Digital Intelligence (DI)

Digital Intelligence (DI)

The term Digital Intelligence (DI) represents the tools and systems that play a key role at intervals the strategic designing methodology of the corporation. These systems allow a corporation to gather, store, access and analyze company info to assist in decision-making. Generally, these systems will illustrate business intelligence at intervals the areas of consumer identification, client support, research, market segmentation, product profit, math’s analysis, and inventory and distribution analysis to decision several. Most corporations collect AN outsized amount of data from their business operations. to remain track of that info, a business and would need to use an honest vary of package programs, like surpass, Access and utterly totally different information applications for various departments throughout their organization. exploitation multiple package programs make it difficult to retrieve information in a {very} very timely manner and to perform analysis of the data.

Area:

  • Data science and business intelligence – theoretical and practical aspects
  • Data science and business intelligence applications and industry experience
  • Business intelligence
  • Business Model Innovation
  • Business intelligence, digitization and data driven business models
  • Business analytics and the new role of IT in enterprises
  • Impacts of business analytics for the performance of profit or non-profit organizations
  • Data warehousing, data mining, online analytical processing, and reporting capabilities
  • Statistical analysis and characterization, predictive analytics and prescriptive analytics
  • Process mining, pattern mining, and swarm intelligence
  • Data quality assessment and improvement: preprocessing, cleaning, and missing data
  • Semi-structured or unstructured data in BI systems
  • Information integration for data and text mining
  • Dynamic pricing: potentials and BI approaches
  • Cloud-computing models and scalability in BI systems
  • Mobile application, smart data, smart services, and smart products
  • Data privacy and security issues in BI systems
  • Digital marketing, new web services, semantic web and data analytics
  • Analytics for healthcare and other public sectors
  • Educational data mining
  • Social network data analysis
  • Web survey methods in business intelligence
  • Organizational and human factors, skills, and qualifications for BI approaches
  • Teaching BI approaches in academic and industrial environments