Business Impact of AI based on Knowledge Graphs

Organizations further on the frontier can expect to derive significant value from knowledge graphs in application areas that enable the construction of intelligence and learning functions, highlighting the following areas:

  • Collaboration, exchange and social learning: the consolidated data in a knowledge graph are interrelated and contextualized, which helps to discover and locate them through implicit and indirect connections. A knowledge graph can function as the "mind" of the company's collaboration and learning applications, such as:
    • Social software in the workplace.
    • Learning and training platforms
    • Learning Analytics Systems
  • Investigation and audit: the ability to capture and disambiguate entities that are assigned to individuals, things, organizations, places, events, manufacturing or distribution processes, etc. in the real world, it allows exploring the relationships between all of them to identify the origin of incidents, fraud, risks in supply chains, defects in manufacturing chains….
  • Analysis and reports: once processed and interpreted, both structured and unstructured data, in a knowledge graph, these can be consulted, interrogated, integrated with third parties and viewed in an unmanaged, expressive and personal way, thus facilitating the extension of intelligence and analysis functions to the entire organization.
  • Integration and management of company content: interoperability and automation. Autonomous reading and "understanding" of data by machines makes the integration and operability of data possible for the organization's set of applications.
  • Content management:
  • Organizational content management: intelligent integration, publication, distribution and notification, based on the profiling of the different audiences, of the organization's content.
  • Communication, web content management and integration with external content.
  • Data reuse and collaboration between industries and companies: data, in a knowledge graph, is linked conceptually or, if you prefer, semantically; this makes it easier for data and metadata to be combined and thus more easily shared and reusable.
  • Customer relationship management: profiling and characterizing the customer experience. The CRM and the characterization of customers and their experience contribute to the design of business strategies that optimize profitability, operational efficiency, customer satisfaction and their loyalty through the implementation of customer-centric processes.