The importance of data accuracy for Hadoop banking analytics
Business analytics solutions, such as those built upon Hadoop architecture, can be a major resource for members of the financial industry. With these tools at their disposal, banks leadership could gain major insights into their operations and market places as well as improving their efforts to effectively engage potential clients.
However, these organizations need access to accurate customer data to gain the full benefits of business analytics solutions. According to a recent Experian QAS survey, many financial institutions have struggled to ensure the accuracy of the data they gather, Credit Union Journal reported. Ninety-one percent of the organizations that participated in the survey suspected that the information they collected was inaccurate in some fashion. While respondents reported that as much as 18 percent of their data could not be ensured for accuracy on average, 27 percent of the total number of participating enterprises could not say how much of their information was compromised.
There are several steps that financial institutions can take to increase the accuracy of their data:
- Establish regular database maintenance tasks to manage files
- Integrate automated verification tools to ensure that client and prospect data is up to date
- Create a full data workflow to prioritize high-volume entry points
Information services expert Thomas Schutz noted in Bank Systems & Technology that banks and other financial institutions could improve the accuracy of their collected information by condensing the number of databases they maintained. This will prevent duplicate entries from being entered into multiple systems. One of the problems with operating multiple databases is that updated information may not be spread to each system, leaving some with inaccurate consumer data. In addition, Schutz recommended that banks place more emphasis on training personnel to enter and access data in a streamlined and uniform process. This will eliminate entry errors and inconsistencies, maintaining the accuracy of gathered data across the enterprise and ensuring that it maximizes the effectiveness of their Hadoop initiatives.
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