Data enrichment refers to the process that is used to refine, enhance and improve raw data. The main idea behind this process is to make data an invaluable asset for any modern business or enterprise so that you can make most out of it. The potential benefits of data enrichment are many.
Let us get to know some of them.
Accuracy of algorithms
As companies bring new changes in an existing data system, algorithms continue to change, and as a result, inaccurate solutions occur. It also causes poor decision making. However, a data enrichment process allows a business to stand a better chance to make the algorithms better.
Accuracy of data
Data enrichment process eliminates outdated facts and irrelevant information. It enables a business to make a well-informed decision that helps in overall growth of an organization.
Identified recurrent entries
Data enrichment process is like cleaning and refining data which automatically highlights flaws in data, like duplicate entries or updates in your file. When you discern duplicate files, you easily get rid of them. This act of eliminating recurring entries becomes a great help in cost cutting.
Here are four ways data enrichment can be done:
Geocoding is one of the first methods an organization can take to enrich their data. If you have an address data, geocoding can help extract numerical and categorical data.
If you seek to calculate route optimization or know the distance between two points; it can help in your data analysis. In that case, you would want to investigate enriching your data with routing.
Improve your point data
Supplementing the data with an area of influence analysis includes creating isolines that display equally calculated levels on a given surface area. It enables you to view different polygons which are calculating the travel time from one point to another within that polygon. For example, you have different types of stores, and you want to see people living within 20-minute walk distance from the store. Creating an area of influence will turn your point data into polygon data which helps you define your area of influence.
Enriching data with demographic measurement can help an analyst to understand their target audience or customer. Popular measures for customer discovery includes – age, gender, race, occupation, average income, etc.
Hope, after going through the post, you have got a better picture of data enrichment.