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Eric Kavanagh: Dame i gospodo, zdravo i dobrodošli još jednom na epizodu 2 iz TechWise-a. Da, doista, vrijeme je da nađete mudre ljude! Danas imamo na raspolaganju gomilu stvarno pametnih ljudi koji će nam pomoći u tom nastojanju. Moje ime je Eric Kavanagh, naravno. Bit ću vaš domaćin, vaš moderator, za ovu sesiju munje. Ovdje imamo puno sadržaja, narode. U poslu imamo nekoliko velikih imena koji su bili analitičari na našim prostorima i četiri najzanimljivija dobavljača. Dakle, danas ćemo imati puno dobrih akcija. I naravno, vi vani u publici igrate značajnu ulogu u postavljanju pitanja.
Dakle, još jednom emisija je TechWise, a tema je danas "Kako analitika može poboljšati poslovanje?" Očito je da je to vruća tema u kojoj će se pokušati razumjeti različite vrste analitike koje možete učiniti i kako to može poboljšati vaše poslovanje, jer o tome se radi na kraju dana.
Tako da možete vidjeti sebe gore na vrhu, to je uistinu vaša. Dr. Kirk Borne, dobar prijatelj sa sveučilišta George Mason. On je podatkovni znanstvenik s ogromnom količinom iskustva, vrlo dubokom stručnošću u ovom prostoru i vađenjem podataka, velikim podacima i svim tim zabavnim stvarima. I, naravno, ovdje imamo našeg dr. Robina Bloora, glavnog analitičara iz Bloor grupe. Koji je trenirao kao aktuar prije puno, mnogo godina. I stvarno je fokusiran na cijeli taj veliki prostor podataka i analitički prostor posljednjih pola desetljeća. Prošlo je pet godina otkad smo sami pokrenuli Bloor Group. Dakle, vrijeme leti kad se zabavljate.
Također ćemo se čuti s Willom Gormanom, glavnim arhitektom Pentaha; Steve Wilkes, CCO of WebAction; Frank Sanders, tehnički direktor tvrtke MarkLogic; i Hannah Smalltree, direktorica tvrtke Treasure Data. Kao što sam rekao, to je puno sadržaja.
Pa kako analitika može pomoći vašem poslu? Pa, kako vam ne mogu pomoći, iskreno? Postoje sve vrste načina na koje se analitika može koristiti za postizanje poboljšanja vaše organizacije.
Dakle, usmjerite operacije. To je ono o čemu ne čujete toliko stvari kao što su stvari poput marketinga ili povećanja prihoda ili čak prepoznavanja mogućnosti. Ali pojednostavljenje vaših operacija ovo je stvarno, stvarno moćna stvar koju možete učiniti za svoju organizaciju, jer možete identificirati mjesta na kojima možete nešto prenijeti ili na primjer dodati podatke u određeni postupak. A to može pojednostaviti tako da netko ne traži da se javi na telefon ili da mu netko pošalje e-poštu. Postoji toliko različitih načina da možete pojednostaviti svoje poslovanje. I sve to zaista pomaže sniziti troškove, zar ne? To je ključno, to smanjuje troškove. Ali to vam također omogućuje da bolje opslužite svoje kupce.
Ako razmišljate o tome kako su postali nestrpljivi ljudi, a to vidim svaki dan u smislu kako ljudi komuniciraju putem interneta, čak i s našim emisijama, pružateljima usluga koje koristimo. Strpljenje koje ljudi imaju, pažnja postaje svakim danom sve kraće. A to znači da kao organizacija morate reagirati na brže i brže razdoblje kako biste mogli zadovoljiti svoje kupce.
Tako, na primjer, ako je netko na vašoj web stranici ili pregledava okolo pokušavajući pronaći nešto, ako se frustriraju i odlaze, pa, možda ste upravo izgubili kupca. Ovisno o tome koliko naplaćujete za svoj proizvod ili uslugu, a možda je to velika stvar. Stoga, suština je da su pojednostavljene operacije, mislim, jedan od najzgodnijih prostora za primjenu analitike. A to činite gledajući brojeve, drobljenjem podataka, zaključujući, na primjer, "Hej, zašto gubimo toliko ljudi na ovoj stranici naše web stranice?" "Zašto upravo primamo neke od tih telefonskih poziva?"
I što više vremena reagirate na takve stvari, to su veće šanse da ćete biti iznad situacije i učiniti nešto u vezi s tim prije nego što bude prekasno. Jer postoji taj vremenski period kada se netko uznemiri zbog nečega, nezadovoljan je ili pokušava nešto pronaći, ali frustriran je; imate mogućnost da im se obratite, zgrabite ih i komunicirate s tim kupcem. A ako to učinite na pravilan način s pravim podacima ili lijepom slikom kupca - razumijevanjem tko je taj kupac, koja je njihova profitabilnost, koje su njihove preferencije - ako se stvarno možete nositi s tim, to ćete učiniti sjajan posao za držanje svojih kupaca i pridobijanje novih kupaca. I o tome se radi.
Dakle, s tim dijelom ću ga predati Kirku Borneu, jednom od naših današnjih znanstvenika podataka. A danas su prilično rijetki, ljudi. Imamo ih barem dvoje na pozivu, tako da je to velika stvar. S tim, Kirk, predaću vam ga kako bih razgovarao o analitikama i kako pomaže u poslovanju. Idi na to.
Dr. Kirk Borne: Pa, hvala vam puno, Eric. Možeš li me čuti?
Eric: To je u redu, samo naprijed.
Dr. Kirk: Dobro, dobro. Samo želim dijeliti ako razgovaram pet minuta, a ljudi mašu rukama prema meni. Dakle, uvodna primjedba, Eric, koji si se stvarno vezao za ovu temu, ukratko ću govoriti u sljedećih nekoliko minuta, a to je upotreba velikih podataka i analitike za podatke za donošenje odluka. Komentar koji ste dali o operativnom usmjeravanju, prema meni, nekako spada u ovaj koncept operativne analitike u kojem možete vidjeti gotovo sve aplikacije u svijetu bilo da se radi o znanosti, poslu, cyber sigurnosti i provedbi zakona i vlada, zdravstvo. Bilo koji broj mjesta gdje imamo tok podataka i donosimo neku vrstu reakcije ili odluke u reakciji na događaje i upozorenja i ponašanja koja vidimo u tom toku podataka.
I tako, jedna od stvari o kojoj bih danas želio razgovarati je vrsta načina na koji izvlačite znanje i uvide iz velikih podataka kako biste došli do točke u kojoj zapravo možemo donositi odluke o postupanju. I često o tome razgovaramo u kontekstu automatizacije. I danas želim spojiti automatizaciju s ljudskim analitičarom u petlji. Pod tim mislim, dok poslovni analitičar ovdje igra važnu ulogu u pogledu klađenja, kvalificiranja, potvrđivanja određenih akcija ili pravila strojnog učenja koje izvlačimo iz podataka. Ali ako stignemo do točke u kojoj smo prilično uvjereni u poslovna pravila koja smo izvukli i mehanizmi za uzbunjivanje nas vrijede, tada to možemo prilično pretvoriti u automatizirani proces. Zapravo radimo ono racionaliziranje koje je Eric govorio.
Dakle, ovdje se malo igram riječima, ali nadam se da ako vam to uspije, govorio sam o izazovu D2D. A D2D, ne samo podaci koji se odnose na odluke u svim kontekstima, gledamo to na dnu ovog dijapozitiva, nadamo se da ćete ga vidjeti, čineći otkrića i povećavajući prihod od prihoda iz našeg analitičkog cjevovoda.
Dakle, u ovom kontekstu, tu ulogu trgovca zapravo imam sada ovdje, s kojim radim, i to je; prvo što želite učiniti je karakterizirati svoje podatke, izdvojiti značajke, izdvojiti karakteristike svojih kupaca ili bilo kojeg entiteta kojega pratite u svom prostoru. Možda je to pacijent u okruženju zdravstvenih analitičara. Možda je to web korisnik ako gledate neku vrstu kibernetske sigurnosti. Ali karakterizirati i izdvojiti karakteristike, a zatim izvući neki kontekst o tom pojedincu, o tom entitetu. A onda skupite one komade koje ste upravo stvorili i stavite ih u neku vrstu zbirke iz koje zatim možete primijeniti algoritme strojnog učenja.
Razlog zašto to kažem je taj što, recimo, na aerodromu imate nadzornu kameru. Sam video je ogromnog, velikog volumena i također vrlo nestrukturiran. Ali možete izvući iz video nadzora, biometriju lica i identificirati pojedince u nadzornim kamerama. Tako, na primjer, u zračnoj luci možete identificirati određene pojedince, a možete ih pratiti kroz aerodrom unakrsnim prepoznavanjem istog pojedinca u više nadzornih kamera. Tako da izvučene biometrijske značajke koje stvarno kopate i pratite nisu sami detaljni videozapisi. Ali nakon što dobijete te ekstrakcije, možete primijeniti pravila strojnog učenja i analitike za donošenje odluka o tome trebate li poduzeti akciju u određenom slučaju ili se nešto dogodilo pogrešno ili nešto što imate priliku ponuditi. Ako ste, na primjer, ako imate trgovinu u zračnoj luci i vidite da vam kupac dolazi, a znate i druge informacije o tom kupcu, možda se stvarno zainteresirao za kupovinu stvari u trgovini bez carina ili nešto takvo, napravite tu ponudu.
Pa kakve bih stvari značio pod karakterizacijom i potencijalizacijom? Karakterizacijom mislim, opet, na vađenje značajki i karakteristika u podacima. A to se može ili generirati strojem, tada njegovi algoritmi mogu zapravo izvući, na primjer, biometrijske potpise iz video analize ili analize osjećaja. Svoje osjećaje možete izvući putem mrežnih pregleda ili društvenih medija. Neke od tih stvari mogu se stvoriti u čovjeku, tako da čovjek, poslovni analitičar, može izvući dodatne značajke koje ću pokazati u sljedećem dijapozitivu.
Neke od njih mogu biti pretrpane. A prema pretrpanoj publici, postoji puno različitih načina na koje možete razmišljati o tome. Na primjer, vrlo jednostavno, na primjer, vaši korisnici dolaze na vašu web stranicu i uvode riječi za pretraživanje, ključne riječi, a oni završavaju na određenoj stranici i zapravo provode vrijeme na toj stranici. Da zapravo barem razumiju da ili pregledavaju, pregledavaju ili klikaju na stvarima na toj stranici. Ono što vam govori je da je ključna riječ koju su upisali na samom početku deskriptor te stranice jer je kupca sletila na stranicu koju je očekivao. I tako možete dodati taj dodatni podatak, odnosno da su korisnici koji koriste ovu ključnu riječ zapravo identificirali ovu web stranicu u našoj informacijskoj arhitekturi kao mjesto na kojem se taj sadržaj podudara s tom ključnom riječi.
I tako je crowdsourcing drugi aspekt koji ponekad ljudi zaborave, takav način praćenja krušnih kruga vaših klijenata; kako se kreću svojim prostorom, bilo da se radi o internetskom ili stvarnom vlasništvu. A zatim upotrijebite takav put koji klijent vodi kao dodatne informacije o stvarima koje gledamo.
Stoga želim reći da su stvari koje su generirali ljudi, ili strojno stvorene, imale kontekst svojevrsne napomene ili označavanja specifičnih granula podataka ili entiteta. Bez obzira jesu li ti subjekti pacijenti u bolnici, kupci ili nešto drugo. I tako postoje različite vrste označavanja i napomena. Nešto od toga se odnosi na same podatke. To je jedna od stvari, kakva vrsta informacija, kakve informacije, koje su značajke, oblici, možda teksture i obrasci, anomalija, neanomalna ponašanja. I zatim izdvojim neku semantiku, tj. Kako se to odnosi na druge stvari koje znam, ili je ovaj kupac kupac elektronike. Ovaj kupac je kupac odjeće. Ili ovaj kupac voli kupovati glazbu.
Tako identificirajući neke semantike o tome, kupci koji vole glazbu obično vole zabavu. Možda bismo im mogli ponuditi neku drugu zabavu. Dakle, razumijevanje semantike i također neke provenijencije, što u osnovi kaže: odakle je ovo došlo, ko je iznio ovu tvrdnju, u koje vrijeme, u koji datum i pod kojim okolnostima?
Pa kad jednom imate sve te napomene i karakterizacije, dodajte tome sljedeći korak, a to je kontekst, vrsta tko, što, kada, gdje i zašto. Tko je korisnik? Kakav je bio kanal na koji su ušli? Koji je bio izvor informacija? Kakvu smo upotrebu vidjeli u ovom konkretnom podatku ili proizvodu podataka? I koja je, svojevrsna, vrijednost u poslovnom procesu? Zatim skupite te stvari i upravljajte njima, a zapravo pomažete u stvaranju baze podataka ako želite tako razmišljati. Neka ih pretražuju, ponovo koriste drugi poslovni analitičari ili automatizirani postupak koji će, sljedeći put kada vidim ove skupove značajki, sustav preduzeti ovu automatsku akciju. I tako dolazimo do takve operativne analitičke učinkovitosti, ali što više prikupljamo korisne, sveobuhvatne informacije, a zatim ih liječimo za ove slučajeve uporabe.
Prelazimo na posao. Radimo analizu podataka. Tražimo zanimljive obrasce, iznenađenja, novitet outliers, anomalije. Tražimo nove klase i segmente u populaciji. Tražimo udruge i povezanosti i veze među različitim entitetima. A onda koristimo sve to kako bismo pokrenuli naš postupak otkrivanja, odlučivanja i donošenja dolara.
Eto, opet, tu smo dobili posljednji slajd podataka koji je u osnovi samo sumiranje, držeći poslovnog analitičara u petlji, opet, vi to ne izdvajate i sve je važno zadržati tog čovjeka unutra.
Dakle, ove značajke pružaju ih svi strojevi ili ljudski analitičari, pa čak i crowd -ourcing. Primjenjujemo tu kombinaciju stvari kako bismo poboljšali naše setove treninga za naše modele i završili s preciznijim prediktivnim modelima, manje lažnih pozitivnih i negativnih efekata, učinkovitijim ponašanjem, učinkovitijim intervencijama s našim kupcima ili bilo kime.
Dakle, na kraju dana, mi stvarno samo kombiniramo strojno učenje i velike podatke s ovom snagom ljudske spoznaje, odakle dolazi takav komad bilježenja s oznakama. A to može dovesti do vizualizacije i vizualne analitike alate ili imerzivne podatkovne sredine ili gužve. I na kraju dana, ovo što uistinu radi generira naše otkriće, uvida i D2D. A to su moji komentari, pa hvala što ste slušali.
Eric: Hej, to zvuči sjajno i pusti me da predajem ključeve dr. Robinu Blooru da i ja pružim njegovu perspektivu. Da, volio bih čuti kako komentirate taj koncept pojednostavljenja operacija i govorite o operativnoj analitici. Mislim da je to veliko područje koje se mora temeljito istražiti. I pretpostavljam, vrlo brzo prije Robin, vratit ću te unutra, Kirk. Potrebno je da imate prilično značajnu suradnju između različitih igrača u kompaniji, zar ne? Morate razgovarati s operativnim ljudima; moraš nabaviti svoje tehničke ljude. Ponekad nabavite svoje marketing ljude ili ljude s vašeg web sučelja. To su obično različite skupine. Imate li neke najbolje prakse ili prijedloge kako nekako natjerati sve da uđu u kožu?
Dr. Kirk: Pa, mislim da to dolazi s poslovnom kulturom suradnje. U stvari, govorim o tri C-a vrste analitičke kulture. Jedan je kreativnost; drugi je znatiželja i treći je suradnja. Dakle, želite kreativne, ozbiljne ljude, ali morate i tim ljudima surađivati. I zaista počinje od vrha, takve vrste izgradnje te kulture s ljudima koji bi trebali otvoreno dijeliti i zajednički raditi na zajedničkim ciljevima poslovanja.
Eric: Sve ima smisla. I doista morate dobiti dobro vodstvo na vrhu da biste to ostvarili. Pa idemo naprijed i predajmo ga dr. Blooru. Robin, kat je tvoj.
Dr. Robin Bloor: Dobro. Hvala na uvodu, Eric. U redu, način na koji se ovi izvlače, to pokazuje, jer imamo dva analitičara; Vidim prezentaciju analitičara koju ostali momci ne čine. Znao sam što će Kirk reći i jednostavno idem potpuno drugačijeg kuta kako se ne bismo previše preklapali.
Dakle, ono o čemu zapravo pričam ili namjeravam razgovarati ovdje je uloga analitičara podataka nasuprot ulozi poslovnog analitičara. A način na koji sam to okarakteriziram, eto, jezik-u-obraz u određenoj mjeri je vrsta Jekyll-a i Hyde-a. Razlika je upravo u tome što znanstvenici, barem u teoriji, znaju što rade. Dok poslovni analitičari nisu tako, dobro je s načinom na koji matematika funkcionira, u što se može vjerovati, a u što se ne može vjerovati.
Dakle, spustimo se na razlog zbog kojeg to radimo, na razlog što je analiza podataka odjednom postala velika stvar osim što možemo zapravo analizirati vrlo velike količine podataka i privući podatke izvan organizacije; isplati li se Način na koji gledam na ovo - i mislim da to tek postaje slučaj, ali definitivno mislim da je to slučaj - analiza podataka je zaista poslovno istraživanje i razvoj. Ono što zapravo radite na ovaj ili onaj način s analizom podataka jeste da gledate poslovni proces na jednu vrstu ili je li to interakcija s kupcem, bilo da je to način na koji obavljate vašu maloprodajnu operaciju, način na koji implementirate. svoje trgovine. Uopće nije važno u čemu je problem. Gledate određeni poslovni proces i pokušavate ga poboljšati.
Rezultat uspješnog istraživanja i razvoja je proces promjena. I možete izrađivati, ako želite, uobičajeni primjer toga. Jer u proizvodnji ljudi skupljaju informacije o svemu kako bi pokušali poboljšati proizvodni proces. Ali mislim da se sve što se dogodilo ili što se događa s velikim podacima sve to sada primjenjuje na sve tvrtke bilo koje vrste na bilo koji način na koji se bilo tko može sjetiti. Stoga je gotovo svaki poslovni proces spreman za ispitivanje ako možete prikupiti podatke o njemu.
Dakle, to je jedna stvar. Ako želite, to je pitanje analize podataka. Što analiza podataka može učiniti za posao? Pa, to može u potpunosti promijeniti posao.
Ovaj poseban dijagram koji neću opisivati u bilo kojoj dubini, ali ovo je dijagram koji smo smislili kao vrhunac istraživačkog projekta koji smo radili u prvih šest mjeseci ove godine. Ovo je način predstavljanja velike arhitekture podataka. I niz stvari koje vrijedi istaknuti prije nego što nastavim na sljedeći slajd. Ovdje postoje dva protoka podataka. Jedan je tok podataka u stvarnom vremenu, koji se odvija vrhom dijagrama. Drugi je sporiji tok podataka koji ide duž dna dijagrama.
Pogledajte donji dio dijagrama. Imamo Hadoop kao rezervoar podataka. Imamo razne baze podataka. Tamo imamo čitav podatak s čitavim se skupom aktivnosti, od kojih je većina analitička.
Ono što ovdje želim učiniti i jedino što ovdje želim učiniti jest da je tehnologija tvrda. Nije jednostavno. Nije lako. To nije nešto što svatko tko je novi u igri zapravo može samo sastaviti. Ovo je prilično složeno. A ako ćete osnovati posao za obavljanje pouzdane analitike kroz sve ove procese, to se neće dogoditi posebno brzo. Trebat će joj puno tehnologije da se doda u mješavinu.
U redu. Na pitanje što je podatkovni znanstvenik, mogao bih tvrditi da je podatkovni znanstvenik, jer sam zapravo bio obučen u statistici prije nego što sam se ikad obučio u računarstvu. I obavljao sam aktuarski posao neko vrijeme, tako da znam način na koji se poslovanje organizira, statističke analize, također kako bi se pokrenuo. Ovo nije trivijalna stvar. A tu je i gomila najboljih primjera iz prakse s ljudske i tehnološke strane.
Dakle, postavljajući pitanje "što je podatkovni znanstvenik", postavio sam Frankenstein sliku jednostavno zato što je to kombinacija stvari koje moraju biti spojene zajedno. Tu je uključeno i upravljanje projektima. U statistici postoji duboko razumijevanje. Postoji stručnost u domenu poslovanja, koja je nužno više problem poslovnog analitičara nego znanstvenika podataka. Postoji iskustvo ili potreba da se razumije arhitektura podataka i da biste mogli graditi arhitekt podataka i tu je uključen softverski inženjering. Drugim riječima, vjerojatno je to tim. Vjerojatno nije pojedinac. A to znači da je to vjerojatno odjel koji se mora organizirati i o njegovoj organizaciji treba razmišljati prilično opsežno.
Ubacivanje u mješavinu činjenica strojnog učenja. Mislim, strojno učenje nije novo u smislu da je većina statističkih tehnika koje se koriste u strojnom učenju poznata već desetljećima. Postoji nekoliko novih stvari, mislim na neuronske mreže relativno nove, mislim da imaju tek oko 20 godina, tako da su neke od njih relativno nove. Ali problem s strojnim učenjem bio je u tome što mi u stvari nismo imali snage računala da to radimo. Ono što se dogodilo, osim svega ostalog, jest da je sada snaga računala. A to znači grozno od onoga što smo, kaže, znanstvenici s podacima učinili prije, u smislu modeliranja situacija, uzorkovanja podataka i zatim to maršare radi stvaranja dublje analize podataka. Zapravo, možemo jednostavno baciti računar na to u nekim slučajevima. Samo odaberite algoritme strojnog učenja, bacajte ih na podatke i pogledajte što dolazi. I to je nešto što poslovni analitičar može učiniti, zar ne? Ali poslovni analitičar mora razumjeti što rade. Mislim, mislim da je to uistinu problem, više nego išta drugo.
Pa, ovo je samo znati više o poslovanju iz njegovih podataka nego na bilo koji drugi način. Einstein to nije rekao, rekao sam to. Samo sam mu postavio sliku radi vjerodostojnosti. Ali situacija se zapravo počinje razvijati ona je u kojoj će tehnologija, ako se pravilno koristi, i matematika, ako se pravilno koristi, moći voditi posao kao i svaki pojedinac. To smo gledali s IBM-om. Prije svega, mogao je pobijediti najbolje momke u šahu, a onda je mogao pobijediti i najbolje momke u Jeopardyju; ali na kraju ćemo moći pobijediti najbolje dečke u tvrtki. Statistika će na kraju trijumfirati. I teško je vidjeti kako se to neće dogoditi, jednostavno se još nije dogodilo.
Dakle, ono što govorim i ovo je neka vrsta kompletne poruke mog izlaganja, jesu li ova dva pitanja poslovanja. Prvi je, možete li shvatiti tehnologiju? Možete li natjerati tehnologiju da radi za tim koji će joj zapravo moći predsjedati i steći koristi za posao? I onda drugo, možete li ispraviti ljude? I to su oba pitanja. I oni su problemi koji do sad nisu, kažu da je riješen.
Ok Eric, vratit ću ti ga. Ili bih ga možda prenio Willu.
Eric: Zapravo, da. Hvala, Will Gorman. Da, evo, Will. Pa da vidimo. Dopustite da vam dam ključ za WebEx. Pa, što se događa? Pentaho, očito ste vi već neko vrijeme i otvoreni je izvor BI-a odakle ste i započeli. Ali dobili ste puno više nego što ste nekada imali, pa da vidimo što ste dobili ovih dana za analitiku.
Will Gorman: Apsolutno. Bok svima! Moje ime je Will Gorman. Ja sam glavni arhitekt u Pentahu. Za one od vas koji nisu čuli za nas, upravo sam spomenuo da je Pentaho velika tvrtka za integraciju podataka i analitiku. U poslu smo već deset godina. Naši proizvodi razvijali su se paralelno sa zajednicom velikih podataka, počevši od platforme otvorenog koda za integraciju podataka i analitiku, inovirajući se sa tehnologijama poput Hadoopa i NoSQL-a, čak i prije nego što su se komercijalni subjekti formirali oko te tehnologije. I sada imamo preko 1500 komercijalnih kupaca i mnogo više proizvodnih imenovanja kao rezultat naše inovacije oko otvorenog koda.
Naša je arhitektura izrazito ugradljiva i proširiva, a izgrađena je na namjeni da bude fleksibilna jer tehnologija velikih podataka posebno se razvija vrlo brzim tempom. Pentaho nudi tri glavna područja proizvoda koja rade zajedno na rješavanju slučajeva korištenja velike analize podataka.
Prvi proizvod u mjeri naše arhitekture je Pentaho integracija podataka koja je usmjerena prema podatkovnom tehnologu i inženjerima podataka. Ovaj proizvod nudi vizualno iskustvo povuci-i-ispusti za definiranje cjevovoda za podatke i procesa orkestriranja podataka iu velikim okruženjima podataka i tradicionalnim okruženjima. Ovaj je proizvod lagana, metapodatkovna platforma, platforma za integraciju podataka izgrađena na Javi i može se primijeniti kao proces unutar MapReduce ili YARN ili Storm i mnogih drugih platformi serije i u stvarnom vremenu.
Naše drugo područje proizvoda odnosi se na vizualnu analitiku. Ovom tehnologijom organizacije i OEM proizvođači mogu ponuditi bogat doživljaj vizualizacije i analitike za poslovne analitičare i poslovne korisnike modernim preglednicima i tabletima, omogućavajući ad hoc kreiranje izvještaja i nadzornih ploča. Kao i prezentacija nadzorne ploče i izvješća savršena za piksele.
Naše treće područje proizvoda usredotočeno je na prediktivnu analitiku namijenjenu znanstvenicima podataka, algoritme strojnog učenja. Kao što je već spomenuto, poput neuronskih mreža i sličnih, mogu se ugraditi u okruženje za transformaciju podataka, omogućujući podacima koji će prelaziti iz modela u proizvodno okruženje, dajući pristup predviđanju, a to može utjecati na poslovne procese vrlo brzo, vrlo brzo.
Svi su ovi proizvodi usko integrirani u jedno agilno iskustvo i našim klijentima za poduzeća nude fleksibilnost koja im je potrebna za rješavanje njihovih poslovnih problema. Primjećujemo brzo razvijajući krajolik velikih podataka u tradicionalnim tehnologijama. Sve što čujemo od nekih tvrtki iz velikog prostora podataka da se EDW bliži kraju. U stvari, ono što vidimo kod naših poslovnih kupaca je da trebaju uvesti velike podatke u postojeće poslovne i IT procese, a ne da ih zamjenjuju.
Ovaj jednostavan dijagram prikazuje točku arhitekture koju često vidimo, a riječ je o vrsti arhitekture EDW-implementacije s integracijom podataka i slučajevima uporabe BI-a. Sada je ovaj dijagram sličan Robinovom dijapozitivu o velikoj arhitekturi podataka, a sadrži podatke u stvarnom vremenu i povijesne podatke. Kako se pojavljuju novi izvori podataka i zahtjevi u stvarnom vremenu, veliki podaci vidimo kao dodatni dio cjelokupne IT arhitekture. Ti novi izvori podataka uključuju strojno generirane podatke, nestrukturirane podatke, standardni volumen i brzinu i raznolike zahtjeve za koje čujemo u velikim podacima; ne uklapaju se u tradicionalne EDW procese. Pentaho blisko surađuje s Hadoopom i NoSQL-om kako bi pojednostavio gutanje, obradu podataka i vizualizaciju tih podataka, kao i miješanje tih podataka s tradicionalnim izvorima kako bi kupci dobili potpuni uvid u njihovo podatkovno okruženje. To radimo na upravljanje tako da IT može ponuditi cjelovito analitičko rješenje za svoju djelatnost.
Zaključno bih želio istaknuti našu filozofiju oko velike analize i integracije podataka; vjerujemo da su ove tehnologije bolje raditi zajedno s jednom jedinstvenom arhitekturom, omogućujući brojne slučajeve uporabe koji inače ne bi bili mogući. Podatkovna okruženja naših kupaca više su od velikih podataka, Hadoopa i NoSQL. Svi podaci su fer igra. A veliki izvori podataka moraju biti dostupni i raditi zajedno kako bi utjecali na poslovnu vrijednost.
Konačno, vjerujemo da bi se ovi poslovni problemi u poduzećima mogli vrlo učinkovito riješiti podacima, informatičkim tehnologijama i poslovnim linijama moraju raditi zajedno na upravljanom, kombiniranom pristupu analitikama velikih podataka. Hvala vam puno što ste nam dali vremena za razgovor, Eric.
Eric: Kladiš se. Ne, to su dobre stvari. Želim se vratiti na onu stranu vaše arhitekture čim stignemo do pitanja i pitanja. Pa krenimo kroz ostatak prezentacije i hvala vam na tome. Vi se definitivno brzo krećete zadnjih par godina, to moram reći sigurno.
Pa Steve, pusti me da ti predam. I samo kliknite tamo strelicu dolje i krenite za njom. Pa Steve, dajem ti ključeve. Steve Wilkes, samo kliknite onu najudaljeniju strelicu dolje na vašoj tipkovnici.
Steve Wilkes: Idemo.
Eric: Evo.
Steve: To je sjajan uvod koji si mi dao.
Eric: Da.
Steve: Dakle, ja sam Steve Wilkes. Ja sam CCO u WebAction-u. Bili smo samo posljednjih nekoliko godina i definitivno smo se brzo kretali od tada. WebAction je platforma za analizu velikih podataka u stvarnom vremenu. Eric je spomenuo ranije, kakav je, koliko je važno u stvarnom vremenu i koliko u stvarnom vremenu dobijaju vaše aplikacije. Naša platforma osmišljena je za izradu aplikacija u stvarnom vremenu. I omogućiti sljedećoj generaciji aplikacija usmjerenih na podatke koje se mogu postupno graditi i omogućiti ljudima da izrade nadzorne ploče od podataka generiranih iz tih aplikacija, ali s naglaskom na realnom vremenu.
Naša je platforma zapravo platforma krajnjeg do kraja, koja radi sve, od prikupljanja podataka, obrade podataka, pa sve do vizualizacije podataka. Omogućuje više različitih tipova ljudi u našem poduzeću da rade zajedno u stvaranju stvarnih aplikacija u stvarnom vremenu, dajući im uvid u ono što se događa u njihovom poduzeću kako su se dogodile.
I to je malo drugačije od onoga što je većina ljudi vidjela u velikim podacima, tako da je tradicionalni pristup - pa, tradicionalan posljednjih nekoliko godina - pristup s velikim podacima bio da ga se uhvati iz čitave gomile različitih izvora i zatim ga skupite u veliko rezervoar ili jezero ili kako god ga želite nazvati. A zatim ga obradite kada trebate pokrenuti upit na njemu; pokretanje povijesnih analiza velikih razmjera ili čak samo ad hoc upiti velike količine podataka. Sada to djeluje za određene slučajeve upotrebe. Ali ako želite biti proaktivni u svom poduzeću, ako želite zapravo da vam se kaže o čemu se radi, a ne da saznate kada je nešto pošlo po zlu prema kraju dana ili kraju tjedna, onda se doista morate pomaknuti u stvarnom vremenu.
A to malo prebacuje stvari. Obradu pomiče u sredinu. Na taj način učinkovito uzimate one tokove velike količine podataka koji se kontinuirano generiraju u poduzeću i obrađujete ih dok ih dobijete. A zato što ga obrađujete dok ga dobijete, ne morate sve skladištiti. Možete jednostavno pohraniti važne podatke ili stvari koje morate zapamtiti da su se zapravo dogodile. Dakle, ako pratite GPS lokaciju vozila koja se kreću niz cestu, zapravo vas nije briga gdje su svake sekunde, ne trebate ih skladištiti tamo gdje su svake sekunde. Samo se trebate brinuti, jesu li napustili ovo mjesto? Jesu li stigli na ovo mjesto? Jeste li autocestom vozili ili ne?
Stoga je zaista važno uzeti u obzir da što se više podataka generira, tada i tri Vs. Brzina u osnovi određuje koliko podataka generira svaki dan. Što se više generira podataka, morate ih pohraniti. I što više morate pohraniti, duže je potrebno za obradu. Ali ako možete obrađivati kako ste ga dobili, tada ćete dobiti zaista veliku korist i na to možete reagirati. Može vam se reći da se stvari događaju, a ne da ih morate kasnije tražiti.
Dakle, naša je platforma zamišljena da bude visoko skalabilna. Sadrži tri glavna komada - komad nabave, komad za obradu, a zatim komade za vizualizaciju isporuke platforme. Na strani akvizicije, mi ne gledamo samo strojno generirane podatke dnevnika poput web dnevnika ili aplikacija koji imaju sve ostale zapisnike koji se generiraju. 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. Hvala vam.
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. Izvoli.
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. U redu?
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. Pravo?
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. U redu. 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?" U redu.
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. Pravo? 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. U redu?
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. I to je to.
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. Odnesi to.
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. To je dobra poanta. 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. Što misliš?
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. Puno ti hvala. We'll catch you next time. Doviđenja.
