DATA ANALYTICS
Delivering deep-link analysis How you can harness the power of graph analytics to achieve a 360 customer view without rebuilding the entire IT system. BY MARTIN DARLING, VP EMEA, TIGERGRAPH ACHIEVING a 360-degree view of their customer base is the dream for many companies wishing to boost sales, drive operational efficiency and ultimately attract more customers. However, they often find it difficult to ask complex questions about the business because their data is trapped in siloed, legacy relational databases that lack the flexibility and power to perform deep link analysis. Graph databases solve this fundamental data link problem and introduce a powerful new computational capability – graph analytics. Relational databases struggle with deep-link analysis because of the very nature of the way data is organised in lists. In list format, drawing a link between one record in one table and another record in another table involves a table join, but relational databases become increasingly inefficient as the number of table joins rises above three or four, especially if the tables are large and the number of queries is high. Graph databases can connect data in multiple relational databases and organise it into a set of vertices representing business objects such as a
customer, payment or order connected by edges that represent the relationships such as this order was placed by this customer. This ability to map the data into pre-connected business entities without disturbing the original datasets effectively turns it into an analytical layer, sitting above the original data. What’s more, it can maintain query performance even as it grows to billions of edges and vertices because, unlike a relational database, it doesn’t have to load entire tables into memory to perform a query. Only the data that is required for the query need be loaded as it ‘hops’ from one node to another, so queries of 10 hops or more are no problem for a graph database. In-query processing adds another dimension to graph analytics, enabling queries to be constructed in such a way that it is not necessary to hop in and out of the database to complete a function. This is a fundamental shift in the way we think about data storage, and it enables the use of a host of computing techniques or algorithms which are incredibly powerful in graph but virtually impossible within a traditional relational database.
Mapping your data There is nothing inherently wrong with relational databases for many business applications. Developed in the 1970s and 80s, relational databases are well suited for the storage and retrieval of data and simple records management. However, to achieve a 360 customer view across multiple databases and unearth new business intelligence, you need to be able to ask questions about the relationships among different pots of data which involves bringing together disparate data held across marketing, sales, accounting, customer service and other business functions. Organisations are harnessing graph analytics to create a digital map of their customer data while leaving existing databases undisturbed. The result is a 360 customer view across accounting, sales, customer service, marketing and other domains – but it doesn’t end there. Businesses are using graph analytics to: 1. Boost the conversion rate for digital channels
62
WWW.DIGITALISATIONWORLD.COM
l
ISSUE VII 2021
l
COPYRIGHT DIGITALISATION WORLD
