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Eric Kavanagh: Senyores i senyors, hola i benvinguts de nou a l'episodi 2 de TechWise. Sí, efectivament, és hora d’aconseguir savis! Avui tinc un munt de persones realment intel·ligents a la línia per ajudar-nos en aquest esforç. Em dic Eric Kavanagh, per descomptat. Seré el vostre amfitrió, el vostre moderador, per a aquesta sessió de llamps. Aquí tenim molt contingut, persones. Tenim alguns grans noms del negoci, que han estat analistes al nostre espai i quatre dels venedors més interessants. Així doncs, actuarem amb molta bona acció en la trucada d'avui. I, per descomptat, els assistents a l'audiència tenen un paper important a l'hora de fer preguntes.
Així doncs, una vegada més, l’espectacle és TechWise i el tema actual és "Com es pot millorar Analytics en els negocis?" Bviament, és un tema candent on tractarà d'entendre els diferents tipus d'analítica que podeu fer i com pot millorar les vostres operacions, perquè això es tracta al final del dia.
Així que em podreu veure a la part superior, és la vostra veritat. Kirk Borne, un bon amic de la Universitat George Mason. És un científic de dades amb una enorme experiència, experiència molt profunda en aquest espai i mineria de dades i big data i tot aquest tipus de coses divertides. I, per descomptat, tenim el nostre propi doctor Robin Bloor, analista en cap del grup Bloor. Qui es va formar com a actuari fa molts, molts anys. I s’ha centrat realment en tot aquest espai de big data i l’espai analític amb molta intensitat durant l’última mitja dècada. Han passat cinc anys gairebé des que vam llançar el grup Bloor per se. Així el temps vola quan us divertiu.
També escoltarem de Will Gorman, arquitecte en cap de Pentaho; Steve Wilkes, CCO de WebAction; Frank Sanders, director tècnic de MarkLogic; i Hannah Smalltree, directora de Treasure Data. Així, com he dit, és molt contingut.
Llavors, com pot ajudar les analítiques a la vostra empresa? Bé, com no pot ajudar la vostra empresa, francament? Hi ha tot tipus de maneres d’utilitzar les analítiques per fer coses que milloren la vostra organització.
Per tant, racionalitza les operacions. Això és el que no se sap tant sobre coses com fer màrqueting, augmentar ingressos o fins i tot identificar oportunitats. Però la racionalització de les vostres operacions és aquesta cosa realment realment potent que podeu fer per a la vostra organització perquè podeu identificar llocs on podeu externalitzar alguna cosa o bé afegir dades a un procés determinat, per exemple. I això pot agilitzar-la sense exigir a algú que agafi el telèfon perquè truqui o algú que li faci un correu electrònic. Hi ha tantes maneres diferents de agilitzar les vostres operacions. I tot plegat ajuda a reduir el vostre cost, oi? Aquesta és la clau, que redueix el cost. Però també permet atendre millor els vostres clients.
I si penseu en com s’han convertit les persones impacients, ho veig cada dia pel que fa a la interacció entre les persones en línia, fins i tot amb els nostres programes de prestació de serveis que utilitzem. La paciència que la gent té, la duració d’atenció, es redueix cada cop més. I el que significa és que, com a organització, heu de respondre en períodes de temps més ràpids i ràpids per poder satisfer els vostres clients.
Així, per exemple, si algú està al vostre lloc web de transmissió web o navega per intentar trobar alguna cosa, si es frustra i se’n va, bé, potser haureu perdut un client. I depenent del que cobreu pel vostre producte o servei, i potser això és un gran problema. Així, doncs, la conclusió és que la racionalització de les operacions és, entre d'altres, un dels espais més calorosos per aplicar analítiques. I ho fas mirant els números, trossejant les dades, esbrinant, per exemple, "Hola, per què perdem tanta gent a aquesta pàgina del nostre lloc web?" "Per què rebem algunes d'aquestes trucades de telèfon ara mateix?"
I, quan més temps puguis respondre a aquest tipus de coses, més probabilitats tindràs de posar-te al capdamunt de la situació i fer alguna cosa al respecte abans que sigui massa tard. Com que hi ha aquesta finestra del temps en què algú es molesta amb alguna cosa, està insatisfet o intenta trobar alguna cosa, però està frustrat; Aquí teniu una finestra d’oportunitat per contactar-los, agafar-los, interactuar amb aquest client. I si ho feu de la manera adequada amb les dades adequades o la bona imatge del client, entenent qui és aquest client, quina és la seva rendibilitat, quines són les seves preferències, si realment podreu aconseguir-ho? un gran treball per aferrar-vos als vostres clients i aconseguir nous clients. I d’això es tracta.
Així, doncs, ho lliuraré a Kirk Borne, un dels nostres científics de dades de la trucada d'avui. I són rars en aquests dies, persones. En tenim dos, com a mínim, a la trucada, per tant és un gran problema. Amb això, Kirk, t’ho lliuraré perquè parli d’analítica i de com ajuda els negocis. Fes-ho.
Dr. Kirk Borne: Moltes gràcies, Eric. Em pots escoltar?
Eric: Està bé, endavant.
Dr. Kirk: D'acord, bé. Només vull compartir si parlo durant cinc minuts, i la gent està donant les mans a mi. De manera que l’obertura remarca, Eric, que realment heu relacionat amb aquest tema, us parlaré breument sobre els propers minuts sobre el que es tracta d’utilitzar analítiques de grans dades i dades per a les decisions a donar suport. El comentari que vau fer sobre la racionalització operacional, per a mi, cau en aquest concepte d’analítica operativa en què podeu veure gairebé a totes les aplicacions del món si es tracta d’una aplicació científica, una empresa, una ciberseguretat i aplicacions de la llei i govern, salut. Qualsevol nombre de llocs on tinguem un flux de dades i estem prenent algun tipus de resposta o decisió en reacció a esdeveniments i alertes i comportaments que veiem en aquest flux de dades.
I, per tant, una de les coses de les quals voldria parlar-ne és com extreure els coneixements i els coneixements de les grans dades per arribar a aquell punt on realment podem prendre decisions per prendre accions. I freqüentment parlem d’això en un context d’automatització. I avui vull barrejar l’automatització amb l’analista humà que es troba en bucle. Així doncs, vull dir que mentre que l’analista de negocis té un paper important en termes d’apostar, qualificar, validar accions específiques o regles d’aprenentatge automàtic que extreiem de les dades. Però si arribem a un punt en què estem prou convençuts de que les regles de negoci que hem extret i els mecanismes per alertar-nos són vàlids, aleshores podem convertir-ho en un procés automatitzat. Realment fem aquesta operació de racionalització de la qual parlava Eric.
Així doncs, tinc un petit joc sobre les paraules aquí però espero que, si funciona per a vosaltres, he parlat del repte de la D2D. I D2D, els no només dades les decisions en tots els contextos, ho estem analitzant a la part inferior d’aquesta diapositiva que esperem que pugueu veure-la, fent descobriments i augmentant els ingressos de dòlars dels nostres canals d’analítica.
Així, en aquest context, realment tinc aquest paper de comercialitzador aquí ara que treballo i és a dir; el primer que voleu fer és caracteritzar les vostres dades, extreure’n les funcions, extreure les característiques dels vostres clients o qualsevol entitat que segueixi al vostre espai. Potser és un pacient en un entorn d’analítica de salut. Potser és un usuari web si està buscant una mena de problema de ciberseguretat. Però caracteritzeu i extreureu les característiques i, a continuació, extraieu algun context sobre aquest individu, sobre aquesta entitat. A continuació, reuneixes les peces que acabeu de crear i les introduïu en algun tipus de col·lecció a partir de la qual podeu aplicar algorismes d'aprenentatge de màquines.
La raó per la qual ho dic d'aquesta manera és que, diguem-ne només, teniu una càmera de vigilància en un aeroport. El vídeo en si és un volum gran i enorme, i també està molt desestructurat. Però podeu extreure de la videovigilància, la biometria facial i identificar els individus de les càmeres de vigilància. Així, per exemple, en un aeroport, podeu identificar persones específiques, podeu fer un seguiment a través de l’aeroport identificant el mateix individu en diverses càmeres de vigilància. De manera que les funcions biomètriques extretes que realment estigueu fent el seguiment no són el vídeo realment detallat. Però, un cop tingueu aquestes extraccions, podeu aplicar regles i analítiques d’aprenentatge automàtic per prendre decisions sobre si heu d’accionar alguna acció en un cas determinat o si alguna cosa va passar incorrecte o alguna cosa que teniu l’oportunitat de fer una oferta. Si, per exemple, si teniu una botiga a l’aeroport i veieu que aquell client s’acosta al vostre camí i sabeu d’altres informacions sobre aquest client, potser es va interessar realment per comprar coses a la botiga sense impostos o alguna cosa així, feu aquesta oferta.
Quin tipus de coses em referiria per caracterització i potencialització? Per caracterització vull dir, de nou, extreure les característiques i les característiques de les dades. I això es pot generar automàticament, aleshores els seus algoritmes poden extreure, per exemple, signatures biomètriques a partir d’anàlisi de vídeo o de sentiments. Podeu extreure el sentiment del client mitjançant ressenyes en línia o en xarxes socials. Algunes d’aquestes coses poden ser generades per l’ésser humà, de manera que l’ésser humà, analista empresarial, pot extreure funcions addicionals que mostraré a la següent diapositiva.
Alguns d'aquests poden ser complets. I a través de multitud de visites, hi ha moltes maneres diferents de pensar-hi. Però molt senzillament, per exemple, els vostres usuaris venen al vostre lloc web i col·loquen paraules de cerca, paraules clau i acaben en una determinada pàgina i passen temps allà a la pàgina. Que, com a mínim, entenen que estan veient, navegant, fent clic a coses de la pàgina. El que et diu és que la paraula clau que van escriure al principi és el descriptor d'aquesta pàgina perquè va posar el client a la pàgina que preveien. I, per tant, podeu afegir aquesta informació addicional, és a dir, que els clients que utilitzin aquesta paraula clau identifiquessin aquesta pàgina web dins de la nostra arquitectura d'informació com el lloc on es corresponia amb aquest contingut.
Per tant, el crowdsourcing és un altre aspecte que de vegades la gent oblida: el seguiment del pa de pessic dels clients, per dir-ho; com es mouen pel seu espai, ja sigui una propietat en línia o una propietat real. A continuació, utilitzeu aquest tipus de camí que el client pren com a informació addicional sobre les coses que estem veient.
Així doncs, vull dir que les coses generades per humans, o màquina generada, van acabar tenint un context en una espècie d’anotar o etiquetar grànuls o entitats de dades específiques. Tant si aquestes entitats són pacients en un centre hospitalari, com a clients o qualsevol altra cosa. I, per tant, hi ha diferents tipus d’etiquetatge i d’anotacions. Alguna cosa d'això tracta de les dades en si. Aquesta és una de les coses, quin tipus d’informació, quin tipus d’informació, quines són les característiques, les formes, potser les textures i els patrons, l’anomalia, els comportaments que no són d’anomalia. A continuació, extreure’n alguna semàntica, és a dir, com es relaciona això amb altres coses que conec, o aquest client és un client d’electrònica. Aquest client és un client de roba. O aquest client li agrada comprar música.
Així doncs, identificant algunes semàntiques al respecte, aquests clients que els agrada la música solen agradar l'entreteniment. Potser els podríem oferir alguna altra propietat d'entreteniment. Entenent així la semàntica i també alguna provenència, que bàsicament es diu: d'on prové, qui va proporcionar aquesta afirmació, a quina hora i quina data, en quina circumstància?
Així, un cop tingueu totes aquestes anotacions i caracteritzacions, afegiu-hi al següent pas, que és el context, el tipus de qui, què, quan, on i per què. Qui és l’usuari? Quin era el canal on van entrar? Quina era la font de la informació? Quin tipus de reutilitzacions hem vist en aquesta informació o producte de dades en concret? I quin és el valor del procés empresarial? A continuació, recopileu aquestes coses i gestioneu-les, i, en realitat, ajudeu a crear bases de dades, si voleu pensar-ho així. Permet que siguin recopilables, reutilitzables, per altres analistes empresarials o per un procés automatitzat que, la propera vegada que vegi aquests conjunts de funcions, el sistema pugui emprendre aquesta acció automàtica. I, per tant, arribem a una mena d'eficiència analítica operativa, però si més recopilem informació útil i completa, la cuidem per a aquests casos d'ús.
Ens posem al negoci. Fem les analítiques de dades. Busquem patrons interessants, sorpreses, detalls de novetats, anomalies. Busquem les noves classes i segments de la població. Busquem associacions i correlacions i enllaços entre les diverses entitats. A continuació, fem servir tot això per impulsar el nostre descobriment, decisió i procés de presa de dòlars.
Aquí hi ha una darrera diapositiva de dades que només tinc resumida bàsicament, mantenint l'analista empresarial al corrent, de nou, no extreureu aquest ésser humà i és important mantenir-lo allà.
De manera que aquestes característiques, totes les proporcionen màquines o analistes humans o fins i tot el grup de serveis. Apliquem aquesta combinació de coses per millorar els nostres conjunts d’entrenament dels nostres models i acabem amb models predictius més precisos, menys falsos positius i negatius, un comportament més eficient, intervencions més eficients amb els nostres clients o amb qui vulgui.
Al final, acabem de combinar l'aprenentatge automàtic i les grans dades amb aquest poder de la cognició humana, que és on arriba aquest tipus d'etiquetatge d'anotacions. I això pot conduir a través de visualització i analítica visual. eines o entorns de dades immersives o el crowdsourcing. Al final del dia, el que realment està fent és generar el nostre descobriment, els coneixements i la D2D. I aquests són els meus comentaris, així que gràcies per escoltar.
Eric: Ei, això sona molt bé i deixa'm avançar i lliurar les claus al doctor Robin Bloor per donar la seva perspectiva també. Sí, m’agrada escoltar-vos comentaris sobre aquest concepte de racionalització d’operacions i parleu d’analítica operativa. Crec que es tracta d’una gran àrea que s’ha d’explorar força a fons. I suposo que, ràpidament abans de Robin, et portaré de tornada, Kirk. Cal que tingueu una col·laboració força important entre diversos agents de l'empresa, oi? Heu de parlar amb la gent d’operacions; heu d'aconseguir la vostra gent tècnica. De vegades us ofereixen persones de màrqueting o persones de la vostra interfície web. Es tracta de grups normalment diferents. Té alguna bona pràctica o suggeriment sobre com es pot aconseguir que tothom posi la pell al joc?
Kirk: Bé, crec que això ve amb la cultura empresarial de la col·laboració. De fet, parlo dels tres tipus C de la cultura analítica. Una és la creativitat; un altre és la curiositat i el tercer és la col·laboració. Així doncs, voleu persones creatives i serioses, però també heu d’aconseguir que aquestes persones puguin col·laborar. I realment comença des de la part superior, aquest tipus de construir aquesta cultura amb persones que haurien de compartir obertament i treballar junts per assolir els objectius comuns del negoci.
Eric: Tot té sentit. I és que realment heu d'aconseguir un bon lideratge al capdamunt perquè això passi. Així que anem endavant i lliurem-lo al doctor Bloor. Robin, el pis és teu.
Robin Bloor: D'acord. Gràcies per aquesta presentació, Eric. D’acord, la manera com s’observen aquestes mostres, perquè tenim dos analistes; Veig la presentació de l'analista que els altres no. Sabia què diria Kirk i només vaig a un angle completament diferent perquè no ens anessin massa.
Llavors, del que estic parlant o que pretenc parlar aquí és el paper de l'analista de dades versus el paper de l'analista de negocis. I la manera que ho caracteritzo, bé, amb la llengua de galta fins a cert punt, és una mica de cosa Jekyll i Hyde. La diferència entre els científics de dades i, en teoria, almenys saben el que fan. Si bé els analistes empresarials no ho són, està bé amb el funcionament de les matemàtiques, en què es pot confiar i en què no es pot confiar.
Així, només ens limitem al fet que anem fent això, perquè l’anàlisi de dades de sobte s’ha convertit en una gran cosa a part que realment podem analitzar quantitats molt grans de dades i extreure’n dades de fora de l’organització; es paga. La manera en què ho miro - i crec que això només s’està convertint en un cas, però crec que és un cas - l’anàlisi de dades és realment una R + D empresarial. El que realment realitzeu d’una manera o altra amb l’anàlisi de dades és mirar un procés empresarial d’una manera o si aquesta és la interacció amb un client, ja sigui amb la forma en què opera la vostra venda al detall, la forma en què es desplega. les vostres botigues. No importa quin és el problema. Esteu veient un procés empresarial determinat i esteu intentant millorar-lo.
El resultat de la investigació i el desenvolupament amb èxit és un procés de canvi. I podeu pensar en fabricar, si voleu, com a exemple habitual d’això. Perquè a la fabricació, la gent recull informació sobre tot per intentar millorar el procés de fabricació. Però crec que el que ha passat o el que està passant a grans dades és que tot això s’aplica a totes les empreses de qualsevol tipus de qualsevol manera que algú pugui pensar. Pràcticament qualsevol procés empresarial està pendent d'examinar si podeu reunir dades al respecte.
Així doncs, això és una cosa. Si voleu, això va a la qüestió de l’anàlisi de dades. Què poden fer les analítiques de dades per al negoci? Bé, pot canviar el negoci completament.
Aquest diagrama en concret que no descriuré en cap detall, però es tracta d’un esquema al qual vam arribar a culminar el projecte de recerca que vam fer durant els primers sis mesos d’aquest any. Aquesta és una forma de representar una arquitectura de big data. I cal assenyalar diverses coses abans de passar a la següent diapositiva. Hi ha dos fluxos de dades aquí. Un és un flux de dades en temps real, que va al llarg de la part superior del diagrama. L’altre és un flux de dades més lent que va al llarg de la part inferior del diagrama.
Mireu la part inferior del diagrama. Tenim Hadoop com a reservori de dades. Tenim diverses bases de dades. Tenim tot un conjunt de dades amb una gran quantitat d’activitats que hi passen, la majoria de les quals són activitats analítiques.
El punt que plantejo aquí i l'únic punt que vull fer aquí és que la tecnologia és difícil. No és senzill. No és fàcil. No és una cosa que algú nou al joc pugui simplement combinar. Això és bastant complex. I si voleu instrumentar un negoci per fer analítiques de confiança en tots aquests processos, no és una cosa que passi específicament ràpidament. Necessitarà molta tecnologia per afegir a la barreja.
Bé. La pregunta de què és un científic de dades, podria afirmar ser un científic de dades, perquè realment m’havien format en estadístiques abans que m’hagués format en informàtica. I vaig fer un treball actuarial durant un període de temps, per tant sé la forma que organitza un negoci, l'anàlisi estadística, també per funcionar. No és una cosa trivial. I hi ha una gran quantitat de bones pràctiques relacionades tant des del punt de vista humà com tecnològic.
Així que, en plantejar-me la pregunta "què és un científic de dades", he plantejat la foto de Frankenstein simplement perquè és una combinació de coses que cal teixir. Hi ha una gestió de projectes implicada. La estadística té un gran coneixement. Hi ha una experiència empresarial del domini, que necessita més que un analista empresarial que un científic de dades. Hi ha experiència o necessitat d’entendre l’arquitectura de dades i poder construir l’arquitecte de dades i hi ha enginyeria de programari. És a dir, probablement sigui un equip. Probablement no sigui un individu. I això vol dir que probablement sigui un departament que hagi d’organitzar-se i que la seva organització s’hagi de pensar bastant àmpliament.
Llançar el fet de l’aprenentatge automàtic. No podríem fer, vull dir, l’aprenentatge automàtic no és nou en el sentit que es coneixen des de fa dècades la majoria de les tècniques estadístiques que s’utilitzen en l’aprenentatge de màquines. Hi ha algunes coses noves, vull dir que les xarxes neuronals són relativament noves, crec que només tenen uns 20 anys, de manera que algunes són relativament noves. Però el problema de l'aprenentatge de màquines era que realment no teníem la potència de l'ordinador per fer-ho. I el que ha passat, a part de qualsevol altra cosa, és que el poder de l’ordinador ja està al seu lloc. I això vol dir una gran quantitat d’allò que, segons els científics de dades, hem fet abans en termes de modelització de situacions, mostreig de dades i, a continuació, la realització de proves per tal de produir una anàlisi més profunda de les dades. De fet, en alguns casos, hi podem alimentar l’ordinador. Només cal triar algoritmes d’aprenentatge automàtic, llençar-lo a les dades i veure què surt. I això és una cosa que pot fer un analista empresarial, no? Però l’analista empresarial ha d’entendre què fan. Vull dir, crec que aquest és el problema, més que res.
Bé, només es tracta de conèixer més coses sobre el negoci de les seves dades que de qualsevol altre mitjà. Einstein no ho va dir, ho vaig dir. Acabo de posar la seva foto per credibilitat. Però la realitat que comença a desenvolupar-se és la que la tecnologia, si s’utilitza adequadament, i les matemàtiques, si s’utilitzen adequadament, podran executar un negoci com qualsevol persona. Ho hem vist amb IBM. Primer de tot, va poder vèncer als millors nois en els escacs i, després, va poder vèncer als millors nois de Jeopardy; però, finalment, podrem vèncer els millors nois en dirigir una empresa. Les estadístiques acabaran triomfant. I és difícil veure com això no passarà, simplement encara no ha passat.
Llavors, el que dic, i aquest és un missatge complet de la meva presentació, són aquests dos temes del negoci. El primer és que podeu aconseguir la tecnologia, no? Pot fer que la tecnologia funcioni per a l’equip que realment pugui presidir-la i obtenir beneficis per al negoci? I, en segon lloc, podeu aconseguir que la gent no sigui correcta? Ambdues són qüestions. I són qüestions que, fins a aquest moment, no es resolen.
D'acord, Eric, us ho passaré. O potser ho hauria de passar a Will.
Eric: En realitat, sí. Gràcies, Will Gorman. Sí, hi aneu, Will. Així doncs, anem a veure Permeteu-me donar-vos la clau de WebEx. I què vas passar? Pentaho, òbviament, vosaltres heu estat durant una estona i heu de començar la BI de codi obert. Però teniu molt més del que abans, així que anem a veure què heu obtingut aquests dies per a les analítiques.
Will Gorman: Absolutament. Hola a tothom! Em dic Will Gorman. Sóc l’arquitecte en cap de Pentaho. Per aquells que no heu sentit a parlar, només he esmentat que Pentaho és una empresa d’analítica i integració de grans dades. Portem deu anys en el negoci. Els nostres productes han evolucionat al costat de la comunitat de grans dades, començant com una plataforma de codi obert per a la integració i analítica de dades, innovant amb tecnologia com Hadoop i NoSQL fins i tot abans que entitats comercials formades al voltant d'aquesta tecnologia. I ara tenim més de 1500 clients comercials i moltes més cites de producció com a resultat de la nostra innovació en codi obert.
La nostra arquitectura és altament incrustable i extensible, construïda a la mida per ser flexible, ja que la tecnologia de dades grans, especialment evoluciona a un ritme molt ràpid. Pentaho ofereix tres grans àrees de producte, que treballen conjuntament per abordar els casos d’ús d’analítica de dades grans.
El primer producte de la nostra arquitectura és Pentaho Data Integration, orientat a tecnòlegs de dades i enginyers de dades. Aquest producte ofereix una experiència visual, arrossega i deixa anar la definició de canals de dades i processos per a orquestrar dades en entorns de dades grans i entorns tradicionals també. Aquest producte és una plataforma lleugera, de metadatabase, d’integració de dades integrada a Java i es pot desplegar com a procés dins MapReduce o YARN o Storm i moltes altres plataformes de lots i temps real.
La nostra segona àrea de productes es basa en l’analítica visual. Amb aquesta tecnologia, les organitzacions i els OEM poden oferir una rica experiència de visualització i analítica arrossegada i analítica per a analistes de negocis i usuaris de negocis mitjançant navegadors i tauletes moderns, permetent la creació ad hoc d’informes i taulers de comandament. A més de la presentació de taulers de taula i informes perfectes per a píxels.
El nostre tercer àmbit de producte se centra en analítiques predictius dirigides a científics de dades, algoritmes d’aprenentatge de màquines. Com s'ha esmentat abans, com les xarxes neuronals i tals, es poden incorporar a un entorn de transformació de dades, permetent als científics de dades passar del modelat a l'entorn de producció, donant accés a predir, i això pot afectar els processos empresarials molt immediatament, molt ràpidament.
Tots aquests productes estan estretament integrats en una única experiència àgil i proporcionen als nostres clients empresarials la flexibilitat que necessiten per fer front als seus problemes empresarials. Estem veient un panorama de grans dades en evolució ràpida en tecnologies tradicionals. Tot el que escoltem d’algunes empreses de l’espai de big data que l’EDW està a punt de finalitzar. De fet, el que veiem als nostres clients empresarials és que han d’introduir grans dades en processos empresarials i informàtics existents i no substituir aquests.
Aquest senzill diagrama mostra el punt en l’arquitectura que veiem sovint, que és un tipus d’arquitectura de desplegament EDW amb integració de dades i casos d’ús de BI. Ara aquest diagrama és similar a la diapositiva de Robin sobre arquitectura de dades grans, incorpora dades en temps real i històriques. A mesura que apareixen noves fonts de dades i requisits en temps real, veiem dades grans com una part addicional de l'arquitectura informàtica global. Aquestes noves fonts de dades inclouen dades generades per màquines, dades no estructurades, el volum estàndard i la velocitat i la varietat de requeriments que se senten a grans dades; no entren en els processos tradicionals d'EDW. Pentaho treballa estretament amb Hadoop i NoSQL per simplificar la ingestió, el processament de dades i la visualització d’aquestes dades, així com barrejar aquestes dades amb fonts tradicionals per oferir als clients una visió completa del seu entorn de dades. Ho fem de manera governada perquè les TI puguin oferir una solució analítica completa a la seva línia de negoci.
Per tancar, voldria destacar la nostra filosofia al voltant de l’analítica i la integració de big data; creiem que aquestes tecnologies funcionen millor junts amb una única arquitectura unificada, cosa que permet una sèrie de casos d’ús que d’altra manera no serien possibles. Els entorns de dades dels nostres clients són molt més que grans dades, Hadoop i NoSQL. Qualsevol dada és un joc just. I les fonts de dades grans han d'estar disponibles i treballar conjuntament per tenir un impacte en el valor del negoci.
Finalment, creiem que per resoldre aquests problemes empresarials a les empreses de manera molt eficaç mitjançant dades, informàtica i línies de negoci han de treballar junts amb un enfocament governat i combinat de les analítiques de grans dades. Bé, moltes gràcies per donar-nos el temps de parlar, Eric.
Eric: Tu apostes. No, això és bo. Vull tornar a aquest costat de la vostra arquitectura a mesura que arribem a les preguntes i preguntes. Per tant, passem per la resta de la presentació i moltes gràcies per això. Sens dubte, vosaltres us heu avançat ràpidament els darrers dos anys, ho he de dir segur.
Steve, deixa'm avançar i t'ho lliuro. Feu clic aquí a la fletxa cap avall i aneu a buscar-la. Així, Steve, us dono les claus. Steve Wilkes, simplement feu clic sobre la fletxa cap a baix del teclat.
Steve Wilkes: Hi anem.
Eric: Allà vas.
Steve: Aquesta és una gran introducció que m'has donat.
Eric: Sí.
Steve: Així que sóc Steve Wilkes. Sóc CCO a WebAction. Només portem uns quants anys i definitivament també ens hem avançat ràpidament des de llavors. WebAction és una plataforma d’anàlisi de dades de grans dades en temps real. Eric va mencionar anteriorment, quin tipus d’important és el temps real i la importància de les aplicacions en temps real. La nostra plataforma està dissenyada per crear aplicacions en temps real. I per habilitar la propera generació d’aplicacions basades en dades que es poden crear de forma incremental i permetre als usuaris crear taulers a partir de les dades generades a partir d’aquestes aplicacions, però centrant-se en temps real.
La nostra plataforma és realment una plataforma de final a extrem complet, des de l'adquisició de dades, el processament de dades, fins a la visualització de dades. I permet que diversos tipus de persones dins de la nostra empresa treballin junts per crear veritables aplicacions en temps real, donant-los a conèixer les coses que succeeixen a la seva empresa tal i com van passar.
I això és una mica diferent del que la majoria de la gent ha vist en grans dades, de manera que l’enfocament tradicional (bé, tradicional els darrers dos anys) - amb grans dades ha estat capturar-lo d’entre un munt de fonts diferents i A continuació, amunteu-lo a un gran embassament o llac o com vulguis anomenar. A continuació, processeu-lo quan hagueu d'executar una consulta. per realitzar anàlisis històriques a gran escala o, fins i tot, fer consultes ad hoc sobre grans quantitats de dades. Ara, funciona per a certs casos d’ús. Però si voleu ser proactiu a la vostra empresa, si realment voleu que us expliqui què passa, més que no pas esbrinar quan alguna cosa va anar malament cap al final del dia o al final de la setmana, aleshores us heu de moure a temps real.
I això canvia una mica les coses. Mou el processament al mig. Així que efectivament esteu prenent aquests fluxos de grans quantitats de dades que es generen contínuament a l'empresa i el processeu a mesura que s'aconsegueixi. I perquè el vagis processant a mesura que ho aconsegueixes, no has de guardar-ho tot. Només podeu emmagatzemar la informació important o les coses que cal recordar que han passat realment. Així, si feu un seguiment de la ubicació GPS dels vehicles que es desplacen per la carretera, no us importa realment on es troben cada segon, no heu d’emmagatzemar on es troben cada segon. Només cal que es preocupi, han deixat aquest lloc? Han arribat a aquest lloc? Han conduït, o no, l’autopista?
Així que és realment important tenir en compte que a mesura que es generen més i més dades, les tres Vs. La velocitat bàsicament determina la quantitat de dades que es generen cada dia. Com més dades es generin, més s'ha d'emmagatzemar. I, més temps heu d’emmagatzemar, més temps triga a processar-se. Però si podeu processar-lo mentre l’obteniu, obteniu un benefici realment gran i podreu reaccionar a això. Se li pot dir que les coses estan passant en lloc d’haver-les de buscar més endavant.
De manera que la nostra plataforma està dissenyada per ser altament escalable. Té tres peces importants: la peça d'adquisició, la peça de processament i després les peces de visualització de lliurament de la plataforma. Al costat de l'adquisició, no només estem buscant les dades de registre generades per màquines, com ara els registres web o les aplicacions que tinguin la resta de registres que es generen. 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. Gràcies.
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. Allà vas.
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. Bé. 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?" Bé.
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. Emporta-t'ho.
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. Què penses?
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. Adeu.