Abstract
Media industries and advertisers are increasingly turning to big data analytics to better understand audience media consumption patterns, as evidenced by Canada’s Globe and Mail’s applications Sophi and TasteGraph [1, 2]. Data analytics and interface design provide complementary perspectives for large datasets. HCI design principles have been applied to Business Intelligence (BI) platforms, including techniques which filter and summarize large data sets, and are equally relevant to media informatics platforms for advertisers, buyers, sellers, and planners [1, 7,8,9]. This analysis becomes more challenging when managing highly scalable and multi-dimensional audience survey data [3,4,5,6]. According to Dewdney and Ride visualization tools are essential for effective decision making in the communications industry as these ease cognitive load and decision-making [15]. Kirk proves that a visualization system becomes a successful tool when it builds on the user’s extant domain knowledge, providing enhanced insights [13]. The research aims to leverage visualization design principles as defined by Tulp and Meirelles [6, 7] and in order to improve the UI/UX and visual analytic capabilities of a leading media analytics platform providing planners, advertisers, and media buyers with an interface to better understand their audience. We have analyzed and assessed the different application report parameters that explore television and radio survey datasets from a leading analytics firm. We propose design prototypes which are comprised of enhanced symbolic icons [9] through badges and glyphs, consistent colours [10], and layouts which maintain a visual hierarchy and filtration techniques [10, 11, 14] in order to minimize information clutter and cognitive overload. We propose a variety of interface designs that address user needs using HCI, heuristic design principles and novel visualization techniques [6, 7, 12, 15]. Next steps include validating our design prototypes through rigorous user testing and building high fidelity prototypes.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Peter, J., Szigeti, S., Jofre, A., Edall, G., Diamond, S.: The Sophi HUD: a novel visual analytics tool for news media. OCAD University Open Research Repository (2017)
Karawash, A., et al.: TasteGraph: a visual analytics tool for profiling media audiences’ tastes. In: Proceedings of the IEEEVis, InfoVis, Berlin (2018)
Edge, D., Larson, J., White, C.: Bringing AI to BI: enabling visual analytics of unstructured data in a modern business intelligence platform. In: Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems, p. CS02. ACM, April 2018
Delen, D., Moscato, G., Toma, I.L.: The impact of real-time business intelligence and advanced analytics on the behaviour of business decision makers. In: 2018 International Conference on Information Management and Processing (ICIMP), pp. 49–53. IEEE, January 2018
Marjanovic, O., Dinter, B., Ariyachandra, T.R.: Introduction to the minitrack on organizational issues of business intelligence, business analytics and big data (2018)
Tulp, J.W.: Designing for small and large datasets. In: Bihanic, D. (ed.) New Challenges for Data Design, pp. 377–390. Springer, London (2015). https://doi.org/10.1007/978-1-4471-6596-5_20
Meirelles, I.: Hierarchical structures: trees. In: Design for Information: An Introduction to the Histories, Theories, and Best Practices Behind Effective Information Visualizations, pp. 5–15. Rockport publishers (2013)
Nielsen, J.: 10 usability heuristics for user interface design. Nielsen Norman Group 1(1) (1995). https://www.nngroup.com/articles/ten-usability-heuristics/
Cairo, A.: The Truthful Art: Data, Charts, and Maps for Communication. New Riders, San Francisco (2016)
Gray, C.C., Teahan, W.J., Perkins, D.: Understanding out analytics: a visualization survey. J. Learn. Anal. School Comput. Sci. (2017). https://research.shadowraider.com/jspui/handle/1471/19
Ware, C.: Color. In: Information visualization: perception for design, pp. 95–138. Morgan Kaufmann (2013)
Andrienko, G., Andrienko, N., Bak, P., Keim, D., Wrobel, S.: Visual analytics infrastructure. In: Andrienko, G., Andrienko, N., Bak, P., Keim, D., Wrobel, S. (eds.) Visual Analytics of Movement, pp. 103–129. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37583-5_4
Kirk, A.: The context of visualization. In: Data Visualization: A Successful Design Process. Packt Publishing Ltd. (2012)
Munzer, T.: What’s vis, and why do it? In: Visualization Analysis and Design, pp. 1–18. AK Peters/CRC Press (2014)
Dewdney, A., Ride, P.: Digital media as a subject. In: Curran, J. (ed.) The Digital Media Handbook, pp. 18–28 (2014). Chap. 2
Zuk, T., Schlesier, L., Neumann, P., Hancock, M. S., Carpendale, S.: Heuristics for information visualization evaluation. In: Proceedings of the 2006 AVI Workshop on Beyond Time and Errors: Novel Evaluation Methods for Information Visualization, pp. 1–6. ACM, May 2006
McCarthy, J., Wright, P.: Technology as Experience. MIT Press, Cambridge (2007)
LaValle, S., Lesser, E., Shockley, R., Hopkins, M.S., Kruschwitz, N.: Big data, analytics and the path from insights to value. MIT Sloan manage. Rev. 52(2), 21 (2011)
Garrett, J.J.: Elements of User Experience: User-Centered Design for the Web and Beyond. Pearson Education, London (2010)
Gray, J., Chambers, L., Bounegru, L.: The Data Journalism Handbook: How Journalists Can Use Data to Improve the News. O’Reilly Media, Inc., Newton (2012)
Tourangeau, R., Couper, M.P., Conrad, F.: Spacing, position, and order: interpretive heuristics for visual features of survey questions. Public Opin. Quart. 68(3), 368–393 (2004)
Sun, Z., Sun, L., Strang, K.: Big data analytics services for enhancing business intelligence. J. Comput. Inform. Syst. 58(2), 162–169 (2018)
Keim, D., Kohlhammer, J., Ellis, G., Mansmann, F.: Mastering the information age solving problems with visual analytics (2010). http://www.vismaster.eu/wp-content/uploads/2010/11/title-page-to-chapter-1.pdf
Yau, N.: Choosing tools to visualize data. In: Visualize This: The FlowingData Guide to Design, Visualization, and Statistics, pp. 53–89 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Hussain, A. et al. (2019). HCI Design Principles and Visual Analytics for Media Analytics Platform. In: Stephanidis, C. (eds) HCI International 2019 - Posters. HCII 2019. Communications in Computer and Information Science, vol 1034. Springer, Cham. https://doi.org/10.1007/978-3-030-23525-3_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-23525-3_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-23524-6
Online ISBN: 978-3-030-23525-3
eBook Packages: Computer ScienceComputer Science (R0)