Representational quality challenges of big data: insights from comparative case studies
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Details
| Original language | English |
|---|---|
| Title of host publication | Challenges and Opportunities in the Digital Era - 17th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society, I3E 2018, Proceedings |
| Publisher | Springer Verlag |
| Pages | 520-538 |
| Number of pages | 19 |
| ISBN (Print) | 9783030021306 |
| DOIs | |
| Publication status | Published - 2018 |
| Publication type | A4 Article in a conference publication |
| Event | Conference on e-Business, e-Services, and e-Society - Kuwait City, Kuwait Duration: 30 Oct 2018 → 1 Nov 2018 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 11195 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Conference
| Conference | Conference on e-Business, e-Services, and e-Society |
|---|---|
| Country | Kuwait |
| City | Kuwait City |
| Period | 30/10/18 → 1/11/18 |
Abstract
Big data is said to provide many benefits. However, as data originates from multiple sources with different quality, big data is not easy to use. Representational quality refers to the concise and consistent representation of data to allow ease of understanding of the data and interpretability. In this paper, we investigate the challenges in creating representational quality of big data. Two case studies are investigated to understand the challenges emerging from big data. Our findings suggest that the veracity and velocity of big data makes interpretation more difficult. Our findings also suggest that decisions are made ad-hoc and decision-makers often are not able to understand the ins and outs. Sense-making is one of the main challenges in big data. Taking a naturalistic decision-making view can be used to understand the challenges of big data processing, interpretation and use in decision-making better. We recommend that big data research should focus more on easy interpretation of the data.
ASJC Scopus subject areas
Keywords
- Big data, Interpretation, Naturalistic decision making, Sense-making