Data 101

Graph Technology

01
What is graph technology?

Graph technology captures data relationships to model real-world complex and dynamic relationships between entities. With each node representing an entity (person, organization, product, or location), and each edge representing the relationship between two connected nodes (affiliation, personal relation, complementary product), the structure enables real-time discovery of relationships across data points for deeper insights.

02
Why is graph technology important?

The relationships between the data points can provide higher business value than the individual data points themselves, empowering enterprises to maximize their data assets. Enterprises leverage the relationships and derived insights to make real-time operational decisions such as customer recommendations, product bundling, or logistical support.

Social media networks and their ranking algorithms, sophisticated supply chain networks, and cloud-driven business services have established the benefits of graph technology in discovering the powerful relationships between distributed large volumes of data.

Big data benefits from the high flexibility, advanced indexing capabilities, and faster searches the modern graph technology offers.

03
Where can graph technology be used?

Graph technology can be applied to any vertical and any business operation to uncover deeper insights about relationships. Some examples of innovative use of graph technology in business include:

  • Life sciences: Graph technology can offer valuable insights about affiliations and connections to identify participation, preferences, and influence. These insights can be leveraged for marketing, R&D, infrastructure management, and compliance.
  • Hospitality and travel industry: With graph technology, they can discover guest preferences, create precise segmentation, and identify opportunities for customized services. These insights can be leveraged to offer superior experiences, personalize guest services, and maximize marketing investments.
  • Retail: Stores can identify households, and reward loyal customers, upsell, cross-sell, and encourage visits to other outlets or affiliated stores.
  • CPG: Uncovered relationships can be leveraged to reward omnichannel customer experiences, as well as enrich segmentation to systematically identify and target influencers or decision-makers. Supply chain optimization through graph technology can improve distribution efficiency and inventory management.
  • Financial Services: Real-time analysis of data relationships can help risk management, fraud identification, and discover upsell as well as cross-sell opportunities.