What does it take to make an organization “smart”?

The website “Answers.com” defines the word “smart” as “characterized by sharp, quick thought”. From the standpoint of those executives like the Chief Operations Officer, this definition means that all the required data to make a sharp, quick decision is at hand and organized to enable them to reach the right decision quickly.

Disproving Myths of Big Data

At a recent conference, I heard a dozen speakers interchangeably refer to big data as “e-commerce” or “digital marketing”. Those are components of big data, but the terms should not be used interchangeably. Unstructured big data refers to information in the cloud, including e-commerce, digital marketing, geo-spacial information, RFID, mobile offers, video, mobile wallets and payments, comments, tweets, NFC (Near Field Communication), blogs, “likes”, schematics, photos, infographics, clicks, QR codes, online searches and much more. Structured big data mostly refers to internal data, including shipments,…

A view on “Analytics 3.0″ and our approach towards successful implementation

Authors: Dr. Günter Koch and Andreas Hufenstuhl It appears that a new term “Analytics 3.0”[1] has been recently coined within the Big Data domain.  Herein  “1.0” relates to classical business intelligence and data warehousing solutions. As a next step “2.0” refers to new, mainly opensource based solutions, where   massive amounts of unstructured data are being analyzed, that specialized companies like start-ups and online traders have deployed. “3.0” would mean a combination of classical and new solutions that would optimize the business performance of enterprises in…

Transparenz statt Bauchgefühl – Entscheidungsfindung durch IT-Kartografie

Management hat die Aufgabe, nachhaltige Entscheidungen zu treffen. Die Komplexität unserer Umwelt und dadurch auch unserer IT-Systeme ist immens. Und sie ist so wenig durchschaubar, dass  oft diese Entscheidungen aus dem Bauchgefühl heraus statt auf Basis von Fakten getroffen werden.

Neue Umfrage – Datenvisualisierung für Big Data

Autoren: Frank Hajen und Andreas Hufenstuhl Ein Bild sagt mehr als 1.000 Worte – aber was hat das mit Big Data zu tun? Sind Visualisierungen der Schlüssel, um aus Massendaten Mehrwert zu ziehen? Zumindest kann man sagen, dass schicke Grafiken von mehr Menschen verstanden werden als ellenlange Ergebnistabellen. Damit ließen sich immerhin schon mehr Ressourcen an die Analyse setzen. Dagegen spricht, dass komplexe Dinge auch komplexe Lösungen erfordern (Ashby) – und dass gerade die Chancen von Big Data Spezialistenwissen erfordern (Stichwort: Data Science).

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