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The myths about big data are many. The bigger the publicity, the greater the fallacies, and big data is no exception here. Today, data analytics is the buzzword in the finance industry and results in business gains and customer satisfaction. However, there are many misconceptions surrounding big data technologies. It makes companies, especially banks, finance companies, and lenders skeptical. They feel uneasy when it comes to adopting big data into their business operations or not. After all, finance companies and Fintechs deal with customers’ personal and financial information. They cannot afford to take any risks.

The reality is that big data if used in the right way and actionable insights derived, would benefit finance companies and customers alike. You can do your research and find out how big data is playing a pivotal role in banking and finance.

Customer satisfaction matters most to the banks and Fintechs. Therefore, they are trying their best to gain useful insights to offer lucrative financial services and deals to their customers.

According to an article published on, millennials today want more customized financial products and services from finance companies. This is where big data helps in offering the same, but customers often start believing the myths and feel skeptical. Therefore, here are the three biggest myths surrounding big data, which finance companies must dispel:

  1. Requires very big investment

Nowadays, it is believed that every technological initiative calls for huge investment and financial spending. It is not true all the time. It is a myth surrounding big data. The cost is one of the initial doubts that bother business and IT managers of banks and Fintechs. They think and spend a lot of time thinking about whether to implement big data or not. They are in two minds when it comes to starting a project or deploying a tool.

Many people assume that data analytics is inherently expensive and impossible to implement. It is one of the greatest lies surrounding big data. Therefore, it’s believed that big data is applicable to multinational finance companies and not meant for small finance firms or Fintechs. It’s also perceived that big data requires many internal resources, which is another lie. Then, not all big data investments are costly.

Today, there are numerous open-source and free tools in the market that can be used to implement data analytics. Even if they are not free, the premium versions are affordable and not blow your budget. If you have a good knowledge and understanding of data storage in your company and what challenges you face, you can make the best use of big data without creating a dent in your wallet. You can use the cloud to experiment with analytics to address a business challenge.

Today analytics are cloud-based and rely on big data. You have affordable options and need not choose expensive ones such as traditional data warehouses. You need to look, research, and find out what data analytics you need for your bank or finance company.

Moreover, data, as well as for analytics, are used to realize three key results such as process efficiency, enhanced revenue, and risk management. It means that big data offers these benefits to finance companies and therefore, a little investment is worth it. You can research on websites such as lending organization, and see how they leverage big data without spending heavily.

  1. Need to work with huge data loads to become successful

There are many people, who believe that you need loads of information to use big data. The perception is flawed and far from the truth. There is no shred of doubt about it.  Did you know you could gain actionable insights and positive results even from moderate data volume? You will use only that much information that is necessary.

The benefits of big data are known and many banks and Fintechs are adopting this technology at a fast pace. The finance companies having the resources can gain a competitive advantage by reaping the maximum benefits out of big data. Then, you require huge data volumes to implement analytics is a myth. Certainly, the advantages of big data are popular and known for some time, and banks and finance firms will benefit a lot from data analysis and implementation.

Many banks and Fintechs try to capture as much data as possible. Then, all that information is not required. The excitement makes people explore and understand more.

The reality is more the data, the better. Then, it is up to the machines to analyze the information and sort that out in no time. Even big data and business intelligence experts in the finance industry do believe that huge volume of data is not always necessary for actionable insights.

It is not about more data but useful data. Most people are searching for information that is pertinent to their job role. The Fintechs use only that info that helps in the decision-making and performance of the business. The experts in the finance industry cite that instead of being petrified about more data, the banks and finance companies must consider what data they should leverage ad make it structured.

Overloading with too much info and in numerous formats is overwhelming for banks and finance companies, thus affecting operations in an adverse manner. It is more important to present data to customers in a simple format that helps them to choose the best financial products or services.

  1. Data analytics means no human bias

Automated systems should not have a bias, ideally. Then, human beings develop technology after all. You cannot deny that. Therefore, removing human bias is not possible. However, some data systems and machine learning solutions eliminate human bias. The scenario is rare.

All these things are myths. Analytics and algorithms are made using training data and will create whatever features the training info contains.

In some situations, humans introduce a benign bias into the data analytics results. It is not necessary that just because some algorithm states something, it does not mean that it is true or beneficial.


Now that you have these myths in front of you, you can separate fact from fiction. Fintechs should learn to use facts and real-time data to make proper business decisions.