With applications in all industries, Big Data is changing our society to its core. Maybe one of the greatest beneficiaries of Big Data applications is the financial industry. Today, players in the banking and financial sector can offer more flexible and personalized products, can change and adapt to their clients’ needs and expectations. But, in spite of all the advantages brought by these technological advancements, there are some challenges financial players have to overcome when it comes to implementing such technologies. Below are some of the biggest challenges that banks, and not only, have in terms of using Big Data to their advantage.
One of the main strategic challenges of using Big Data in financial ventures is tightly related to integrating it with systems already available within these establishments. Sometimes, existing tools can be difficult to integrate with big data technologies. Since a growing number of similar institutions already have automation tools (according to a recent McKinsey study, more than 30%), integrating big data with all of them might become difficult.
Fragmented business processes and data create new challenges in properly implementing this technology. For instance, although many financial institutions already have large volumes of data, they face difficulties in integrating it with financial institutions’ core systems.
Most frequently, this is the biggest challenge financial players must overcome. Core systems must be changed and adapted in such a way to make data accessible in a usable form when thinking about implementing Big Data initiatives in your company.
Another big challenge posed by big data integration with financial services comes under the form of regulatory compliance.
Financial players must ensure they are up to date with regulatory requirements in terms of data retention, processing, and usage. They also must ensure they offer proper security on personal and sensitive data and information. However, financial players put increasing efforts into following private data regulations and that they are always prepared to meet further regulatory requirements.
Another challenge of Big Data implementation with financial services is marketing and advertising new applications and functions. Because not everybody is familiar with the terminology used by professionals, people might be reluctant in engaging with the new systems developed by banking institutions.
To successfully market similar solutions, the experts at PickWriters recommend using a more accessible language when implementing different marketing and advertising strategies.
High-quality content should be complemented by infographics and how-to videos that show users how to make the most of the new technologies brought by big data. Financial institutions should aim to expose the perks and advantages brought by big data in such a fashion to make users interested in using them. Try to educate users how can big data improve customer experience, how it can help them achieve a more personalized experience when using a financial institution’s services, and so on.
Finding the necessary financial resources to develop systems that use Big Data can be a difficult part of integrating similar solutions with existing processes within the banking sector. According to this paper, development costs for similar solutions can range anywhere between $5million and $100 million, depending on complexity.
The high costs associated with Big Data integration are mainly caused by the lack of trained and knowledgeable teams within banking institutions that can help them implement similar solutions at more affordable prices.
Besides, companies in the sector can find it difficult to access legacy systems and incorporating these with their internal processes.
Although financial companies can pride themselves on large volumes of data, not all can pride themselves on relevant data or on using it in the most efficient and relevant way. Some of the banking institutions today may find it difficult to source data and integrate it with their internal systems from third-party players.
Apart from that, data quality matters. A smart system is just as useful as the data it receives. Data must meet several standards to become useful in this new context:
- It has to be accurate
- It has to be valid
- It must be complete
- It must be reasonable
- It must be valid
In systems where this type of data lacks, the insights offered by the system might be incomplete, inaccurate or, sometimes, even useless.
Lack of expertise and talent
In spite of the fact Big Data is around for some time, the number of experts in the field is still limited. Unfortunately, not all banks can enjoy proper expertise from these professionals. People with analytical and technical skills are mandatory. However, the expertise gap will soon be closed, thanks to professionals catching up with this trend at a fast pace. This particular limitation will soon be overcome.
Making the most of Big Data in the financial industry
Making the most of Big data seems to rely more and more on the so-called 3Vs of data management: variety, velocity, and volume, variables introduced first by Dough Laney and Gartner.
- Variety – data variety can bring higher value to systems that enjoy Big Data as their core function. Data coming from different sources (social media, CRM systems, bank-customer interactions, etc.) offers banks actionable insights and can open new development opportunities for them.
- Velocity – the speed at which data is processed can clearly determine how useful and relevant the system as a whole is. And it can also help establishments in the field offer better services: transactions, applications, customer interactions, and payments.
- Volume – data volume also matters in the new environment Big Data opens for financial institutions. Although banks are very familiar with traditional data processing, new, less traditional data entry points are now available. The data captured today by banks is not only available in larger volumes, but it is also deeper, complex and broader.
Big Data has transformed the financial industry to its core. Although many traditional banks still need time to catch up with these developments and trends, new emerging players have adapted easier. FinTech organizations are the new-age financial players and thanks to Big Data, they are more prepared than ever to meet consumer needs and tailor personalized services and products.