In today’s digital world, data has transformed to be the new currency. With the advent of technology, the volume of data generated by financial institutions has grown leaps and bounds. In this climate, banks and other financial institutions are being forced to adapt to a data-driven organization. Big data is making inroads into providing critical information for marketing & sales, operations, business performance, and risk management.
But being a data-driven organization brings forth the challenges of managing big data and its compound characteristics. While the 3 V’s of big data, namely volume, variety, velocity define big data, the most important aspect of it comes down to the value it delivers – the fourth V.
Big data is worthless if stakeholders cannot derive value from it. This is where big data visualizations give form to this unstructured data, moulding it into something visual, tangible, and relatable. Visualization provides key actionable insights from complex datasets for all business users and not just data scientists. In the banking sector, data visualization projects can range from visualizing historical trends to real-time analysis of transactions to complex network analytics. With visualization, banks can now make sense of large volumes of data, making it easier to spot patterns and trends.
For instance, visualization is essential for the prevention and detection of money laundering. According to PWC, money laundering accounts for 2 to 5 percent loss of global GDP, or $800 billion to $2 trillion. Visualizing data has helped in finding patterns in unstructured transactions, track relations, identify below the threshold smurfers, and communicate results for quicker action. Visualizing this unstructured data provides deep insights for enhanced due diligence.
Today’s data visualization tools go beyond the conventional static visualization methods (tables, histograms, line charts, bar graphs etc.,) to interactive data querying and dynamic visualizations. For example, real-time dashboards can help portfolio managers to uncover risk concentrations and highlight portfolio improvement opportunities.
A good data visualization is part of delivering a data-driven story to your business users and consumers, alike. It forms the ‘front-end’ of data that relays a simple and quick snapshot of data insights. Business users can make key observations about their customers to help deliver a better customer experience. For instance, a marketing analyst could map historical data to identify spending patterns and gather insights towards financial management (and potential cross-selling opportunities such as fixed deposits, savings bonds etc.,). On the other hand, consumers are presented a visual representation of their spending in an interactive dashboard; improving customer experience and engagement.
Big data analytics has become the main driver for innovation in the banking domain. But, the very sensitive nature of the financial data brings in the challenges of scalability, dynamics, functionalities, and response time for visual analytics. As banks assimilate data across channels, the diversity and heterogeneity (structured, semi-structured, and unstructured) of data, the need for better visualization is crucial.
The first step for banks to step into data visualization would be to incorporate and adhere to the data analytics and visual practices. The key to simplify the monitoring and visualization of vast data is an intuitive dashboard that is accessible for all end-users to map the entire customer journey.
In this data-driven world, data analytics is critical for a better understanding of consumer behavior, meeting regulatory requirements, and generate new sources of revenue. Across industries, business leaders are working on embedding analytics into decision making and operational workflows. And driving these analytics are visualization engines that empower users to derive insights toward better risk management, increased profitability, and enhanced growth performance.