Big Data – Von der Datenmasse zur Datenklasse!

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Viele beschreiben Big Data lediglich als große strukturierte und unstrukturierte Datenmengen und wie diese optimal für die Unternehmen respektive dem Management ausgewertet können, um entscheidungsrelevante Erkenntnisse zu ziehen. Big Data beschreibt neben den rasant wachsenden Datenmengen auch neue IT-Lösungen mit deren Hilfe Unternehmen und Organisationen, diese Datenflut so effizient wie möglich für sich nutzen können. Die Geschichte von Big Data und der Umgang mit großen Datensätzen gehen zurück in die 1960er und 1970er Jahre, als man damit begann erste Rechenzentren einzurichten und die relationale Datenbank entwickelte.

Um die Definition etwas genauer zu spezifizieren haben Analysten von Gartner das 3V-Modell ins Leben gerufen. Das 3V-Modell bedeutet Volume, Velocity und Variety:

    • Volume: Eine wichtige Charaktereigenschaft von Big Data ist die große Menge der betrachteten Daten. Sie beginnt im Terabytebereich und ist nach oben nicht begrenzt.
    • Velocity: Die hohen Geschwindigkeiten, mit denen Daten erzeugt und verarbeitet werden, sind ebenso ein Merkmal von Big Data. Abhängig von ihrer Quelle können Daten mit unterschiedlichen Geschwindigkeiten erzeugt werden.
    • Variety: Eine weitere Eigenschaft von Big Data besteht in der Heterogenität der Datenbeschaffenheit. Die betrachteten Daten kommen oft aus sehr verschiedenartigen Quellen, wie z. B. sozialen Netzwerken, elektronischen Handelsplätzen oder Sensoren, unterscheiden sie sich insbesondere in Struktur und Format essenziell voneinander.

Diesem Modell sind mittlerweile zwei entscheidende Faktoren hinzuzufügen.

    • Validity: Hierbei geht es um die Sicherstellung der Datenqualität oder wahlweise die Wahrhaftigkeit und Glaubwürdigkeit von Daten.
    • Value: Das Value bezeichnet den unternehmerischen Mehrwert. Viele Unternehmen haben mittlerweile eigene Datenplattformen aufgebaut. Nun gilt es, daraus auch Business Value zu generieren.

Mit der fortschreitenden Digitalisierung und der damit verbunden und gesammelten Datenmengen werden auch in Zukunft noch viel stärker an Relevanz erhalten. Insbesondere für Unternehmen hat die Entwicklung große Auswirkungen auf die Unternehmenslandschaft.

Heutzuttage werden die großen Datenmengen sowohl von Privatpersonen als auch von Unternehmen täglich produziert. Auf dem Privatsektor entstehen beispielsweise über Social Media, wie z.B. Facebook oder Twitter Unmengen an Daten. Erst mit Eintritt von Facebook bemerkte man erst, wie viele Daten tatsächliche durch die vielen Nutzer erstellt werden. Facebook als ein großes Datensilo von mittlerweile ca. 2 Mrd. Nutzer stellt somit auch Unternehmen vor komplett neuen Möglichkeiten, um die Gewinnung vom Konsumnverhalten sicherzustellen. Nicht nur im Privatsektor werden viele Daten erzeugt, sondern auch bei Unternehmen werden im Sekundentakt Tausende von Informationen, die bspw. während der kompletten Wertschöpfungskette und darüber hinaus fabriziert werden. Insbesondere in der Industrie spielen die großen Datenberge im Rahmen der digitalen Transformation, auch als Industrie 4.0 bezeichnet, eine immer wichtigere Rolle. Dadurch stehen Unternehmen vor neuen Herausforderungen, wie u.a.:

    • Was sind die wesentlichen Einflussfaktoren auf die Produktivität meiner Produktionsstätte?
    • Predictive Analytics: Wie können Maschinenausfälle vermieden bzw. minimiert werden ?
    • Wie kann die Qualität der Produkte erhöht werden?
    • Welche Prozesse innerhalb der Produktionsstätte können optimiert werden?

Dies ist nur ein Auszug dessen was durch Big Data alles angestoßen wird. Die Bedeutung von Big Data hat somit in den unterschiedlichen Bereichen der Gesellschaft Einzug erhalten. Die effiziente Nutzung der Daten können für Unternehmen somit auch Wettbewerbsvorteile darstellen.

 

Chancen von Biga Data

Um das hohe Datenaufkommen in den Griff zu bekommen und so eine optimale Nutzung der Datenberge sicherzustellen, sind innovative Big-Data-Strategien zu konzipieren. Ohne eine passenden Strategie verliert das Unternehmen den Überblick der Daten, was letztlich zur Folge haben kann, dass auf wesentliche Markt – bzw. Kundenveränderungen zu spät reagiert wird.

Mithilfe einer Big-Data-Strategie können Unternehmen Veränderungen frühzeitig erkennen und entsprechend reagieren. Durch Big Data erhofft sich die Wirtschaft neue Erkenntnisse hinsichtlich der bei Interessenten – und Kundenverhalten durch die Generierung benutzerbasierten Profile. Jeder kennt es wahrscheinlich aus den sozialen Netzwerken, dass der eben gesuchte Begriff bei Google (z.B. Herrenuhr) überall auf den Werbebanner von Facebook oder Instagram erscheint. Auch im Rahmen von Industrie 4.0 soll durch Big Data eine Optimierung und Flexibilität der Produktion, Innovationen sowie durch predictive maintenance, um deine effektivere Marktpositionierung zu schaffen. Die Wissenschaft hat ebenso ein großes Interesse an Big Data-Analysen, da auf diese Weise z.B. der Klimawandel oder Bevölkerungswanderungen und Verkehrsstaus untersucht werden können.

Die Chancen für Wirtschaft, Wissenschaft und Politik sind sehr umfassend: Sie reichen von der Produktivitätserhöhung, der Kostenersparnis sowie einer performanten Kundenansprache bis hin zu besseren Prognosen von Krankheiten oder wissenschaftlichen Modellanalysen.

 

Risiken und Ausblick auf Big Data

Die immer schnellere Ansammlung von Daten wird in der Gesellschaft häufig kritisiert und stellt eine besonders große Herausforderung für den Datenschutz und das Persönlichkeitsrecht dar. In den meisten Fällen sind die betroffen Personen, was den Umgang der Daten angeht, nicht einverstanden. Oftmals ist die Verwendung der Daten unklar, so dass sie nicht in der Lage sind diese Daten zu identifizieren und zu kontrollieren. Betroffenen ist meist nicht bewusst, welche Daten sie wirklich bewusst oder unbewusst freigeben. Grundsätzlich sollten nur Daten erhoben werden, dessen Sicherheit man selber gewährleisten kann. Auch für Unternehmen birgt neben den Chancen auch Risiken, wenn es etwa um die Bewältigung der Datenmengen angeht.

Klar ist, dass im Bereich Big Data die Meinungen stark auseinander gehen. Die Befürworter von Big Data sehen das Riesenpotenzial das Big Data mit sich bringt. Neben der Effizienzsteigerungen in den jeweiligen Unternehmensgrößen- und Bereichen, stellt Big Data auch eine Erleichterung der Entscheidungsfindung für Unternehmen dar. Ebenso im Bereich Forschung und Entwicklung, wie z.B. im Bereich der Medizin ist hier ein deutlicher Mehrwert bei der Heilung von Krankheiten offensichtlich. Auch ist die gezielte Vermarktung von bestehenden oder neuen innovativen Produkten durch Big Data für Unternehmen eine vielversprechende Lösung, um z.B. das Kundenverhalten oder mögliche Trends besser zu bewerten, ergo lässt sich die Unternehmensstrategie anpassen. Den größten Vorteil was Big Data mitbringt ist, aber die Transparenz. Nicht nur für Unternehmen, sondern auch im Behördentum spielt das Thema immer eine wichtigere Rolle, um z.B. die Korruption bzw. Terrorismus besser einschätzen zu können. Besonders in den Entwicklungsländern spielt dies eine besondere Rolle.

Ferner kann gesagt werden, dass durch Big Data ein massiver Eingriff in die Privatsphäre jedes einzelnen erfolgt. Daher stellt der Datenschutz ein großes Risiko von Big Data dar. Im Rahmen der DSGVO (Datenschutz- Grundverordnung) sind die Länder der Europäischen Union aufgefordert ihre Datenschutzrichtlinien an die heutigen Verhältnisse anzupassen, damit die Sicherheit und Anonymität der Bürger gewährleistet ist. Ein weiteres großes Risiko ist die rasant wachsende Zahl durch Hackerangriffe. Große Datenbanken sind für Hacker sehr attraktiv, wodurch beim Überqueren der Sicherheit durch ein Hackerangriff ein größerer Schaden entstehen kann. Aus diesem Grund sind neue Sicherheitsvorkehrungen zu treffen, um einen Hackerangriff zu erschweren. Die großen und unterschiedlichen Datenmengen sind für Big Data „Fluch und Segen“ zu gleich. Aufgrund dem hohen Datenaufkommen ist es teilweise unmöglich mithilfe eines Algorithmus optimale Ergebnisse zu erzielen. Innerhalb der großen Datenmenge sind nicht alle Daten für jeden Anwendungsfall nützlich und können so bestimmte Ergebnisse verändern. Damit können falsche Ergebnisse erzielt werden bzw. richtige Muster können nicht erkannt werden.

Hiermit verbunden ist auch die Erkennung von Muster, wo eigentlich keine vorhanden sind. Falsche Verbindungen zwischen unterschiedlichen Daten sind ein Risiko der Big Data Analytics, was letztendlich zu falschen Schlussfolgerungen und Entscheidungen führen kann.

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