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.

Data seldom create value by themselves. They need to be linked and combined from multiple sources, which can often come with variable data quality. The task of improving data quality is a recurring challenge. In this paper, we use a case study of a large telecom company to develop a generic process pattern model for improving data quality. The process pattern model is defined as a proven series of activities, aimed at improving the data quality given a certain context, a particular objective, and a specific set of initial conditions. Four different patterns are derived to deal with the variations in data quality of datasets. Instead of having to find the way to improve the quality of big data for each situation, the process model provides data users with generic patterns, which can be used as a reference model to improve big data quality.

Organizations are looking for ways to gain advantage of big and open linked data (BOLD) by employing statistics, however, how these benefits can be created is often unclear. A reference architecture (RA) can capitalize experiences and facilitate the gaining of the benefits, but might encounter challenges when trying to gain the benefits of BOLD. The objective of the research to evaluate the benefits and challenges of building IT systems using a RA. We do this by investigating cases of the utilization of a RA for Linked Open Statistical Data (LOSD). Benefits of using the reference architecture include reducing project complexity, avoiding having to “reinvent the wheel”, easing the analysis of a (complex) system, preserving knowledge (e.g. proven concepts and practices), mitigating multiple risks by reusing proven building blocks, and providing users a common understanding. Challenges encountered include the need for communication and learning the ins and outs of the RA, missing features, inflexibility to add new instances as well as integrating the RA with existing implementations, and the need for support for the RA from other stakeholders.

Big data has been acknowledged for its enormous potential. In contrast to the potential, in a recent survey more than half of financial service organizations reported that big data has not delivered the expected value. One of the main reasons for this is related to data quality. The objective of this research is to identify the antecedents of big data quality in financial institutions. This will help to understand how data quality from big data analysis can be improved. For this, a literature review was performed and data was collected using three case studies, followed by content analysis. The overall findings indicate that there are no fundamentally new data quality issues in big data projects. Nevertheless, the complexity of the issues is higher, which makes it harder to assess and attain data quality in big data projects compared to the traditional projects. Ten antecedents of big data quality were identified encompassing data, technology, people, process and procedure, organization, and external aspects.

Organizations become more data-intensive and companies try to reap the benefits from this. Although there is a large amount of data available, this data has often different qualities which hinders use. Creating value from big data requires dealing with the variations in quality. Depending on their quality, data need to be processed in various ways to prepare this data for use. Although the processes vary, dealing with certain levels of data quality is a recurring challenge for many organizations. By developing generic process patterns organizations can reuse each other solutions. In this paper, process patterns for dealing with various levels of data quality are derived based on a case study of a large telecom company that employs all kinds of data to create operational value. The process patterns can possibly be used by other organizations.