FACTORS CONDITION THE ADOPTION OF BUSINESS AND BIG DATA ANALYTICS BY SMES

Various factors condition the poor adoption of business and big data analytics by SMEs. Among these, we have identified the following ones as being more pervasive and relevant [9]: [1]

set themselves up purely as data industries and thus, when they decide to commit to a big data project, they realize that their data is not accessible or in the format that is desired; when a data specialist comes into the organization. It is not a simple task to extract value from the data.

  • 4. Shortage of In-House Data Analytics Expertise: Most SMEs have few or no in-house data-analytic expertise to approach advanced big data analytics. Various factors hamper the creation of adequate in-house expertise: (i) high set-up costs relative to uncertainty in future returns from data analytics; (ii) lack of management expertise to design, establish, and monitor a data analytic unit; (iii) shortage of qualified workers, excessive staff costs.
  • 5. Bottlenecks in the Labor Market: There is an increased shortage of qualified data analysts in the labor market. Predict that in 2018, the e-skills UK8 study expects that the demand for big data specialists will increase from 2013 until 2018 by 243. The e-skills UK8 survey reveals that 57% of all recruiters experienced difficulties in filling big data analysis positions.
  • 6. Lack of Business Cases: The availability of exemplary case studies and success stories is an important factor for the successful propagation of innovation in business and industry.
  • 7. Shortage of Useful and Affordable Consulting and Business Analytics Services: A major part of consulting sendees used by SMEs concerns the operational level, for example, accounting or hardware-related and software-related IT issues. Management and business analytic consulting are less considered by SMEs. One major reason is that the large consulting companies dominate the management-consulting sector whose business practices are not in line with SMEs’ needs and financial capacities.
  • 8. Non-Transparent Software Market: Plenty of business analytics software solutions exist on the market. For users with little or no expertise, it is hard to select a product with a good price-performance ratio and to separate the wheat from the chaff. The existing comparison and evaluation platforms are strongly vendor biased. Independent evaluations and selection schemes are hard to find.
  • 9. Lack of Intuitive Software: The present market offer in business and big data analytics is split into two extreme parts: potentially useful but highly complex solutions requiring the expertise of knowledgeable data scientists and some simple but less-effective implementations. Solutions with both an intuitive user interface and a strong analytical potential are rare. IBM’s Watson Analytics is one of the few exceptions. Market analysts emphasize the need for predictive analytics software with intuitive user interfaces and a shorter learning curve.
  • 10. Lack of Management and Organizational Models: To make business analytics an economic success, a company needs an appropriate management concept and organizational structure. Management challenges in business analytics and big data analytics have been addressed in the literature. Organizational issues have been considered particularly in the context of maturity models. However, the discussion hitherto concentrates exclusively on the requirements of large companies. For instance, issues like leadership, allocation to departments, horizontal, and vertical relationships, and centralized versus distributed functions have little or no relevance for SMEs. The suggested maturity models have rather an assessment purpose than the purpose of providing constructive, detailed advice on how to build up and maintain business analytics in a company.
  • 11. Concerns on Data Security: Data security concerns are a key obstacle in the SMEs’ path to big data analytics. Ira an international survey among 82 companies, 22 about 50% of the respondents identified data protection and data security concerns as a barrier for big data analytics. The data security issue is more serious for SMEs than for larger companies. In general, the conditions of and expertise in IT security are at a lower level in SMEs than in bigger companies. An important security gap at SMEs is the use of outdated and unsupported database management systems. Microsoft Windows Server 2003, a major platform used by SMEs, is a notorious example of the aforementioned situation because Microsoft is ending regular support for that software in nrid-2015. Consequently, SMEs are more exposed to data breaches and are more vulnerable to intrusion and cyber-attacks. According to recent surveys, 80% of SME's cyberattacks resulted in PCI (payment card industry) compliance fines, 62% of breaches were targeted at SMEs, 60% of this close within six months of an attack, and 40% of all targeted cyber-attacks was directed to SMEs. The big data environment implies further challenges. Large volumes of data are transmitted through multi-user and multi-owner channels, particularly in supply chains. Being unable to create an in-house data analytic environment, SMEs will resort to outsourcing analytics services, with a further loss of control over data. Security concerns are particularly serious with respect to cloud services.
  • 12. Data Privacy and Data Protection: Customer data processing and analysis have to obey legal constraints on data privacy and protection. In 2012, the European Commission initiated an extensive reform of the data protection rules in the EU that should lead to a single law, the General Data Protection Regulation. The reform should be completed by 2015.

The present EU data protection regulations and their implications are considerably intricate and not easily accessible for judicially untrained persons. The Handbook on European data protection law 27 has over 200 pages. Many SMEs cannot afford the expert lawyer support needed to understand all the requirements of the legislation.

  • 13. Different Venture Concept: The business model for SMEs is often built around specific market opportunities or the existence of differentiating skills and strategic resources that make them competitive in the local or global market. This focused venture perspective creates the idea that business is only dependent on the w’ay they excel in such dimensions, eventually overlooking other resources at their disposal, as well as new opportunities to improve and diversify their activity.
  • 14. Financial Barriers: Numerous studies have identified financial barriers as the main issue for SME growth, for instance. SMEs have less access to debt finance than larger companies, particularly because of imperfect information between banks and SMEs. Limited financial resources cause SMEs to be veiy carefiil about new investments beyond their specific business scope.

  • [1] Lack of Understanding: The e-skills UK8 survey highlights anextremely low understanding of big data analytics by SME representatives. It is clear that SMEs will not step into a domain whichthey seemingly do not understand. Most SMEs are unsure whethertheir data has at least one of the big data dimensions, and therefore,whether investing in data science is going to bring the benefitsclaimed by big data enthusiasts. 2. The Dominance of Domain Specialists: Operating in a highlyspecialized field is a particular strength of many SMEs. The majorpart of the staff is domain specialists. General management functionsare poorly covered. Hence, there is reduced awareness of new business trends and opportunities, such as business and big data analytics. 3. Cultural Barriers and Intrinsic Conservatism: Domain-specialized SMEs often used to have little interest and confidence inmanagement trends. This attitude can lead them to classify businessand big data analytics as management hype rather than as a perspective opportunity. Another issue is infrastructure. Few organizations
 
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