Bijna 70% van de bedrijven mist de “big data” trein
Big data is hot. Uit thesisonderzoek van Jonas Vandenbruaene blijkt dat ook bedrijven massaal op zoek gaan naar toepassingen. Echter, de implementatie van de technologie die nodig is om grote hoeveelheden informatie te analyseren verloopt vaak moeizaam. Hierdoor slaagt slechts 30% van de organisaties erin de hype te valoriseren.
“I’ll be back”. Met deze woorden uit de ‘The Terminator’ maakte Arnold Schwarzenegger zich meer dan onsterfelijk. De film uit 1984 gaat (#spoileralert) over slimme computerprogramma’s (artificiële intelligentie) die op eigen houtje een kernoorlog starten. De visuele effecten mogen dan wel archeologisch aanvoelen, de film geeft een mooi inzicht in hoe Jan Modaal vroeger over artificiële intelligentie dacht: als iets onbegrijpelijks, duister en vooral bijzonder gevaarlijk.
Van opgepast naar toegepast
Deze Hollywood-definitie van artificiële intelligentie werd de laatste decennia beetje bij beetje onder de mat geveegd. Door de grote vooruitgang binnen het domein zijn we collectief anders over artificiële intelligentie gaan denken: van een mysterieuze, bedreigende entiteit naar een handig, alledaags gebruiksvoorwerp. Een zakrekenmachine voor gevorderden. De soms vergezochte sci-fi’s werden in sneltempo vervangen door een stortvloed aan artikels waarin de praktische toepassingen van artificiële intelligentie uiteengezet werden. Geniale schaakcomputers, chatbots, zelfrijdende auto’s of verbeterde kankerdiagnoses, de mogelijkheden lijken eindeloos.
Surfen op de data-tsunami
In het eerste deel van het onderzoek wordt gepeild naar de interesse van bedrijfsleiders in analytics: de dataverwerking. Deze tak van de artificiële intelligentie is relevanter dan ooit doordat steeds meer mensen vergroeid raken met hun laptop of smartphone. Hierdoor worden er meer data gecreëerd dan ooit tevoren. Om je een idee te geven: elke dag worden er zo’n 205 miljard (!) e-mails verstuurd en wordt er zo’n 400.000 uur aan Youtube-video’s geüpload. Deze data-tsunami, soms ook wel big data genoemd, is een goudmijn voor bedrijven die weten hoe ze ermee om moeten gaan.
Uit het thesisonderzoek blijkt dat meer dan 70% van de bedrijven analytics als zeer of extreem belangrijk beschouwd. Verder merken we dat organisaties analytics willen inzetten om een opmerkelijk breed spectrum van doelen te bereiken: van het beter begrijpen van de klant tot het verhogen van de omzet. (En om de paranoïde lezer gerust te stellen, het uitroeien van de mensheid werd door geen van de onderzochte bedrijven vermeld als doel.) Echter, slechts de helft van alle analytics-gerelateerde ambities die organisaties hebben, wordt gerealiseerd. Dit brengt ons bij het tweede deel van de thesis, namelijk hoe bedrijfsleiders ervoor kunnen zorgen dat de implementatie van analytics in hun firma succesvol verloopt. Wat moet de CEO van een Vlaams familiebedrijfje doen om zijn firma om te bouwen van een Volkswagen Kever naar een blitse Tesla? Oftewel, hoe kan deze CEO de maturiteit van zijn bedrijf met betrekking tot analytics verhogen.
Een rups leren vliegen
Bedrijfswetenschappers bestuderen de implementatie van nieuwe technologieën in organisaties typisch door gebruik te maken van maturiteitsmodellen. Dit zijn patronen die beschrijven hoe bedrijven veranderen doorheen de tijd. Het is een landkaart die laat zien welk pad er gevolgd wordt voordat een organisatie iets volledig onder de knie heeft. Dit klinkt zeer abstract, laat ik er een voorbeeld bijhalen. Elke vlinder wordt geboren uit een ei. Na verloop van tijd komt er een rups uit het ei. Die rups kruipt in een tentje: de pop, waaruit later een prachtige vlinder tevoorschijn komt. Voilà, een maturiteitsmodel met vier fases.
Zo bestaan er ook in het domein van de analytics verschillende maturiteitsmodellen die beschrijven hoe een bedrijf volwassen wordt. Wat de meeste van deze modellen gemeen hebben is dat ze opgesteld worden door deductie. Dit wil zeggen dat een wetenschapper vanuit bepaalde theoretische aannames een model fabriceert. Deze modellen zijn heel mooi op papier, maar het is niet volledig duidelijk of ze er ook in slagen om de realiteit op een waarheidsgetrouwe manier te beschrijven. En laat dit nu net het doel van de wetenschap zijn!
Bedrijven zijn geen vlinders
Om te begrijpen hoe bedrijven transformeren, is het een goed idee om te werken met inductieve maturiteitsmodellen. Dit zijn modellen die opgebouwd zijn uit het veralgemenen van zeer veel observaties. In plaats van te starten vanuit een theorie kijken we naar hoe het er in de praktijk aan toegaat. Het hoofdgerecht van deze thesis is een nieuw, inductief maturiteitsmodel. Dit werd ontwikkeld door alle data die verzameld werden tijdens de marktstudie samen te voegen en ze te verwerken met een algoritme (een soort computerprogramma). Wat blijkt nu? Bedrijven zijn geen vlinders: groei verloopt vaak niet rechtlijnig. Veel maturiteitsmodellen stellen transformaties voor als eenrichtingsverkeer: een pop wordt een vlinder, en nooit andersom. Als we naar de realiteit kijken zien we dat dit soort terugval weldegelijk voorkomt.
Hierna kunnen we het model gebruiken om te kijken in welke fases bedrijven zich op dit moment bevinden. Welk percentage van de vlinders zitten er nog in een ei, hoeveel rupsen zijn er al, dit soort uitspraken. Na deze analyse merken we dat bijna 70% van de organisaties zich nog op een zeer laag maturiteitsniveau bevinden met betrekking tot big data en analytics. Voor deze groep is het zeer belangrijk om snel na te denken over hoe ook zij de vruchten van analytics kunnen plukken, willen ze op de lange termijn overleven. Het opgestelde maturiteitsmodel kan een wegenkaart zijn voor de bedrijven die hun maturiteit met betrekking tot analytics willen verhogen.
Conclusie
Wat kunnen we uit deze onderzoeksresultaten leren? Ten eerste is de interesse in big data en analytics zeer groot, maar er is nog veel werk aan de winkel. Ten tweede is het voor bedrijfsleiders belangrijk om in te zien dat de ontwikkeling van een bedrijf zeker niet altijd rechtlijnig gebeurt. Ten derde is het voor onderzoekers interessant om te onthouden dat patronen die in theorie goed klinken vaak niet in de realiteit weerspiegeld worden.
En om af te sluiten, hoeveel Terminator-films zullen er gemaakt zijn voordat artificiële intelligentie een bedreiging vormt voor de mensheid? Moeilijk te voorspellen, maar wees maar zeker: they’ll be back.
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