Abstract
The main purpose of the chapter is to discuss the nature of analytical systems based on data lake architecture and their abilities to support different business objectives. In software engineering, empirical methods play a significant role in order to reach research objectives (Wohlin et al. in Empirical methods and studies in software engineering. Springer, Berlin, 2013 [1]). We decided to examine the proposed ideas using a prototyping approach. Software prototypes can be perceived as design artefacts and they can support creativity, communication and early evaluation (Beaudouin-Lafon and Mackay in Human computer interaction handbook: fundamentals. CRC Press, 2007 [2]). Prototyping can be a good way to merge this process with agile principles (Böhmer et al. in Proceedings of the 21st international conference on engineering design (ICED 17). Design methods and tools. The Design Society, Vancouver, 2017 [3]; Tanvir et al. in International conference on engineering, computing & information technology (ICECIT 2017), pp. 50–54, 2017 [4]). This research method has been applied in the chapter, and the final prototype of a light data lake (LDL) is presented in the third section. Additionally, the authors present maturity level modeling as a supported process to implement LDL in SME organizations. The last section describes the maturity model that was achieved by the LDL prototype.
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References
Wohlin, C., Höst, M., & Henningsson, K. (2013). Empirical research methods in software engineering. In R. Conradi & A. I. Wang (Eds.), Empirical methods and studies in software engineering. Lecture notes in computer science (vol. 2765). Berlin: Springer.
Beaudouin-Lafon, M., & Mackay, W. (2007) Prototyping tools and techniques. In Human computer interaction handbook: Fundamentals. CRC Press.
Böhmer, A. I., Hostettler, R., Richter, C., Lindemann, U., Conradt, J., & Knoll, A. (2017). Towards agile product development—The role of prototyping. In A. Maier, et al. (Eds.), Proceedings of the 21st International Conference on Engineering Design (ICED 17). Design Methods and Tools (pp. 1–10). Vancouver: The Design Society.
Tanvir, S., Safdar, M., Tufail, H., & Quamar, U. (2017). Merging prototyping with agile software development methodology. In International Conference on Engineering, Computing & Information Technology (ICECIT 2017) (pp. 50–54). https://www.researchgate.net/publication/319288275_Merging_Prototyping_with_Agile_Software_Development_Methodology.
Olszak, C. M., & Ziemba, E. (2012). Critical success factors for implementing business intelligence systems in small and medium enterprises on the example of upper Silesia, Poland. Interdisciplinary Journal of Information, Knowledge, and Management, 7, 129150.
Ramamurthy, K., Sen, A., & Sinha, A. P. (2008). An empirical investigation of the key determinants of data warehouse adoption. Decision Support Systems, 44, 817–841.
Beckett, H. (2003). Half of SMEs have no IT Strategy. Computer Weekly, 1, 34. https://bit.ly/2WX3RUZ. Access April 19, 2019.
Brynjolfsson, E., Hitt, L. M., & Kim H. H. (2011) Strength in numbers: How does data-driven decision making affect firm performance? https://bit.ly/2NlfCAj. Access April 10, 2019.
Davenport, T. H., & Harris, J. G. (2007). Competing on analytics: The new science of winning. Boston, MA: Harvard Business Review Press.
Big Data Analytics Market Study. (2017). MicroStrategy Website: https://bit.ly/2xcYe5y. Access April 10, 2019.
Rizkallah, J. (2017). The big (unstructured) data problem. Forbes Technology Council. https://www.forbes.com/sites/forbestechcouncil/2017/06/05/the-big-unstructured-data-problem/#5e631803493a. Access February 11, 2016.
Davenport, T. H. (2013, December). Analytics 3.0. Harvard Business Review.
Turban, E., Sharda, R., Delen, D., Aronson, J. E., Liang, T., & King, D. (2015). Business intelligence and analytics: systems for decision support. Pearson.
Davenport, T. H. (2017, June 22). How analytics has changed in the last 10 years (and How It’s Staved the Same). Harvard Business Review. https://bit.ly/2sG6FUb. Access December 20, 2018.
Fowler, M. (2015). Data Lake. https://martinfowler.com/bliki/DataLake.html. Access April 25, 2019.
Tomcy, J., & Pankaj, M. (2017). Data lake for enterprises. Packt Publishing.
Pasupleti, P., & Purra, B. S. (2015). Data lake development with big data. Birmingham: Packt Publishing.
Pólkowski, Z., & Nycz, M. (2017). Distance management of SMEs using ICT solutions. Iranian Journal of Optimization, 9(1), 1–12. http://ijo.iaurasht.ac.ir/article_529145_d1bfe68e8bb29997ade9757ad9f14342.pdf. ISSN:2008-5427.
Rising, C. J., Kristensen, M., & Tjerrild-Hansen, S. (2014). Is big data too big for SMEs? Leading Trends in Information Technology. Stanford University (Online). https://web.stanford.edu/class/msande238/projects/2014/GainIT.pdf. Access April 4, 2019.
Miloslavskaya, N., & Tolstoy, A. (2017). Big data, fast data and data lake concepts. Procedia Engineering, 88, 300–305.
Serra, J. (2018). When should we load relational data to a data lake? https://www.sqlchick.com/entries/2018/11/13/when-should-we-load-relational-data-to-a-data-lake. Access May 16, 2019.
Mason, R. T. (2015). NoSQL databases and data modeling techniques for a document-oriented NoSQL database. In Proceedings of Informing Science & IT Education Conference (InSITE) (pp. 259–268). https://bit.ly/2Jfjiyf. April 25, 2019.
Raj, R., Wong, S. H., & Beaumont, A. J. (2016). Business intelligence solution for an SME: A case study. In Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2016) (pp. 41–50). SciTe-Press. https://doi.org/10.5220/0006049500410050.
Kandel, S., Heer, J., Plaisant, C., Kennedy, J., van Ham, F., Riche, N. H., et al. (2011). Research directions in data wrangling: Visualizations and transformations for usable and credible data. Information Visualization, 10(4), 271–288.
Press, G. (2016, March 23). Cleaning Big data: Most time-consuming, last enjoyable data science task, survey says. “Forbes”. https://www.forbes.com. Access September 21, 2017.
Lismont, J., Vanthienen, J., Baesens, B., & Lemahieu, W. (2016). Defining analytics maturity indicators: A survey approach. https://www.sciencedirect.com/science/article/pii/S0268401216305655#bib0025.
Comuzzi, M., & Patel, A. (2016). How organizations leverage Big Data: A maturity model. https://www.emerald.com/insight/content/doi/10.1108/IMDS-12-2015-0495/full/html.
Wand, Y., & Wang, R. (1996). Anchoring data quality dimensions in ontological foundations. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.391.6006&rep=rep1&type=pdf.
Gartner1. (2014). Business analytics, from business to value. https://www.slideshare.net/sucesuminas/business-analytics-from-basics-to-value.
Gartner2. (2016). Data and analytics leadership vision for 2017. https://www.gartner.com/binaries/content/assets/events/keywords/business-intelligence/bie18i/gartner_data-analytics_research-note_da-leadership-vision_2016.pdf.
PwC. (2015). Data lakes and the promise of unsiloed data. http://usblogs.pwc.com/emerging-technology/data-lakes-and-the-promise-of-unsiloed-data/. Access July 16, 2017.
Gartner3. (2017). Planning guide for data and analytics. https://www.gartner.com/binaries/content/assets/events/keywords/catalyst/catus8/2017_planning_guide_for_data_analytics.pdf.
Acknowledgements
The project is financed by the Ministry of Science and Higher Education in Poland under the programme “Regional Initiative of Excellence” 2019–2022 project number 015/RID/2018/19 total funding amount 10,721,040.00 PLN.
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Sitarska-Buba, M., Zygała, R. (2020). Data Lake: Strategic Challenges for Small and Medium Sized Enterprises. In: Hernes, M., Rot, A., Jelonek, D. (eds) Towards Industry 4.0 — Current Challenges in Information Systems. Studies in Computational Intelligence, vol 887. Springer, Cham. https://doi.org/10.1007/978-3-030-40417-8_11
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