Data science is changing our world in many different ways. Data and the associated data science innovations are changing everything: the way we work, the way we move, the way we interact, the way we care, the way we learn, and the way we socialize. As a result, many professions will cease to exist. For example, today’s call centers will disappear just like video rental shops disappeared. At the same time, new jobs, products, services, and opportunities emerge. Hence, it is important to understand the essence of data science. This extended abstract discusses the four essential elements of data science: “water” (availability, magnitude, and different forms of data), “fire” (irresponsible uses of data and threats related to fairness, accuracy, confidentiality, and transparency), “wind” (the way data science can be used to improve processes), and “earth” (the need for data science research and education). Next to providing an original view on data science, the abstract also highlights important next steps to ensure that data will not just change, but also improve our world.
In two sessions the International Professional Practice Partnership (IP3) of IFIP addressed a number of frameworks that provide definitions of ICT competences and typical profiles. These frameworks contribute to establishing an ICT profession that consists of competent and responsible professionals who can demonstrate the necessary skills and competences.
In today’s global, competitive economy, downtime has been identified as a key performance indicator for field service organisations. The emergence of an Internet of Things (IoT) has brought new enhancement possibilities to various industries such as the manufacturing and field service industry. This paper provides a vision and motivation for using IoT in Field Service Management (FSM) in order to address data quality and service delivery issues. The theory of information quality was used to undergird the research and a model for the optimisation of downtime management in the field service industry using the IoT is proposed. The model was used to drive the design of a “proof of concept” prototype, the KapCha prototype. The paper also includes a report on an empirical study of the application of the proposed IoT model in FSM. The experiment findings showed that the prototype reduced the round trip delay time for sending and receiving data and was scalable. As a result, access to quality information supporting advanced data analytics and artificial intelligence was provided. Therefore, service technicians can be alerted more quickly as soon as any potential technical problems occur. In turn improved diagnostics and more efficient decision making can be achieved. The model and the lessons learned provide valuable guidance to other researchers and fill the gap in research of empirical studies conducted on IoT implementations.
AbstractData science is changing our world in many different ways. Data and the associated data science innovations are changing everything: the way we work, the way we move, the way we interact, the way we care, the way we learn, and the way we socialize. As a result, many professions will cease to exist. For example, today’s call centers will disappear just like video rental shops disappeared. At the same time, new jobs, products, services, and opportunities emerge. Hence, it is important to understand the essence of data science. This extended abstract discusses the four essential elements of data science: “water” (availability, magnitude, and different forms of data), “fire” (irresponsible uses of data and threats related to fairness, accuracy, confidentiality, and transparency), “wind” (the way data science can be used to improve processes), and “earth” (the need for data science research and education). Next to providing an original view on data science, the abstract also highlights important next steps to ensure that data will not just change, but also improve our world.
