Getting clean information that is correct and valid is more important and more challenging than ever as masses of information are flowing through various applications. Having incorrect or incomplete information to work with, from transaction validation to analysis and identification of trends and important milestones, is, perhaps, the costliest mistake you could make that will bring your projects into high risk.
The need for managing contact information and ensuring data quality is a critical issue that if ignored the customer impression of your business will be less complimentary. A comprehensive data quality approach, on the other hand, results in working more effectively with your customers, for example, in response to a transaction that has occurred, or the follow-up contact.
So, as you might already conclude, it’s all about reliability, completeness, and accuracy of information flowing through your systems. An incorrect approach -to the personally identifiable information elements, like address, email, and phone- is able to immediately invalidate information. On the other hand, proper approaches to data management significantly maximise the value you get from that data.
Data quality checks must be applied to transactional and application-to-application information, making sure transaction values are valid and you don’t have conflicting information across data sources. Not having the proper contact information, especially when mail is addressed incorrectly, costs in returned mail or failed transactions.
The key to success is having the right address for the right person. As customers now enter their own address information, data quality applications are getting even more challenged. How can an application possibly know which, if any, of the addresses on file for a customer are correct? How should corrections be suggested?
More than 70% of the businesses believe that having a single view of the customer would lead to cost savings, while more than 90% admit having duplicate records in their system. It is critical however that banks can determine and establish a ‘Golden Record’ for the customer across all their systems, to ensure optimal customer experience. Every customer wants to be treated as an individual and not as series of disconnected accounts.
One of the major obstacles to a single customer view is customer data that has been stored in different formats, meaning that the quality of data on each customer is different and data are stored on disparate IT systems and applications, both in-house and outsourced.
Banks will need to integrate all the data sources – internal and external – that all contain different parts of the truth.
Banks that have been able to establish a single customer view will be able to set risk exposure limits for customers much more accurately, based on everything they know about them. For example , they will be able to increase lending to lower risk customers or reduce exposure to higher risk customers by reducing their lending limit.
This means that banks will be able to improve their ability to deliver a more personalized and engaging customer experience with highly relevant and targeted offers. Especially at a time when all banks have tightened their lending policies, which means that they don’t have that many new customers, and they need to derive value from their existing ones.
Validata is a data platform that is able to provide the ‘single version of the truth’ on your customer data across different systems and applications. Leveraging machine learning it solves the challenges of incorporating, aggregating and analysing both structured and unstructured data, and provides a true 360 view of the customer giving banks a competitive advantage to customer retention.
So, as you might already conclude, it’s all about reliability, completeness, and accuracy of information flowing through your systems. An incorrect approach -to the personally identifiable information elements, like address, email, and phone- is able to immediately invalidate information. On the other hand, proper approaches to data management significantly maximise the value you get from that data.
Data quality checks must be applied to transactional and application-to-application information, making sure transaction values are valid and you don’t have conflicting information across data sources. Not having the proper contact information, especially when mail is addressed incorrectly, costs in returned mail or failed transactions.
The key to success is having the right address for the right person. As customers now enter their own address information, data quality applications are getting even more challenged. How can an application possibly know which, if any, of the addresses on file for a customer are correct? How should corrections be suggested?
More than 70% of the businesses believe that having a single view of the customer would lead to cost savings, while more than 90% admit having duplicate records in their system. It is critical however that banks can determine and establish a ‘Golden Record’ for the customer across all their systems, to ensure optimal customer experience. Every customer wants to be treated as an individual and not as series of disconnected accounts.
One of the major obstacles to a single customer view is customer data that has been stored in different formats, meaning that the quality of data on each customer is different and data are stored on disparate IT systems and applications, both in-house and outsourced.
Banks will need to integrate all the data sources – internal and external – that all contain different parts of the truth.
Banks that have been able to establish a single customer view will be able to set risk exposure limits for customers much more accurately, based on everything they know about them. For example , they will be able to increase lending to lower risk customers or reduce exposure to higher risk customers by reducing their lending limit.
This means that banks will be able to improve their ability to deliver a more personalized and engaging customer experience with highly relevant and targeted offers. Especially at a time when all banks have tightened their lending policies, which means that they don’t have that many new customers, and they need to derive value from their existing ones.
Validata is a data platform that is able to provide the ‘single version of the truth’ on your customer data across different systems and applications. Leveraging machine learning it solves the challenges of incorporating, aggregating and analysing both structured and unstructured data, and provides a true 360 view of the customer giving banks a competitive advantage to customer retention.