Abstract
Big Data Analytics is widely adopted by large companies whilst only 10% of Small and Medium-sized Enterprises (SMEs) have adopted the technology, despite the benefits reported such as increased efficiency and profitability. SMEs are the backbone of the global economy, consisting of 90% of all businesses worldwide. In the UK, SMEs (0–250 employees) comprise 99% of all businesses and employ 61% of the country’s workforce. The barriers to SMEs adoption of Big Data Analytics reported in the literature include financial barriers, lack of top management support and the lack of business cases. There appear to be a lack of performance measures of the benefits that can be achieved from Big Data Analytics for SMEs to evaluate and ascertain its commercial value to them in relation to the IT investment. This paper suggests two techniques the Balanced Scorecard and Benchmarking to assist in determining quantifiable measures for SME in adopting Big Data Analytics. These measures are widely adopted by large companies (UK >250 employees), however this paper highlights how smaller companies (<250 employees and the majority are micro companies (1–10 employees)) can utilise them. These measures have been applied to two real world SMEs in the UK to illustrate how the Balanced Scorecard and benchmarking could be adopted for the purpose of setting targets and measuring performance of Big Data Analytics in conjunction with a software scoring tool. Use of these performance measures may also help to identify ‘hidden benefits’ which were not initially expected by adopting Big Data Analytics. Additionally, the performance measures outlined could be utilised to assess the adoption of other technologies by SMEs.
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Adnan, K., & Akbar, R. (2019). An analytical study of information extraction from unstructured and multidimensional big data. J Big Data 6(1). https://doi.org/10.1186/s40537-019-0254-8
AlRasheed AA, Atkins A, Campion R (2017) Using knowledge management and visualisation concepts to improve patients and hospitals staff workflow. https://doi.org/10.5281/ZENODO.1132284
Anand G, Kodali R (2008) Benchmarking the benchmarking models. Benchmark Int J 15(3):257–291. https://doi.org/10.1108/14635770810876593
Arunachalam D, Kumar N, Kawalek JP (2018) Understanding big data analytics capabilities in supply chain management: unravelling the issues, challenges and implications for practice. Transp Res Part E: Logist Transp Rev. https://doi.org/10.1016/j.tre.2017.04.001
Atkins A, Zhang L, Yu H (2010) Application of RFID and Mobile technology in Tracking of Equipment for Maintenance in the Mining Industry. 10th Underground Coal Operators’ Conference, University of Wollongong & the Australasian Institute of Mining and Metallurgy, 350–358
Axelos (2021) What is ITIL | IT service management | AXELOS. https://www.axelos.com/best-practice-solutions/itil/what-is-itil. Accessed 1 Dec 2022
Babovic J, Raicevic V, Caric M (2012) Benchmarking as a function of competitiveness and efficiency in business. Ekonomika Poljoprivrede 59(1):115–127. http://www.ea.bg.ac.rs/index.php/EA/article/view/599/528. Accessed 1 Dec 2022
Bange C, Grosser T, Janoschek N (2015) Big data use cases 2015 - A BARC research study. http://barc-research.com/research/big-data-use-cases-2015/. Accessed 1 Dec 2022
Bianchini M, Michalkova V (2019) OECD SME and Entrepreneurship Papers No. 15 data analytics in SMEs: Trends and policies. https://doi.org/10.1787/1de6c6a7-en
BMC Software (2016) ITIL® Capacity Management – BMC Software | Blogs. https://www.bmc.com/blogs/itil-capacity-management/. Accessed 1 Dec 2022
Chen H, Chiang RHL, Storey VC (2012) Business intelligence and analytics: from big data to big impact. MIS Q Manag Inf Syst 36(4):1165–1188. https://doi.org/10.2307/41703503
Clarke V, Braun V (2013) Teaching thematic analysis: over-coming challenges and developing strategies for effective learning. Psychologist. https://doi.org/10.1191/1478088706qp063oa
Coffey H (2019) Tourists waste more than £100,000 getting the London Underground between capital’s two closest Tube stops. The Independent. https://www.independent.co.uk/travel/news-and-advice/london-underground-tube-stops-close-covent-garden-leicester-square-tourists-a9170876.html. Accessed 1 Dec 2022
Coleman S, Göb R, Manco G, Pievatolo A, Tort-Martorell X, Reis MS (2016) How can SMEs benefit from big data? Challenges and a path forward. Qual Reliab Eng Int 32(6):2151–2164. https://doi.org/10.1002/qre.2008
Daniel E (2018) Just 12% of businesses experiencing benefits of big data, study finds. Verdict. https://www.verdict.co.uk/big-data-in-business/. Accessed 1 Dec 2022
Danziger C (2019) How amazon used big data to rule E-Commerce - insideBIGDATA. InsideBigData. https://insidebigdata.com/2019/11/30/how-amazon-used-big-data-to-rule-e-commerce/. Accessed 1 Dec 2022
De Mauro A, Greco M, Grimaldi M (2016) A formal definition of big data based on its essential features. Libr Rev 65(3):122–135. https://doi.org/10.1108/LR-06-2015-0061
European Commission (2021) Entrepreneurship and Small and Medium-sized Enterprises (SMEs) | Internal Market, Industry, Entrepreneurship and SMEs. https://ec.europa.eu/growth/smes_en. Accessed 1 Dec 2022
Gandomi A, Haider M (2015) Beyond the hype: big data concepts, methods, and analytics. Int J Inf Manag 35(2):137–144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007
Ge D, Pan Y, Shen Z-J (Max), Wu D, Yuan R, Zhang C (2019) Retail supply chain management: a review of theories and practices. J Data Inf Manag 1(1):45–64. https://doi.org/10.1007/S42488-019-00004-Z
Giannopoulos G, Holt A, Khansalar E, Cleanthous S (2013) The use of the balanced scorecard in small companies. Int J Bus Manag 8(14). https://doi.org/10.5539/IJBM.V8N14P1
Henning S, Hasselbring W, Burmester H, Möbius A, Wojcieszak M (2021) Goals and measures for analyzing power consumption data in manufacturing enterprises. J Data Inf Manag 3(1):65–82. https://doi.org/10.1007/S42488-021-00043-5/FIGURES/7
Hutton G, Ward M (2022) Business statistics. https://commonslibrary.parliament.uk/research-briefings/sn06152/. Accessed 1 Dec 2022
Impact Networking (2021) The state of data analytics adoption and what it means. https://www.impactmybiz.com/blog/data-analytics-adoption-what-it-means/. Accessed 1 Dec 2022
Iqbal M, Kazmi SHA, Manzoor A, Soomrani AR, Butt SH, Shaikh KA (2018) A study of big data for business growth in SMEs: Opportunities & challenges. 2018 International Conference on Computing, Mathematics and Engineering Technologies: Invent, Innovate and Integrate for Socioeconomic Development, ICoMET 2018 - Proceedings, 2018-Janua, 1–7. https://doi.org/10.1109/ICOMET.2018.8346368
Kaplan RS, Norton DP (1992) The balanced scorecard--measures that drive performance. Harv Bus Rev 70(1):71–79
Keen PGW (1991) Shaping the future: business design through information technology. Harvard Business Review Press, Brighton
Kitchin R (2013) Big data and human geography: opportunities, challenges and risks. Dialogues Hum Geogr 3:262–267. https://doi.org/10.1177/2043820613513388
Krishnamoorthy B, D’Lima C (2014) Benchmarking as a measure of competitiveness. Int J Process Manag Benchmark 4(3):342–359. https://doi.org/10.1504/IJPMB.2014.063240
Lee I (2017) Big data: dimensions, evolution, impacts, and challenges. Bus Horiz 60(3):293–303. https://doi.org/10.1016/j.bushor.2017.01.004
Li W, Liu K, Tang Y, Belitski M (2017) E-leadership for SMEs in the digital age. Palgrave Handbook Manag Cont Bus Transform 375–416. https://doi.org/10.1057/978-1-137-60228-2_17
Liang TP, Liu YH (2018) Research landscape of business intelligence and big data analytics: a bibliometrics study. Expert Syst Appl 111:2–10. https://doi.org/10.1016/j.eswa.2018.05.018
Maire JL, Bronet V, Pillet M (2008) Benchmarking: methods and tools for SME. Benchmarking 15(6):765–781. https://doi.org/10.1108/14635770810915931
Malagueño R, Lopez-Valeiras E, Gomez-Conde J (2018) Balanced scorecard in SMEs: effects on innovation and financial performance. Small Bus Econ 51(1):221–244. https://doi.org/10.1007/S11187-017-9921-3
Mamaghani ND, Samizadeh R, Saghafi F (2011) Developing a combined framework for evaluating IT projects based on IT-BSC and COBIT. Int J Digit Content Technol Appl 5(5):10–22. https://doi.org/10.4156/jdcta.vol5.issue5.2
Marr B (2021) Balanced scorecard: how many companies use this tool? https://bernardmarr.com/balanced-scorecard-how-many-companies-use-this-tool/. Accessed 1 Dec 2022
Mention (2023) Monitoring and social media management | Manage your brand online. https://mention.com/en/. Accessed 1 Dec 2022
Mikalef P, Boura M, Lekakos G, Krogstie J (2019) Big data analytics and firm performance: findings from a mixed-method approach. J Bus Res 98:261–276. https://doi.org/10.1016/j.jbusres.2019.01.044
Noonpakdee W, Phothichai A, Khunkornsiri T (2018) Big data implementation for small and medium enterprises. 2018 27th Wireless and Optical Communication Conference, WOCC 2018, 1–5. https://doi.org/10.1109/WOCC.2018.8372725
O’Connor C, Kelly S (2017) Facilitating knowledge management through filtered big data: SME competitiveness in an Agri-food sector. J Knowl Manag 21(1):156–179. https://doi.org/10.1108/JKM-08-2016-0357
OECD (2021) The digital transformation of SMEs. https://www.oecd.org/industry/smes/PH-SME-Digitalisation-final.pdf. Accessed 1 Dec 2022
Olufemi A (2018) Considerations for the Adoption of Cloud-based Big Data Analytics in Small Business Enterprises. Electron J Inf Syst Eval 21(2), 63–79. www.ejise.com. Accessed 1 Dec 2022
Organisation for Economic Cooperation and Development (2022) Financing SMEs and entrepreneurs 2022 : an OECD scoreboard. https://www.oecd-ilibrary.org/sites/8ae4e97d-en/index.html?itemId=/content/component/8ae4e97d-en. Accessed 1 Dec 2022
Pham Q (2022) Using big data and data analytics for better business decisions. https://www.forbes.com/sites/forbesbusinessdevelopmentcouncil/2022/08/29/using-big-data-and-data-analytics-for-better-business-decisions/. Accessed 1 Dec 2022
Polkowski Z., Nycz M (2016) Big data applications in SMEs. Sci Bull - Econ Sci 15(3):13–24. http://economic.upit.ro/repec/pdf/2016_3_2.pdf. Accessed 1 Dec 2022
Ragowsky A, Gefen D (2008) What makes the competitive contribution of ERP strategic. ACM SIGMIS Database: DATABASE Adv Inf Syst 39(2):33–49. https://doi.org/10.1145/1364636.1364641
Rejeb A, Keogh JG, Rejeb K (2022) Big data in the food supply chain: a literature review. J Data Inf Manag 4(1):33–47. https://doi.org/10.1007/S42488-021-00064-0
Saggi MK, Jain S (2018) A survey towards an integration of big data analytics to big insights for value-creation. Inf Process Manag 54(5):758–790. https://doi.org/10.1016/j.ipm.2018.01.010
Seritag (2021) Warehouse tag - Seritag. https://seritag.com/nfc-tags/warehouse-tag?vat=true&gclid=Cj0KCQjwpreJBhDvARIsAF1_BU3yK5VStc7Dg9GRDgoL5aKD9CNmFxSExhThFs_01C1U-o5DixKVaK8aAsFnEALw_wcB. Accessed 1 Dec 2022
Shah G, Shah A, Shah M (2019) Panacea of challenges in real-world application of big data analytics in healthcare sector. J Data Inf Manag 1(3):107–116. https://doi.org/10.1007/S42488-019-00010-1
Sivarajah U, Kamal MM, Irani Z, Weerakkody V (2017) Critical analysis of big data challenges and analytical methods. J Bus Res 70:263–286. https://doi.org/10.1016/J.JBUSRES.2016.08.001
Song J, Xia S, Vrontis D, Sukumar A, Liao B, Li Q, Tian K, Yao N (2022) The source of SMEs’ competitive performance in COVID-19: matching big data analytics capability to business models. Inf Syst Front 1:3. https://doi.org/10.1007/s10796-022-10287-0
Tan KH, Zhan Y (2017) Improving new product development using big data: a case study of an electronics company. R&D Manag 47(4):570–582. https://doi.org/10.1111/radm.12242
The World Bank (2019) Small and Medium Enterprises (SMEs) Finance. The World Bank. https://www.worldbank.org/en/topic/smefinance. Accessed 1 Dec 2022
The World Bank (2022) Small and Medium Enterprises (SMEs) Finance. https://www.worldbank.org/en/topic/smefinance. Accessed 1 Dec 2022
Tsai C-WW, Lai C-FF, Chao H-CC, Vasilakos AV (2015) Big data analytics: a survey. Journal of Big Data 2(1):21. https://doi.org/10.1186/s40537-015-0030-3
University of Cambridge (2016) Porter’s value chain. https://www.ifm.eng.cam.ac.uk/research/dstools/value-chain-/. Accessed 1 Dec 2022
Villaespesa E (2015) An evaluation framework for success: Capture and measure your Social Media strategy using the Balanced Scorecard. MW2015: Museums and the Web 2015. Published February 8, 2015. Consulted April 5, 2023. https://mw2015.museumsandtheweb.com/paper/an-evaluation-framework-forsuccess-capture-and-measure-your-social-media-strategy-using-the-balanced-scorecard/
Wenger E, Trayner-Wenger B (2015) Communities of practice: a brief introduction. Communities Pract 15(5):1–8. Available: http://wenger-trayner.com/wp-content/uploads/2015/04/07-Brief-introduction-to-communities-of-practice.pdf. Accessed 28 Jul 2021
Whishworks (2019) The state of big data in the UK 2019. www.whishworks.com. Accessed 1 Dec 2022
Willetts M, Atkins AS (2023) Qualitative study on barriers of adopting big data analytics for UK SMEs. Int J Big Data Manag 3(1). https://doi.org/10.1504/IJBDM.2024.10052988
Willetts M, Atkins AS, Stanier C (2020a) A strategic big data analytics framework to provide opportunities for SMEs. INTED2020 Proc 1:3033–3042. https://doi.org/10.21125/inted.2020.0893
Willetts M, Atkins AS, Stanier C (2020b) Barriers to SMEs adoption of big data analytics for competitive advantage. 4th International Conference on Intelligent Computing in Data Sciences, ICDS 2020, 1–8. https://doi.org/10.1109/ICDS50568.2020.9268687
Willetts M, Atkins AS, Stanier C (2021) Teaching and Learning Case Study on Social Media Analytics for Small And Medium-Sized Enterprises. ICERI2021 Proceedings, 3158–3167. https://doi.org/10.21125/iceri.2021.0785
Willetts M, Atkins AS, Stanier C (2022a) Big data, big data analytics application to smart home technologies and services for geriatric rehabilitation. In: Choukou M-A, Syed-Abdul S (eds) Smart home technologies and Services for Geriatric Rehabilitation (pp. 205–230). Academic Press. https://doi.org/10.1016/b978-0-323-85173-2.00001-1
Willetts M, Atkins AS, Stanier C (2022b) A teaching and learning case study on data mining using association rules for SMEs. 16th International Technology, Education and Development Conference, 1401–1410. https://doi.org/10.21125/inted.2022.0417
Willetts M, Atkins AS, Stanier C (2022c) Quantitative study on barriers of adopting big data analytics for UK and Eire SMEs. In: Sharma N, Chakrabarti A, Balas VE, Bruckstein AM (eds) Data management, analytics and innovation. Springer, Singapore, pp 349–373. https://doi.org/10.1007/978-981-16-2937-2_23
Xiang Z, Schwartz Z, Gerdes JH, Uysal M (2015) What can big data and text analytics tell us about hotel guest experience and satisfaction? Int J Hosp Manag 44:120–130. https://doi.org/10.1016/j.ijhm.2014.10.013
Yan Z, Ismail H, Chen L, Zhao X, Wang L (2019) The application of big data analytics in optimizing logistics: a developmental perspective review. J Data Inf Manag 1(1):33–43. https://doi.org/10.1007/S42488-019-00003-0
Zhang L, Atkins AS (2015) A decision support application in tracking construction waste using rule-based reasoning and RFID technology. Int J Comput Intell Syst 8(1):128. https://doi.org/10.2991/IJCIS.2015.8.1.11
Zhou S, Qiao Z, Du Q, Wang GA, Fan W, Yan X (2018) Measuring customer agility from online reviews using big data text analytics. J Manag Inf Syst 35(2):510–539. https://doi.org/10.1080/07421222.2018.1451956
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Willetts, M., Atkins, A.S. Performance measurement to evaluate the implementation of big data analytics to SMEs using benchmarking and the balanced scorecard approach. J. of Data, Inf. and Manag. 5, 55–69 (2023). https://doi.org/10.1007/s42488-023-00088-8
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DOI: https://doi.org/10.1007/s42488-023-00088-8