The number of companies that use Big Data in their work is constantly growing. Recent years have shown that the implementation of big data analysis results can give substantial effect which will have a positive influence on company’s bank accounts. But entrepreneurs are also shocked to discover that besides a number of advantages there are a lot of big data problems the solving of which requires quite a lot of resources.

So, here are the things you should know before you start using Big Data:

1. Storing and managing

This is when you are forced to admit — big data big problems. The bigger the amount of data becomes, the more demanding the system of storing and managing of these data needs to be. You will have to buy expensive equipment or tolerate all the drawbacks of storing the data in the cloud. You will need specialists able to foresee possible problems of big data and able to organize everything to make it possible for you to use the data as efficiently as possible.

2. Prejudice

Prejudice is another rather serious problem with big data. It is rather easy to come to a certain conclusion if you use the results of one or two surveys, but if their number grows, it creates enough room for interpretation of the general meaning of the results by altering the way the data are presented. That is why it is very important to make sure that the results are not influenced by the opinion of any interested party.

3. Premature verdicts

Though this is more a problem of specialists, than one of the problems of big data, you should still be aware of it. Quite often conclusions are made on the basis of insufficient data or data based on a certain period of time. For example, it is quite logical to suggest that if you consider the feasibility of opening a new ice cream selling spot and take only the results of winter time surveys, the results will be far from motivating.

4. Noise

The more data you have, the harder it is to highlight the area you need at the moment. Of course, the nature of this big data problem is directly linked to big data and data mining, but you should also keep it in mind.

5. Accuracy

The specific feature of big data and data mining lies in the fact that the analysis is run on the basis of the algorithm that lacks the freedom of actions and cannot take a number of factors into account. Besides, if the algorithm is too complicated it increases the risk of missing one factor or another. Imagine the situation when you need to drive down a road with heavy traffic and your navigator informs you about a detour. You head over there only to find out that the road is still under construction.

It is worth noting that discovered correlation is not always connected with the real correlation between occurrences: for example, in the USA they found the correlation between the share of Microsoft browser at the market and the number of committed murders. This problem with big data should be taken especially seriously because it threatens the feasibility of all decisions made on the basis of collected data.

Here is another manifestation of this problem in big data: if you know the algorithm of work you can fool the system easily. During the trial period of the system students started writing long complicated sentences because they had noticed that the system used this factor as one of the criteria. As the result the quality of their works dropped, but the grades improved.

Conclusion

After reading this article you might think — big data big problems, but keep in mind that if you use it wisely, it can become a powerful and effective tool which will help you to make efficient decisions. For example, in order to work with big data you need to have good understanding of certain markets and business, that is why many analysts recommend nurturing data analysis specialists inside your company, not recruiting them from outside.

Another important factor that will help you to avoid problems in big data is having a clear understanding of the fact that effective implementation requires certain corporate culture in relation to the analytical department and, however obvious this might sound, adequate funding. This sphere is based on modern technologies and complicated algorithms, that is why it simply is not capable of producing positive results if there is the lack of resources.

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