Big Data Analytics Challenges That Businesses Have To Deal With In 2021

Many businesses struggle to utilize business intelligence analytics on a strategic level, especially in their project's initial stage. This is because they are not aware of the big data analytics challenges and are not well-prepared to tackle these problems. This article would discuss the top big data challenges that most businesses will encounter in 2021. 

The big data analytics field in 2021

This year holds great potential for big data analytics to grow; however, many hurdles are needed to overcome. So, it’s time to explore the most common big data analytics problems together. As a big data consulting services provider, we would like to help you understand all root causes and provide you with the best solution to these problems.

When your big data analytics systems are at the concept stage, it’s a good idea to think smart and act fast. Any fixes after the system are already running can be expensive.

In today’s fast-growing world, businesses can improve decision-making and productivity, increase accountability and productivity to make better predictions, and track performance to gain a competitive advantage by using big data analytics. However, many businesses are having difficulty in using business intelligence analytics. 

A survey conducted by Gartner revealed that 87 percent of companies have low business intelligence and analytics maturity. They also lack data analyzing experts to support them. The main problem relies on deep systems or infrastructure instead of analytics itself.

New data analytics solution that fails to provide new and timely insight

Quite a few companies are investing in business analytics solutions striving to gain valuable insights. From there, they can make better business decisions. However, sometimes, it seems that the details the new solution provides are of the same data quality that you had. Depending on the root cause, this issue can be resolved through a business or technology lens.

Data missing: Due to the lack of data integration or poor data organization, there might be a lack of data to be analyzed and generated new insights.

If this happens, you should run the data test to see if the existing data integrations can provide the necessary insights. Incorporating new data sources can also eliminate data shortages. It is also worth seeing how the raw data comes into the system and seeing that all metrics and indicators can be put in place for analysis. Lastly, diversity in data storage can also be an issue. This problem can be solved with Data Lake.

Long data response time: When your business analytics system is designed for batch processing, your system will encounter a long data response time when you need real-time insights. Therefore, the data requested is still being collected or preprocessed and is not yet available. 

What you can do is make sure that your ETL (Extract - Transform, Load) can process your data on a more persistent schedule. In some cases, a batch solution allows you to reschedule twice as fast. Another option for you is to combine an existing batch pipeline with a fast real-time stream using data-processing architecture like Lambda architecture.



This approach is known for its ability to handle massive quantities of data. Lambda architecture emergence has been associated with the dynamics that minimize delays in big data development, real-time analysis, and the drive to minimize map-reduce latencies.

The old-school approach applied to the new system: Although you have transferred your report to the new integration, it will be difficult to get new answers and generate new insights when asking the same old questions. This is mostly a business problem, and the possible solutions to this problem vary widely from case to case. Therefore, it is best to consult a big data analyst experienced in analytical methods and understand your business area.

Incorrect data analysis

There is nothing worse for a business than incorrect data analysis. If your business encounters such a problem, you need to fix it as soon as possible. 

Poor quality data source: Most of the time, you’ll get poor results if your system relies on flawed, errors, or incomplete data. Data quality management and validation process cover all stages of the ETL process that can help ensure the incoming data’s quality at different levels such as semantics, grammatical, business, etc. This makes the data accurate and accurate by identifying and eliminating errors, making sure that one field's modifications are immediately displayed across the board.

Data flow affected by system errors: When system requirements are ignored or not fully met due to human error intervention during development, testing, and verification, you’ll encounter this problem.

The development life cycle’s high-quality testing and verification can minimize data processing problems by reducing the number of such issues. Even when working with high-quality data, the results of your analysis can be inaccurate. In this case, you’ll need to carefully review the system and make sure that there are no errors in implementing the data processing algorithm.

Expensive maintenance

All systems require non-stop investment in maintenance and infrastructure. And all business owners want to minimize these investment costs. As a business consultant ourselves, we strongly recommend you review the system and make sure that you aren’t paying too much, even if you’re happy with the maintenance and infrastructure cost. 

Obsolete technologies: New technologies that can process large amounts of data in a faster and cheaper way appear every day. Therefore, why do you have to pay for outdated technologies that cost you much more? Sooner or later, the technology on which big data analytics is based will become outdated.

These technologies will then require more hardware resources and cost you more to maintain than the latest ones. Moreover, it’s even more difficult to find experts who are willing to develop and support solutions based on previous technologies.

The best solution is to keep up with the latest market trends and try to migrate to the new one, of course. In the long run, it will cost you less to maintain while increasing reliability, availability, and scalability. It is also essential to gradually carry out a system redesign, slowly replacing the old elements with the new ones.

Infrastructure which is not optimal: Infrastructure always costs more when it comes to optimization. If you’re still working on-premises, moving to the cloud can be a smart move. With cloud solutions, you can pay for what you use and reduce costs. Even if you have security-related restrictions, you can still go with the private cloud option. And if you’re already using the cloud, make sure you’re using it effectively and have already implemented all the best practices to reduce your spending.

The selected system is underutilized: You will continue to pay for the infrastructure your system uses even if you don’t use most of the system’s features. It’s better to revise your business metrics and optimize the system as needed. You can substitute some of the elements with a simpler version that suits your business needs better.

Conclusion

In a nutshell, big data analytics is a wide domain. It can bring you benefits, but it can also be a challenge if you miss some fundamental points at the new solution initial implementation phase. We hope that this article has provided you with the information you need. 

If you need any technology-related advice, feel free to talk to one of our consultants. We are proud to be among the top-ranked software development companies in Vietnam.