Abstract
Internet of Things (IoT) data visualization is gaining popularity among researchers and industrialists across various disciplines. Present-day businesses already know that data visualization in business intelligence forms an integral part of competitive success. Yet, several organizations are subsiding behind due to their lack of ability to cope up with the information demand. The major pitfall resides in the fact that most organizations fail to acquire the current trend of information from valid resources. In recent decades, the evolution of IoT devices acts as a destination for data resources with the most real-time information. The process of IoT data visualization greatly influences the success of business organizations with state-of-the-art data resources. It is primarily due to the reason that IoT data visualization enhances the productivity and efficiency of the business in a most significant way. With the help of modern IoT technologies, business organizations could gain deeper access to the data by utilizing the human sense of vision, sound, and touch factors.
In a business intelligence environment, organizations usually collect data from various sources; the collected data is then stored and processed for business, economic, social, legal, technological, and environmental purposes with a wide range of business tools. The process of IoT data analytics and visualization helps organizations to explore profound insights into the most recent unprocessed data. Such kind of data-driven results provides progressive developments to organizations. However, researchers suggest that there are numerous challenges in IoT data visualization for business analytics processes. Some of them include the size of the IoT data (volume), heterogeneity of the IoT devices, analytics at the edge devices, need for scalable and efficient storage infrastructures, data security, data privacy, reliable validity of marketing segmentation, and financial management issues.
Moreover, the dynamic macro level parameters external to the organizations also play a major part in the extent to which IoT data visualization could be used in business decision-making activities. Hence, it is mandatory to identify various micro and macro-level factors that affect the use of the IoT data visualization for business intelligence processes.
Data analytics in business intelligence does not work in isolation; in contrast, it considerably impacts the growth of business outcomes. Also, there are very limited works available in the past that confluences IoT data visualization for business intelligence processes. This special issue aims to bring out various factors that affect the use of IoT data visualization in business and to explore various effective implications for IoT data visualization in business intelligence.
Reliable and efficient implementation frameworks for IoT data visualization in business intelligence
Critical examination of dynamic factors in the field of marketing, finance, human resource and organizational human behaviour and operations that affects the growth of IoT data analytics across business organizations
State-of-the-art tools and techniques intended for effective IoT data visualization processes for business intelligence
Marketing and management of business process with IoT data visualization techniques
Implication and frontiers in IoT data visualization for business intelligence
Big data analytics and management techniques for IoT data visualization in business intelligence
IoT data visualization for decision making and business support
Security and privacy of IoT data in business intelligence
Data Visualization for Enterprise resource planning (ERP) and customer relationship management (CRM) to improvise business intelligence
Innovative methods for IoT data visualization and business analysis
IoT data mining and knowledge discovery for effective data visualization and analysis processes
Integrating Business forecasting models with IoT business intelligence systems
Machine learning and artificial intelligence for IoT visualization and business intelligence
Achieving a secure and efficient IoT business intelligence environment with blockchain and federated learning methodologies
Methods and practices for mining unstructured, spatial-temporal, streaming and multimedia IoT data for business intelligence
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