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Big Data is the talk of the town nowadays. The hype is on the verge. However, the concept of ‘big’ data is creating and resulting in some huge costs that will be unavoidable in near future. This article will discuss the problems and myths associated with Big Data and how we can improve them with Deep Data.
Starting off with the common myths about Big Data:
All data can and should be stored and captured
The cost of storage of extensive data is none
The cost of computation of big data is none
More data will be helpful always in resulting in a better and accurate predictive model
However, the truth is:
Not all data needs to be stored and captured. It is important to be smart and prioritize the data depending upon the volume. Furthermore, if you are repeating the same data every time, it will not improve or enhance the predictive model’s accuracy. Moreover, the cost associated with capturing data is not only limited to the one that Amazon Web Service charges. The cost of management of multiple databases is often a lot higher than the computation and storage costs.
Believing in these myths is problematic as you’ll be making your information systems such that they look better on papers but are cumbersome in the long-term.
When it comes to big data, some problems associated with it include:
More but repetitive data can be of no help to your model. Furthermore, if the new data consists of errors, it will not be helpful in increasing the accuracy of the model. Such noisy data can have a negative impact on the model. Moreover, if the data is more, it will eventually be slowing everything down. For example, it is better and more efficient to build a model on a gigabyte of data than on a terabyte of data as it would take double the amount of time and would make the whole process slow.
In order to make things better, you need to adopt the mindset of Deep Data instead of Big Data. This would help you avoid all the problem associated with more data. Of the various actions you could take in order to avoid the negative side of big data, 4 ways include:
Firstly, you need to understand the accuracy you want to achieve in the model. You can initiate with ROI expectations that are explicit.
Secondly, it is not necessary to use all the big data that you have. It would only pile up costs.
Create each model with a sample that is random.
Finally, you need to search for more sources of data.
The benefits of doing so will include an increase in efficiency and the processes of training, experimenting and scoring of models being quicker than usual. With less data, there will be little requirement of storage and compute and less pressure on data scientists.