1. Why is a strong analytics effort key to competitive advantage for banking and financial services companies?
As banking and financial services companies search for new revenue streams, analytics projects let them deliver more personalized products in a shorter time to market. Analytics help banks present the customer with the right product at the right time via the right channels. Also, thanks to the growing body of application programming interfaces (APIs) with the ability to connect disparate data sets, banks can create added services for existing customers, often using financial tech companies as sales channel partners.
Machine learning, combined with analytics, teaches banks much about customer behavior and preferences, so the banks can continuously learn and fine tune analytical models to optimize products and services, plus optimize the cost of offering products in various channels. Cloud-based analytics platforms give banks flexibility and elasticity to work with big data workloads at high velocity and reap business value faster.
2. What types of new data sources are banking and financial services companies using to wring more value from data analysis projects?
Given the valuable data now residing in sources ranging from social media posts to mobile applications, banks need to acquire more data types, from more channels. For example, banks work with a growing amount of NoSQL data to harness value from semi-structured data, from sources such as website clickstream tracking reports, weather and news data, product reviews, and social media posts. Hadoop tools can help banks glean insights from unstructured data such as call center customer inquiries, video clips, image scans, and text messages. To deal with these data types efficiently, the unified data architecture approach has become popular. This lets the bank create a unified semantic layer for internal and external data, using APIs to deliver the data to various business groups based on need.
3. What are the biggest worries for BFS companies around analytics right now?
First, data management is a key concern for BFS organizations. As banks use big data tools to analyze data in increasing variety, velocity and volume, governance and data quality become key challenges.
Second, banks must meet significant legal, risk and compliance requirements on the security, auditing, retention, and backup of analytic data. Customer data privacy and security, as well as granular security for employees, also pose key worries as companies explore wider use of analytics.
Finally, BFS companies are struggling to find talented people who can transform analytics data into actionable business data.
4. What is holding back better analytics ROI for these companies?
Several factors hold BFS companies back from realizing more ROI on analytics projects. At some companies, federated business structures and a lack of accountability cause delays in reaping ROI. A unified framework that includes various business processes and points teams toward the same business goals is essential. Additionally, BFS companies struggle with deciding which key performance indicators (KPIs) are critical for managing analytics projects and evaluating analytics project ROI. Until ROI measurement matures, banks will struggle to demonstrate business impact.
Within BFS companies, a great deal of data remains cordoned off. It still does not flow through analytics tools or influence business decisions. Also, some banks remain far away from using big data analytics, machine learning, or real-time analytics because their infrastructure concerns or governance, data privacy, and security worries hinder progress. These banks struggle exploring how to monetize analytics.
Finally, self-service analytics tools have not become a reality for most business users yet. The democratization of data, where people at all levels of a BFS company can access huge amounts of data and derive insights, has not arrived because self-service tools are still emerging. Until those tools mature further, APIs can help banks connect analytics data to day-to-day business processes and automate decision making.