Steve Wilkes, bare klik på den fjerneste pil ned der på dit tastatur.

Steve Wilkes: Der går vi.

Eric: Der går du.

Steve: Det er en god introduktion, du har givet mig.

Eric: Ja.

Steve: Så jeg er Steve Wilkes. Jeg er CCO på WebAction. Vi har kun eksisteret i de sidste par år, og vi har bestemt også bevæget os hurtigt siden da. WebAction er en real-time big data-analyse platform. Eric nævnte tidligere, slags, hvor vigtig realtid er, og hvor realtid dine applikationer får. Vores platform er designet til at bygge apps i realtid. Og for at aktivere den næste generation af datadrevne apps, der kan bygges trinvis på, og for at give folk mulighed for at bygge dashboards fra de data, der genereres fra disse apps, men som fokuserer på realtid.

Vores platform er faktisk en komplet ende-til-ende-platform, der gør alt fra dataindsamling, databehandling, hele vejen til datavisualisering. Og gør det muligt for flere forskellige typer mennesker i vores virksomhed at arbejde sammen om at skabe ægte apps i realtid, hvilket giver dem indsigt i ting, der sker i deres virksomhed, som de skete.

Og dette er lidt anderledes end hvad de fleste har set i big data, så den traditionelle tilgang - ja, traditionel de sidste par år - med Big Data har været at fange den fra en hel masse forskellige kilder og derefter hældes det op i et stort reservoir eller sø eller hvad du end vil kalde det. Og behandle det derefter, når du har brug for at køre en forespørgsel om det; at køre storstilet historisk analyse eller endda bare ad hoc-forespørgsel efter store mængder data. Det fungerer nu i visse tilfælde. Men hvis du ønsker at være proaktiv i din virksomhed, hvis du rent faktisk vil have at vide, hvad der foregår snarere end at finde ud af, når noget gik galt slags mod slutningen af ​​dagen eller ugens slutning, er du virkelig nødt til at flytte til realtid.

Og det skifter lidt rundt. Det flytter behandlingen til midten. Så effektivt tager du strømme af store mængder data, der genereres kontinuerligt inden for virksomheden, og du behandler dem, når du får dem. Og fordi du behandler det, som du får det, behøver du ikke opbevare alt. Du kan bare gemme de vigtige oplysninger eller de ting, du har brug for for at huske, at der faktisk skete. Så hvis du sporer GPS-placeringen af ​​køretøjer, der bevæger sig ned ad vejen, er du ligeglad med hvor de er hvert sekund, du behøver ikke at gemme, hvor de er hvert sekund. Du skal bare passe på, har de forladt dette sted? Er de ankommet til dette sted? Har de kørt eller ej, motorvejen?

Så det er virkelig vigtigt at overveje, at når flere og flere data genereres, så er de tre Vs. Hastighed bestemmer dybest set, hvor meget data der genereres hver dag. Jo flere data der genereres, jo mere skal du gemme. Og jo mere du skal opbevare, jo længere tid tager det at behandle. Men hvis du kan behandle det, som du får det, får du en rigtig stor fordel, og du kan reagere på det. Du kan få at vide, at tingene sker snarere end at skulle søge efter dem senere.

Så vores platform er designet til at være meget skalerbar. Det har tre større stykker - erhvervelsesstykket, behandlingsstykket og derefter leveringsvisualiseringsdelene på platformen. På overtagelsessiden ser vi ikke kun på maskingenererede logdata som weblogs eller applikationer, der har alle de andre logfiler, der genereres. 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. Tak skal du have.

Eric: Yes, indeed. Godt arbejde. 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. Værsgo.

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. Okay. 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?" Okay.

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. Tage det væk.

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. Hvad synes 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. Mange tak. We'll catch you next time. Hej hej.