Organizations collect massive amounts of data to gain insights and make better business decisions. Companies’ data, on the other hand, maybe erroneous, lacking in gaps, or outdated. This creates a bottleneck for businesses who wish to better understand their target market to develop the best products and services. As a result, firms may not be able to differentiate themselves in the competitive landscape by depending simply on internal data.
As a result, businesses gather data from a variety of sources to supplement their existing data and provide better customer service. Address Data Enrichment is a method for businesses in transforming pre-existing data into a comprehensive profile that can be used to enhance data analytics for in-depth insights. Businesses can utilize Data Enrichment to refine, improve, and reuse raw data to achieve their objectives.
Data enrichment is the process of adding context to existing data by augmenting missing or incomplete data with information from other sources. It is the process of upgrading, refining, and augmenting basic data in layman’s terms. This is accomplished by combining third-party data from an external authoritative source with a first-party customer database already in place. Data enrichment’s main goals are to improve data accuracy, quality, and value.
Today, there are various types of data enrichment that are commonly employed. The following are a few of them:
- Contact Enrichment is the process of adding contact information to an existing database to create a comprehensive database of clients or leads.
- Geographic Enrichment is the process of adding address data, as well as latitude and longitude data, to customer and contact information.
- Behavioral Enrichment is the study of client behavioral patterns such as prior purchases and browsing behaviors. This typically requires following a user’s purchasing habits to identify key areas of interest for each client.
- Demographic Enrichment is the process of enhancing the value of consumer datasets by adding information such as marital status, family size, income level, credit score, and so on.
In the Big Data diaspora, data enrichment is accomplished by incorporating taxonomies, ontologies, and third-party libraries into the data processing architecture. Organizations can offer superior business alternatives and customized consumer experiences when accurate and authoritative data is analyzed.
Based on in-house sales statistics, the demand for two alternative zip code locales for a meal delivery firm can be the same. However, when the corporation supplements its existing data by obtaining demographic data for the two places from reliable public sources, the data becomes more valuable. Because of the changing population count, they may see a shift in the percentage of demands. Such information can help businesses make better marketing decisions to increase demand.
Data enrichment is done by businesses to improve the information they already have so they can make better decisions. Aside from that, it aids organizations in doing the following tasks:
- Hierarchies must be defined and managed.
- Create new business rules on the fly for labelling and sorting data.
- Investigate and analyze multilingual and multi-structured data.
- Improve the efficiency of text and semi-structured data processing.
- Reduce expenditures while increasing sales.
- Predictive Analysis should be carried out.