Process of CDI

Customer data integration provides the infrastructure for transforming raw data into accurate, consistent, and usable corporate information assets. The goal is a unified, complete data repository (or customer data hub) of your company’s customer data. The fundamental foundation for customer data integration consists of six building blocks of customer data management:

Data Cleansing is used to identify and define data definitions and standardize customer information. A common customer data standardization initiative includes creating a single view of customer name and address information across the organization. Data cleansing activities include removing duplicate records as well as fixing common spelling errors.

Data profiling is the logical first step to any project, as it allows you to discover, understand and document the sources around your organization that maintain customer information. Data profiling can encompass something as simple as an inventory of the data source. This analysis phase can also include frequency and distortion reports to understand data characteristics, table relationships, phrase and element analysis, and business rule discovery. Only once you fully understand the sources and characteristics of customer data can you proceed with integration of the data.

Data quality begins the process of correcting the data. After profiling, you can begin to make the data useful. Data is often invalid, out of range, incompatible, or inconsistent with current business rules. The data quality process brings your customer data to a standard that meets your business requirements.

Data integration is where the CDI solution truly takes shape. This phase involves identity management or customer matching to discover the same customer within and across the data sources. The full understanding of your customer requires a full collection of all of the data from all of the sources.To understand the true view of your customers, you must remove duplicates and consolidate customer information across data sources. And you can provide links between data sources to gain an aggregate understanding of interrelationships between customers. Linking (also known as clustering) can occur at different levels depending on the need: at the
customer level, at the household level (all customers at the same address, for example), at the business or corporate level, or some other combination of attributes

Data enrichment adds value to the consolidated view of the customer. Here, you can enhance your customer relationship by understanding more about the individual, their preferences and characteristics. There are many data sources that provide geographic, demographic, financial and lifestyle information about business or consumers. Augmenting your customer data hub with this type of information gives you additional capabilities to improve your relationship.

Data monitoring is the final, ongoing phase for any CDI project. You must continuously identify and correct the problems in your data sources while also addressing the processes that created the poor data. Data monitoring builds on your initial data management initiatives by providing the technology needed to examine your data over time – and alert you when good data goes bad.

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