The financial services sector benefits greatly from using data analytics in its business models. The financial reporting systems used by banks and hedge funds help them understand the bigger picture as well as the small details. This technology helps them gain a competitive edge over other companies through a better understanding of needs and risk.
However, not every financial executive is completely sold on the idea of big data. According to a recent poll by CDW, only 66 percent of banking and capital markets executives use analytics platforms such as business intelligence software to influence their decision-making. While a great number, it's far lower than the 90 percent of CEOs in the sector who say the same thing. The disagreement can be a critical issue when incorporating analytics into a company's business model, so it's essential to figure out ways to describe the benefits of data analytics to financial services executives.
"Seventy-two percent of banking executives thought data management was challenging."
Understanding the challenges at stake
Financial executives offer various reasons as to why they feel uncomfortable with big data. The most significant of these is the overwhelming amount of information involved in the process. The CDW survey pointed out 72 percent of banking executives thought of data management as extremely challenging. Because there are so many different data choices and models to consider, data analytics seems confusing to a lot of people, especially leaders. Getting more data scientists who can effectively explain the data in understandable terms will help make things easier. Another idea is to appoint a chief data officer who can oversee all analytics initiatives. Forty-three percent of executives who took that course of action had successful results, compared to a success rate of 31 percent for those that didn't.
A strategy of business
Another issue that financial executives may have about analytics is the inability to understand where data actually fits in the business model . For example, how does predictive analytics help customers? Or how much certainty can a bank truly glean from risk assessments? In order to best answer these concerns, IBM suggests creating a blueprint of the big data strategy with help from executive leadership, data scientists and key decision-makers. It should have the vision and plans for big data, along with the list of resources the company needs to execute them. This outline should place analytics in the context of the business itself. That means identifying pain points that predictive modeling, forecasting and performance management can help address. {something off here} Once the financial leadership of the company understands where systems like IBM Cognos BI can fit, they'll feel more comfortable with them.
