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
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.