Top 4 Mistakes Banks Make with their Data
date: June 1, 2018
Collecting, sorting, and analyzing large volumes of sensitive data is part of every bank and financial services company’s operations. However, how this data is managed is of increasing concern as security regulations become stricter and banks continue to use legacy systems that are quickly becoming outdated. It’s now critical for banks to build a scalable, hybrid cloud database that’s always on and efficiently managing customer data.
Here are the top four mistakes the banking and financial services industry (BFSI) is making with its data and effective solutions:
1. Maintaining Multiple Data Silos Vulnerable to Fraud
Many financial services institutions still house data in multiple silos. This potentially exposes that data to hackers and compromises security measures. Protecting and monitoring all of this data stored in so many locations can be tricky and can make maintaining legal compliance a challenge.
Solution: Using an integrated and unified data collection and storage system easily solves this problem. A dedicated cloud database for banks like DataStax Enterprise centralizes all of this information and greatly reduces complexity. This mitigates risk of fraudulent behavior and help you meet compliance requirements.
2. Having a Limiting Amount and Granularity of Data Collection
Many banks implement systems that can store or analyze large amounts of data but a lot of this data is collected in batches—not in real-time—so its relevance is compromised. As a result, banks often end up limiting the data they collect or throwing it away, or, even worse, dumping data into a low value data lake solution where data sits without being accessed for long periods of time.
Solution: Banks need to use a scalable, multi-model database and hybrid cloud solution that collects and analyzes data in real time. Collecting data at a granular level can provide more accurate insights into consumer behavior so banks can better target their products and services while providing a more personalized experience. Cloud database and NoSQL database systems like DataStax Enterprise make this possible.
3. Data Collection Across Multiple Systems
Many financial services institutions using legacy systems are collecting data across dozens, even hundreds, of systems. This makes it extremely difficult for analysts to derive actionable insights from relevant data because they may not be able to access certain types of data easily and may not have all the data points they truly need.
Solution: This is solved with cloud databases for banks like DataStax Enterprise, an integrated system that reduces complexity and facilities the process of collecting actionable data. Extrapolating and analyzing this data can provide a 360-degree view of the customer which in turn helps companies develop AI based solutions and design more personalized, targeted customer experiences.
4. Scheduling Maintenance Windows
Customers need bank services around the clock so any interruptions in service because of scheduled maintenance periods greatly impacts the amount of data collected and overall transaction capabilities of the bank. Maintenance windows are simply no longer acceptable to today’s consumer and banks need a system that stays on 24/7.
Solution: Investing in DataStax Enterprise, with its always-on architecture, allows for delivering around-the-clock services and coordinating accurate data collection processes. Scheduling maintenance windows that shuts down some or all data collection completely becomes obsolete.
Intelligent data management is an important driving force behind the future of financial services companies and banks. Being able to access data in real time, analyze large volumes of data, and produce detailed reports of this data is critical for companies that want to transform the customer experience and maintain regulatory compliance. Using cloud applications designed with always-on architectures can prevent many common mistakes the BFSI industry is making with its data management systems.
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