Trust is a defining factor in the financial services industry, where data plays a critical role in enabling crucial business decisions, and therefore, maintaining the accuracy of the source of data becomes much more relevant. With the establishment of global financial regulations, there is a standard process of data governance that intends to assure the trust factor that every financial organization has to abide by. Therefore, the financial services industry must understand and verify the sources of data and their authenticity, bringing in the relevance of data quality engineering in financial services. Additionally, with evolving data governance regulations, financial services need to go a step further to maintain data quality and security through data quality engineering services. This blog post focuses on the key factors impacting data quality engineering in the financial services industry, which helps it maintain enhanced transparency and data authenticity.
Key Factors Influencing Data Quality Engineering in Financial Services
Data quality is necessary for all businesses across industries to make informed decisions. As international governing bodies strive for increased transparency in all financial services industry operations, the financial services sector must strictly abide by regulatory compliance. If they identify any irregularity in meeting the compliance regulations, the penalty imposed on financial organizations is severe. Not only the financial penalty is imposed, but also the breach of customers’ trust takes place, which severely damages the reputation and trust factor of financial organizations, making it challenging for businesses to garner the trust of their customers again. Therefore, maintaining data quality is critical for financial institutions to stay relevant, competitive, and well-organized, and here data quality engineering plays an influential role.
So, let’s take a quick look at the key factors that play a momentous role in optimizing data quality engineering for financial organizations below:
- Accuracy of Data: To ensure data quality in financial services, there is a need to maintain a specific set of rules that define the accuracy and consistency of data across all the datasets of the organization. There should be automation of business logic that establishes specific conditional values to maintain error-free data. Furthermore, a consistent approach should be followed for maintaining the authenticity of data garnered from both internal and external sources. Financial organizations have troves of customer data; therefore, it is vital to maintain the authenticity of data across all data sets. Suppose a customer lives in a particular area, but after a year, he relocates to another location, and this information needs to be updated in the data records of financial services. Otherwise, these organizations carry outdated customer data that proves to be irrelevant for business use. Any change in customer records must be updated across all the datasets of financial organizations. It is equally important for financial organizations to use cross-reference to validate the accuracy of customer data. For example, an address change for a customer must be cross-verified with external data sources like databases reflecting the official addresses of customers to ensure the authenticity of the data for further use and decision-making. Therefore, harnessing data quality engineering enables the financial service industry to maintain data accuracy efficiently.
- Ensuring Exhaustive Information: Data accuracy can be assured only if exhaustive customer information is available. If there is missing information, let’s say a customer’s phone number is missing, then automated workflows are harnessed to find the missing information. As the automated workflows follow pre-defined rules, they check all the databases to get the required information to fill the information gap. Furthermore, these workflows also go for cross-reference, like checking the customer’s information in official databases, to confirm the validity of the information. But the challenge comes in a scenario where business logic is added after the data is ready to use, and now the existing system can’t be realigned to bring in new logic. Therefore, leveraging data quality engineering services is critical to identifying the key missing information at the very beginning and addressing it immediately by routing it to the required personnel and triggering the workflow designed for the same. It is important to consider that there can be changes in customer information throughout the customer lifecycle, and appropriate data engineering services help to cater to such situations by filling in the required information.
- Ensuring Standardization of Information: Data is critical for financial organizations, and maintaining the authenticity of that data is far more important to making optimized business decisions. If the data is not standardized across all datasets of financial organizations, then it becomes challenging to maintain its accuracy and validity. Furthermore, in the absence of data standardization, there can be chaos, leading to misrepresentation of data across the organization that can affect the operational capabilities of financial organizations, leading to misinformed decision-making. Therefore, using data engineering tools, financial institutes can determine the specific standardization of data coming from multiple sources. Therefore, maintaining the data models and bringing a standardized approach to them becomes critical to ensuring data accuracy. It enables the entire organization to maintain data records in a standardized manner that helps in the effective maintenance of data, leading to optimal data organization.
Conclusion
To conclude, financial institutions heavily depend on data for their operational efficiency, and relying on data that is not accurate, authentic, and verified can bring numerous challenges to their overall functioning. Therefore, leveraging data quality engineering services empowers financial institutes to optimize their operations with enhanced productivity, improved decision-making, and compliance with global financial regulations. Financial organizations can harness their data engineering capabilities to maintain data sanctity, but if they lack in-house capabilities, then relying on an expert IT partner is highly recommended. The IT partner comes with domain expertise, a skilled team of professionals, and technological advancements that accelerate the data efficiency of the financial services industry. Therefore, by leveraging the data engineering consulting services of an IT partner, financial organizations can promote data accuracy throughout their operations and make informed decisions based on verified data insights to confidently navigate the future and deliver exceptional customer experiences.
No comments yet