Kaip analitika gali pagerinti verslą? techwise 2 epizodo nuorašas

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Ericas Kavanaghas: Ponios ir ponai, sveiki ir dar kartą sveikinkis „TechWise“ 2 serijoje. Taip, iš tikrųjų laikas sulaukti išmintingų žmonių! Šiandien turiu krūvą tikrai protingų žmonių, kurie padėtų mums stengtis. Mano vardas, žinoma, Ericas Kavanaghas. Aš būsiu jūsų šeimininkas, jūsų moderatorius šiam žaibiškam seansui. Čia turime daug turinio, žmonės. Versle turime keletą didelių vardų, kurie buvo analitikai mūsų erdvėje ir keturi įdomiausi pardavėjai. Taigi, šiandien su skambučiu turėsime atlikti daug gerų veiksmų. Ir, žinoma, jūs, auditorija, vaidinate nemažą vaidmenį užduodant klausimus.

Taigi dar kartą rodo, kad „TechWise“, o šios dienos tema yra „Kaip„ Analytics “gali pagerinti verslą?“ Akivaizdu, kad tai yra aktuali tema, kurioje bus bandoma suprasti įvairius analizės būdus, kuriuos galite atlikti ir kaip tai gali pagerinti jūsų operacijas, nes būtent tai ir yra dienos pabaigoje.

Taigi jūs galite pamatyti save viršuje, tai tikrai jūsų. Dr. Kirkas Borne'as, geras draugas iš George'o Masono universiteto. Jis yra duomenų mokslininkas, turintis didžiulę patirtį, labai gilias žinias šioje erdvėje, duomenų gavybą ir didelius duomenis bei visa tai įdomią medžiagą. Ir, be abejo, mes turime savo „Dr. Bloin Bloor“ vyriausiąjį analitiką. Kas prieš daugelį metų mokėsi kaip aktuarijus. Ir jis per pastaruosius pusmetį iš tiesų labai daug dėmesio skyrė šiai didelėms duomenų ir analitinėms erdvėms. Jau praėjo penkeri metai nuo tada, kai mes patys sukūrėme „Bloor“ grupę. Taigi laikas sklinda, kai jums smagu.

Mes taip pat išgirsime iš Pentaho vyriausiojo architekto Willo Gormano; Steve'as Wilkesas, „WebAction“ CCO; Frankas Sandersas, „MarkLogic“ techninis direktorius; ir „Treasure Data“ direktorė Hannah Smalltree. Taigi, kaip jau sakiau, tai yra daug turinio.

Taigi, kaip analizė gali padėti jūsų verslui? Na, kaip atvirai kalbant, tai negali padėti jūsų verslui? Yra daugybė būdų, kaip analizė gali būti naudojama atliekant dalykus, gerinančius jūsų organizaciją.

Taigi supaprastinkite operacijas. Tai yra tas, apie kurį jūs ne tiek girdite, kiek apie tokius dalykus kaip rinkodara ar pajamų didinimas ar net galimybių nustatymas. Tačiau jūsų operacijų supaprastinimas yra tikrai labai stiprus dalykas, kurį galite padaryti savo organizacijai, nes galite nustatyti vietas, kur, pavyzdžiui, galite ką nors perduoti trečiosioms šalims arba galite pridėti duomenų, pavyzdžiui, tam tikram procesui. Tai gali supaprastinti, nereikalaujant, kad kas nors pakviestų telefoną, kad paskambintų, arba kažkas, kad išsiųstų el. Yra tiek daug skirtingų būdų, kaip patobulinti savo veiksmus. Ir visa tai tikrai padeda sumažinti jūsų sąnaudas, tiesa? Tai yra svarbiausia, tai sumažina sąnaudas. Bet tai taip pat leidžia geriau aptarnauti klientus.

Ir jei jūs galvojate apie tai, kaip nekantrūs žmonės tapo, ir aš tai matau kiekvieną dieną kalbant apie tai, kaip žmonės sąveikauja internete, net su mūsų pasirodymais, paslaugų teikėjais, kuriuos mes naudojame. Žmonių kantrybė ir dėmesys kasdien vis trumpėja. Ką tai reiškia, kad jūs, kaip organizacija, turite reaguoti greičiau ir greičiau, kad galėtumėte patenkinti savo klientus.

Taigi, pavyzdžiui, jei kas nors lankosi jūsų internetinėje transliacijos svetainėje ar naršo bandydamas ką nors surasti, jei nusivilia ir išeina, gerai, galbūt jūs tiesiog praradote klientą. Ir tai priklauso nuo to, kiek mokate už savo produktą ar paslaugą, ir galbūt tai yra didelis dalykas. Taigi esmė yra ta, kad operacijų supaprastinimas, manau, yra viena iš šilčiausių erdvių analizės taikymui. Ir jūs tai darote žiūrėdami į skaičius, susmulkindami duomenis, išsiaiškindami, pavyzdžiui, „Ei, kodėl mes prarandame tiek daug žmonių šiame mūsų svetainės puslapyje?“ "Kodėl mes dabar gauname kai kuriuos iš šių telefono skambučių?"

Ir kuo realesniu laiku galėsite reaguoti į tokią medžiagą, tuo didesnė tikimybė, kad susitvarkysite su situacija ir padarysite ką nors su ja, kol dar ne vėlu. Nes yra tas laiko langas, kai kažkas dėl kažko susierzina, yra nepatenkintas ar bando ką nors surasti, bet yra nusivylęs; jūs gavote progos langą susisiekti su jais, patraukti juos, bendrauti su tuo klientu. Ir jei jūs tai darote tinkamai, turėdami tinkamus duomenis ar gražų kliento vaizdą - suprasdami, kas yra šis klientas, koks yra jų pelningumas, kokie yra jų pasirinkimai - jei tikrai su tuo susitvarkysite, ketinate padaryti puikus darbas išlaikyti savo klientus ir pritraukti naujų klientų. Ir štai apie ką.

Taigi aš iš tikrųjų perduosiu tai Kirkui Borne'ui, vienam iš mūsų skambučių duomenų žinovų šiandien. Ir jie šiais laikais yra gana reti, žmonės. Turime bent du iš jų bent jau skambučiu, kad tai yra didelis dalykas. Turėdamas tai, Kirk, perduosiu tau pasikalbėti apie analizę ir kaip ji padeda verslui. Pirmyn.

Dr Kirkas Borne'as: Labai ačiū, Ericai. Ar tu mane girdi?

Erikas: Puiku, pirmyn.

Dr Kirkas: Gerai, gerai. Aš tik noriu pasidalinti, jei kalbuosi penkias minutes, ir žmonės mostuoja rankomis į mane. Taigi, įžanginė pastaba, Ericai, kad jūs tikrai susiejote su šia tema, artimiausiomis minutėmis trumpai pakalbėsiu apie tai, koks yra didžiųjų duomenų ir duomenų analizės panaudojimas priimtiems sprendimams. Jūsų komentaras, susijęs su operatyviniu supaprastinimu, man atrodo, kad patenka į šią operatyvinės analizės koncepciją, kurioje beveik kiekvienoje pasaulio programoje galite pamatyti, ar tai mokslo programa, verslas, kibernetinis saugumas ir teisėsauga bei vyriausybė, sveikatos apsauga. Bet koks skaičius vietų, kuriose yra duomenų srautas ir mes priimame tam tikrą reakciją ar sprendimą reaguodami į įvykius ir perspėjimus bei elgesį, kuriuos matome tame duomenų sraute.

Taigi vienas iš dalykų, apie kuriuos šiandien norėčiau pakalbėti, yra tarsi tai, kaip jūs kaupiate žinias ir įžvalgas iš didelių duomenų, kad pasiektumėte tą vietą, kur mes iš tikrųjų galime priimti sprendimus imtis veiksmų. Ir dažnai apie tai kalbame automatizavimo kontekste. Ir šiandien noriu automatizavimą suderinti su žmogaus analitiku. Taigi turiu omenyje, kol verslo analitikas čia vaidina svarbų vaidmenį lažybų, kvalifikacijos kėlimo, konkrečių veiksmų patvirtinimo ar mašininio mokymosi taisyklių, kurias mes gauname iš duomenų, prasme. Bet jei mes pasiekiame tašką, kuriame mes esame gana įsitikinę dėl mūsų išskleistų verslo taisyklių ir įspėjimo mechanizmai yra galiojantys, tada mes galime tai gana pakeisti automatiniu procesu. Mes iš tikrųjų darome tą operatyvinį supaprastinimą, apie kurį kalbėjo Ericas.

Taigi aš čia šiek tiek žaidžiu su žodžiais, bet tikiuosi, jei jis jums tinka, aš kalbėjau apie D2D iššūkį. Ir D2D, ne tik duomenų apie sprendimus bet kuriame kontekste, mes pažvelgsime į tai skaidrės apačioje, tikėkimės, kad ją pamatysite, todėl galite rasti atradimų ir padidinti pajamas iš mūsų analizės sistemos.

Taigi šiame kontekste aš dabar turiu šį pardavėjo vaidmenį čia, kai dirbu su tuo, tai yra; pirmas dalykas, kurį norite padaryti, yra apibūdinti savo duomenis, išgauti ypatybes, išskleisti savo klientų ar bet kokio subjekto, kurį stebite savo erdvėje, ypatybes. Gal tai pacientas sveikatos analizės aplinkoje. Galbūt tai interneto vartotojas, jei ieškote tam tikros kibernetinės saugos problemos. Bet apibūdinkite ir išskirkite charakteristikas, tada ištraukite tam tikrą kontekstą apie tą individą, apie tą subjektą. Tada jūs surenkate tuos kūrinius, kuriuos ką tik sukūrėte, ir sudedate juos į kažkokį rinkinį, iš kurio galite pritaikyti mašininio mokymosi algoritmus.

Priežastis, kodėl taip sakau, yra ta, kad, sakykim, oro uoste turite stebėjimo kamerą. Pats vaizdo įrašas yra milžiniškas, didelės apimties ir taip pat labai nestruktūrizuotas. Bet jūs galite išgauti iš vaizdo stebėjimo, veido biometrijos ir atpažinti asmenis stebėjimo kamerose. Pvz., Oro uoste galite atpažinti konkrečius asmenis, juos galite sekti per oro uostą kryžminiu būdu identifikuodami tą patį asmenį keliose stebėjimo kamerose. Taigi išgautos biometrinės savybės, kurias jūs tikrai gavote ir stebite, nėra tikrasis išsamus vaizdo įrašas. Bet kai turėsite tuos ištraukimus, galėsite pritaikyti mašininio mokymosi taisykles ir analizę, kad galėtumėte priimti sprendimą dėl to, ar konkrečiu atveju reikia imtis veiksmų, ar kažkas nutiko neteisingai, ar ką turite galimybę pasiūlyti. Pavyzdžiui, jei esate parduotuvėje oro uoste ir matote, kad klientas ateina į jūsų kelią, ir iš kitos informacijos apie tą klientą žinote, kad galbūt jis tikrai susidomėjo daiktų pirkimu neapmuitinamoje parduotuvėje ar kažką panašaus, padaryk tą pasiūlymą.

Taigi kokius dalykus turėčiau galvoje apibūdindamas ir potencializuodamas? Apibūdindamas turiu omenyje dar kartą išskirti duomenų bruožus ir ypatybes. Tai gali būti sukurta mašina, tada jo algoritmai iš tikrųjų gali išgauti, pavyzdžiui, biometrinius parašus iš vaizdo įrašo ar sentimentų analizės. Kliento požiūrį galite gauti naudodamiesi internetinėmis apžvalgomis ar socialine žiniasklaida. Kai kurie iš šių dalykų gali būti žmonių sukurti, todėl žmogus, verslo analitikas, gali išskleisti papildomas funkcijas, kurias parodysiu kitoje skaidrėje.

Kai kurie iš jų gali būti gaunami šaltinių. Jei jūs ieškote minios, jūs galite galvoti apie tai įvairiais būdais. Pvz., Labai paprastai, jūsų vartotojai ateina į jūsų svetainę ir įdeda paieškos žodžius, raktinius žodžius, o paskui atsiduria tam tikrame puslapyje ir iš tikrųjų praleidžia ten laiką tame puslapyje. Tai, kad jie bent jau supranta, kad jie arba peržiūri, naršo, spustelėja dalykus tame puslapyje. Tai jums sako, kad raktinis žodis, kurį jie įvedė pačioje pradžioje, yra to puslapio aprašymas, nes jis nukreipė klientą į puslapį, kurio jie tikėjosi. Taigi galite pridėti tą papildomą informaciją, ty klientai, kurie naudoja šį raktinį žodį, iš tikrųjų nustatė šį tinklalapį mūsų informacijos architektūroje kaip vietą, kur tas turinys atitinka tą raktinį žodį.

Taigi minios rinkimas yra dar vienas aspektas, kurį kartais pamiršta žmonės, kad būtų galima sekti jūsų klientų džiūvėsėlius; kaip jie juda per savo erdvę, ar tai internetinė nuosavybė, ar nekilnojamasis turtas. Tada naudokitės tokiu keliu, kuriuo klientas naudojasi kaip papildoma informacija apie dalykus, kuriuos mes žiūrime.

Taigi noriu pasakyti, kad žmonių sugeneruoti dalykai arba mašinų sugeneruoti kontekstai buvo tokie, kad jie anotavo ar žymėjo konkrečias duomenų granules ar subjektus. Nesvarbu, ar šie subjektai yra ligoninės ligoniai, klientai ar kas kita. Taigi yra įvairių žymėjimo tipų ir komentarų. Iš dalies tai susiję su pačiais duomenimis. Tai yra vienas iš dalykų, koks informacijos tipas, kokia informacija, kokie yra bruožai, formos, galbūt tekstūros ir modeliai, anomalija, neanomalinis elgesys. Ir tada ištraukite šiek tiek semantikos, tai yra, kaip tai susiję su kitais dalykais, kuriuos aš žinau, ar šis klientas yra elektronikos klientas. Šis klientas yra drabužių pirkėjas. Arba šis klientas mėgsta pirkti muziką.

Taigi, identifikuodami tam tikrą semantiką, šiems klientams, kuriems patinka muzika, dažniausiai patinka pramogos. Gal mes galėtume jiems pasiūlyti kokį nors kitą pramogų objektą. Taigi suprantant semantiką ir tam tikrą kilmę, iš esmės sakoma: iš kur tai atsirado, kas pateikė šį teiginį, kada, kokia data ir kokiomis aplinkybėmis?

Taigi, kai turėsite visas šias pastabas ir apibūdinimus, pridėkite prie to tada kitą veiksmą, tai yra kontekstą, rūšiuokite, kas, kas, kada, kur ir kodėl. Kas yra vartotojas? Kokiu kanalu jie prisijungė? Koks buvo informacijos šaltinis? Kokius pakartotinius naudojimo atvejus matėme šioje konkrečioje informacijos ar duomenų dalyje? O kas yra, tarsi, vertė verslo procese? Ir tada surinkite tuos dalykus ir tvarkykite juos, ir iš tikrųjų padėkite sukurti duomenų bazę, jei norite apie tai pagalvoti. Padarykite, kad jų galėtų ieškoti, pakartotinai naudoti kiti verslo analitikai arba automatizuotas procesas, kuris, kitą kartą pamačius šiuos funkcijų rinkinius, sistema galės atlikti šiuos automatinius veiksmus. Taigi mes pasiekiame tokio tipo analitinį efektyvumą, bet kuo daugiau mes renkame naudingos, išsamios informacijos ir kuruojame ją šiems naudojimo atvejams.

Mes pradedame verslą. Atliekame duomenų analizę. Mes ieškome įdomių modelių, netikėtumų, naujovių pašalinių vietų, anomalijų. Ieškome naujų klasių ir segmentų populiacijoje. Mes ieškome įvairių subjektų asociacijų ir koreliacijų bei sąsajų. Ir tada mes visa tai naudojame norėdami paskatinti savo atradimus, sprendimus ir dolerių priėmimo procesą.

Taigi, vėlgi, čia mes gavome paskutinę duomenų skaidrę, kurią turiu tik iš esmės apibendrindama, laikydamasi verslo analitiko žvilgsnio, vėlgi, jūs neišgaunate to žmogaus ir labai svarbu išlaikyti tą žmogų ten.

Taigi šias funkcijas teikia visos mašinos, žmonių analitikai ar net minios specialistų paslaugos. Mes naudojame tą dalykų derinį, kad patobulintume savo modelių mokymo rinkinius ir galų gale pateiktume tikslesnius nuspėjamuosius modelius, mažiau klaidingų teigiamų ir neigiamų, efektyvesnį elgesį, veiksmingesnes intervencijas su klientais ar kuo kitu.

Taigi dienos pabaigoje mes iš tikrųjų tiesiog deriname mašininį mokymąsi ir didelius duomenis su šia žmogaus pažinimo galia, būtent čia ir atsiranda toks žymėjimo anotacijos kūrinys. Tai gali paskatinti vizualizaciją ir vizualinės analizės tipą. įrankiai ar svaiginanti duomenų aplinka arba minios šaltiniai. Ir dienos pabaigoje tai, ką tai iš tikrųjų daro, sukuria mūsų atradimams, įžvalgoms ir D2D. Tai yra mano komentarai, todėl ačiū, kad klausėtės.

Ericas: Ei, jis skamba puikiai ir leisk man eiti į priekį ir perduoti klavišus gydytojui Robinui Bloorui, kad jis taip pat suteiktų savo požiūrį. Taip, man patinka girdėti jus komentuojant apie tą operacijų koncepcijos supaprastinimą ir jūs kalbate apie operatyvinę analizę. Manau, kad tai yra didelė sritis, kurią reikia gana nuodugniai ištirti. Manau, kad tikrai prieš Robiną aš jus sugrįšiu, Kirk. Tai reikalauja, kad jūs turėtumėte gana reikšmingą įvairių įmonės žaidėjų bendradarbiavimą, tiesa? Jūs turite kalbėtis su operacijas atliekančiais žmonėmis; jūs turite gauti savo techninius žmones. Kartais jūs sutinkate rinkodaros ar interneto sąsajos žmones. Paprastai tai yra skirtingos grupės. Ar turite geriausios praktikos pavyzdžių ar pasiūlymų, kaip priversti kiekvieną įsidėti savo žaidimą?

Dr Kirkas: Aš manau, kad tai ateina su verslo bendradarbiavimo kultūra. Tiesą sakant, aš kalbu apie tris analitinės kultūros rūšies C. Viena jų yra kūrybiškumas; kita yra smalsumas, o trečia - bendradarbiavimas. Taigi jūs norite kūrybingų, rimtų žmonių, tačiau taip pat turite priversti šiuos žmones bendradarbiauti. Ir tai iš tikrųjų prasideda nuo viršaus, tos kultūros kūrimo su žmonėmis, kurie turėtų atvirai dalintis ir dirbti kartu siekdami bendrų verslo tikslų.

Erikas: Visa tai turi prasmę. Ir jūs tikrai turite gauti gerą vadovavimą viršuje, kad tai įvyktų. Taigi, eikime į priekį ir perduokime gydytojui Bloorui. Robinai, grindys tavo.

Dr Robin Bloor: Gerai. Ačiū už šį įžangą, Ericai. Gerai, kad tai, ką šie pakeitė, tai rodo, nes mes turime du analitikus; Aš matau analitiko pristatymą, kad kiti vaikinai to nedaro. Aš žinojau, ką ketina pasakyti Kirkas, ir aš tiesiog einu visiškai kitu kampu, kad per daug nesimatytų.

Taigi tai, apie ką aš iš tikrųjų kalbu ar ketinu kalbėti čia, yra duomenų analitiko ir verslo analitiko vaidmuo. Ir tai, kaip aš apibūdinu tai, gerai, kad kalba yra skruoste tam tikru mastu, yra savotiškas Jekyll ir Hyde dalykas. Skirtumas yra tas, kad duomenų mokslininkai bent jau teoriškai žino, ką jie daro. Nors verslo analitikai nėra tokie, gerai, kaip veikia matematika, kuo galima pasitikėti ir kuo negalima pasitikėti.

Taigi, nusileiskime prie priežasties, kad mes tai darome, dėl to, kad duomenų analizė staiga tapo didžiuliu dalyku, išskyrus tai, kad mes iš tikrųjų galime analizuoti labai didelius duomenų kiekius ir surinkti duomenis iš organizacijos išorės; ar moka. Tai, kaip aš į tai žiūriu - ir manau, kad tai tik tampa pavyzdžiu, bet neabejotinai manau, kad tai yra atvejis - duomenų analizė iš tikrųjų yra verslo MTTP. Tai, ką jūs iš tikrųjų darote vienaip ar kitaip vykdydami duomenų analizę, yra tai, kad žiūrite į verslo procesą vienodai, ar tai yra sąveika su klientu, nesvarbu, ar tai yra jūsų mažmeninės prekybos veikla, ar tai, ką naudojate jūsų parduotuvėse. Visiškai nesvarbu, kokia tai problema. Jūs žiūrite į nurodytą verslo procesą ir bandote jį patobulinti.

Sėkmingų tyrimų ir plėtros rezultatas yra pokyčių procesas. Jei norite, galite pagalvoti apie gamybą, kaip įprastą to pavyzdį. Nes gamindami žmonės renka informaciją apie viską, kad pabandytų patobulinti gamybos procesą. Bet aš manau, kad tai, kas atsitiko ar kas vyksta dideliais duomenimis, visa tai dabar taikoma visoms bet kokio pobūdžio įmonėms bet kokiu būdu, apie ką bet kas gali pagalvoti. Taigi bet kokį verslo procesą reikia išnagrinėti, jei galite surinkti duomenis apie jį.

Taigi tai vienas dalykas. Jei norite, tai bus duomenų analizės klausimas. Ką duomenų analizė gali padaryti verslui? Na, tai gali visiškai pakeisti verslą.

Ši konkreti schema, kurios aš neketinu išsamiai apibūdinti, tačiau tai yra schema, kurią mes sugalvojome kaip tyrimo projekto, kurį atlikome per pirmuosius šešis šių metų mėnesius, kulminaciją. Tai būdas atvaizduoti didelę duomenų architektūrą. Ir nemažai dalykų, į kuriuos verta atkreipti dėmesį prieš pereinant prie kitos skaidrės. Čia yra du duomenų srautai. Vienas iš jų yra realiojo laiko duomenų srautas, einantis palei schemą. Kitas yra lėtesnis duomenų srautas, einantis palei diagramos apačią.

Pažvelkite į schemos apačią. Mes turime „Hadoop“ kaip duomenų rezervuarą. Turime įvairių duomenų bazių. Turime ištisų duomenų, kuriuose aprašyta visa krūva veiklos, iš kurių daugiausia yra analitinė veikla.

Čia noriu pasakyti tik tai, kad labai noriu pasakyti, kad ši technologija yra sunki. Tai nėra paprasta. Tai nelengva. Tai nėra kažkas, ką kiekvienas žaidimo naujokas iš tikrųjų gali tiesiog sudėti. Tai gana sudėtinga. Ir jei jūs ketinate pritaikyti verslą, kad atliktumėte patikimą analizę visuose šiuose procesuose, tai nėra kažkas, kas atsitiks ypač greitai. Norint, kad mišinys būtų pridėtas, reikės daug technologijų.

Gerai. Į klausimą, kas yra duomenų mokslininkas, galėčiau teigti, kad esu duomenų mokslininkas, nes buvau iš tikrųjų mokomas statistikos, prieš tai buvau išmokytas skaičiuoti. Ir tam tikrą laiką dirbau aktuarinį darbą, todėl žinau būdą, kurį organizuoja įmonė, statistinę analizę, taip pat norėdama valdyti save. Tai nėra nereikšmingas dalykas. Ir čia yra nepaprastai daug geriausios praktikos pavyzdžių, susijusių tiek su žmonėmis, tiek su technologijomis.

Taigi uždavęs klausimą „kas yra duomenų mokslininkas“, aš pateikiau Frankenšteino paveikslėlį vien todėl, kad tai yra dalykų derinys, kuriuos reikia megzti kartu. Dalyvauja projekto valdymas. Statistikoje yra gilus supratimas. Yra domeno verslo ekspertizė, kuri, be abejo, yra ne verslo duomenų analitiko, o ne verslo duomenų analitiko problema. Yra patirties ar poreikio suprasti duomenų architektūrą ir sugebėti sukurti duomenų architektą, taip pat programinės įrangos inžinerija. Kitaip tariant, greičiausiai tai yra komanda. Tikriausiai tai nėra individas. O tai reiškia, kad turbūt reikia organizuoti skyrių, o jo organizavimą reikia gana plačiai apgalvoti.

Pasinėrimas į mašinų mokymosi faktą. Turėjome omenyje, kad mašinų mokymasis nėra naujas dalykas ta prasme, kad dauguma statistinių metodų, naudojamų mašininiame mokyme, buvo žinomi dešimtmečius. Yra keletas naujų dalykų, turiu omenyje, kad neuroniniai tinklai yra palyginti nauji, manau, kad jiems yra tik apie 20 metų, taigi kai kurie iš jų yra palyginti nauji. Bet mašininio mokymosi problema buvo ta, kad mes iš tikrųjų neturėjome kompiuterio galios tai padaryti. Ir kas atsitiko, be viso kito, dabar yra kompiuterio galia. Ir tai reiškia nepaprastai daug to, ką mes, tarkime, duomenų mokslininkai, darėme modeliuodami situacijas, imdami duomenis ir vėliau rinkdami juos, kad gautume gilesnę duomenų analizę. Tiesą sakant, kai kuriais atvejais galime tiesiog sunaikinti kompiuterio galią. Tiesiog pasirinkite mašininio mokymosi algoritmus, permeskite juos į duomenis ir pažiūrėkite, kas išeis. Verslo analitikas tai gali padaryti, tiesa? Tačiau verslo analitikas turi suprasti, ką jie daro. Aš turiu omenyje, manau, kad tai tikrai daugiau nei kas nors kitas.

Na, tai yra tik žinoti daugiau apie verslą iš jo duomenų nei bet kuriomis kitomis priemonėmis. Einšteinas to nesakė, aš pasakiau. Aš tiesiog pateikiau jo nuotrauką dėl patikimumo. Tačiau padėtis, kuri iš tikrųjų pradeda formuotis, yra tokia, kai tinkamai naudojant technologiją ir, jei tinkamai naudojama, matematika galės valdyti verslą kaip ir kiekvienas asmuo. Stebėjome tai kartu su IBM. Visų pirma, tai galėtų įveikti geriausius vaikinus prie šachmatų, o tada galėtų įveikti geriausius vaikinus Jeopardijoje; bet galų gale mes sugebėsime įveikti geriausius vyrukus vadovaujant įmonei. Galiausiai statistika triumfuos. Ir sunku suvokti, kaip tai neįvyks, tiesiog dar neįvyko.

Taigi tai, ką aš sakau, ir tai yra visiškai mano pranešimo pranešimas, yra šie du verslo klausimai. Pirmasis yra, ar galite tinkamai pritaikyti technologijas? Ar galite priversti technologijas veikti komandai, kuri iš tikrųjų ketina jai pirmininkauti ir gauti naudos verslui? Ir, antra, ar galite sutvarkyti žmones? Ir jie abu yra klausimai. Jie yra problemos, kurios iki šiol nėra išspręstos.

Gerai, Ericai, aš jums tai grąžinsiu. Arba turėčiau perduoti tai Willui.

Erikas: Tiesą sakant, taip. Ačiū, Will Gorman. Taip, jūs eisite, Will. Taigi, pažiūrėkime. Leiskite man duoti jums „WebEx“ raktą. Taigi, ką jūs ėmėtės? „Pentaho“, aišku, jūs, vaikinai, jau kurį laiką buvote atviroje programinėje įrangoje, kur pradėjote. Bet gavote daug daugiau, nei turėjote anksčiau, todėl pažiūrėkime, ką šiais laikais gavote analizei.

Willas Gormanas: Visiškai. Sveiki visi! Mano vardas Willas Gormanas. Aš esu vyriausiasis „Pentaho“ architektas. Tiems iš jūsų, kurie apie mus negirdėjote, aš ką tik paminėjau, kad „Pentaho“ yra didelė duomenų integravimo ir analizės įmonė. Mes versle dirbame dešimt metų. Mūsų produktai vystėsi greta didžiųjų duomenų bendruomenės, pradedant kaip atvirojo kodo duomenų integravimo ir analizės platforma, kuriant naujoves tokioms technologijoms kaip „Hadoop“ ir „NoSQL“, net prieš tai susikūrus komerciniams subjektams. Dabar mes turime daugiau nei 1500 komercinių klientų ir daug daugiau susitikimų dėl gamybos, nes mūsų naujovės yra atviro kodo.

Mūsų architektūra yra lengvai įterpiama ir plečiama, pritaikyta tam, kad būtų lanksti, nes ypač didžiųjų duomenų technologijos vystosi labai sparčiai. „Pentaho“ siūlo tris pagrindines produktų sritis, kurios veikia kartu spręsdamos didelių duomenų analizės naudojimo atvejus.

Pirmasis produktas, apimantis mūsų architektūrą, yra „Pentaho Data Integration“, kuris yra skirtas duomenų technologui ir duomenų inžinieriams. Šis produktas siūlo vaizdinę, nuimamą, duomenų apibrėžimo duomenų bazę ir duomenų tvarkymo procesus didelėse duomenų ir tradicinėse aplinkose. Šis produktas yra lengva, metaduomenų bazė, duomenų integravimo platforma, sukurta „Java“ ir kurią galima naudoti kaip procesą „MapReduce“ ar „YARN“ ar „Storm“ ir daugelyje kitų paketinių ir realaus laiko platformų.

Antroji mūsų produktų sritis yra vizualinė analizė. Naudodamiesi šia technologija, organizacijos ir originalios įrangos gamintojai gali pasiūlyti turtingą vizualizacijos ir analizės patirtį verslo analitikams ir verslo vartotojams naudodamiesi šiuolaikinėmis naršyklėmis ir planšetiniais kompiuteriais, leisdami ad hoc kurti ataskaitas ir prietaisų skydelius. Taip pat puikiai pritaikytų prietaisų skydelio ir ataskaitų pateikimas.

Trečioji mūsų produktų sritis yra nukreipta į numatomą analizę, skirtą duomenų mokslininkams, mašininio mokymosi algoritmus. Kaip minėta anksčiau, tokius kaip neuroniniai tinklai ir tokius, galima integruoti į duomenų transformavimo aplinką, leidžiančią duomenų mokslininkams pereiti nuo modeliavimo prie gamybos aplinkos, suteikiant prieigą prie prognozių, ir tai gali turėti tiesioginį poveikį verslo procesams, labai greitai.

Visi šie produktai yra tvirtai integruoti į bendrą greitą patirtį ir suteikia mūsų verslo klientams lankstumo, reikalingo jiems sprendžiant verslo problemas. Matome greitai besikeičiančią tradicinių technologijų duomenų bazę. Viskas, ką girdime iš kai kurių bendrovių didelėje duomenų erdvėje, kad EDW yra arti pabaigos. Tiesą sakant, tai, ką matome savo įmonės klientuose, yra tai, kad jie turi įvesti didelius duomenis į esamus verslo ir IT procesus, o ne pakeisti šiuos procesus.

Ši paprasta schema parodo dažnai matomą architektūros tašką, kuris yra EDW diegimo architektūros tipas su duomenų integracija ir BI naudojimo atvejais. Dabar ši schema yra panaši į Robino skaidrę apie didelių duomenų architektūrą, joje yra realiojo laiko ir istoriniai duomenys. Atsiradus naujiems duomenų šaltiniams ir realaus laiko reikalavimams, didelius duomenis matome kaip papildomą bendros IT architektūros dalį. Šie nauji duomenų šaltiniai apima mašinų sugeneruotus duomenis, nestruktūrizuotus duomenis, standartinį tūrį, greitį ir įvairių reikalavimų, apie kuriuos girdime dideliuose duomenyse, įvairovę; jie netelpa į tradicinius EDW procesus. „Pentaho“ glaudžiai bendradarbiauja su „Hadoop“ ir „NoSQL“, kad būtų supaprastintas šių duomenų pateikimas, apdorojimas ir vizualizavimas, taip pat derinant šiuos duomenis su tradiciniais šaltiniais, kad klientams būtų suteikta visa informacija apie jų duomenų aplinką. Mes tai darome tvarkingai, kad IT galėtų pasiūlyti išsamų analizės sprendimą jų verslo srityje.

Baigdamas norėčiau pabrėžti mūsų filosofiją apie didžiųjų duomenų analizę ir integraciją; mes tikime, kad šios technologijos yra geriau suderintos su viena bendra architektūra, leidžiančia panaudoti daugybę naudojimo atvejų, kurie kitaip būtų neįmanomi. Mūsų klientų duomenų aplinka yra ne tik dideli duomenys, „Hadoop“ ir „NoSQL“. Bet kokie duomenys yra sąžiningas žaidimas. O dideli duomenų šaltiniai turi būti prieinami ir veikti kartu, kad būtų daroma įtaka verslo vertei.

Galiausiai, mes tikime, kad norint efektyviai išspręsti šias verslo problemas įmonėse naudojant duomenis, IT ir verslo kryptis, reikia dirbti kartu vadovaujantis, mišriu požiūriu į didžiųjų duomenų analizę. Na, labai ačiū, kad suteikėte mums laiko pasikalbėti, Eric.

Erikas: Tu lažinkis. Ne, tai geri dalykai. Norėčiau grįžti į tą jūsų architektūros pusę, kai pateksime į klausimus ir atsakymus. Taigi pereikime prie likusio pristatymo ir labai už tai ačiū. Jūs, vaikinai, neabejotinai greitai judėjote pastaruosius porą metų, turiu tai pasakyti.

Taigi, Steve'as, leisk man eiti į priekį ir perduok tau. Ir tiesiog spustelėkite ten rodyklę žemyn ir eikite jos ieškoti. Taigi, Steve'as, aš tau duodu raktus. Steve'as Wilkesas, tiesiog spustelėkite toliausiai žemyn nukreiptą rodyklę ten, klaviatūroje.

Steve'as Wilkesas: Ten mes einame.

Erikas: Ten tu.

Steve'as: Vis dėlto tai puikus įvadas, kurį man davei.

Erikas: Taip.

Steve: Taigi aš esu Steve Wilkes. Aš esu „WebAction“ CCO. Mes gyvename tik pastaruosius porą metų ir nuo to laiko neabejotinai sparčiai judame. „WebAction“ yra realaus laiko didelių duomenų analizės platforma. Erikas jau anksčiau minėjo, koks yra tikrasis laikas ir koks yra jūsų programų realus laikas. Mūsų platforma skirta kurti realaus laiko programas. Be to, įgalinti naujos kartos duomenų valdomas programas, kurias galima laipsniškai kurti, ir leisti žmonėms kurti prietaisų skydelius iš tų programų sugeneruotų duomenų, tačiau daugiausia dėmesio skiriant realiajam laikui.

Mūsų platforma iš tikrųjų yra visa apimanti platforma, atliekanti viską nuo duomenų rinkimo, duomenų apdorojimo iki duomenų vizualizacijos. Ir tai suteikia galimybę keliems skirtingiems įmonės tipo žmonėms dirbti kartu kuriant tikras realiojo laiko programas, suteikiant jiems galimybę suprasti, kas vyksta jų įmonėje.

Ir tai šiek tiek skiriasi nuo to, ką dauguma žmonių matė dideliais duomenimis, todėl tradicinis požiūris - na, tradicinis per pastaruosius porą metų - metodas, turintis didelius duomenis, turėjo jį sugauti iš daugybės skirtingų šaltinių ir tada supilkite jį į didelį rezervuarą ar ežerą ar bet ką, ką norite vadinti. Ir tada apdorokite, kai jums reikia paleisti užklausą; atlikti didelės apimties istorinę analizę ar net tiesiog atlikti ad hoc užklausas dėl didelių duomenų kiekių. Dabar tai veikia tam tikrais atvejais. Bet jei norite būti iniciatyvūs savo įmonėje, jei norite, kad jums iš tikrųjų būtų pasakyta, kas vyksta, o ne sužinoti, kai dienos ar savaitės pabaigoje kažkas nutiko ne taip, tada jums tikrai reikia judėti į realų laiką.

Ir tai šiek tiek keičia aplinkybes. Tai perkelia apdorojimą į vidurį. Taigi efektyviai naudojate didelius duomenų srautus, kurie nuolat generuojami įmonėje, ir apdorojate juos, kai tik gaunate. Ir todėl, kad apdorojate kaip tik gaunate, todėl neprivalote visko saugoti. Galite tiesiog išsaugoti svarbią informaciją ar dalykus, kuriuos jums reikia atsiminti, kad iš tikrųjų įvyko. Taigi, jei stebite keliais judančių transporto priemonių GPS vietą, jums visiškai nesvarbu, kur jos yra kiekvieną sekundę, jums nereikia laikyti ten, kur jos yra kiekvieną sekundę. Jums tiesiog reikia rūpintis, ar jie paliko šią vietą? Ar jie atvyko į šią vietą? Jie važiavo autostrada, ar ne?

Taigi tikrai svarbu atsižvelgti į tai, kad kai sugeneruojama vis daugiau duomenų, tada trys V. Greitis iš esmės lemia, kiek duomenų sugeneruojama kiekvieną dieną. Kuo daugiau duomenų sugeneruota, tuo daugiau turėsite saugoti. Ir kuo daugiau turite laikyti, tuo ilgiau reikia apdoroti. Bet jei jūs galite apdoroti, kai tik gaunate, tada jūs gaunate tikrai didelę naudą ir galite į tai reaguoti. Jums gali būti pasakyta, kad dalykai vyksta, o ne ieškoti jų vėliau.

Taigi mūsų platforma yra sukurta taip, kad būtų lengvai keičiama. Jį sudaro trys pagrindiniai elementai - įsigijimo, perdirbimo ir vėliau pristatymo vizualizacijos platformos elementai. Įsigijimo pusėje mes ne tik žiūrime į mašinų sugeneruotus žurnalų duomenis, tokius kaip žiniatinklio žurnalai ar programos, kuriose yra visi kiti kuriami žurnalai. 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. Ačiū.

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. Prašom.

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. Gerai. 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?" Gerai.

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. Pašalink.

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. Ką tu manai?

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. Thank you so much. We'll catch you next time. Iki.