Abstract
An incredible amount of data has constantly been generated. There is a gap between the abundance of data and the needs of users. At the same time a particular user needs not only a relevant part of the available data but (s)he needs higher-level (inferred) data in order to use this data in support of decision-making. Analytics comes to bridge that gap, by means of tools. Those tools link what the user needs to what is available as data; generate higher-level (inferred) data based on the raw data; provide visualization that is useful especially to users that have limited knowledge in mathematics and data science. The abundance of definitions that concern analytics as well as the numerous terms and concepts confuse users. Often different concepts are inconsistent with regard to each other. Inspired by this problem, the current paper: provides a systematic analysis featuring analytics in general and business analytics, in particular, developing this also from the perspective of visualization-related needs. We propose a layered business analytics architecture named General Architectural Framework for Business Visual Analytics. The framework presents the main interrelationships between the elements for designing and modeling Business Visual Analytics. We propose a conceptual definition that combines the semantics of the multiple recurring and overlapping definitions of analytics, named Business Visual Analytics (BVA). This is a new conceptual viewpoint mainly focused on the innovative data visualization techniques, business analytics capabilities and achieving business goals and performance. The paper is reporting research in progress and for this reason, further architectural developments and related validations are planned for further research.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Davenport, T.H., Harris, J.G.: Competing on Analytics: the New Science of Winning. Harvard Business School Press Boston, Boston (2007)
Cooper, A.: What is analytics? definition and essential characteristics. CETIS Anal. Ser. 1(5), 1–10 (2012). ISSN 2059214
Laursen, G., Thorlund, J.: Business Analytics for Managers: Taking Business Intelligence Beyond Reporting, 2nd edn. Wiley, Hoboken (2017). ISBN 9781119298588
Vanthienen, J., Witte, K.: Data Analytics Applications in Education, 1st ed. CRC Press/Taylor Francis Group/Auerbach Publications, New York (2017). ISBN 9781498769273
Somani, A., Deka, G.: Big Data Analytics: Tools and Technology for Effective Planning. CRC Press/Taylor & Francis Group, New York (2018). ISBN 9781138032392
IBM Software Group: Descriptive, Predictive, Prescriptive: Transforming Asset and Facilities Management with Analytics. IBM Corporation Software Group, New York (2013)
Chowdhury, M., Apon, A., Dey, K.: Data Analytics for Intelligent Transportation Systems, 1st edn. Elsevier Science Publishers, Amsterdam (2017). ISBN 9780128097151
Sami, M.: The Evolution of Analytics. https://melsatar.blog/2017/07/30/the-evolution-of-analytics/. Accessed 3 May 2018
Dankov, Y., Birov, D.: Extended conceptual framework for business analytics supporting innovations. In: Ketikidis, P., Solomon, A., Thessaloniki, G. (eds.) Proceedings of International Conference for Entrepreneurship, Innovation and Regional Development ICEIRD10, 31 August–1 September 2017 (2017). ISBN 9789609416115, ISSN 24115320
Peña-Ayala, A. (ed.): Learning Analytics: Fundaments, Applications, and Trends. SSDC, vol. 94. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-52977-6
Dodd, J.: Healthcare It Transformation: Bridging Innovation, Integration, Interoperability, and Analytics, 1st ed. CRC Press/Taylor & Francis Group/Productivity Press, New York (2017). ISBN 9781498778442
Lowman, M.: A Practical Guide to Analytics for Governments: Using Big Data for Wiley and SAS Business Series. Wiley, Hoboken (2017). ISBN 9781119362821
Yan, W.Q.: Introduction to Intelligent Surveillance: Surveillance Data Capture, Transmission, and Analytics, 2nd edn. Springer International Publishing AG, Switzerland (2017). https://doi.org/10.1007/978-3-319-60228-8. ISBN 9783319602271
Thuraisingham, B., Parveen, P., Masud, M., Khan, L.: Big Data Analytics with Applications in Insider Threat Detection, 1st ed. CRC Press/Taylor & Francis Group/Auerbach Publications, Boston (2017). ISBN 9781498705479
Alsmadi, I., Karabatis, G., Aleroud, A.: Information Fusion for Cyber-Security Analytics. Studies in Computational Intelligence, vol. 691, 1st edn. Springer International Publishing, Switzerland (2017). https://doi.org/10.1007/978-3-319-44257-0. ISBN 9783319442563, 9783319442570
Schniederjans, M., Schniederjans, D., Christopher, J.: Business Analytics Principles, Concepts and Applications. Pearson Education Inc, USA (2014). ISBN 9780133552188
Stubbs, E.: The Value of Business Analytics. Wiley, Hoboken (2011)
Bichler, M., Heinzl, A., van der Aalst, W.M.P.: Business analytics and data science: once again? Bus. Inf. Syst. Eng. 59(2), 77–79 (2017). https://doi.org/10.1007/s12599-016-0461-1
Shmueli, G., Bruce, P.: Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner, 3rd edn. Wiley, Hoboken (2016). ISBN 9781118729274
Evans, J.: Business Analytics: Methods, Models, and Decisions, 2nd edn. Pearson Education, Boston (2017). ISBN 9781292095448
Bayrak, T., A review of business analytics: a business enabler or another passing fad. In: World Conference on Technology, Innovation and Entrepreneurship, Procedia - Social and Behavioral Sciences vol. 195, pp 230 – 239, Elsevier Ltd (2015). ISSN: 18770428
Sugumaran, V., Sangaiah, A., Thangavelu, A.: Computational Intelligence Applications in Business and Big Data Analytics, 1st edn. CRC Press/Taylor & Francis Group, FL (2017). ISBN 9781498761017
Keim, D.; Mansmann, F.; Stoffel, A., Ziegler, H.: Visual Analytics., Encyclopedia of Database Systems/M. Tamer Özsu et al. (ed.) - Heidelberg: Springer, (2008). http://nbn-resolving.de/urn:nbn:de:bsz:352-opus-68335. Accessed 2 Nov 2018
Keim, D., Kohlhammer, J., Ellis, G., Mansmann, F.: Mastering the Information Age – Solving Problems with Visual Analytics. Eurographics Association, Goslar (2010). ISBN 9783905673777
Thomas, J., Cook, K.: Illuminating the path: the research and development agenda for visual analytics. In: National Visualization and Analytics Center (2005). ISBN:0769523234
Keim, D., Andrienko, G., Fekete, J.-D., Görg, C., Kohlhammer, J., Melançon, G.: Visual Analytics: Definition, Process, and Challenges. In: Kerren, A., Stasko, John T., Fekete, J.-D., North, C. (eds.) Information Visualization. LNCS, vol. 4950, pp. 154–175. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-70956-5_7
Telea, A., Ersoy, O., Voinea, L.: Visual analytics in software maintenance: challenges and opportunities. In: International Symposium on Visual Analytics Science and Technology, Kohlhammer, J., Keim, D. (eds.), the Eurographics Association (2010)
Dill, J., Earnshaw, R., Kasik, D., Vince, J., Chung Wong, P.: Expanding the Frontiers of Visual Analytics and Visualization. Springer, London Dordrecht Heidelberg New York (2012). https://doi.org/10.1007/978-1-4471-2804-5. ISBN 9781447128038
Heer, J., Agrawala, M.: Design considerations for collaborative visual analytics, information visualization. In: Proceedings of IEEE Symposium on Visual Analytics Science and Technology 2007, Sacramento, CA, USA, vol. 7 (2008). https://doi.org/10.1057/palgrave.ivs.9500167
Suh, S., Anthony, T.: Big Data and Visual Analytics, 1st edn. Springer International Publishing AG, Switzerland (2017). ISBN 9783319639154
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Dankov, Y., Birov, D. (2018). General Architectural Framework for Business Visual Analytics. In: Shishkov, B. (eds) Business Modeling and Software Design. BMSD 2018. Lecture Notes in Business Information Processing, vol 319. Springer, Cham. https://doi.org/10.1007/978-3-319-94214-8_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-94214-8_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-94213-1
Online ISBN: 978-3-319-94214-8
eBook Packages: Computer ScienceComputer Science (R0)