Abstract

Today’s financial service organizations have a data deluge. A number of V’s are often used to characterize big data, whereas traditional data quality is characterized by a number of dimensions. Our objective is to investigate the complex relationship between big data and data quality. We do this by comparing the big data characteristics with data quality dimensions. Data quality has been researched for decades and there are well-defined dimensions which were adopted, whereas big data characteristics represented by eleven V’s were used to characterize big data. Literature review and ten cases in financial service organizations were invested to analyze the relationship between data quality and big data. Whereas the big data characteristics and data quality have been viewed as separated domain ours findings show that these domains are intertwined and closely related. Findings from this study suggest that variety is the most dominant big data characteristic relating with most data quality dimensions, such as accuracy, objectivity, believability, understandability, interpretability, consistent representation, accessibility, ease of operations, relevance, completeness, timeliness, and value-added. Not surprisingly, the most dominant data quality dimension is value-added which relates with variety, validity, visibility, and vast resources. The most mentioned pair of big data characteristic and data quality dimension is Velocity-Timeliness. Our findings suggest that term ‘big data’ is misleading as that mostly volume (‘big’) was not an issue and variety, validity and veracity were found to be more important.

Highlights

  • Todays’ organizations are harvesting more and more data using technologies such as mobile computing, social networks, cloud computing, and internet of things (IoT) (Akerkar 2013)

  • Our objective is to investigate the complex relationship between big data and data quality

  • Literature review and ten cases in financial service organizations were invested to analyze the relationship between data quality and big data

Read more

Summary

Introduction

Todays’ organizations are harvesting more and more data using technologies such as mobile computing, social networks, cloud computing, and internet of things (IoT) (Akerkar 2013) This data deluge can be used to create a competitive advantage over competitors and create significant benefits (LaValle et al 2013) such as better understanding of customer’s behavior, more effective and efficient marketing, more precise market forecasting, and more manageable asset risks (Beattie and Meara 2013; PricewaterhouseCoopers 2013). Our objective is to understand the relationship between big data and data quality in financial service organizations. This research is among the first that studied the relationship between big data and data quality For this purpose, we formulated a research approach which is presented in Sect.

Research Approach
UBS Bank
Big Data Concept
Data Quality (DQ) Concept
Volume Volume was not frequently mentioned affecting DQ issue in the case
Velocity
Variety
Variability
Veracity
Validity
Visibility
Vast Resource
Volatility, Viability, Value
Mapping Big Data and Data Quality
Conclusion

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.