Customer Data in Banking

Reference data, sometimes referred to as ‘master data’ or ‘static data’, is fundamental to financial trading, with an estimated 70% of data used in financial transactions being reference data*.

Reference data encompasses a range of details such as a unique security number for the financial instrument or product being traded, the name of the buyer, seller, or any other party involved in the contract, and the costs involved in the trade. Reference data can become far more detailed however, sometimes including information regarding the complex conditions of the trade and facts about all the relevant parties involved.

Reference data is also divisible into two essential types:

- Static data – this type of data won’t change throughout the course of the transaction, and includes specifications such as the names of the parties contracted in the financial transaction (ie. ‘counterparties’) and those in the financial supply chain, as well as information related to the financial products involved, such as their expiration dates or type.

- Dynamic data – information that changes throughout the course of the transaction, such as credit ratings or the pricing of the instrument at different times or at the close of the deal.

Practically speaking, reference data can be understood as boundary conditions required to undertake a transaction or trade in the financial arena, with correct data contributing to an environment for smooth transactional flow. Reference data is continuously being accessed and altered by systems linked to trading desks, risk departments, middle and back offices in financial institutions, with new data usually being added constantly throughout the trade. It is important to protect this static reference data (or Master Data as it is commonly called) and create a single version of truth.

Many of Banking and Financial Customers struggle to ensure that their data is easy to find, current, accurate and shared with only those who need it. A whopping 80% of an analyst’s time is spent solely on discovering and preparing data, according to the Harvard Business Review. At the same time, having unnecessary information on-hand makes it more challenging to locate the information that you do need. Fortunately, these problems can be addressed by implementing Customer Master Data Management (MDM). MDM creates a common definition and a single view of the customer by centralising data and creating governance, compliance and security. Given the amount of data and the number of systems that may be involved, MDM projects can be daunting. So where do you begin? Let’s take a look at the five steps to customer master data management success.

An MDM project’s starting point will depend on where the business falls in the enterprise data management maturity model. Every organisation may be at a different stage and very few are starting from scratch or have only one existing system/application/master list. Here are a few common scenarios:

- Lines of business have been focused on their products and services only, so they aren’t sharing client data with other departments and divisions;

- A company has great data, but has poor governance, so data gets duplicated and you end up with out-of-sync data;

-M&A activity has resulted in an accumulation of data from different firms—but this data has not yet been integrated; and

-A company has grown so quickly that it has outpaced its system’s ability to keep up with its business needs.

Easier transfers: If you are a manufacturer or a supplier, providing the right classification information and a list of characteristics as extensive as possible to your retailers also simply makes sense in order to ease the customer’s journey to your products. You might not have the exact same category tree with your retailers, or the same attribute names but if you give them a clear and organized set of categories and description of your products, you’ll save their time and ensure your products are given the exposure they deserve.

Being in an industry that is getting transformed, are you and your team receiving more business questions that require deep analytics with short turnaround times? Do you grapple with data silos and a lack of tools to bridge those silos and foster collaboration among analytics team members?

DDC MDM360 helps business discover and merge data from multiple disparate databases, making it possible to discover single version of truth that would otherwise go undetected. With robust integrations with a wide range of data sources and Business Intelligence and Data Science tools that your team relies on every day, DDC MDM360 serves as a hub that can make identifying your customer better than ever.