Business
analytics is all about transforming irrelevant and meaningless data into
valuable insights. For analytical practices
and approaches to reach their full and optimum potential, business analytics
tools and platforms are required to make it easy for customers to take action
based on their new insights. They must analyze, feed and monitor the final
results of actions taken back into the system in an automated and systematic
way to improve and inform the predictive models that led to the insights at the
very first place. One can actually consider this process as of closing the big
data analytical loop. For an example – A business consumer at a big vendor uses
self business intelligence tools to decide whether to offer or not a new
promotional campaign. In between user analyzes that the three hour online sale will
likely result in significant revenue generation based on the intelligence yielded
by the underlying analytics engine. The user has to implement the campaign,
keep a track of its performance and makes necessary adjustments to future ones
as assured and guaranteed.
In existing
environments, the foremost step needs the user to exit the business
intelligence application and log into a different campaign management system to
raise the sales and its related promotional efforts. The user afterwards waits
for a day or two to get the canned and static report with metrics and finally
suggest to a central analytics team to adjust the analytical models based on
the positive or negative results. If the user is lucky, those suggestions might
make their way into the analytic process and loop will never be closed. Customers
continue to make decisions entirely based on the analytics and intelligence essentially
frozen and numb in time till someone lifts the chain to redo the entire process.
Again the whole process gets repeated.
Closing the
business analytical loop needs essential anticipation into how data and
application infrastructures are crafted and defined. Big data visualization
strategies, analytical engines and transactional systems should be capable to bi
directionally interact in real time situations. The transactional and BI
applications should be tightly blended with the functionality built into BI applications.
In an ideal manner, non relational databases should play a role to board multi
structured data including social and machine generated data. Cultural issues should
be overcome and no longer be an excuse acceptable from IT pros, analysts and business
user’s side for clinging on inefficient, outdated and hoaxed analytic
processes. So the next time the user conduct a similar kind of analysis, the
big data recommendation engine suggestions generated will be informed by the
relative success or failure of real world campaigns and the entire process will
get repeated. Moreover more iteration in the recommendations and analytics improvises
and increases the revenue generation.
Along with this, big
data pioneers and supporters maintain that the sheer volume of data and
information diminishes the effects of occasional poor quality of data. If you
are browsing for peta bytes of data to determine historical trends, a few data input
issues will barely list as a glitch on a dashboard or reports. It does not mean
that the data quality is not vital to big data. It is true in real time
transactional situations. Likewise big data applications that recommend doses
and medicines for critically ill patients are better be entrusting over good
data. Same goes for big data operational applications that affirm commercial
aviation, industrial internet use cases and the power grid. It is mandatory for
professionals to acknowledge the source and structure of the data along with
the data quality prerequisites for apt big data use cases including
recommendation engine in order to evaluate the type and quality of tools and
measures to apply. And, the biggest key players dealing in big data and its
real time use cases are IBM, Oracle, Microsoft, CSC,
IB Technology , Informatica
and Infosys.
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