The digital age has paved the way for enterprises and individuals communicating through networked environment. Digital communications through mobile networks, information exchange through corporate networks, use of smart cards at train stations, banks and departmental, grocery shops and online stores is today part of our everyday actions and this has led to large volumes of data being generated.

1. Big Data Analytics -Efficient Handling of Data in Digital Age
The digital age has paved the way for enterprises and individuals communicating through
networked environment. Digital communications through mobile networks, information
exchange through corporate networks, use of smart cards at train stations, banks and
departmental, grocery shops and online stores is today part of our everyday actions and
this has led to large volumes of data being generated. Business houses today can gather
abundance of information about the target market from various sources. Nevertheless,
most enterprises are yet to grasp the idea of gathering useful information by putting
together small bits of structured and unstructured data together to understand the larger
customer market.’
Such collection of structured and unstructured data forms what is today referred to as “big
data”. The analysis of such large data volumes will provide enterprises with the hidden key
to understand, acquire and retain their customers and would also give them a competitive
edge. Wikipedia defines big data analytics as the process of collecting, organizing and
analyzing large sets of data to discover patterns and other useful information. Besides, this
process also enables enterprises to identify the data that is vital for decision making.
However, for most enterprises big data analytics is a challenge. Irrespective of the type of
product engineering services, big data analytics involves breaking down the data silos to
gain access to all data stored across the globally spread enterprise locations, systems and
stakeholders. Further, enterprises also need to pull in all unstructured data and
structured data together through common platforms.
The size of big data, is in itself a major challenge faced by enterprises. However, analysts
across industries also point to variety and velocity of such voluminous data as challenges
faced by them. the heterogeneity of data types, the semantic interpretation and the rate
of data transfers cannot be handle by traditional data base applications or software tools.
Deployment of right big data analytics platform will help enterprises with actionable
insights to brand image, steps needed for increasing sales intelligence or methods to close
more deals, increase operational efficiencies and risk management.
To analyze such large volume of data enterprises need to deploy specialized software tools
that will facilitate predictive analytics, data mining, forecasting and data optimization. The
big data analytic tools from the leaders in the industry enables enterprises to process both
unstructured and structured data collected from various sources to arrive at better
business decisions. The leading service provider leverages on big data and cloud to
facilitate enterprises to architect, develop and deploy products and business services. The
open source big data analytics software gives business houses the capabilities to combine
skills and market knowledge and exploit new technologies for both start-up product
development companies to established corporate.
Related links - Enterprise Software, Consumer Electronics