Data Enrichment

Data Enrichment Meaning

Data enrichment is a technique in data management that involves enhancing the reliability and accuracy of the existing raw information stored by a business. It ensures that the data is capable of sufficiently representing the customers and audience. This process is instrumental in adding value to the business.

The enrichment process improves data by adding relevance to it. It gives organizations deeper insights into the lives of their customers and helps them understand the customers better. The process involves adding new and supplementary data to existing data sets. The organizations also cross-check the information with data from third-party sources to enhance the overall value.

• Data enrichment is a technique that includes enhancing the quality and accuracy of the existing raw information recorded by a company.
• This procedure entails adding the missing information to fill discrepancies in the data collection. It intends to increase data richness by including more details.
• The process helps to supplement the present data with additional relevant data. It helps to increase the scope and utility of the information.
• Enriching the data helps to improve targeted marketing. It facilitates effective data segmentation, which helps improve the targeted marketing process.

Data Enrichment In Finance Explained

Data enrichment is a data management approach that improves the accuracy and quality of a company's existing and unprocessed data. It ensures that the data can accurately represent the target audience and consumers. This technique helps to add value to the business.

Customer data originates in raw form, irrespective of the source. When consumer information is gathered, it is stored in a central database and is primarily useless. To provide more valuable information, raw data is first cleaned and organized before it is enriched with external data. Data enrichment helps businesses learn more about their customers by providing them with deeper insights into their lives.

Techniques

The data enrichment techniques are:

• Businesses may engage in appending data. This process involves bringing several information sources together and creating a more consistent and accurate data set. It also includes gathering data from third-party sources.
• Data segmentation is a valuable technique in this process. It includes dividing a data object into several groups depending on a standard set of pre-determined variables. The segmentation may occur based on demography, geographic location, and behavioral characteristics.
• The data manipulation process can be employed for enriching data. This ṃethod replaces values for irregular or missing data within the data fields.
• Data categorization is one of the most common data enrichment techniques. It involves labeling the unstructured information so that it takes a structured form and becomes accessible for analysis.

Examples

Let us study the following examples to understand this process:

Example #1

Suppose Jake is a manager in a cosmetics company named Roses and Lily. The company stores customer information so that it can offer better products and services to its consumers. Jake created a team to enrich the data, and the team conducts surveys with existing customers frequently to keep the database updated with relevant information. The business tracks the changes, augments the existing information, and uses the insights derived from the data sources to improve its offerings. This is a data enrichment example.

Example #2

Even though the Internet has generated a lot of data about social and human behavior, this data is not organized and classified in a way that makes it useful for training artificial intelligence (AI) models. Millions of people have been hired globally as data enrichment workers to help reduce the gap by classifying, labeling, annotating, enriching, and validating the datasets on which AI models are based. The data enhancement work is still underpaid, unappreciated, and underestimated despite the essential role that human intelligence played in enabling the AI advancements that grabbed our attention.

Benefits

Some benefits of the data enrichment process include the following:

• Enriching the data enables a business to cut down expenses as the database system does not require the storage of any data that is not beneficial for the business. The business can improve the internal data with external sources of information to add value to the company.
• This process fosters personalized communications and increases the possibility of valuable customer relationships and business opportunities. Acquiring relevant customer information can aid the business in developing communication strategies that align with consumer needs and preferences.
• The process assists in enhancing targeted marketing. It aids in segmenting the data effectively, which contributes to the success of the targeted marketing process.
• This technique ensures that a business has a clean and accurate customer information list that aids in increasing sales and return on investment. It also enables a business to upsell and cross-sell because the company has access to the correct information and knows its customers well.

Best Practices

The data enrichment process best practices are:

• The enriching process must be duplicable and produce the same expected results invariably. The company must generate a process that is sufficiently driven by rules. As a result, the business can run the process repeatedly while anticipating the same results.
• An enrichment process must contain clear criteria for its assessment. The business must be able to evaluate the success of running the enrichment process.
• Each enrichment task must be scalable for the expenses, resources, and timeliness. The business must assume that the data set will expand over time. Thus, any process that the business develops must be capable of maintaining the data as it expands.
• The task must be concluded in terms of the information that is fed into the system and generating results with the same attributes. Moreover, the enrichment process must apply to various data sets.

Data Enrichment vs Data Cleansing

The differences between the two are as follows:

Data Enrichment 

• This process involves adding missing data to fill the gaps in the data set.
• It aims to improve data richness by adding extra details.
• The process assists in augmenting the existing information with additional relevant data. It aids in expanding the information's scope and usefulness.

Data Cleansing

• Data cleansing aids in correcting the errors, gaps, and irregularities in the existing data.
• It aims to enhance the data quality and data consistency.
• The process is primarily employed to clean and standardize existing information. It aids in ensuring that the data is accurate and reliable for further analysis.

Frequently Asked Questions (FAQs)

Recommended Articles

This article has been a guide to Data Enrichment and its meaning. We explain its examples, techniques, benefits, differences with data cleansing, and best practices. You may also find some useful articles here –

• Data Fusion
• Data Distribution
• Longitudinal Data Analysis