Ottenere dati, decodificarli e farli divenire informazione è sempre meno costoso, le fonti dalle quali attingere i dati sono sempre più numerose, si stanno intrecciando flussi provenienti da Open data, altri da IOT, altri generati da sistemi di intelligenza artificiale e la manipolazione di grandi masse di dati è sempre meno costosa. Le piccole e medie imprese possono trovare un proprio spazio e collocarsi efficacemente lungo la catena del valore che questa economia dell’informazione sta generando ma non tutte riusciranno. Come attrezzarsi per cogliere appieno i benefici della rivoluzione in atto?
Cosa sono i “Big Data”?
Benché il termine big data sia popolare, non vi sono definizioni standard; come dimostra la lista che segue, il termine è stato usato per indicare una ampia varietà di cose:
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Forrester: “Tecniche e tecnologie che rendono gestibile l’utilizzo di grande mole di dati”
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Gartner: “Grande volume, velocità, varietà di informazioni che viene utilizzata per migliorare il processo decisionale”
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IBM: “Più di una questione di dimensioni … si tratta di una opportunità di trovare spunti grazie a dati e contenuti che rendano il business più agile e diano risposta a ciò che prima si riteneva al di fuori di ogni possibilità”.
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Office of the Director of National Intelligence: “un concetto che abilita l’analisi massiva all’interno e tra dati finalizzata all’integrazione delle informazioni
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McKinsey: “Dataset la cui dimensione sia al di sopra delle tipiche capacità di calcolo dei softwaer di catturare, mantenere, gestire e analizzare il dato.
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Wikipedia: “Una collezione di dati così grande e complessa che rendere difficile procedere utilizzando uno strumento di gestione di database tradizionale”
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ZDNet: “Tecnologie e pratiche per il trattamento dei dati che i sistemi di database convenzionali non riescono a gestire efficientemente.”
Big data implica una base dati così ampia che stressa i limiti dei tradizionali database su quattro parametri:
- Volume dei dati
- Varietà di formati, risorse e tipologie
- Velocità della ricerca
- Verifica della veridicità delle conclusioni basata sui dati.
Quanto è grande “…Big”?
Per comprendere quanto sia grande il set di dati in esame quando si discute di Big Data basti pensare che la marina militare Americana dichiara di trattare quotidianamente 200 terabyte di informazioni, equivalenti alla digitalizzazione di tutti i libri contenuti presso la libreria del Congresso, notoriamente una delle più grandi biblioteche del mondo.
Ma anche questo importo, così grande, è da considerarsi minuscolo se paragonato all’intero universo digitale che tratta miliardi di terabyte ogni giorno ed è in crescita esponenziale.
Una delle questioni cruciali diventa, quindi, come dare forma a questi dati affinché siano comprensibili al lettore. Il primo a porsi il problema alla fine del 1800 fu Pearson che – grazie all’utilizzo delle tecniche inventate da Gauss – mostrò come risolvere una equazione capace di descrivere una nuvola di punti su un insieme molto ampio di dati. Cinquant’anni dopo, Togerson propose un sistema multidimensionale per visualizzare le relazioni tra set sempre più ampi di dati.
Il problema computazionale è fondamentalmente un problema di costi: determinare le componenti principali di una nuvola di punti cresce al cubo della dimensione dei punti della nuvola. Avanzamenti nelle scienze matematiche hanno generato nuove risposte relativamente a come sintetizzare e esporre i dati, riducendo i costi computazionali al crescere della quantità di numeri trattati, anche se questo appare come uno dei temi sui quali le scienze matematiche e statistiche stanno ancora lavorando per affinamenti successivi. Nel contesto dell’analisi sui big data dobbiamo scoprire la struttura sottostante ai dati per riuscire a trattarli.
Nel suo articolo del 2013 intitolato ‘A Necessary Disenchantment: Myth, Agency and Injustice in a Digital World’, Nick Couldry discute di quanto la stessa attribuzione del termine “big” rischi di far apparire mitologico l’ambito dei big data, come capace di realizzare una diversa e migliore forma di conoscenza sociale.
E’ evidente che dall’uso automatizzato di una così grande mole di dati si possano ottenere benefici prima non accessibili, la questione è che il dato, una volta sintetizzato, deve essere interpretato.
E trattare il dato è possibile a due condizioni (che in effetti ne racchiudono molteplici al loro):
- primo, che i dati siano raccolti costantemente e che ogni variazione significativa venga tracciata;
- secondo, che i dati vengano aggregati e le correlazioni siano correttamente interpretate
Non tutti i set informativi riescono ad avere le due macro caratteristiche summenzionate.
Visto che solo alcuni set informativi sono in grado di generare informazioni di tal genere, i big data saranno sempre una selezione dell’immensa moltitudine di dati che la vita ci presenta e – se vogliamo – la stessa scelta che noi facciamo nell’osservare un set di dati escludendone altri può fuorviare l’interpretazione.
Come ammette anche la marina militare americana; tecnicamente, l’ammontare di dati è illimitato, ma al crescere del volume di dati cresce la difficoltà di comprenderli, fonderli e usarli efficacemente in tempo. Tutti i dati e le informazioni raccolte dalla marina militare sono potenzialmente utili, ma processarli e farli divenire conoscenza fruibile è limitata dall’investimento in risorse umane che l’ente può mettere a disposizione. La marina sente di essere sempre meno capace di processare tutti i suoi dati dalle fonti possibili.
Può prendere dati da Twitter, GeoEye, National Oceanic and Atmospheric Administration per nominarne alcuni, ma quanti di questi dati possono essere usati correttamente? I dati che collezionano forniscono ai comandanti informazioni sulla posizione del nemico e sulle sue attività, abilitano a migliorare la precisione nella localizzazione di un target nemico, consentono di ottenere informazioni vitali nella localizzazione delle forze alleate. Gli altri sviluppi vengono perduti ed è così anche nella esperienza quotidiana di ciascuno di noi. Davanti a tante informazioni dobbiamo selezionare quelle importanti affinché divengano conoscenza e quotidianamente ne abbandoniamo altre affinchè il nostro cervello riesca a concentrarsi; è alla base di questo sistema che si giustifica per quale ragione dimentichiamo.
Big data e innovazione “data driven”
Guardiamo adesso alle implicazioni che i big data hanno sulla società della conoscenza, soprattutto con riferimento al settore profit.
Il tema dei Big data e dello sfruttamento commerciale delle innovazioni “data-driven” è ancora nella fase di sviluppo iniziale, sebbene tutti si attendano un impatto ampio e molto positive. Nel 2014 Gartner ha previsto un periodo tra i cinque e i dieci anni di intenso sviluppo prima che la produttività si appiattisca.
Chiaramente tale crescita non è dovuta unicamente all’uso dei Big data, ma al modo in cui tale tecnologia si coniuga con numerose altre tecnologie emergenti, dalle automobili a guida autonoma all’uso dell’Internet of Things (IOT), ai sistemi avanzati di analitica a servizio della logistica (Gartner 2015).
In sede europea I Big Data sono stati definiti “Il Nuovo oro” (Kroes 2011) e il “nuovo petrolio” (Kroes 2013) poichè le potenzialità di generare reddito predicono opportunità per €140 miliardi di profitti aggiuntivi’ (Nagy-Rothengass 2014). La rapida espansione del mercato online così come dei canali tradizionali, tocca tutti gli aspetti del business. La crescente collezione e uso di dati sulle transazioni da parte dei consumatori per fini commerciali sta dando prova di essere una fondamentale risorsa di vantaggio competitivo (EDPS 2014, p. 32). I dati sono il capitale del 21mo secolo, capaci di creare lavoro e costruire nuovi mercati, rendono più facile per gli imprenditori analizzare dati finalizzati a creare e vendere prodotti (BusinessZone 2012).
Molteplicità di fonti
Le fonti informative tendono a fondersi: tramite i Big Data possiamo mappare un comportamento e geo riferirlo, tramite l’IOT possiamo automatizzare un flusso di informazioni provenienti dai nostri clienti. Facendo il matching tra le due fonti possiamo aiutare il nostro cliente a costruirsi un benchmark con soggetti che abbiano abitudini simili alle sue senza dover limitare il campo d’osservazione ai clienti dell’azienda stessa.
L’ecosistema
Le imprese di grandi dimensioni stanno fronteggiando la sfida necessaria a raccogliere e gestire i dati e vi sono evidenze che mostrano come anche le piccole e medie imprese stiano gestendo questa sfida e riescano a trovare spazi di innovazione sfruttando gli open data. (Kroes 2013, 2011). Le PMI e le start-up colmano il vuoto tra grandi database costruendo applicazioni che le collegano.
L’uso di Google Maps fornisce un buon esempio: la possibilità di gestire dati geo-localizzati tramite servizi, piattaforme o API era inizialmente a esclusivo beneficio della Google stessa, il valore per le PMI si è evoluto successivamente, quando i dati sono stati collegati alle imprese dando informazioni su dove mangiare, dove fare acquisti, dove passare il tempo libero, etc. Oggi i dati di google maps possono essere consultati tramite API.
Uber, nata nel 2009 ha costruito il suo successo grazie all’uso dei dati di Google Maps, divenendo una impresa globale. Di converso, Uber e le altre aziende che usano dati da Google Maps, stanno sviluppando applicazioni per la guida autonoma (Camhi 2015; Miller 2014). Quindi, la base dati originaria di Google Maps è stata accresciuta con l’innesto di altre fonti informative e ciò sta generando un circuito virtuoso che produce tecnologie nuove e nuovi modelli di business in nuovi settori.
Questo esempio suggerisce che gli investimenti e le infrastrutture originate dalle grandi aziende e dalle altre istituzioni possono dischiudere nuove opportunità non ancora osservabili e condurre a business model innovativi ai quali hanno accesso piccole e medie imprese. Queste opportunità emergono prima in un ecosistema e poi contagiano la collettività nel suo complesso.
Dato, informazione, business
Ciò che fino a questo momento abbiamo sottaciuto è l’articolato nesso che conduce il dato a divenire informazione e quindi business.
E’ assodato che all’interno di un determinato contesto innovativo alcune aziende emergeranno e riusciranno a inferire sulle grandi basi dati messe a disposizione tanto dalla pubblica amministrazione quanto alle grandi imprese secondo l’ordine visto nel grafico precedente.
Ciò accade sul piano mesoeconomico, ma cosa accade nel piano micro? Fin ora abbiamo dato per scontato che la “data driven society” produca innovazioni, ma perché alcune organizzazioni riescono a costruire progetti innovativi e godere dei sempre maggiori benefici provenienti dai Big Data ed altre no?
Alcuni individui sono immersi in ambienti che favoriscono il pensiero innovativo, che consentono di identificare nuovi progetti oggi realizzabili anche grazie alla tecnologia e ai big data.
Per sfruttare appieno le potenzialità dei Big Data deve esser estremamente chiaro il beneficio che l’azienda intende soddisfare e il target al quale si rivolge, al contrario, può (ed è auspicabile che lo sia) essere fumoso l’insieme di strumenti con i quali creare ciò che possa soddisfare il bisogno. Tali fumi si dissolveranno grazie al processo di prova ed errore che verrà affrontato dall’azienda.
Affinchè ciò accada, l’azienda deve divenire una learning organization, i suoi membri devono sentirsi liberi di esprimere le proprie idee senza temere ritorsioni o giudizi in un clima capace di catturare da ciascuno la parte migliore.
Bisogna stendere periodicamente un elenco di problematiche da affrontare, stilare indicatori che rendano possibile cogliere se i progetti attivati siano coerenti con la visione aziendale e a quel punto definire azioni e progetti da raggiungere.
Oggi il costo di accesso al dato sta divenendo vicino allo zero, il costo di elaborazione del dato si assottiglia grazie agli algoritmi e ai processori, ciò che diviene cruciale per lo sviluppo sono idee coerenti che soddisfino bisogni ad alto valore.
La sfida è quindi solo in parte tecnologica, è in larga parte spostata sulla maniera nella quale la leadership viene esercitata, come viene favorito e nutrito un ambiente felice, poiché se è felice è inevitabilmente proattivo e poco burocratico, capace di guardare oltre la visione a tunnel, tipica dell’ambiente stressato.
Piano strategico
Molte aziende perseguono un modello che andrebbe ormai abbandonato, interagiscono con i clienti su base occasionale e, dopo aver identificato i loro bisogni, cercano di soddisfarli fornendo loro i beni o servizi che reputano più idonei. È una strategia che Nicolaj Siggelkow e Christian Terwiesch (Harvard Business Review Maggio – Giugno 2019) definiscono “Acquista ciò che abbiamo”. L’azienda prova a soddisfare un bisogno generico tramite l’offerta di prodotti di alta qualità a prezzi competitivi e basa il marketing e tutti i processi operativi sull’assunzione che sarà possibile avere solo un fugace e superficiale rapporto con il cliente.
Il cliente sente il bisogno di acquistare un determinato bene, si dirige verso il negozio, guarda tra gli scaffali, si perde un po’, poi trova un prodotto simile a ciò che fa il paio con il suo reale bisogno, si informa con il commesso che sparisce per qualche minuto nel magazzino per cercane uno più idoneo a soddisfare il bisogno, riemerge con il prodotto che ritiene più simile oppure non riesce a trovare niente di meglio di quel succedaneo già presente sullo scaffale e gli suggerisce garbatamente di accontentarsi, indorando la pillola alla bell’e meglio. Il cliente acquista, ma rimane insoddisfatto per aver dovuto acquistare un ripiego. Ovviamente, se non è mosso da una emergenza che lo costringa a comprare, deciderà di non acquistare rivolgendosi ad altri. Siggelkow e C. Terwiesch (2019) evidenziano quattro alternative praticabili e più efficaci della strategia “acquista ciò che abbiamo”.
Strategia “Respond to desire”.
Con tale strategia l’azienda provvede a soddisfare i bisogni del cliente il più velocemente possibile. Consegna rapida, flessiblità, esecuzione perfetta. I clienti che amano sedere al posto di guida ed essere padroni delle proprie scelte apprezzano molto questa soluzione. Gli utenti di Uber vogliono che l’auto che hanno prenotato arrivi prontamente, i pazienti vogliono poter contattare un medico in qualunque momento, i clienti del settore commerciale al dettaglio desiderano che i prodotti ordinati online giungano a destinazione prima possibile, un desiderio sul quale Amazon ha basato il suo sviluppo. Ancor più di prima, oggi puoi impostare una funzione che consenta ad Alexa di acquistare direttamente un determinato prodotto al semplice comando vocale e il software farà il resto (dialogando con gli operatori e rispondendo a tutti i passaggi della customer journey al posto del cliente reale).
Ipotizzando una mappa strategica sulla quale il management volesse inserire le principali iniziative guidate da un approccio Respond to Desire, potrebbe avere una forma come questa.
Strategia “Curated offering”
In questo caso le aziende interpretano il bisogno “al posto” del cliente. Partendo dal bisogno, viene identificato il prodotto più idoneo a soddisfarlo. Immaginando di dover acquistare una cartuccia per la stampante, avendo già memorizzato i dati degli acquisti precedenti suggerisce di acquistare lo stesso prodotto autocompilando i campi ed evitando al cliente di digitarli ogni volta. Sono soluzioni che partono dall’esigenza finale del cliente, come ad esempio Blue Apron che permette di acquistare tutti gli ingredienti necessari a fare una determinata ricetta. L’utente non deve creare una lista di ingredienti, sceglie solo la ricetta e tutti gli ingredienti necessari per farla vengono proposti. Diversamente da altri servizi di consegna della spesa a domicilio, che sono più comodi di far la spesa al supermercato, ma non sono diversi nell’approccio, Blue Apron è come se avesse un assistente che si limiti a chiedere cosa vuoi per cena, definendo in autonomia la lista degli ingredienti correlati.
Strategia “Coach Behavior”
Le due strategie precedenti richiedono che il cliente identifichi il proprio bisogno in tempo, cosa che non è sempre in grado di far bene. Con la strategia Coach Behavior l’assistente informatico ricorda proattivamente ai clienti i loro bisogni incoraggiandoli ad andare oltre, proseguendo i passi che li portino all’acquisto. Nike, ad esempio, riesce a seguire gli sviluppi sportivi dei clienti fruitori della sua App e può quindi suggerire abiti idonei allo sport definito dall’utente, mantenendoli sempre motivati e fidelizzati.
Strategia “Automatic Execution”
Tutte le strategie precedenti richiedono il coinvolgimento del cliente. Quest’ultima, invece, consente di soddisfare i bisogni del cliente ancor prima che egli li avverta. In una strategia automatica, i clienti autorizzano l’azienda a prendersi cura di qualcosa e dal quel punto in poi l’azienda si occupa del resto. E’ implicito il forte legame di fiducia tra il cliente e l’azienda così come un flusso informativo molto ricco dal cliente all’azienda e l’abilità di anticipare le esigenze del cliente senza commettere errori.
I clienti più inclini ad accettare tali servizi sono quelli che non hanno remore ad dare costantemente un flusso dati utili all’azienda per calibrare i servizi da offrire. Immaginiamo di avere un IOT in grado di segnalare il livello di consumo di una cartuccia da inchiostro per stampanti, che prima di giungere a fine vita suggerisca l’acquisto della cartuccia nuova. Se il cliente desse l’autorizzazione a spedire il ricambio in automatico non appena si raggiunge il livello minimo, saremmo in presenza di un servizio che applica tale strategia. Stessa strategia si applica nel caso in cui il frigorifero si autoalimenti con il cibo mancante, oppure una stazione abilitata a monitorare i dati biometrici preveda l’arrivo automatico dei paramedici in caso di necessità. A genitori con elevato livello di istruzione si associano spesso alti livelli di reddito e scarso tempo a disposizione per i propri figli. Ciò giustifica l’abbonamento a servizi che inviino con cadenza periodica dei pacchi regali per i figli contenenti giochi educativi (esperimenti di chimica e fisica). Il genitore in questo caso delega ad un automatismo la selezione mensile del gioco.
Le quattro strategie viste sopra possono combinarsi in vario modo e sono comunque rese possibile dal sempre più ridotto costo di ottenimento del dato (diretto dal cliente e indiretto tramite basi dati di terzi) e il suo uso volto a spronare il cliente o – come abbiamo visto nell’ultimo paragrafo – a sostituirlo.
Come possiamo osservare dai quattro casi precedenti, in presenza di una strategia definita, i sistemi di analisi dei dati consentiranno di migliorare la produttività e la conoscenza delle abitudini di consumo del cliente.
Bibliografia
Big Data: Challenges and Opportunities Book Title: Data Flood Book Subtitle: Helping the Navy Address the Rising Tide of Sensor Information Book Author(s): Isaac R. Porche Published by: RAND Corporation. (2014)
Making sense of big data Author(s): Patrick J. Wolfe Source: Proceedings of the National Academy of Sciences of the United States of America, Vol. 110, No. 45 (November 5, 2013), pp. 18031-18032 Published by: National Academy of Sciences
The Myth of Big Data Chapter Author(s): Nick Couldry Book Title: The Datafied Society
Book Subtitle: Studying Culture through Data Book Editor(s): Mirko Tobias Schäfer, Karin van Es Published by: Amsterdam University Press. (2017)
Book Title: Data Flood, Chapter Title: Big Data: Challenges and Opportunities. Book Subtitle: Helping the Navy Address the Rising Tide of Sensor Information Book Author(s): Isaac R. Porche <suffix>III</suffix>, Bradley Wilson, Erin-Elizabeth Johnson, Shane Tierney and Evan Saltzman
Hype Cycle for Emerging Technologies’ in 2014, Gartner 2014
Nicolaj Siggeljow e Christian Terwiesch, Harvard Business Review May – June 2019.
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