Big Data: Major Challenges and Solutions

Big data is the foundation for the next disruption in the field of Information Technology. Big data analytics has become a part of more than 95% of professional corporations because of the value of the data in the market today. With the increase in the amount of data, the demand for its analysis and interpretation has also increased. As organizations go on collecting, evaluating, and utilizing unstructured data, a new set of problems arise due to the amount of raw information.

According to an NVP survey in 2017, only 37% of the companies achieved success in their big data endeavors because of the lack of measurable results and failed implementation of projects.

Learning of the blog

  • Data Storage
  • Valuable Insights
  • Integration of data sources and platforms
  • Selecting the right tool
  • Data security
  • Data quality
  • Cost
  • Conclusion

Before indulging in big data, each decision-maker should be sure of its challenges and solutions to draft the right strategy and maximize its potential. Here, we have a list of prominent big data challenges and their possible solutions, as proposed by a big data expert.

If you are new to big data analytics, it would be worth checking out a big data certification course alongside. With this, let us dig deep into the article.


Data Storage


The rate of generation of big data has surpassed the capabilities of computing and storage systems. The primary issue faced by organizations is managing unstructured data in multiple formats because it can’t be saved in the database. This implies that this data can’t be directly searched or analyzed.

As a solution, organizations are using software-defined storage and resort to dynamic databases like MongoDB. Hadoop is further used to handle computation and analysis.


Valuable Insights


The end goal is to utilize data for profitable business, reduce overhead cost, innovation, and acceleration of operations. There is a great deal of complexity involved, as there is a requirement for a data analytics tool that can handle highly accurate data. This is not the only challenge, but also faster decision making is required, especially in banking and healthcare. The idea here is to have a proper system of data sources and factors which ensure that nothing falls out of scope.

To fulfill the aim, new-gen ETL engines such as AWS Glue are in the market. These, along with others like Xplenty, decrease report generation time and ease integrations.


Integration of Data Sources and Platforms


The data source for businesses are emails, documents, social media, enterprise applications, etc. The compiling of data and creating actionable insights that everyone in the company can utilize is a challenge. There are plenty of backend dispersed data stores, but it is not ideal for accessing and putting away information in unsupported data stores as it slows advancement cycles.

Since it is hard to rearrange IT infrastructure for significant data process streams, it is advisable to use a python based API with automation tools for a wide range of records. These tools can handle a considerable part of the work.


Selecting the right tool


We need a non-relational SQL system to capture, analyze, and process data. It is possible to make a poor decision when choosing the optimum NoSQL tool because of the variety. While there are many such tools available in the market, choosing one is often challenging. Businesses can’t find the right one because every tool has some shortcomings.

To choose the right big data technology, you should be aware of your organization’s requirements, and seeking the help of a big data expert would be the way to go. This way, you can work out a strategy and then choose the needed technology stack.


Data security


Here we look at the big data security problems on a large scale as it is quite vast. Due to several data sources, it is very challenging to adjust the inconsistencies. Another issue is the security and integrity of data because of many channels and interconnected nodes. A small security loophole can lead to huge losses and even legal implications in case of data theft. So, it is best to put security first from the very start and not on the application level.

Companies are now using machine learning techniques such as Amazon Macie to fight cybercrimes. It is a cloud-based service and can intelligently discover, categorize, and protect valuable data.


Data Quality


For accuracy of big data, it must be cleaned, prepared, verified, reviewed for compliance, and maintained. The rate of intake of data is so fast that these steps go generally missing before storage. At any given point, you can face data integration problems if you don’t have characterized pre-processing data rules leading to inferior quality data and poor decisions.

Big data should have a proper model, and then data cleaning techniques can be applied to it. Comparison, matching, and merging of records are then possible without losing performance.




Big data deployment projects could be weighty on the organization’s wallet, depending on your organization’s technological requirements and business targets. There are new hardware and employment costs involved along with software development expenses for an existing arrangement. In the case of a cloud-based solution also setup and staff costs are prevalent. To save a fortune, future developments have to be considered.

A financially smart decision is to handle data on the cloud in parts unless the organization has a severe security necessity. Data lakes can provide cheap storage, and optimized algorithms can reduce computing power – both cost-effective.


Lack of Understanding


A lesser acknowledged challenge in the big data industry is the organization’s resistance to understand the value and to pivot its architecture for the implementation. The absence of basic knowledge of the benefits and infrastructure of big data leads to a waste of time and resources.

To understand the middle management, the change should come as a part of vertebrae such that the control is with top management and those with a Big Data Certification. The implementation should be monitored to map its acceptance.




For a robust system, it is essential to identify and remove the above challenges. These obstacles arise due to a lack of certified big data professionals. As a solution, higher management wants its employees to go for a data analytics certification. If you want to enter the industry, you know what to do now!

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