As you can see, financial companies nowadays are living with a huge amount of data, including products, service purchase history, client information, financial transactions as well as marketing campaigns and so on. This source of data comes from a lot of different mobile apps and devices. This big amount of data offers valuable chances yet it can also generate more difficulties if the important data is not consistent throughout various systems and used unsuitably.
Moreover, financial services companies are required to comply with intense regulations. If they do not, they would end up big loss or huge fines and so on. The risk of those problems can be minimized to a large extent by taking advantage of master data management for financial services providers.
The cost of bad data
Poor quality data is a big problem, which is undeniable. However, it is your own responsibility and fault if you underestimate how big a problem caused by bad data really is. In fact, poor quality data costs the U.S economy 3.1 trillion dollars a year, which is a significant number. And the bad results of poor quality data are not just financial figures. For financial services providers, bad data comes with more risks related to decision making, customer disappointment and regulatory non-compliance.
Fortunately, inaccurate data is not a long-term issue. Its leading causes such as human error, departmental silos, data copies and so on can be identified easily so that you can plan ahead. First and foremost, you should make sure that you current data and any new data flowing into your systems should have the following factors.
Firstly, data must be accurate and non-error. Secondly, data must be complete and comprehensive without gaps in collecting. Data must be consistent, which means that data stored in one system should not impact negatively on the same data stored in another system. Moreover, data must be standardized and input in the right format. Data must be timely, which means being gathered at the right moment in time. What is more, data must be updated so that you can gain approach to the most relevant information. Last but not least, an indispensable factor of an effective data strategy is the suitable data governance, which directs organizations to master data management.
The definition of master data management
Master data management relates to collaborating throughout business units as well as departments in a company, together with the orchestration, enablement and workflow of the data domain. When it comes to financial services, data domains come with customers, product and assets. Mastering all these domains will offer a whole view of all the data saved in those domains. Having a consolidated database will allow business users to identify the relationships between customers, take a look at the products that a customer has already had, define which regulations to apply to each transaction regarding compliance requirements, detect mistakes, make requests be achieved simpler and so on.
The most important factor of master data management is security. Controlling who has approach to the important information of your business, who can make changes to that data, what kind of changes should be made is not easy at all. Master data management offers a single location to deal with those changes and gives clarity on how they influence all business unit.
Master data management is defined as an active data solution that helps company systems be synced. For instance, you can make use of a data service to capture, confirm and store your customer details automatically while categorizing them by using the information of where they are in the sales process. As a result, the right data will be taken at the right time, which is also accurate, updated and consistent. This would be a difficult task as the financial institutions often get data from a wide variety of sources, some of which may be inconsistent information. This data should be cleaned up so that business workers can make use of it to generate better decisions regarding which markets to target, how to avoid errors and so on. Thus, a key element of master data management is having data screened before allowing it to enter the system so that the data quality standards can be satisfied properly.
The route leading to financial services data management
Similar to successful data strategy, suitable financial services data management asks for detailed consideration and planning. First of all, you need to set up your master data management project. In other words, you need to clarify the main business areas as well as which data should be governed. In order to determine which data to be governed, you need to be clear about the critical domains to master, by what ways those domains will have an influence on your company and what the possible risks of not controlling the data are.
After that, you need to set up an inventory of all of your current data sources and understand which ones you are able to reduce and which ones you need to consolidate. In most cases, it is possible to substitute different smaller systems with a single but quicker solution. The fewer systems that need tracking, the easier it would be to create a central repository.
In the scope stage, you need to opt for your implementation style. There are four common master data management implementation styles.
First and foremost, master data is consolidated from different sources to make a golden record, which is a single source of truth. Similar to this style, master data is also consolidated from different sources and then delivered to a central master data management repository and updated in its source systems.
Moreover, copies will then be defined and removed by making a comparison for the data throughout different systems. This is called registry style. Last but not least, master data is saved in a central repository, in which it is enhanced and returned to its respective source system.