The Role of Data Analytics in Banking World

Banking customers generate huge amounts of data every day via hundreds of thousands of individual transactions. Falling under the umbrella of big data, this data is defined as large and diverse sets of information that multiplies at an ever-increasing rate. This humongous amount of data holds untapped potential for banks and other financial institutions seeking a better understanding of their customer base, market trends, and product performance.

The question is what’s the source of all this data? The rapid advancements in technology in tablets, smartphones, and the internet of things (IoT) have made it easier for the customers to utilize online resources to communicate with companies, purchase items, research products, and even perform banking tasks. Customer profiles are developed using these activities to track trends, predict behaviors, and help banks better understand their customers.

Types of Big Data

Approximately 2.5 quintillion bytes of data is generated every day. It is difficult to put all of it in a single category. Following are the three ways big data is classified:

·         Structured: This is the type of data that is highly organized and has a fixed format.

·         Unstructured: This type of data lacks a well-defined format. Emails are an example since they are tough to process.

·         Semi-structured: Many people confuse this data type with unstructured data. 

However, it contains keywords that prove useful for processing.

The staggering volume of data available at our fingertips can be translated into valuable, actionable information with advanced processing techniques. By using the right business tools, all types of big data can be filtered through efficiently.

Advanced Analytics In Banking:

Data analytics has remained a key aspect in many financial institutions through the ages. From securities trading to investment banking and credit scoring, data analytics in banking has played a key role in arriving at a data-driven decision.  With rapid advancements in technology, big data has gained significant ground in the banking sector, which has opened up enormous potential for banks to grow. For banks to embrace advanced analytics, it’s important for them to have a deep understanding of the various components that make up the technology. Following are the chief components of advanced analytics that can be broken down into four main categories:

Reporting: It focuses on converting raw data into information and building data repositories using data analytics.

Descriptive Analytics: It entails processing and identifying patterns and summarizing information gathered in reporting.

Predictive Analytics: It uses the above-mentioned patterns for predicting future actions or scenarios.

Prescriptive Analytics: It gathers results obtained from descriptive and predictive analytics to determine what, why, and how a situation is likely to occur.

Banks all across the globe are recognizing the importance of analytics and increasingly investing in advanced analytics investments that are helping the banks improve their decision making to a considerable extent. Whether you are looking for advanced analytics solutions or data migration software, companies such as Impetus Technologies are the most trusted sources to leap in big data.