Yearly Archives: 2016

Data Management
Jim Harris 0
Pushing data quality beyond boundaries

Throughout my long career of building and implementing data quality processes, I've consistently been told that data quality could not be implemented within data sources, because doing so would disrupt production systems. Therefore, source data was often copied to a central location – a staging area – where it was cleansed, transformed, unduplicated, restructured

Customer Intelligence
Interview 0
Workshop: Die digitale Customer Journey begleiten – an allen Punkten und in Echtzeit

Omnichannel, Personalisierung und Echtzeit sind heute die wichtigsten Hürden, die der Marketier bei jeder Kundeninteraktion überspringen muss. Woher die Sprungkraft kommt, erfahren Teilnehmer praxisnah bei einem Workshop von Cintellic und SAS im Vorfeld des Dialog Summit am 2. Mai in Frankfurt, der die Digital Customer Journey ins Visier nimmt. Seminarleiter

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Advantages of a standard insurance data model

In my first blog article I explained that many insurance companies have implemented a standard data model as base for their business analytics data warehouse (DWH) solutions. But why should a standard data model be more appropriate than an individual one designed especially for a certain insurance company?

Analytics
Andreas Gödde 0
6 Tipps für modernes Datenmanagement

Erst kürzlich habe ich mit einem CIO über die Zukunft seiner IT-Infrastruktur gesprochen. In einem erstaunlich offenen Gespräch war fast schon Verzweiflung zu hören: Jedes Jahr kämen neue Trends, alle seien megawichtig, und dazu werde neuerdings alles im Big-Data-Umfeld mit CEO-Blick durchleuchtet.

Advanced Analytics
Andrew Pease 0
What is scale?

I've got scale on my mind! While speeding down the rails from Brussels to Paris on the TGV (the sleek, high-speed train), the scale of speed is breathtaking. In previous generations, going from Brussels to Paris for a single-day meeting would have inevitably involved a plane, with check-ins, security, travel

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