Digitalizing Swedish industry: What is next?: Data analytics readiness assessment of Swedish industry, according to survey results

Abstract

Digitalization refers to enabling, improving, and transforming operations, functions, models, processes, or activities by leveraging digital technologies. Furthermore, digitalization is considered one of the most powerful drivers of innovation with the potential to trigger the next wave of innovation.Today, the importance of digitalization is well-understood in Swedish government agencies and industry. Although there are several initiatives working to actively drive change, one question is key: What is the next step? Data analytics is a promising way to turn information into outcomes, enhance decision-making, make data-driven discoveries, minimize risk, and unearth valuable insights that would otherwise remain hidden. This paper presents survey results on data analytics adoption and usage within Swedish industry, to highlight post-digitalization industry needs. To this end, a questionnaire was designed and distributed. Answers from more than 100 respondents from the manufacturing, technology, engineering, telecommunications, and automotive industries in Sweden were collected and analyzed.The assessment results show that Swedish industry has a high resources readiness score. This suggests that the necessary tools, and human resources are in place. Moreover, its cultural readiness level, which focuses on the acceptance of data-driven decision-making, scores between high and very high. At the same time, the information systems readiness level is in between medium and high, except in the telecommunication domain. However, the organizational readiness level is between medium and low, which shows that the organizations are not structured to enable the adaptation of data analytics and the business impacts of data analytics are not in place yet. These findings suggest that the industry should use the advantages of the current cultural, information systems, and resources readiness capabilities and concentrate efforts on exploring the business impacts of data analytics, ensuring the support from executive managers, and implementing data analytics protocols to improve organizational readiness. Moreover, the industry should consider structural changes in organizations, in addition to systematically initiating proper planning, timing, budgeting, and setting of clear key performance indicators/metrics in order to ameliorate the organizational readiness of data analytics.