Data enrichment is a process to improve, refine or enhance the data. It is something like adding some additional details to the existing data. It also includes adding external data from some trusted sources to the existing data. Data enrichment helps you to have complete and accurate data. More informed decisions can be made by having enriched data. As data is the most valuable asset in the Big Data world, it must be ensured that the data is in good condition. It should not be incomplete, missing, redundant or inaccurate. If we do not have good data, we can not expect good results out of it.  What we mean by good data is that it should be complete and accurate. The process of data enrichment helps us to add more details to the existing data so that it becomes a complete data. Incomplete or little data can not give a bigger or complete picture of your customer. If you have insufficient information about your customers, you may not be able to give the expected service or customized offerings. This affects the business conversion rate and ultimately the business revenue. So having data in a  good and complete condition is a must for Big Data analytics to give the correct insights and hence produce the expected results. Data enrichment involves data refinement that may be insufficient, inaccurate or may have small errors. Extrapolating data is also a kind of data enrichment. Here we produce more data from the available raw data.  There are several types of data enrichment methods. Out of these, the two significant methods are: Demographic Data Enrichment Geographic Data Enrichment It is up to you to decide what kind of data enrichment you need depending on your business requirements and objectives. Data enrichment is not a one time process, it is to be done continuously because the customer data tends to change with time. There are several data enrichment tools available. Some of these are: Clearbit Datanyze LeadGenius Leadspace Reachforce FullContact RingLead DemandGen ZoomInfo etc.