Towards Process Patterns for Processing Data Having Various Qualities
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 th
- PDF / 947,818 Bytes
- 12 Pages / 439.37 x 666.142 pts Page_size
- 45 Downloads / 159 Views
Abstract. 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. Keywords: Big data Telecom
Data quality
Data processing
Process patterns
1 Introduction Today’s organizations collect more and more data due to datafication. Datafication refers to activities that digitalize all objects which are related to the organizations’ processing chain [1, 2]. Data can originate from internal and external sources and might have different qualities. Data quality refers to data that are fit for use by data users or data consumers [3, 4]. The definition of data quality captures a broad perspectives by including by the quality conveyed by the data and the use of the data. Many studies suggest that organizations can gain benefits from the data if they succeed in unlocking value from the data. This can result in greater efficiency and profits [5] as well as competitive advantages [6–8]. Therefore, organizations are seeking ways to realize the value from their big data [8]. Value creation requires the processing of data. Data can be processed in various ways (e.g. [9–11]). Although the idea of drawing value from the data seems to be straightforward, many organizations failed to do so. According to a recent study by Reid et al. [12], two third of businesses across Europe and North America failed to unlock value from big data. In this paper, we identify generic process patterns that can be used by any organization to deal with data which have various data qualities. The various data qualities of internal and external sources require organizations to deal with them in various manners. Which process should be followed depends on the data © IFIP International Federation for Information Processing 2016 Published by Springer International Publishing Switzerland 2016. All Rights Reserved Y.K. Dwivedi et al. (Eds.): I3E 2016, LNCS 9844, pp. 493–504, 2016. DOI: 10.1007/978-3-319-45234-0_44
494
A. Wahyudi and M. Janssen
qualities. These variations have some similarities that create process patterns of how organizations deal with them. Which process should be followed depends on the data qualities. The data quality provides the initial set of conditions to select the process steps that are necessary to prepare the data for use. Such patterns can be vi
Data Loading...
