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Top Big Data Challenges 2021

ByDave Stopher

Nov 26, 2021

Despite the fact that Big Data is at its peak of efficiency and demand in 2021, many companies are still afraid to tackle this specialized niche without having the necessary knowledge. On top of that, there are several more challenges that can be solved only with the acquisition of the required minimum of knowledge and the help of big data solution providers. Let’s take a look.

Challenge #1: Profiled education

Big Data tools are very diverse and not always that easy to use. Terabytes of memory are filled with data instances that may not be related to each other in any way. There are many aother difficulties that arise when you first get to know Big Data. It also happens that business owners, having adopted trendy technology, forget about the main thing – qualification training of managers and other personnel.

Solution: Company owners should educate themselves in the basics of introducing Big Data. They should be aware that training is one of the main parts of the implementation. Or, you may as well hire only the employees who already know how to handle one or another Big Data platform. Trained employees can help educate those who are not yet familiar with the technology.

Challenge #2: Confusing range of opportunities

The enterprise is ready for training, but training for what? There are many models for managing Big Data, from Cassandra to Hadoop MapReduce. The mentioned models and many others have different advantages, the choice becomes more difficult as more options become known to the owner.

Solution: This potential issue is not so significant as big data solution providers can help with the choice appropriate for a business that has decided to use the technology. Most likely, without prior consultation, the owner will experience many difficulties, not to mention the effectiveness of implementation and the time spent.

Challenge #3: Data integration

The amount of data will grow, as will the number of formats in which this data is received. There also may be an issue with data duplication, and minor changes in names can entail significant problems with their organization. Add the accumulation of a large amount of erroneous data on top of this, which leads to noticeable distortions in the analysis.

Solution: Data always contains errors, this forces specialists to constantly take measures to minimize errors and possible damage from them. Again, the ability to minimize errors depends on the model chosen. Different models can be selected for different needs, which take into account these problems to different degrees. In addition, there are additional tools created to help you troubleshoot issues, such as Data Integration or arcESB.

Challenge #4: Analytics

The data may be all-encompassing, but it may not provide the necessary insights, for instance, in trading. It happens that terabytes of data are not as practical as they could be, which is caused by the inept use of the assets provided by Big Data.

Solution: Data filtering and properly selected input sources will fix this problem. Big Data is unnecessary if it doesn’t increase analytics revenue. Analytics should lead to quick action, but you need to be aware of trends ahead of time. There are also events that cannot be predicted by trends, some changes occur too quickly. This forces you to be especially careful about how relevant the used sources are.

Challenge #5: Security

The received data is also a responsibility, if you do not accept it in time, the losses can be irreparable. The trouble is that there are too many stages at which data is compromised. This includes the security of cloud storage and the security of physical media, security during data migration, etc. In short, security should be a priority area when implementing Big Data because data can be lost even due to an elementary oversight of employees, and not a weakness in the system itself.

Solution: qualified Big Data solution providers should take care of basic security measures, but they can also offer you useful add-ons. The larger the scale of the enterprise, the higher the risk of data loss so it is worth tackling this issue from the very beginning. As in the case of data errors, the owners do not have absolute security, but it is possible to minimize the risks.

It is High Time for Big Data Implementation

Almost all existing issues in this area find their solution in one way or another. The successful application of the technology can be provided by professional Big Data solution providers since working with Big Data has long been based on serious previous experience. This is why finding professionals is no longer as difficult as it used to be, which allows you to worry less about the optimality of the chosen model for interacting with Big Data.