Working with an Enterprise Commercial Graph
Written by Ajay Khanna for icrunchdata; originally published at https://icrunchdata.com/working-enterprise-commercial-graph/
Graph technology powers the most popular consumer data-driven applications today, such as Facebook (Social Graph), Google (Knowledge Graph) and LinkedIn (Economic Graph). And today’s enterprise, data-driven applications need an enterprise-wide Commercial Graph.
Most of the operational business applications run on relational, columnar databases, but they do not manage relationships well. Graph databases, while suited for uncovering and handling relationships, don’t have the horizontal scalability to meet enterprise needs. With graph databases, managing complete customer information at a big data scale becomes a challenge. This scalability issue limits its usage for storing and serving petabytes of data gathered from omnichannel interactions, e-commerce transactions and social networks.
This limitation is leading to the emergence of Modern Data Management platforms built on columnar-graph hybrid stores, and other forms of polyglot persistence. An example of such polyglot data storage approach is using Cassandra as a columnar storage of all data which provides a schema-less model to store information at a big data scale. It supports elastic storage for fast access and ability to perform analytics for aggregated insights.
Moreover, adding graph technology helps model and visualize real-world relationships. A platform with hybrid columnar and graph store enables schema-on-read, graph relationship modeling and infinite horizontal scalability across all business entities with unlimited attributes.
Building a Commercial Graph on a modern data management platform requires blending data from all internal, external, third-party data subscriptions and social sources, and then matching and merging to consolidate the information. The next effort is to establish many-to-many relationships across real-life entities like peoples, products, stores, suppliers and contracts to create the graph. When you start connecting all business entities, you start getting newer insights into your customer’s needs and preferences.
For organizations, graph technology can make it easy to describe and visualize complex relationships in an easy to use interface. The simplicity, elegance and power of graph technology help solve many complex data problems with ease.
Once you create data-driven applications on a reliable data foundation of a Commercial Graph, you can quickly pivot from one application to the other. You can see all the purchased products and stores visited in a consumer’s profile, and with a simple click, you can drill into the product profile with details about the suppliers. Or click on a store to see all information about it and the products it carries. Such an auto-subscription when you start building more and more applications is another cool benefit of the Commercial Graph. You can pivot around profile attributes to answer questions like who are the customers in a zip code that purchased a blue car of a particular model from all dealerships in a date range. You do not have to write complex reports to answer such questions – a polyglot Commercial Graph can easily handle these for you. With Apache Spark and its graph analytics libraries, you can utilize algorithms such as triangles, page ranks, node connectivity, node inbound and the outbound degree to know more about the relationships between data entities.
Various uses of the Commercial Graph in modern data management include:
Understand customer preferences such as channels, content they consume, their influence in social media, products they endorse and their locations.
Identity resolution to identify if a customer engaging with you through different channels is the same person or not.
Grouping individual consumers into households based on their social connections, locations and purchased products
Quick segmentation based on any attribute or determine top-selling products in your target segments
Roll-up of dynamic hierarchical information like revenue, value and credit risk
Finding key influencers by calculating scores, relationship strengths of various data elements
Understanding relationships between different data entities is critical for decision-making in any organization. Sales, marketing, finance and support waste countless hours searching for information and requesting new reports. This information should be readily available, on-demand.
Data-driven applications running on Commercial Graph deliver such information enabling informed decision-making across the organization.