Answer the Tough Questions about Your Customers with a Single Customer View
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. Customer identity must be linked to the original source systems.
The problem of achieving a single customer view
What are the challenges in achieving a single customer view? The problems lie in multiple databases, a common occurrence in the enterprise.
Let’s say you have a customer named Thomas Smith. He lives at 1234 Briar Crescent, Cleveland, Ohio, 09876. You’ve also got another customer named Tom Smith, who lives at 1324 Bryant Court, Albuquerque, New Mexico, 68970. These two customer entries are stored in different databases, but what happens when you integrate those data repositories?
Will your new database recognize that Tom and Thomas aren’t the same person? If you send marketing materials intended for one Thomas Smith to the other, you could have a problem on your hands; he’ll feel as though you don’t understand him or his needs anymore, and he may not want to do business with a firm that doesn’t know what he wants.
To address these challenges, implementations with multiple data sources are often mapped to the same logical customer model.
Taking this approach ensures the entity resolution process can master customer data from multiple existing sources based on a common input data schema. These data quality tools 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)
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Meeting the challenge of integration
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 data quality tools 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 data quality tools as a repository for the mastered customer data. Why? Because data quality tools 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 data quality tools suited to dealing with complex relationships has increased with the growth of big data.
Using data quality tools also provides the opportunity to view, search, and analyze big data that is physically stored in a non-graph repository such as a 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.
Data quality tools: Helping you answer tough questions
The challenges mentioned above are not insurmountable. Data quality tools help you untangle your information so that you gain a 360° degree view of the customer (ie. customer 360). How do they do that?
The right data quality tools allow you to access information located anywhere in your organization and assess its accuracy and completeness. In three steps, you can gain insight into your data and evaluate its viability for critical use cases. You can also uncover defects in your information as well as relationships across data sets in a single source or multiple sources. For example, you’d be able to figure out if you had more than one Tom Smith, and take the necessary steps to fix the problem.
Moreover, you don’t need to be a specialist to use data quality tools. Business analysts, data analysts, IT professionals, and data stewards can all assess information for accuracy and completeness. You don’t have to wait until the IT department comes across the two Tom Smiths; someone from Marketing or Sales could discover it sooner. The right data quality tools can work on-premises or in the cloud too, so you don’t have to worry that your information isn’t available or correctable.
Creating a single customer view for each of your customers is possible, even if you have conflicting, tangled data. The secret to unraveling the knots in your data is choosing the right data quality tools. Market-leading data quality tools give you access to information anywhere in your organization so you can evaluate its viability and uncover defects and relationships across a single source (or multiple sources).
Read our eBook, Exceeding Expectations: Four Ways Data Quality Promotes Customer Loyalty, to learn more about how a single customer view can help build customer relationships.