The implementation of a Single Customer View provides organizations the ability to have a more consistent and accurate understanding of, and communications with, their customers. However, the successful implementation of a single customer view can be challenging where representations of a customer are held in more than one system. In this case, discrepancies in customer data quality and identity must be resolved both within and between systems. In addition, customer identity must be linked to the original source systems. To address these challenges, implementations with multiple data sources are often mapped to the same logical customer model.
Taking this approach ensures an entity resolution process can master customer data from multiple existing sources based on a common input data schema. These data sources technologies can include:
- Relational and analytical DBMS (e.g. Oracle and data warehouse appliances, respectively)
- Big data non-relational data management platforms (e.g. Hadoop platform)
- NoSQL data store (e.g., graph database, such as Neo4j)
- Applications (e.g. SAP)
- Cloud (e.g. Azure
- Text based (e.g. XML)
Then comes the challenge of integration. These data sources can be integrated into a single logical model via batch ETL, real time web service requests, or increasingly by virtualizing the data using Federation methodologies. In this approach, these capabilities can be used to execute queries against multiple data sources to create a virtual, integrated, logical view of the data in memory without having to create another physical copy of the original source data.
Increasingly forward thinking organizations are using Graph data base technologies as a repository for the mastered customer data. Why? Because Graph databases excel at Relationship Analysis. Enabling users to store, view, search, and analyze customer related entities, and their complex relationships, offers the chance to uncover important relationships and trends in complex data. The importance of technologies suited to dealing with complex relationships has increased with the growth of Big Data.
Using Graph also provides the opportunity to view, search, and analyze big data that is physically stored in a non-graph repository such big data non-relational data management platform (e.g. Hadoop platform). Banks do this with billions of transactions. Storing the data in a Hadoop repository provides speed and scalability at low cost, while master customer and account data can be stored and maintained in a graph database.
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This paper explores how the successful implementation of a Single Customer View – as well as the connected multiple views of customers that arise in different contexts – can be achieved using effective data integration, data cleaning, data enrichment, entity resolution, Master Data Management and graph database, as well as location intelligence visualization and analysis.