Big Data: why data sorting is essential for a relevant analysis

The Big Data encompasses all the data collected. However, there are data that can be used as data to be discarded. In this flow of information, it is therefore essential to know how to sort it in order to make the analysis and thus obtain a relevant line of work.

The praise of Big Data risks giving business leaders unrealistic expectations often masking that it can be It’s hard to separate the “good” data from the “bad” data. in order to achieve good results.
High-speed data analysis is changing the scope of possibilities in many industries. Businesses are increasingly able to collect and exploit insights into their customer bases on an unprecedented scale.

Who's afraid of Big Data?
© Daniel Burrus

Sorting is essential

The main challenge is to refine all this data. It has become increasingly difficult to separate relevant data from unusable data.

High-speed data analysis must meet the challenge of analyzing all data while taking into account the way in which poor data can affect overall conclusions. Indeed, even if the data are 90% good, the results obtained may appear radically different from those expected.

Perfecting this type of data analysis has become a science that today is far from perfect.

Within the company

When you record each consumer’s click, mouse movement, purchase, and search queries, you will be able to build a fairly reliable picture of that customer, and be able to serve them much better. But if your high-speed data analytics technology isn’t able to separate the good data from the bad before developing refined results, then you could end up with an incorrect evaluation of your customer, and in the end, damage your turnover.

Of course we don’t play with lives when we’re using the wrong target for a targeted advertisement. But it becomes a real problem when mismanagement of data occurs in areas such as the health or insurance sectors.

There is a basic trend that sees Big Data increasing exponentially. Today’s large data volumes will look like small data in the next two years.

The importance of the data and its number will therefore explode. The amount of bad data collected will invariably grow as fast as the amount of good data. Sorting the data and refining the results will therefore be increasingly difficult to achieve..

By being aware of how errors in information collection and analysis can occur and by strengthening safeguards to ensure that you have the right data collected, you will soon be able to ride the wave of Big Data that is about to break.


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