Big data is huge, complicated. It not only collects data but also has a great impact on banking and finance. Studies showthat the total spending on data analytics was a whopping $89.6 billion in 2018. The amount has increased more in 2019 and beyond. Therefore, the implementation of big data has now become imperative for finance companies, online lenders, and Fintechs. There is no doubt about this fact anymore.
Though some finance firms are yet to implement big data and business intelligence, many banks and Fintechs have adopted the technology for better business performance and providing outstanding customer service. They are making the best use of data analytics to boost revenue streams.
According to an article published on https://www.huffpost.com, big data helps in investment advice. Then, to implement the same, the finance companies must have the right data analysts to interpret data and derive actionable insights.
Then, there are many pitfalls of big data. The finance companies should overcome these challenges. Based on the findings of Gartner, big data is hard to leverage because 70 percent of Hadoop deployments could not realize cost savings and profit generation. The reason is the lack of big data skills and integration issues. Therefore, banks and finance must have the right skills and integration infrastructure to make big data happen and benefit the business as well as customers. Here are four big data pitfalls that finance companies must avoid when implementing the technology:
- No organizational platform or data-focused architecture
Hadoop normallyis implemented in a bank or Fintechas a prototype for a particular use-case, nothing more, and nothing less. Gradually, it takes center stage, attracts more info, and translates into a huge number-crunching machine. Skilled data scientists in a bank or finance company operate the machines. It is the key to the success of big data implementation in the finance sector.
Therefore, banks and Fintechs must have an organizational platform plan as well as a data-focused architecture to break down the silos widespread in most financial concerns. These pitfalls need quick addressing and then big data needs to be implemented.
When it comes to data, it needs the potential to parallel process, with the least obstruction as possible in a distributed and scalable setting. There is not a shred of ambiguity about it. These things matter before the implementation of big data.
Big data platforms are not controlled like conventional databases or application islands in a data-focused platform. It is not schema-bound or locked. As already cited, 70 percent of Hadoop deployments failed to meet cost savings and generation of profits. The lack of skills and integration are the root cause of such challenges or pitfalls.
- Collating less info and depending more on complex algorithms
Another challenge is that intricate and difficult algorithms would solve all banking or financial problems. Wrong! It will not, but many people believe so. They end up performing poorly and annoying their customers. When it comes to PCs, they function on rational processes and undoubtedly would process unintentional, even illogical input data and generate unwanted, useless output or results. If you have taken a loan from https://libertylending.com, you will know how data is used to offer the best deals and financial products to their customers.
In computer science and IT, it is known as garbage in and garbage out. Yes, it is true because not all the data produced means that they are useful or perfect for actionable insights. Big data helps in structuring such cluttered or unclean data for taking strategic business decisions. Therefore, banks and finance firms should rely more on clean data and not algorithms.
Therefore, the finance industry should not rely on unfounded assumptions and weak relevance. The banks and Fintechs must collate as much data as possible, of course the most relevant information, and let the data do the job. It is easy for making financial decisions and affordable when you have a proper data platform.
- Compromising on data quality
When it comes to bank or finance company data, it is all about accuracy and quality of the information. The finance sector cannot take it for granted. Poor data quality or incorrect info can lessen the impact of analytics in any business, leave alone banks and finance companies.
When it comes to big data, the overall quality of information may plummet because both semi-structured and unorganized information is included in sets of data. It is the greatest mistake banks or finance companies may commit.
The finance sector must take appropriate steps to avoid these pitfalls, and this is when big data plays a pivotal role. The banks and Fintechs should know which data to use and which to throw away to improve the quality of information.
- No vision for Data Lake
When it comes to a data lake, it is a game-changer in the finance sector and has a changing nature for banks or for that matter any business. It is a vital target for information and offers the much-deserved association of various kinds of data or information. It includes organized, unstructured, clean, unclean, and semi-structured data.
The data lake ware house offersrobustrewards and perquisites via the economies of big data with up to 30 times or 50 times expenses to store and assess info compared to customary setups.
When it comes to a data lake, it has the potential to capture raw or as-is data before any in formation conversion or schema generation. Before capturing essential data with automatic quick, ingest methods in position. Therefore, Data Lake plays a significant role in linking finance company data with flawless data access, frequentative algorithm buildup and swift deployment.
These are the pitfalls banks and finance companies, must avoid when implementing big data. You should start everything right from the initial stages.It will really help banks and Fintechs make the most out of big data and improve business and customer service.
Big data solutions could be fully incorporated into banking and finance for the accurate reason. Else, the result is disappointing. Most essential, it could lead to incorrect analytical assessments lead to more problems.