Big Data problems

Big Data is a unique instrument for processing huge amounts of structured, semi-structured and unstructured information. Big Data analytics allows making predictions and behavioral patterns. Thanks to Big Data, we can teach neural networks (DNNs), use Machine Learning (ML), Artificial Intelligence (AI) and Data Science (DS).

An experienced Big Data Company can provide ML, AI and DS for your business and you’ll get the ability to increase your efficiency exponentially. But are there any challenges or problems? Of course, nothing is perfect and there are some problems with Big Data you might face. Let’s analyze them beforehand, so you know what to look for and avoid.

Big Data problems and solutions

Incorrect implementation of Big Data can bring your company a lot of trouble. If you get acquainted with Big Data’s big problems, you will get the ability to avoid them timely. There are common challenges:

  1. Data Silos. We can call it one of the biggest challenges. It means you keep all the collected data separately on different disparate units and doesn’t integrate to the back end. It slows down making C-level decisions and makes all the processes less efficient. The solution is to integrate data into an end-to-end system.
  2. The imperfection of Big Data analytics. Big Data Scientists try to improve the quality of Big Data analytics but there are still problems with the quantity and quality of information. People tend to think that the more data they have, the better the analytics will be. It’s not always true. Sometimes you might face system overtraining when the quantity of some unnecessary data prevails over necessary and the system gives you unexpected results. To solve this problem, Big Data Scientists change weights of links between nodes in the whole neural network. As a result, you have a trained and predictable system.
  3. Rapid technological advancement. Big Data is still evolving at high speed. This leads to the absence of qualified specialists. We can compare this with buying a new smartphone – you bought the newest model and in a year it becomes out-of-date. Thus, Big Data specialists must keep abreast of tendencies and learn very quickly. Companies that want to implement Big Data should carefully look for specialists in this area.
  4. Privacy and security intricacy. This is not a secret that Big Data is used for collecting users information for contextual advertising, for example. Such Big Data visualization can scare some users who worry a lot about their private data. As for the businesses, the founders are concerned about the security of sensitive data. To protect them, Big Data analytics systems are using duplication (in case of deletion from one place) and encryption.
  5. Implementation cost. This is not a secret that Big Data implementation a quite an expensive thing. You need to get both a qualified team of specialists and a lot of computing recourses too. Thus said, Big Data will cost you a lot and it is very important to understand the value of implementation and profit after that. Unfortunately, sometimes profit does not worth the effort.

Wrap Up: is Big Data so problematical?

No, it isn’t. Big Data analytics is a complex methodology that can bring you big profit as a result. Like any other complex approach, Big Data needs a lot of attention and qualified specialists, especially in the early stages. 

If you want to implement it in your company, the best way to do it is by referring to the specialists which can estimate all pluses and minuses in your case, make the right implementation strategy and deliver the system. We described the basic challenges and solutions to Big Data problems, so you can navigate the issue and be prepared for what is waiting for you.