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In the past few decades, one of the prominent issues regarding Big Data management has been the storage, and especially in regard to the exponential growth and size of amorphous data. This data also at times fails to fit into the databases thus creates problems for the end users. However today, the competitive landscape is entirely different from one that was followed in the past. Data storage and management are now merely preconditions to find and analyse the real hidden jewels in Big Data which include converting data from giant streams and algorithms into defined knowledge.
Real time predictive analyses enable better transparency into business operations and provide an excellent understanding on the true nature of data. No wonder, in a dynamic market like today’s’, the enterprises must prepare for the future. Below are few important steps which are essential for mastering big data and predictive analytics for driving the business growth.
Enduing C-Level Predictive Analytics
As big data analytics is experiencing changes at a very high speed, and the corporate houses and government certainly needs to incorporate top-management muscle for data initiatives. A strong business background which usually comes from supply chain and analytics background must have the power to lead model analytics centers. Corporate who wish to succeed must possess a transparent business strategy with defined initiatives to achieve the best results. Moreover, the management priorities must have to reinforce functional level goals with targets and metrics to meet the organizational goals.
Assessing the Strength of Supply Chain
Addressing weaknesses with analytics is always considered a smart choice for the growth and expansion of the business. One should never examine the supply chain without taking in knowledge the logistics at a macro level. A corporate should never fail in maximizing the knowledge in its data contributes to necessary logistics costs. People must always have a comprehensive view of their data to better study the predictive analyses. A deep analysis of the supply chain indirectly includes relationships with parties including manufactures, retailers and customers.
Inferring Actionable Intelligence from Data Initiatives
Consumers’ first need to understand the fact that not all Big Data is useful data as it includes a wide spectrum of reports and analyses. However, the companies do have some advanced analytics that permit inferences from granular data. The inference transforms data into knowledge, that further results in greater business process transparency and improvements. One must always remember that when evaluating the need to institute analytics, the actionable knowledge is not being inherited in data per se. The extraction and inference requires a similar type of open innovation platform.
By objecti vity
