If you google Big Data you will notice big corporations like IBM, SAS, Oracle providing enterprise solutions with punch lines “Transform Your Business” and “Big Data Can Generate Big Brainstorms” promising business growth. The reality behind investing in Big Data analytics is still hidden. Today we have the tools to analyze massive amount of data but we still lack the expertise to derive correlations from it.
In the public realm finding relation between health and air pollution can be an example of Big Data analytics whereas in the market, companies like google, amazon and Netflix use “Recommendation” software engines to suggest products based on the interests of customers. An article by Jonathan Shaw mentions a credit card companies finding …show more content…
In his article “What is Big Data” Edd Dumbill characterizes different aspects of Big Data as three V’s. Volume, Velocity and Variety. The volume itself is a challenge to IT infrastructure as many corporations have tantamount data in form of logs but do not have the necessary tools to process it. Velocity corresponds to the rate at which new data is gathering. Many times the data gathered is not in a uniform-relational structure and rarely does data present itself in an orderly form for processing. It can vary from a media file to raw input from a sensor to an archived email. Willy Shih a professor at Harvard Business School states “Big Data use involves a fundamentally different way of doing experimental design.” In the past, scientists would arrange an experiment, decide what data to collect along with parameters, and analyze the data. In recent times, the cost to store bits of information has dropped 60 percent a year for six decades and this reduction in cost has led to a new trend of store everything and then search for pattern …show more content…
Even employees who work in big corporations lack the contextual knowledge and business insight which is required to make customer segments even after being comfortable with data-analytics tools. Prof. Eagle, provides us with one of the pitfalls of working with huge datasets is that you may find correlations in very large linked datasets without understanding causation. Applying BI tools to complex structure such as web requires a strong foundation in statistics and mathematics no doubt, but this foundation should be complemented by sound judgment and deep understanding of business.
Effectiveness of Big data lies not in tools but in actual analysis, as Shah States “Big data needs to be complemented with Big Judgment” And there is a long way to go in this
In the public realm finding relation between health and air pollution can be an example of Big Data analytics whereas in the market, companies like google, amazon and Netflix use “Recommendation” software engines to suggest products based on the interests of customers. An article by Jonathan Shaw mentions a credit card companies finding …show more content…
In his article “What is Big Data” Edd Dumbill characterizes different aspects of Big Data as three V’s. Volume, Velocity and Variety. The volume itself is a challenge to IT infrastructure as many corporations have tantamount data in form of logs but do not have the necessary tools to process it. Velocity corresponds to the rate at which new data is gathering. Many times the data gathered is not in a uniform-relational structure and rarely does data present itself in an orderly form for processing. It can vary from a media file to raw input from a sensor to an archived email. Willy Shih a professor at Harvard Business School states “Big Data use involves a fundamentally different way of doing experimental design.” In the past, scientists would arrange an experiment, decide what data to collect along with parameters, and analyze the data. In recent times, the cost to store bits of information has dropped 60 percent a year for six decades and this reduction in cost has led to a new trend of store everything and then search for pattern …show more content…
Even employees who work in big corporations lack the contextual knowledge and business insight which is required to make customer segments even after being comfortable with data-analytics tools. Prof. Eagle, provides us with one of the pitfalls of working with huge datasets is that you may find correlations in very large linked datasets without understanding causation. Applying BI tools to complex structure such as web requires a strong foundation in statistics and mathematics no doubt, but this foundation should be complemented by sound judgment and deep understanding of business.
Effectiveness of Big data lies not in tools but in actual analysis, as Shah States “Big data needs to be complemented with Big Judgment” And there is a long way to go in this