Big data capabilities not available - Organizations need more analytics capabilities (Business Analytics Research Agenda for 2013)
Visualization with the right tools | Business Intelligence, where is the innovation?
EfraudBox address internet fraud (detection engine, investigative tools).
EfraudBox is an ANR project in collaboration with the GIE Cartes Bancaires, Thales, KXEN, Paris laboratories LIP6, LIP13 and LIPN, the National Gendarmerie and the Judicial Police.
Technologies used by Altic: Hadoop, Mahout, Rhipe, SpagoBI, Palo GPU.
Les meilleurs logiciels de DataViz, selon Forrester (Q3, 2012)
Social Analytics Framework for Marketing and Sales Effectiveness
Analytics Model
Differences in use case for Column and Row-Oriented Databases.
Column-Oriented Databases are best used for analytics against large data volumes. Row-oriented Databases are best suited for transaction processing with low-complexity, high-volume datasets. NoSQL databases are best suited for high-complexity, high-volume datasets.
Source: The emerging database landscape
Potential Use Cases for Big Data Analytics
Business Intelligence Maturity
Data & Information 2.0
Site Extension: Big Data in Practice
the concept of site extension is to identify a custom network of sites whose visitor populations are very similar. If a campaign is successful on one site in the network, that performance can then be extended across the other similar sites. The solution requires two key ingredients - a massive data warehouse containing information on billions of monthly impressions and a flexible analytics platform that enables event-level reporting.
Career of the Future: Data Scientist
The Emerging Big Data Stack
The competitive axes and representative technologies on the Big Data stack are illustrated here. At the bottom tier of data, free tools are shown in red (MySQL, Postgres, Hadoop), and we see how their commercial adaptations (InfoBright, Greenplum, MapR) compete principally along the axis of speed; offering faster processing and query times. Several of these players are pushing up towards the second tier of the data stack, analytics. At this layer, the primary competitive axis is scale: few offerings can address terabyte-scale data sets, and those that do are typically proprietary. Finally, at the top layer of the big data stack lies the services that touch consumers and businesses. Here, focus within a specific sector, combined with depth that reaches downward into the analytics tier, is the defining competitive advantage.
