The logic behind this tool is to blend comprehensive raw data acquisition with a structured data transformation process so that
>risk identification and business projection becomes easier. The purpose is to bridge the gap between empirical data sourcing and optimal statistical processing of business data in order to find out the strengths and weaknesses of an organisation.
A Big Data tool simplifies the task of a business analyst or strategist by providing him with the empirical proofs he needs to locate the potential risk areas and design solutions.
It occurs in four simple steps.
The first step is a complete acquisition of the entire raw data generated in and around the organisation. It does not leave aside a single data stream or a single information, emerged during the course of operation.
Raw data serves no good in terms of analyses. You cannot work with this form of data since you will be unable to figure out the correlations between the variables. So, after acquisition, a Big Data tool processes and transforms the raw data into optimal data structures, suitable for a number of complex statistical analyses.
Big Data analyses indicate the vulnerabilities and strengths of the process, resources and the products of an organisation. It brings out the hidden dynamics between the components of the business operation, the loopholes in strategies and all the factors that are detrimental for the business.
Analysts and strategists work upon the Big Data findings and suggest solutions for implementation.
The virtues of this tool are best experienced by medium to large scale companies that have highly complex operational structures but if you talk about a systematic approach and an analysis closest to the ground realities, we would rather say even the ‘Mom & Pop’ shops need Big Data tools.
If you seek more clarity on this issue please contact us and learn how we can help you implement Big Data tool in your organisation.