Operational Data in Banking to Provide High-Class Customer-Focused Service
In the past, banks earned and maintained the trust of customers by their personal contact with bank employees. Today banks have to rely on a new customer service model. They have to continuously store and analyze information coming from both traditional and digital sources to create an electronic trail for every client. Their data consists of an ever-growing volume of structured and unstructured data in various formats.
Banks realize that they can no longer afford to rely on legacy systems where data is fragmented. If banks want to provide clients with the services and advice they need when they need it, they have to make changes. The use of operational data in a modern operational database provides them with the throughput, availability, agility and scalability they need.
What is operational data?
Operational data is the type of data produced by daily operations, such as customer interactions. An operational data store (ODS) provides a consolidated repository into which previously isolated IT systems can feed. In a bank, all the structured and unstructured data from every system of record is integrated with an ODS. As the data comes from multiple sources, integrating it requires cleaning, resolving redundancies and checking against business integrity rules.
The ODS delivers the best available instance of a data element at any given moment. For instance, the ODS will have an account balance for each customer checking account from the checking account system and a balance for every savings account from the savings account systems. The various systems send account balances to the ODS and a user can look in one place to see each bank customer’s complete profile, such as basic information and the balance for each account type. The frequency of data load can vary from a few minutes to a few hours.
Operational data uses
The operational data in an ODS isn’t complex, historical data and as new data comes in, it overwrites existing data. The pace of updates in a batch-oriented data warehouse is too slow for operational requirements. The data in an ODS is current and less complex but it offers a level of detail necessary to support operational business functions. There are many practical uses of an ODS in banking.
Serving data to customer-facing apps
The proportion of customers using banking apps is increasing all the time because they provide an easy-to-use tool for everyday basic banking activities. They can use them to pay bills, make transfers, check balances and contact customer advice.
High speed and performance: As the operational data store consolidates data from many disparate systems, it removes friction from the customer journey. When a customer interacts with the bank through its app, the app sends an API call to the operational data store and it sends the 360-degree data specific to the customer back at speed.
Low latency: When traditional systems handle large amounts of data, they experience high latency and current customer-facing applications demand low latency. A modern ODS offers quick response time to customers.
Scale easily: Modern operational data stores can scale easily and accommodate unexpected loads and peak volumes. Using traditional operational data stores means peaks in user volume can impact performance and customers don’t get the response time they expect.
High availability: Services are available 24/7 and customers can access them from wherever they are. Even if one system of the record goes down, applications keep working because, in a modern ODS, the API layer is decoupled from the systems of record.
Make customized offers to the right customers: Customers are willing to share data with banks in exchange for personalized, convenient services. By using insights from operational data, banks can customize their offers to customers. Automated bots running in the operational data layer can regularly flag cross-selling opportunities and make appropriate offers that specifically fit a customer’s current needs.
Give advice: Customers can not only personalize their requests but get advice on issues like financial literacy, investment, and saving. For example, customers starting a new business could get advice about the best account to open for business purposes.
Serving data to employee-facing apps
Secure, bank-branded workplace apps on the phones of employees enhance their operational efficiency.
Tactical decision-making: An ODS provides a snapshot of the most current data at a given moment and this gives employees the opportunity to make the most tactical decisions because they have access to near real-time data. The ability to make reliable, accurate and timely decisions is more important than ever before.
Better reporting: As an ODS offers a non-historical, integrated view of the data in legacy systems, it enables employees to complete reporting at a higher level due to a more comprehensive view of operational processes than when using individual underlying systems of record.
Easier problem diagnosis: Employees don’t have to go into component systems to identify problems and troubleshoot.
Risk assessment and fraud prevention: Knowing the usual financial behavior patterns of clients helps employees to know when something goes wrong. For example, if a “cautious” investor withdraws all the money from an account, this could indicate fraud.
Better compliance: Regulators require banks to integrate data and have a comprehensive view of customers. Compliance improves when using an ODS as employees can cope better with processes that require compliance verification, auditing, and reporting. This simplifies operations and reduces overhead costs.
More security: Using an ODS is a risk-controlled approach compared with serving data directly from a legacy application store. The backend data stays secure and protected from threats at all times because customer requests terminate at the operational data layer.
Conclusion
Many banks are still struggling to fill the operational gap between storing and deploying data. In an increasingly complex marketplace, using operational data that resides in an operational data store gives banks the ability to adapt to changing business needs, improve customer experiences, navigate the increasingly complex regulatory landscape and transform digitally. Many banks are starting to differentiate themselves from the competition by innovating their banking models and introducing new online services.
