{"id":17598,"date":"2022-04-22T09:30:00","date_gmt":"2022-04-22T07:30:00","guid":{"rendered":"https:\/\/www.codemotion.com\/magazine\/?p=17598"},"modified":"2022-04-21T12:10:26","modified_gmt":"2022-04-21T10:10:26","slug":"how-data-observability-can-boost-it-performance-in-the-banking-sector","status":"publish","type":"post","link":"https:\/\/www.codemotion.com\/magazine\/data-science\/how-data-observability-can-boost-it-performance-in-the-banking-sector\/","title":{"rendered":"How Data Observability Can Boost IT Performance in the Banking Sector"},"content":{"rendered":"\n

Many industries have found themselves having to move quickly to keep up with changing technologies, and the world of fintech <\/a>has felt this change more than most<\/strong>. Few consumer services command the desire for security that financial institutions such as banks do, while at the same time, customers expect the conveniences that come with instantly available information and services.<\/p>\n\n\n\n

The general shift in all walks of business towards Software as a Service (SaaS)<\/strong> is one that makes economical sense in many cases. There is far more risk when developing cloud applications, making the cost-benefit analysis of having bespoke applications created much less appealing than it has been in the past. When the customers in question are banks, however, the stakes are raised even further.<\/p>\n\n\n\n

Cedacri Group is a company that is tackling this seemingly insurmountable problem\u2014offering IT services to fintech<\/strong>. Even a company considered to be the most important Italian company in this field needs help on occasion, and in this article, we\u2019re going to look at how S2E <\/a>(Solutions2Enterprises) provided that help in the form of tailored data management and engineering<\/strong> services.<\/p>\n\n\n\n

Data Science and Engineering in The Banking Sector<\/strong><\/h2>\n\n\n\n

As the world moves ever more into digital spaces, the science of data<\/a> is becoming increasingly important. The science\u2014perhaps even the art\u2014of analysing data is one that can help to avoid accidents, prevent fraud, make remarkably accurate predictions on future trends, and more.<\/p>\n\n\n\n

The reality of modern business is that companies have access to more data than they could ever have collected in the past. Such a wealth of information is a gold mine for organisations who are looking to make accurate predictions, determine where things aren\u2019t working (and where they are) and more. Paradoxically, the problem with having all this data is that it can be overwhelming<\/strong>, and without an effective approach to analysing the data, it is essentially useless. That is where data engineering comes in.<\/p>\n\n\n\n

Key Concepts Of Data Science In The Banking Sector<\/strong><\/h2>\n\n\n\n

Here are some of the ways in which data science is enabling fintech to keep up<\/strong> with the immense pace of modern technology.<\/p>\n\n\n\n

\"Data
Data Visualization is key in Fintech and the banking sector.<\/figcaption><\/figure>\n\n\n\n

Managing Customer Data<\/strong><\/h3>\n\n\n\n

The collection of customer data is something of a sore subject these days, with consumer sentiment increasingly shifting towards less data being better. Banks, however, are obliged to collect huge amounts of data in order to provide the services they do. These include things like advanced security features, credit services, and more.

Another key aspect to take into account is that customer data needs to be handled by companies and institutions in full compliance with the different international security standards such as GDPR<\/strong> (EU\u2019s data security and privacy law).<\/p>\n\n\n\n

The advent of digital banking has not only increased the amount of data that needs to be held on each customer, but it has also increased the opportunity to collect data. Data science is the mechanism by which banks can isolate what information is relevant<\/strong>. From there, data engineering can be used to identify trends, unlock potential revenue sources, and inform future business decisions.<\/p>\n\n\n\n

Observability<\/strong><\/h3>\n\n\n\n

Data observability was, in the past, the sole responsibility of software engineers.<\/p>\n\n\n\n

Today, Observability involves various stakeholders<\/strong> because it allows organizations to delve into what their systems, services, and apps are really doing.<\/p>\n\n\n\n

Observability means collecting KPIs in a non-invasive way of IT services<\/strong>. Considering that a performing IT is necessary for a performing business, observability is the way to measure and definitively improve the business of the organization.<\/p>\n\n\n\n

Customer Services<\/strong><\/h3>\n\n\n\n

Understanding the customer through data is essential for three main different purposes that involve the marketing, segmentation, and user experience spheres: <\/p>\n\n\n\n

Personalised Marketing<\/strong>: Perhaps one of the most well-known uses of data science and engineering these days is to create a more personalised experience for consumers<\/strong> when it comes to the advertisements they see and the offers they are given. A deep understanding of personal data points enables banks and other financial organisations to make offers that are relevant to their customers in ways that the bank would never previously have known about.<\/p>\n\n\n\n

Customer Segmentation<\/strong> Being able to single out groups of customers based on things like their spending habits, income, age, and a host of other data points enables financial institutions to make far-reaching decisions<\/strong>. These decisions can be made with more agility than would have been possible if every customer was considered individually, but with more accuracy than considering all the customers together with no segmentation.<\/p>\n\n\n\n

Customer Service: <\/strong>Ultimately, banks provide a service, albeit a very specific one. Any public-facing service comes with the need for customer support, and eliminating the need for that customer support is a win-win situation for financial institutions. This is because customers are generally having a much better experience when they don\u2019t need customer support<\/strong>, so reducing the need for it not only reduces costs in terms of employees offering support, it also means customer experience is being inherently improved as a result.<\/p>\n\n\n\n

Data engineering not only makes it easier to identify areas that need work to make the customer experience more intuitive but also makes it easier to automate<\/strong> parts of the customer support experience.<\/p>\n\n\n\n

Actionable Analytics in Real-Time<\/strong><\/h3>\n\n\n\n
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Analysing data in real-time can be key for boosting customer experience and taking more agile actions.<\/figcaption><\/figure>\n\n\n\n

Being able to analyse data in real-time enables large organisations like banks to be more agile<\/strong>, whether it\u2019s responding quickly to what the data says, or taking proactive measures<\/strong> based on predictions.<\/p>\n\n\n\n

Data streaming is an innovative architecture that is now accepted as “real-time”: data is processed as it moves from source to destination<\/strong>, so the data value is immediately extracted making it available instantly to decision-makers. These decision-makers can also be intelligent agents, so not only analysis but also actions become real-time.<\/p>\n\n\n\n

Other Areas<\/strong><\/h3>\n\n\n\n

This is by no means a definitive list of the ways in which data engineering and engineering can help financial institutions improve their service and become more efficient and agile operations<\/strong>. Other examples include;<\/p>\n\n\n\n