Major Big Data Analytics Challenges and Different Ways to Solve Them

Though it might seem a simple job to a simple man, but only a professional understands how complicated it is to carry out big data analytics. That too in this era of advancement.

Big data analysis is not a child’s play at all. One has to be serious when it comes to analyzing the deep down insights of his company’s data. Even though all the experts put their greatest efforts during the analyses, yet they can never be careful enough because big data analytics comes with dozens of challenges and problems no matter what.

big data

So it is better to become aware of the potential challenges before beginning with the task so that you can be ready to face them. Therefore, to help you cope with the big data challenges, we have illustrated some of the major ones below along with their possible solutions, so let’s have a look: 

A Large Amount of Data Handling

The major challenge faced by every big data development company during the data analyses is of course the “large amount of data”. You can’t expect a company’s information to be in small chunks, after all, it’s the whole organization you are dealing with. Therefore, due to the large amount of data, it often becomes difficult for the analyzers to handle it and that’s where the problem begins.

Solution

The most effective solution for this problem is data compression. In data compression, you can reduce the overall size of the data by reducing its bits, thus making it easier for the professionals to analyze a small amount of data easily.

Staying Ahead of Time

Lack of advanced tools and the presence of outdated systems brutally impact big data analyses, and that’s one of the biggest problems many companies face nowadays because the tech world is evolving day by day. Most companies today are still using those old and outdated tools and techniques due to which they are unable to reach the insights efficiently. 

Solution

The only solution for this problem is to modernize your legacy tools and applications so that you can match your speed with the fast occurring technological changes.

Lack of Professionals

The absence of the right people is also a major problem in big data analyses. Handing over the task that cost you your whole future to some immature is for sure the most imprudent thing you’ll ever do, yet people do make such mistakes and hire the wrong professionals who aren’t even experts of data analyses, all of this happens due to the lack of experience.

Solution

To avoid this situation, you’ll have to gather complete information regarding the task and seek expert opinions before hiring the professional, you can also opt for data science consulting agencies for this purpose.

Selection for the Right Tools

Though choosing the advanced tools is necessary for successful data analytics, yet picking up just any modern tools and systems without proper research is not appreciable too. Many companies’ regrets after making the decision, just because they came to know that they have ended up with the wrong supplies. 

Solution

If you don’t want to end up with the wrong tools, then it is suggested to hand over the task to someone having expertise in the field or do extensive research before bringing the supplies.

Security breaches and Holes

Another major challenge faced during big data analysis is the unsupervised process and lack of proper security protocols. As already mentioned, the world of technology is advancing day by day, and so are the frauds and hackers, so you can’t expect to launch those outdated security measures to protect your big data.

Solution

If you want to ensure the security of your big data, then make sure to introduce your company to the best data security practices which include even more complex coding and encryption than ever.

About Mohit Tater

Mohit is the co-founder and editor of Entrepreneurship Life, a place where entrepreneurs, start-ups, and business owners can find wide ranging information, advice, resources, and tools for starting, running, and growing their businesses.