There is perhaps no one size fits all Big Data solution; but you may craft a holistic analytics strategy.
Business leaders at all sectors are pondering weather they have data, capability and infrastructure ready to explore the big opportunities Big Data may bring up. The forward-look businesses will put a holistic and
comprehensive Big Data strategy in place to ensure that they are
getting the most out of their collective investment along with the necessary
governance needed to execute the strategy across the organization. But
tactically, will organization have on Big Data initiative or many?
The data analytics
can apply to different business perspective: Analytics helps optimize
various business management, directly or indirectly related to long-term
revenue. Include traditional optimization, root cause analysis, and statistical
analysis / machine learning / data mining to boost efficiency of marketing
campaigns, price optimization, inventory management, finance and tax engineering,
sales forecasts, product reliability, fraud and risk management, user
retention, product design, ad spend, employee retention and predicting success
of new hires, competitive intelligence leveraging external data source,
guessing new trends based on automated analysis of user feedback and much more.
And the role as an analyst is to understand the business context, leverage
data, and come up with recommendations that can be acted on
Consolidation and integration is necessity, but there is no one size fits all analytics solution. While the
technologies applied may vary for a given problem, there is also a vast amount
of shared architecture /infrastructure. It would be very unfortunate if the
initiatives worked in silos, you never know what kind of insight can be gained
from combining data and you never know what kind of BI/Analytic tools might
need to be supported. On the other hand, the concept of a single
one-size-fits-all technology solution for Big Data is a fallacy especially if
you consider the three Vs of Big Data. If you consider the reason NoSQL
technologies, for example, have become so popular due to the support of data
"Variety", you can easily begin to understand that no one solution
will really deliver on an Enterprise
The evolution of the
data pool into a centralized form that allows cross silo analysis is growing.
The mechanics of that evolution seem to have many paths, some are definitely working better than others. Ideally, it would be great to be able to
combine data from all corners of the business in new ways into new insight; abstract
the way data is accessed in the company wide architecture to allow data-sources
to support multiple access strategies. However, it takes time to mature
analytics technologies. It is also the most complex and challenging as new
methods and technologies are "integrated" into the existing estate. In
addition, poor planning and coordination are organization and project
management related, the nature of most big data projects requires the
ability to read and rapidly process variety of data, high volume data sources,
making coordination critical. Things get complicated very quickly as the
structure of these data sources and the business needs continue to evolve.
There are many challenging pieces in Big Data. Analytics
is effectively one piece; but it is only part of the equation. And
analytics is multi-dimensional -engineering, art and management disciplines.From management perspective, keep the business end in mind
by framing the right questions, enforce data governance, experiment to
learn, and take the integral approach for building a solid long term analytics
portfolio based on a cohesive Big Data strategy.
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