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Ēriks Kavanaghs: dāmas un kungi, sveicināti un vēlreiz sveicam TechWise 2. epizodē. Jā, patiešām, ir pienācis laiks iegūt gudrus cilvēkus! Man šodien ir līnijas ķekars patiešām gudru cilvēku, kas mums palīdzēs šajā centienā. Mans vārds, protams, ir Ēriks Kavanaghs. Es būšu jūsu saimnieks, jūsu moderators šai zibenīgajai sesijai. Mums šeit ir daudz satura, ļaudis. Mums biznesā ir daži lieli vārdi, kas ir bijuši analītiķi mūsu telpā un četri visinteresantākie pārdevēji. Tāpēc šodien mums būs jāveic daudz labu sarunu. Un, protams, jums, kas atrodas auditorijā, ir būtiska loma jautājumu uzdošanā.
Tātad atkal šovs ir TechWise, un šodien galvenā tēma ir "Kā Analytics var uzlabot biznesu?" Acīmredzot tā ir karsta tēma, kurā mēģinās izprast dažādu veidu analītiku, ko varat veikt, un kā tas var uzlabot jūsu operācijas, jo dienas beigās tas viss ir domāts.
Tātad jūs varat redzēt sevi augšā, tas ir patiesi jūsu. Dr Kirks Borne, labs draugs no Džordža Meisona universitātes. Viņš ir datu zinātnieks ar milzīgu pieredzi, ļoti dziļu kompetenci šajā telpā, datu ieguvi un lieliem datiem, kā arī visu šāda veida izklaidi. Un, protams, mums šeit ir pats Dr. Robins Bloors, Bloor grupas galvenais analītiķis. Kurš apmācīts kā aktuārs pirms daudziem, daudziem gadiem. Un viņš pēdējo desmit gadu laikā ir ļoti koncentrējies uz visu šo lielo datu un analītisko telpu. Ir pagājuši pieci gadi, kopš mēs izveidojām Bloor Group per se. Tātad laiks rit, kad jums ir jautri.
Mēs uzklausīsim arī Pentaho galveno arhitektu Will Gorman; Stīvs Vilks, WebAction CCO; Frenks Sanderss, MarkLogic tehniskais direktors; un Hannah Smalltree, uzņēmuma Treasure Data direktore. Tātad, kā jau teicu, tas ir daudz satura.
Tātad kā analītika var palīdzēt jūsu biznesam? Nu kā, atklāti sakot, tas nevar palīdzēt jūsu biznesam? Ir dažādi veidi, kā analītiku var izmantot, lai veiktu lietas, kas uzlabo jūsu organizāciju.
Tātad racionalizējiet operācijas. Tas ir tāds, par kuru jūs nedzirdat tikpat daudz kā par mārketingu vai ieņēmumu palielināšanu vai pat iespēju noteikšanu. Bet jūsu darbību racionalizēšana ir šī patiešām ļoti spēcīga lieta, ko varat darīt savai organizācijai, jo jūs varat noteikt vietas, kur, piemēram, kaut ko nodot ārpakalpojumiem vai, piemēram, pievienot datus noteiktam procesam. Un tas var to pilnveidot, neliekot kādam paņemt tālruni, lai piezvanītu, vai kādam pa e-pastu. Ir tik daudz dažādu veidu, kā jūs varat pilnveidot savas darbības. Un tas viss patiešām palīdz samazināt jūsu izmaksas, vai ne? Tas ir galvenais, tas samazina izmaksas. Bet tas arī ļauj labāk apkalpot klientus.
Un, ja jūs domājat par to, cik nepacietīgi cilvēki ir kļuvuši, un es to redzu katru dienu attiecībā uz to, kā cilvēki mijiedarbojas tiešsaistē, pat ar mūsu izrādēm, pakalpojumu sniedzējiem, kurus mēs izmantojam. Cilvēku pacietība un uzmanības spēja ar katru dienu kļūst arvien īsāka. Un tas nozīmē, ka jums kā organizācijai ir jāreaģē ātrāk un ātrāk, lai varētu apmierināt savus klientus.
Tā, piemēram, ja kāds ir jūsu tīmekļa apraides vietnē vai pārlūko kaut ko meklējamu, ja viņi sarūgtina un aiziet, iespējams, jūs tikko esat pazaudējis klientu. Un tas atkarīgs no tā, cik daudz jūs maksājat par savu produktu vai pakalpojumu, un varbūt tas ir daudz. Tātad, es uzskatu, ka operāciju racionalizēšana, manuprāt, ir viena no karstākajām vietām analītikas piemērošanai. Un jūs to darāt, apskatot ciparus, sasmalcinot datus, izdomājot, piemēram, "Ei, kāpēc mēs zaudējam tik daudz cilvēku šajā mūsu vietnes lapā?" "Kāpēc mēs šobrīd saņemam dažus no šiem tālruņa zvaniem?"
Un jo reālākā laikā jūs varat reaģēt uz šāda veida lietām, jo lielākas ir iespējas nokļūt situācijā un kaut ko darīt, pirms nav par vēlu. Tā kā ir tas laika logs, kad kāds kaut ko satrauc, viņš ir neapmierināts vai mēģina kaut ko atrast, bet ir neapmierināts; jūs tur saņēmāt iespēju logu, lai uzrunātu viņus, satvertu viņus un sazinātos ar šo klientu. Un, ja jūs to darāt pareizā veidā, izmantojot pareizos datus vai jauku klienta attēlu - saprotot, kurš ir šis klients, kāda ir viņu rentabilitāte, kādas ir viņu vēlmes - ja jūs patiešām varat tikt galā ar to, jūs to darīsit lielisks darbs, lai noturētu klientus un iegūtu jaunus klientus. Un tas ir tas, par ko ir runa.
Līdz ar to es to patiesībā nodošu Kirkam Bornesam, vienam no mūsu šodienas dežūrdaļas zinātniekiem. Viņi, mūsdienās, ir diezgan reti. Mums vismaz divi ir zvana, tāpēc tas ir liels darījums. Līdz ar to, Kirk, es tev to nodošu, lai runātu par analītiku un to, kā tā palīdz biznesam. Dari tā.
Dr Kirk Borne: Nu, liels paldies, Ēriks. Vai tu mani dzirdi?
Ēriks: Tas ir labi, dodieties tālāk.
Dr Kirks: Labi, labi. Es tikai vēlos padalīties, ja runāju piecas minūtes, un cilvēki man vicina rokas. Tātad, ievada piebilde, Ēriks, ka jūs esat patiešām piesaistījies šai tēmai, es tuvāko minūšu laikā īsi runāšu par to, kas ir lielo datu un datu analītika datu izmantošanai lēmumiem, kurus atbalstīt. Jūsu komentārs, ko jūs sniedzāt par darbības pilnveidošanu, manuprāt, ietilpst šādā operatīvās analīzes koncepcijā, kurā gandrīz katrā lietojumprogrammā visā pasaulē varat redzēt, vai tā ir zinātnes lietojumprogramma, bizness, kiberdrošība un likuma izpildes nodrošināšana un valdība, veselības aprūpe. Jebkurš vietu skaits, kur mums ir datu plūsma un mēs kaut kādu reakciju vai lēmumu pieņemam, reaģējot uz notikumiem un brīdinājumiem un izturēšanos, ko mēs redzam šajā datu plūsmā.
Un tāpēc viena no lietām, par kuru es šodien gribētu runāt, ir sava veida veids, kā jūs iegūstat zināšanas un atziņas no lieliem datiem, lai nonāktu līdz vietai, kur mēs faktiski varam pieņemt lēmumus par rīcību. Un bieži par to mēs runājam automatizācijas kontekstā. Un šodien es vēlos automatizāciju sajaukt ar cilts analītiķi. Ar to es domāju, kamēr biznesa analītiķim šeit ir svarīga loma derību veikšanas, kvalifikācijas iegūšanas, noteiktu darbību apstiprināšanas vai mašīnmācīšanās noteikumu ziņā, kurus mēs iegūstam no datiem. Bet, ja mēs nonākam līdz punktam, kurā mēs esam diezgan pārliecināti par mūsu izveidotajiem biznesa noteikumiem un mūs brīdinošajiem mehānismiem, ir spēkā, tad mēs diezgan daudz to varam pārvērst par automatizētu procesu. Mēs faktiski veicam šo operatīvo pilnveidošanu, par kuru Ēriks runāja.
Tāpēc es šeit nedaudz spēlēju ar vārdiem, bet es ceru, ka, ja tas jums noderēs, es runāju par D2D izaicinājumu. Un D2D, kas nav tikai datu lēmumi visos kontekstos, mēs to aplūkojam šī slaida apakšā, un, cerams, ka jūs to varēsit redzēt, veicot atklājumus un palielinot ieņēmumus dolāros no mūsu analītikas cauruļvadiem.
Tātad šajā kontekstā man patiesībā ir šī tirgotāja loma šeit, kad es sadarbojos, un tas ir; Pirmais, ko vēlaties darīt, ir raksturot savus datus, izvilkt funkcijas, izvilkt klientu vai jebkuras citas entītijas pazīmes, kuras jūs izsekojat savā telpā. Varbūt tas ir pacients veselības analītikas vidē. Varbūt tas ir tīmekļa lietotājs, ja aplūkojat sava veida kiberdrošības problēmu. Bet raksturojiet un iegūstiet raksturlielumus un pēc tam iegūstiet zināmu kontekstu par šo indivīdu, par šo entītiju. Un tad jūs apkopojat tos failus, kurus tikko izveidojāt, un ievietojat tos kaut kādā kolekcijā, no kuras jūs pēc tam varat izmantot mašīnmācīšanās algoritmus.
Iemesls, kāpēc es to saku, ir tāds, ka, teiksim vienkārši, jums lidostā ir novērošanas kamera. Pats video ir milzīgs, liels skaļums, un tas ir arī ļoti nestrukturēts. Bet jūs varat iegūt informāciju no videonovērošanas, sejas biometrijas un identificēt personas novērošanas kamerās. Tā, piemēram, lidostā, jūs varat identificēt konkrētas personas, jūs varat izsekot viņiem caur lidostu, vairākās novērošanas kamerās identificējot vienu un to pašu personu. Tā kā iegūtās biometriskās pazīmes, kuras jūs patiešām iegūstat un izsekojat, nav pats detalizētais video. Kad esat ieguvis šos ieguvumus, varat izmantot mašīnmācības noteikumus un analītiku, lai pieņemtu lēmumus par to, vai jums ir jārīkojas konkrētā gadījumā, vai kaut kas notika nepareizi, vai arī par kaut ko tādu, ko jums ir iespēja izteikt. Piemēram, ja jūs atrodaties veikalā lidostā un redzat, ka klients nāk jūsu ceļā, un no citas informācijas par šo klientu zināt, ka varbūt viņš patiešām bija ieinteresēts iegādāties preces beznodokļu veikalā vai kaut ko tādu, izdari to piedāvājumu.
Tātad, kādas lietas es domāju par raksturojumu un potenciālu? Ar raksturojumu es atkal domāju datu pazīmju un īpašību iegūšanu. Un to var vai nu ģenerēt ar mašīnu, tad tā algoritmi faktiski var iegūt, piemēram, biometriskos parakstus no video vai sentimenta analīzes. Klientu uzskatus varat iegūt, izmantojot tiešsaistes pārskatus vai sociālos saziņas līdzekļus. Dažas no šīm lietām var būt cilvēku radītas, tāpēc cilvēks, biznesa analītiķis, var iegūt papildu funkcijas, kuras es parādīšu nākamajā slaidā.
Dažus no tiem var meklēt sabiedrībā. Un, izmantojot tiešo meklēšanu, ir daudz dažādu veidu, kā jūs varat par to domāt. Bet, piemēram, ļoti vienkārši, piemēram, lietotāji nonāk jūsu vietnē un ieliek meklēšanas vārdus, atslēgvārdus, un nonāk noteiktā lapā un faktiski pavada laiku tajā. Ka viņi vismaz vismaz saprot, ka viņi vai nu skata, pārlūko un noklikšķina uz šīs lapas lietām. Tas, kas jums saka, ir tas, ka atslēgvārds, kuru viņi ievadīja pašā sākumā, ir šīs lapas deskriptors, jo tas klientam nolaida viņu gaidītajā lapā. Tātad jūs varat pievienot šo papildu informāciju, proti, klienti, kuri lieto šo atslēgvārdu, mūsu tīmekļa vietnē mūsu tīmekļa arhitektūrā faktiski identificēja šo vietu kā vietu, kur šis saturs atbilst šim atslēgvārdam.
Tā kā sabiedriskās domas meklēšana ir vēl viens aspekts, kuru cilvēki dažreiz aizmirst, tā sakot, jūsu klientu rīvmaizes izsekošana; kā viņi pārvietojas savā telpā, neatkarīgi no tā, vai tas ir tiešsaistes īpašums vai nekustamais īpašums. Un pēc tam izmantojiet šāda veida ceļu, kuru klients izmanto kā papildu informāciju par lietām, kuras mēs skatāmies.
Tāpēc es gribu pateikt, ka cilvēku radītas lietas vai mašīnu ģenerētas lietas beidzās ar kontekstu, kurā anotēja vai marķēja noteiktas datu granulas vai entītijas. Neatkarīgi no tā, vai šie subjekti ir pacienti slimnīcas vidē, klienti vai kas cits. Tāpēc ir dažādi marķēšanas veidi un anotācijas. Daļa no tā attiecas uz pašiem datiem. Tā ir viena no lietām, kāda veida informācija, kāda veida informācija, kādas ir pazīmes, formas, varbūt faktūras un raksti, anomālija, uzvedība, kas nav anomālija. Pēc tam izvelciet semantiku, tas ir, kā tas attiecas uz citām lietām, kuras es zinu, vai šis klients ir elektronikas klients. Šis klients ir apģērbu klients. Vai arī šim klientam patīk pirkt mūziku.
Tātad, identificējot kādu semantiku šiem klientiem, kuriem patīk mūzika, parasti patīk izklaide. Varbūt mēs varētu viņiem piedāvāt kādu citu izklaides īpašumu. Tātad saprotot semantiku un arī zināmu izcelsmi, kas būtībā saka: no kurienes tas radies, kurš sniedza šo apgalvojumu, kad, kad, kurā datumā, kādos apstākļos?
Tātad, kad esat ieguvis visas šīs anotācijas un raksturojumus, pievienojiet tam nākamo soli, kas ir konteksts, pēc veida, kurš, kas, kad, kur un kāpēc to dara. Kas ir lietotājs? Kāds bija kanāls, kurā viņi ienāca? Kāds bija informācijas avots? Kāda veida atkārtotu izmantošanu mēs esam redzējuši šajā konkrētajā informācijas vai datu produktā? Un kāda ir tā veida vērtība biznesa procesā? Pēc tam savāciet šīs lietas un pārvaldiet tās, un, ja vēlaties par to domāt, faktiski palīdziet izveidot datu bāzi. Padariet tos meklējamus, atkārtoti izmantojamus citu biznesa analītiķu vai automatizētā procesā, kas nākamreiz, kad redzēšu šos funkciju kopumus, sistēma var veikt šo automātisko darbību. Tātad mēs nonākam pie šāda veida analītiskās efektivitātes, bet jo vairāk mēs apkopojam noderīgu, visaptverošu informāciju un pēc tam to izveidojam šiem lietošanas gadījumiem.
Mēs ķeramies pie biznesa. Mēs veicam datu analīzi. Mēs meklējam interesantus modeļus, pārsteigumus, jaunumu novirzes, anomālijas. Mēs meklējam jaunas klases un segmentus populācijā. Mēs meklējam asociācijas un korelācijas un saites starp dažādām entītijām. Un tad mēs to visu izmantojam, lai virzītu savus atklājumus, lēmumu pieņemšanu un dolāru pieņemšanas procesu.
Tātad, atkal, šeit mēs saņēmām pēdējo datu slaidu, kas man ir tikai pamatā, apkopojot, saglabājot biznesa analītiķa redzējumu. Atkal jūs neizmantojat šo cilvēku, un ir svarīgi šo cilvēku turēt.
Šīs funkcijas viņi visi nodrošina mašīnas vai cilvēku analītiķi vai pat kopuzņēmumi. Mēs izmantojam šo lietu kombināciju, lai uzlabotu apmācības komplektus mūsu modeļiem un galu galā iegūtu precīzākus prognozējošus modeļus, mazāk viltus pozitīvu un negatīvu, efektīvāku uzvedību, efektīvāku iejaukšanos ar klientiem vai neatkarīgi no tā.
Dienas beigās mēs patiešām vienkārši apvienojam mašīnmācīšanos un lielos datus ar šo cilvēka izziņas spēku, kur nonāk šāda veida marķēšanas anotācijas gabals. Un tas var novest pie vizualizācijas un vizuālās analītikas veida rīki vai ieskaujoša datu vide vai kopīga pieeja. Un dienas beigās tas, ko tas patiesībā dara, rada mūsu atklājumus, atziņas un D2D. Tie ir mani komentāri, tāpēc paldies, ka uzklausījāt.
Ēriks: Sveiki, tas izklausās lieliski un ļaujiet man iet uz priekšu un nodot atslēgas dakterim Robinam Blooram, lai viņš sniegtu arī savu skatījumu. Jā, man patīk dzirdēt jūs komentārus par šo operāciju koncepcijas pilnveidošanu, un jūs runājat par operatīvo analītiku. Es domāju, ka tā ir liela joma, kas ir jāizpēta diezgan pamatīgi. Un es domāju, tiešām ātri pirms Robina, es jūs atvedīšu atpakaļ, Kirk. Tas prasa, lai jums būtu diezgan ievērojama sadarbība starp dažādiem uzņēmuma dalībniekiem, vai ne? Jums ir jārunā ar operāciju cilvēkiem; jums ir jāiegūst savi tehniskie cilvēki. Dažreiz jūs saņemat savus mārketinga cilvēkus vai tīmekļa interfeisa cilvēkus. Parasti tās ir dažādas grupas. Vai jums ir kāda labākā prakse vai ieteikumi, kā panākt, lai visi iesaistītos spēlē?
Dr Kirks: Es domāju, ka tas nāk ar sadarbības biznesa kultūru. Patiesībā es runāju par trim analītiskās kultūras C veidam. Viens no tiem ir radošums; cits ir zinātkāre un trešais ir sadarbība. Tātad jūs vēlaties radošus, nopietnus cilvēkus, bet jums arī jāpanāk, lai šie cilvēki sadarbotos. Un tas tiešām sākas no augšas, šāda veida kultūras veidošanas ar cilvēkiem, kuriem vajadzētu atklāti dalīties un strādāt kopā, lai sasniegtu biznesa kopīgos mērķus.
Ēriks: Tam visam ir jēga. Un jums patiešām ir jāiegūst laba vadība augšpusē, lai tas notiktu. Tāpēc iesim uz priekšu un nodosim to Dr Bloor. Robin, grīda ir tava.
Dr Robin Bloor: Labi. Paldies par ievadrakstu, Ēriks. Labi, tas, kā šie pankumi notiek, tie parāda, jo mums ir divi analītiķi; Es redzu analītiķa prezentāciju, ka pārējie puiši to nedara. Es zināju, ko Kirks grasās teikt, un es vienkārši eju pavisam citā leņķī, lai mēs pārāk daudz nepārklājamies.
Tātad tas, par ko es faktiski runāju vai plānoju runāt, ir datu analītiķa loma salīdzinājumā ar biznesa analītiķa lomu. Un tas, kā es to raksturoju, labi, ka mēle ar vaigu zināmā mērā ir sava veida Jekyll un Hyde lieta. Atšķirība ir tā, ka datu zinātnieki vismaz teorētiski zina, ko viņi dara. Kamēr biznesa analītiķi nav tādi, labi, kā darbojas matemātika, kam var uzticēties un kam nevar uzticēties.
Tāpēc pievērsīsimies iemesls, kāpēc mēs to darām, iemesls, ka datu analīze pēkšņi ir kļuvusi par lielu lietu, neskaitot faktu, ka mēs faktiski varam analizēt ļoti lielu datu daudzumu un ievilkt datus ārpus organizācijas; vai tas maksā. Tas, kā es uz to raugos - un es domāju, ka tas tikai kļūst par lietu, bet es noteikti domāju, ka tas ir gadījums - datu analīze patiešām ir biznesa pētniecība un attīstība. Tas, ko jūs faktiski darāt vienā vai otrā veidā ar datu analīzi, ir tas, ka jūs aplūkojat biznesa procesu vienā secībā vai arī tā ir mijiedarbība ar klientu, vai tas ir saistīts ar mazumtirdzniecības darbību, izvietošanu jūsu veikalos. Nav īsti svarīgi, kāds ir jautājums. Jūs skatāties doto biznesa procesu un mēģināt to uzlabot.
Veiksmīgas pētniecības un attīstības rezultāts ir pārmaiņu process. Un, ja vēlaties, varat domāt par ražošanu kā parastu piemēru. Tā kā ražošanā cilvēki apkopo informāciju par visu, lai mēģinātu uzlabot ražošanas procesu. Bet es domāju, ka tas, kas noticis vai kas notiek pēc lieliem datiem, tas viss tagad tiek piemērots visiem jebkura veida uzņēmumiem jebkādā veidā, par kuru ikviens var domāt. Tik tiešām, ka jebkurš biznesa process ir jāpārbauda, ja varat apkopot datus par to.
Tātad tā ir viena lieta. Ja vēlaties, tas ir jautājums par datu analīzi. Ko datu analīze var dot biznesam? Tas var pilnībā mainīt biznesu.
Šī konkrētā diagramma, kuru es sīkāk neaprakstīšu, bet tā ir diagramma, kuru mēs izdomājām kā kulmināciju pētniecības projektam, kuru mēs paveicām šī gada pirmajos sešos mēnešos. Tas ir lielās datu arhitektūras attēlošanas veids. Un vairākas lietas, kuras ir vērts norādīt, pirms es dodos uz nākamo slaidu. Šeit ir divas datu plūsmas. Viens no tiem ir reāllaika datu straume, kas iet diagrammas augšpusē. Otra ir lēnāka datu plūsma, kas iet diagrammas apakšā.
Apskatiet diagrammas apakšā. Mums ir Hadoop kā datu rezervuārs. Mums ir dažādas datu bāzes. Mums tur ir veseli dati ar visu darbību virkni, no kurām lielākā daļa ir analītiskā darbība.
Šeit es uzsveru, ka vienīgais, ko es patiešām gribu šeit izteikt, ir tas, ka šī tehnoloģija ir cieta. Tas nav vienkārši. Tas nav viegli. Tas nav nekas tāds, ko ikviens, kurš ir jauns spēles dalībnieks, faktiski var vienkārši salikt. Tas ir diezgan sarežģīti. Un, ja jūs plānojat biznesu uzticamas analīzes veikšanai visos šajos procesos, tad tas nav tas, kas notiks īpaši ātri. Būs nepieciešams daudz tehnoloģiju, lai pievienotu maisījumu.
Labi. Jautājums par to, kas ir datu zinātnieks, es varētu apgalvot, ka esmu datu zinātnieks, jo es faktiski biju apmācīts statistikā, pirms mani kādreiz apmācīja skaitļošanā. Es kādu laiku darīju aktuāru darbu, tāpēc es zinu veidu, kā bizness organizē statistisko analīzi, arī lai pats vadītu. Tā nav nebūtiska lieta. Un tajā ir iesaistīts šausmīgi daudz labākās prakses gan no cilvēku puses, gan no tehnoloģiju puses.
Tāpēc, uzdodot jautājumu "kas ir datu zinātnieks", es esmu uzlicis Frankenšteina attēlu tikai tāpēc, ka tas ir lietu kopums, kas ir jāapvieno kopā. Ir iesaistīta projektu vadība. Statistikā ir dziļa izpratne. Ir domēna biznesa ekspertīze, kas noteikti ir vairāk saistīta ar biznesa analītiķi, nevis datu zinātnieku. Ir pieredze vai nepieciešamība izprast datu arhitektūru un spēt veidot datu arhitektu, un tajā ir iesaistīta programmatūras inženierija. Citiem vārdiem sakot, tā, iespējams, ir komanda. Droši vien tas nav indivīds. Un tas nozīmē, ka, iespējams, ir jāorganizē nodaļa, un par tās organizāciju ir jādomā diezgan plaši.
Iemetot sajaukumā, mašīnmācīšanās fakts. Mēs nevarētu darīt, es domāju, mašīnu apguve nav nekas jauns tādā nozīmē, ka vairums statistisko paņēmienu, ko izmanto mašīnu apguvē, ir zināmi jau vairākus gadu desmitus. Ir dažas jaunas lietas, es domāju, ka neironu tīkli ir salīdzinoši jauni, es domāju, ka viņiem ir tikai apmēram 20 gadu, tāpēc daži no tiem ir salīdzinoši jauni. Bet mašīnu apgūšanas problēma bija tā, ka mums faktiski nebija datora jaudas, lai to izdarītu. Un tas, kas notika, izņemot visu citu, ir tas, ka datora jauda tagad ir vietā. Un tas nozīmē šausmīgi daudz to, ko mēs, teiksim, datu zinātnieki, esam paveikuši iepriekš, modelējot situācijas, atlasot datus un pēc tam to šķirojot, lai iegūtu dziļāku datu analīzi. Patiesībā dažos gadījumos mēs tam varam vienkārši izmantot datora jaudu. Vienkārši izvēlieties mašīnmācīšanās algoritmus, izmantojiet datus un skatiet, kas iznāk. Un tas ir kaut kas, ko biznesa analītiķis var darīt, vai ne? Bet biznesa analītiķim ir jāsaprot, ko viņi dara. Es domāju, es domāju, ka tas ir jautājums patiešām vairāk nekā jebkas cits.
Tas ir tikai tāpēc, lai par biznesu uzzinātu vairāk no tā datiem, nevis izmantojot citus līdzekļus. Einšteins to neteica, es to teicu. Es vienkārši uzliku viņa bildi par ticamību. Bet patiesībā situācija sāk attīstīties, ja tehnoloģija, ja to pareizi izmanto, un matemātika, ja to pareizi lieto, spēs vadīt biznesu kā jebkura persona. Mēs to vērojām kopā ar IBM. Pirmkārt, tas varētu pārspēt labākos puišus šahā, un tad tas varētu pārspēt labākos puišus Jeopardijā; bet galu galā mēs spēsim pārspēt labākos puišus, vadot uzņēmumu. Statistika galu galā triumfēs. Un ir grūti saprast, kā tas nenotiks, tas vienkārši vēl nav noticis.
Tātad tas, ko es saku, un tas ir pilnīgi pilnīgs manas prezentācijas vēstījums, ir šie divi biznesa jautājumi. Pirmais ir tas, vai jūs varat iegūt tehnoloģiju pareizi? Vai jūs varat likt tehnoloģijai darboties tajā komandā, kura faktiski gatavojas spēt to vadīt un gūt labumu biznesam? Un, otrkārt, vai jūs varat sakārtot tautu? Un tie abi ir jautājumi. Un tie ir jautājumi, kas līdz šim nav atrisināti.
Labi, Ēriks, es jums to atdošu. Vai arī man varbūt vajadzētu to nodot Viljam.
Ēriks: Patiesībā jā. Paldies, Will Gorman. Jā, tur jūs ejat, Vils. Tātad, redzēsim. Ļaujiet man dot jums WebEx atslēgu. Tātad, ko jūs turpinājāt? Pentaho, acīmredzot, jūs, puiši, jau kādu laiku atrodaties un atvērtā koda BI veidam, kur sākāt. Bet jūs ieguvāt daudz vairāk, nekā jums bija agrāk, tāpēc redzēsim, ko šajās dienās ieguvāt analītikai.
Will Gorman: Absolūti. Sveiki visiem! Mani sauc Will Gorman. Esmu Pentaho galvenais arhitekts. Tiem no jums, kas par mums neko nav dzirdējuši, es tikko pieminēju, ka Pentaho ir liels datu integrācijas un analītikas uzņēmums. Mēs darbojamies uzņēmējdarbībā desmit gadus. Mūsu produkti ir attīstījušies līdzās lielajai datu kopienai, sākot ar atvērtā pirmkoda platformu datu integrēšanai un analītikai, izmantojot jauninājumus tādās tehnoloģijās kā Hadoop un NoSQL, pat pirms komerciālām vienībām, kas izveidojās ap šīm tehnoloģijām. Un tagad mūsu jauninājumu rezultātā, kas saistīti ar atvērto avotu, mums ir vairāk nekā 1500 komerciālu klientu un vēl daudz citu ražošanas tikšanos.
Mūsu arhitektūra ir ļoti iestrādājama un paplašināma, mērķim pielāgota, lai tā būtu elastīga, jo īpaši lielu datu tehnoloģija attīstās ļoti strauji. Pentaho piedāvā trīs galvenās produktu jomas, kuras darbojas kopā, lai risinātu lielo datu analītikas lietojuma gadījumus.
Pirmais produkts mūsu arhitektūras mērogā ir Pentaho Data Integration, kas ir paredzēts datu tehnologam un datu inženieriem. Šis produkts piedāvā vizuālu, velciet un nometiet pieredzi, lai noteiktu datu cauruļvadus un procesus datu orķestrēšanai lielās datu vidēs un arī tradicionālās vidēs. Šis produkts ir viegla, metadatu bāze, datu integrācijas platforma, kas veidota uz Java, un to var izmantot kā procesu MapReduce vai YARN vai Storm un daudzās citās pakešu un reālā laika platformās.
Mūsu otrā produktu joma ir saistīta ar vizuālo analītiku. Izmantojot šo tehnoloģiju, organizācijas un oriģinālo iekārtu ražotāji var piedāvāt bagātīgu vizualizācijas un analītikas pieredzi biznesa analītiķiem un biznesa lietotājiem, izmantojot modernus pārlūkus un planšetdatorus, ļaujot ad-hoc izveidot pārskatus un informācijas paneļus. Kā arī pikseļu perfekta paneļa un pārskatu prezentācija.
Mūsu trešā produktu joma ir vērsta uz prognozējošo analītiku, kas paredzēta datu zinātniekiem, mašīnmācīšanās algoritmiem. Kā jau minēts iepriekš, tāpat kā neironu tīklus un tādus, var iekļaut datu pārveidošanas vidē, ļaujot datu zinātniekiem pāriet no modelēšanas uz ražošanas vidi, dodot piekļuvi prognozēšanai, un tas var ietekmēt biznesa procesus ļoti nekavējoties, ļoti ātri.
Visi šie produkti ir cieši integrēti vienā elastīgā pieredzē un sniedz mūsu uzņēmuma klientiem elastību, kas nepieciešama viņu biznesa problēmu risināšanai. Mēs redzam strauji mainīgo lielo datu ainavu tradicionālajās tehnoloģijās. Viss, ko mēs dzirdam no dažiem uzņēmumiem lielajā datu telpā, ka EDW ir tuvu beigām. Faktiski tas, ko mēs redzam mūsu uzņēmuma klientiem, ir tas, ka viņiem ir jāievieš lieli dati esošajos biznesa un IT procesos, nevis jāaizstāj šie procesi.
Šī vienkāršā diagramma parāda bieži sastopamo arhitektūras punktu, kas ir EDW izvietošanas arhitektūras tips ar datu integrācijas un BI lietošanas gadījumiem. Tagad šī diagramma ir līdzīga Robina slaidam par lielo datu arhitektūru, tajā ir iekļauti reālā laika un vēsturiskie dati. Tā kā parādās jauni datu avoti un reālā laika prasības, lielos datus mēs redzam kā papildu daļu no kopējās IT arhitektūras. Šajos jaunajos datu avotos ietilpst mašīnas ģenerēti dati, nestrukturēti dati, standarta apjoms un ātrums, kā arī prasību dažādība, par kurām mēs dzirdam lielos datos; tie neiederas tradicionālajos EDW procesos. Pentaho cieši sadarbojas ar Hadoop un NoSQL, lai vienkāršotu šo datu ievadīšanu, datu apstrādi un vizualizāciju, kā arī sajauktu šos datus ar tradicionālajiem avotiem, lai klientiem būtu pilnīgs ieskats viņu datu vidē. Mēs to darām pārvaldītā veidā, lai IT viņu biznesam varētu piedāvāt pilnu analītisko risinājumu.
Noslēgumā es vēlētos izcelt mūsu filozofiju ap lielo datu analītiku un integrāciju; mēs uzskatām, ka šīs tehnoloģijas ir labākas kopā strādājot ar vienotu arhitektūru, ļaujot izmantot vairākus gadījumus, kas citādi nebūtu iespējami. Mūsu klientu datu vide ir kas vairāk nekā tikai lielais dators, Hadoop un NoSQL. Jebkuri dati ir godīga spēle. Un lieliem datu avotiem jābūt pieejamiem un jāstrādā kopā, lai ietekmētu biznesa vērtību.
Visbeidzot, mēs uzskatām, ka, lai ļoti efektīvi risinātu šīs biznesa problēmas uzņēmumos, izmantojot datus, IT un biznesa virzieniem ir jāstrādā kopā, lai pārvaldītu, jauktu pieeju lielo datu analītikai. Nu, liels paldies, ka veltījāt mums laiku sarunai, Ēriks.
Ēriks: Jūs derējat. Nē, tas ir labs sīkums. Es vēlos atgriezties tajā jūsu arhitektūras pusē, kad mēs nokļūsim Jautājumu un atbilžu sadaļā. Tāpēc pārcelsimies pārējā prezentācijas daļā un par to sakām lielu paldies. Jūs, puiši, noteikti esat ātri pārvietojušies pēdējos pāris gadus, man tas jāsaka noteikti.
Tāpēc Stīvs, ļaujiet man iet uz priekšu un nodot to jums. Un vienkārši noklikšķiniet uz lejupvērstās bultiņas un dodieties uz to. Tātad Stīvs, es tev dodu atslēgas. Stīvs Vilkes, vienkārši noklikšķiniet uz vistālākās uz leju vērstās bultiņas tur, uz tastatūras.
Stīvs Vilks: Turp mēs ejam.
Ēriks: Tur jūs ejat.
Stīvs: Tomēr tas ir lielisks ievads, ko jūs man esat devis.
Ēriks: Jā.
Stīvs: Tāpēc es esmu Stīvs Vilks. Es esmu WebAction CCO. Mēs esam bijuši tikai pēdējos pāris gadus, un kopš tā laika mēs noteikti arī strauji virzāmies uz priekšu. WebAction ir reāllaika lielo datu analīzes platforma. Ēriks jau iepriekš pieminēja, kāda veida reālais laiks ir svarīgs un cik reālā laikā kļūst jūsu lietojumprogrammas. Mūsu platforma ir paredzēta reāllaika lietotņu izveidošanai. Un lai iespējotu nākamās paaudzes uz datiem balstītās lietotnes, kuras var pakāpeniski veidot, un ļaut cilvēkiem veidot paneļus no datiem, kas ģenerēti no šīm lietotnēm, bet koncentrējoties uz reālo laiku.
Mūsu platforma faktiski ir pilnīga visaptveroša platforma, kas dara visu, sākot no datu iegūšanas, datu apstrādes, līdz pat datu vizualizēšanai. Un tas ļauj vairākiem dažādu veidu cilvēkiem mūsu uzņēmumā strādāt kopā, lai izveidotu īstas reāllaika lietotnes, sniedzot viņiem ieskatu par lietām, kas notiek viņu uzņēmumā, kā tas notika.
Un tas nedaudz atšķiras no tā, ko lielākā daļa cilvēku ir redzējuši lielos datos, tāpēc tradicionālā pieeja - labi, tradicionālā pēdējo pāris gadu laikā - pieeja ar lielajiem datiem ir bijusi tās uztveršana no vesela daudz dažādu avotu un tad sakrauj to lielā rezervuārā vai ezerā vai kā vien to vēlies saukt. Un pēc tam apstrādājiet to, kad jums par to jāuzsāk vaicājums; lai veiktu liela mēroga vēsturisko analīzi vai pat tikai ad hoc vaicājumus par lielu datu daudzumu. Tagad tas darbojas noteiktos lietošanas gadījumos. Bet, ja jūs vēlaties būt proaktīvs savā uzņēmumā, ja vēlaties patiesībā pateikt, kas notiek, nevis uzzināt, kad dienas vai nedēļas beigās kaut kas notika nepareizi, tad jums patiešām ir jāpārceļas uz reālo laiku.
Un tas nedaudz mainās. Tas pārvieto apstrādi uz vidu. Tik efektīvi jūs uzņematies lielu datu daudzumu straumēm, kuras uzņēmumā tiek nepārtraukti ģenerētas, un apstrādājat tās, tiklīdz iegūstat. Un tāpēc, ka jūs to apstrādājat, kā jums pienākas, jums viss nav jāuzglabā. Jūs varat vienkārši saglabāt svarīgu informāciju vai lietas, kas jums jāatceras, kas patiesībā notika. Tātad, ja jūs izsekojat GPS atrašanās vietu transportlīdzekļiem, kas pārvietojas pa ceļu, jums nav īsti vienalga, kur tie atrodas katru sekundi, jums nav jāuzglabā, kur tie atrodas katru sekundi. Jums vienkārši jārūpējas, vai viņi ir pametuši šo vietu? Vai viņi ir ieradušies šajā vietā? Vai viņi ir braukuši pa automaģistrāli vai nē?
Tāpēc ir patiešām svarīgi ņemt vērā, ka, iegūstot arvien vairāk datu, tad trīs Vs. Ātrums galvenokārt nosaka, cik daudz datu katru dienu tiek ģenerēts. Jo vairāk datu tiek ģenerēts, jo vairāk jums būs jāuzglabā. Un jo vairāk jums ir jāuzglabā, jo ilgāks laiks to apstrādā. Bet, ja jūs varat to apstrādāt tā, kā jūs to iegūstat, tad jūs saņemat patiešām lielu labumu un jūs varat uz to reaģēt. Var pateikt, ka lietas notiek, nevis vēlāk tās meklēt.
Tātad mūsu platforma ir veidota tā, lai tā būtu ļoti pielāgojama. Tam ir trīs galvenie gabali - ieguves gabals, apstrādes gabals un pēc tam platformas piegādes vizualizācijas gabali. Ieguves pusē mēs aplūkojam ne tikai mašīnu ģenerētus žurnālu datus, piemēram, tīmekļa žurnālus vai lietojumprogrammas, kurām ir visi pārējie ģenerētie žurnāli. 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. Paldies.
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. Lūdzu.
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. Labi. 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?" Labi.
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. Ņem to prom.
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. Ko tu domā?
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. Liels tev paldies. We'll catch you next time. Labdien!
