Big data analytics is the process of examining large data
sets to uncover hidden patterns, unknown correlations, market trends,
customer preferences and other useful business information. The analytical
findings can lead to more effective marketing, new revenue opportunities,
better customer service, improved operational efficiency, competitive
advantages over rival organizations and other business benefits.
The
primary goal of big data analytics is to help companies make more informed
business decisions by enabling data scientists predictive modelers and
other analytics professionals to analyze large volumes of transaction data, as
well as other forms of data that may be untapped by conventional business
intelligence programs. That could include Web server logs and Internet
stream data, social media content and social network activity reports,
text from customer emails and survey responses, mobile-phone call detail
records and machine data captured by sensors connected to the Internet of
things.
Big data
can be analyzed with the software tools commonly used as part of advanced
analytics disciplines such as predictive analytics, Data
mining, Text analytics and Statistical Analysis.
Potential
pitfalls that can trip up organizations on big data analytics initiatives
include a lack of internal analytics skills and the high cost of hiring
experienced analytics professionals. The amount of information that's typically
involved, and its variety, can also cause data management headaches, including
data quality and consistency issues. In addition, integrating Hadoop
systems and data warehouses can be a challenge, although various vendors now
offer software connectors between Hadoop and relational databases, as well as
other data integration tools with big data capabilities.
SAP has whole range of solution for taking care of entire analytics scope.
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