Data Enrichment

Normally Data Cleansing and Data Enrichment are confused with each other.

‘Data Enrichment is about enhancing the data whereas Data Cleansing is about correcting the data.’

Data Cleansing has been covered in one of the previous topics, so we will focus only on Data Enrichment. As the definition explains itself, Data Enrichment is about adding more data into your first-hand data set from other internal or third-party data sources to add additional context. In this era of social media and unstructured sources, there is always additional data available which can enhance the context of your data generated from internal systems.

‘Data Enrichment must depend on Business Goals.’

For example, if a brand wants to open a new branch, it can buy geographical data from third parties. If a bank wants to offer Credit Cards deals, it can buy crediting rating data from third parties.

Please note, there can be many sources of data sets for Data Enrichment and of course not all can be Enriched in your data stores. Just like any other implementation, Data Enrichment must follow business requirements.

Few Types of Data Enrichment

• Demographic Data Enrichment: It contains information like marital status, income level, family hierarchy, cars driven, house value etc.
• Geographic Data Enrichment: It contains location details like latitude, longitude, postal code, ZIP code, geographic boundaries, cities, towns etc.
• Behavioral Data Enrichment: It contains information like buying/ spending pattern, interest behavior, travelling trends etc.

Few top challenges in Data Enrichment

• Data Quality: As data is coming from third parties, one must make sure data is of high quality or else it will impact the quality of your internal data sets, as well as may impact the decision making.
• Data Integration or Correlation: As data is coming from third parties, there should be some linkage between internal and external data sets else data won’t match and it won’t be worth storing third party data sets.

Few Benefits of Data Enrichment

• Data Accuracy: By adding additional information, the accuracy of data enhances better decision making.
• Reduce Cost: Traditionally, we store a lot of internal data for decision making but with the help of a small size of precise external data sets, we can purge huge data sets.
• Increase Customer Relationship: With the use of e.g., customer data coming social media, companies can create stronger relationship with their customers.
• Enhance Customer Segmentation: Using external data sets assists in maturing customer engagement by offering relevant information only to relevant customers.
• Targeted Marketing: Having an additional behavioral pattern of customer helps in conducting targeted campaigning saving a lot of money as well.
• Updated Communication Channel: Sometimes, rather most of the time, a customer’s contact details are not up to date, means all the marketing campaigns are not reaching the right customer. Having additional details helps organizations to reach customers to their latest communication channels.
• Better Customer Experience: We the latest details from multiple source systems either internal or external, when organizations have complete and updated customer details, it can enhance Customer Experience.