Feature image Data enrichment

What is Data Enrichment?
Definition, Process & Examples

By Brian Laleye · August 2, 2022 · 9 min read

Data enrichment is the process of adding or updating data to existing datasets.

It is also called data enhancement, data cleansing or data tagging.

The main goal of this process is to add missing information and correct errors in the existing datasets.

This can be done by adding new relevant data or by correcting the existing values based on some business rules.

Data enrichment can be used to improve customer experience, understand customer behavior, find new prospects and customers, reduce attrition rate and increase sales.

Enriching your data by activating them helps you gain deeper insights into your business and customers.

This will help you reduce costs and improve the efficiency of your operations for instance.

In any case, you’ll lose a lot by not taking the trend of using external data as a complementary source of insights!

Let’s dive into the external sources that can be added / mixed to your current pile of data! 

Let’s discover how data enrichment can help you succeed in your day-to-day business and make you closer to a data-driven decision making organization!

What is Data Enrichment? And Why Do Businesses Need It


Data enrichment is the process of adding value to raw data by enriching it with information from external sources.

It is the process of enhancing and enriching the quality of existing data.

It is a form of data augmentation that improves the quality of existing data sets, for example, by enhancing the information content in them.

Data enrichment methods can be used in a variety of situations, such as:

  • To add context to your data set by incorporating information from other sources (e.g., demographics and location) and activate them properly
  • To fill in missing values or enrich categorical variables (e.g., product category or spend)
  • To improve predictions by adding new features (e.g., insurance risk score).

For example, when we are looking for a particular product on Amazon or Flipkart, we will get the relevant results based on our query.

How does it happen?

The search engine gets our query and searches the database for the product name and other relevant information like price, ratings etc.


Build Better Data Pipelines

With RestApp, be your team’s data hero

by activating insights from raw data sources.

Types of Data Enrichment

Enrichment involves processing large amounts of structured or semi-structured data that exists within your database or elsewhere (files, websites, tools…).

This can include everything from email addresses, phone numbers and postal codes to GPS coordinates and social media handles.

The goal is always the same — identify new information about your contacts (their interests, demographics etc.) so that you can build better relationships with them! 

Here are 3 common types of Data Enrichment:

  • Behavioral

Enriching behavioral data adds consumer behavioral patterns to their user profile.

By adding behavioral patterns to a user’s profile, you may determine the customer’s areas of interest as well as their path leading up to their overall buying decision.

For example, enriching behavioral data is vital since it helps you determine the performance of advertising efforts and justify marketing spending.

  • Demographic

Data enrichment helps you to focus communications to specific demographic groups.

This helps firms ensure that their marketing and messaging are relevant to their customers.

Demographic data enrichment also allows you to tailor communications to the users signing up to your product or service.

For example, when a user from another business enters their work email for a free trial, for example, you may use data enrichment tools to determine the size of that organization. You may then tailor your messaging accordingly.

  • Geographic

Businesses that enrich their geographic data can target different geographic groups with their messaging.

Users will view material that is relevant to their nation, time zone, and city as a result of this.

Companies can use geographic data to seek up an IP address and determine the user’s location.

For example, when prospective consumers or users visit your site, you may tailor content depending on what they might be interested in. 

What about GDPR and Privacy Policy?

As the number of data breaches suffered by large & small corporations continues to rise, governments have been forced to intervene and implement stringent safeguards to secure user data, such as the GDRP or ISO 27001.

Your data enrichment service should now obtain its data from open and social sources to guarantee compliance with both of these standards.

If you do not follow this guidance, you risk infringing the regulations in your region, which might result in penalties and unnecessary legal fights.

3 key benefits of Data Enrichment

Improved analysis capabilities

Data enrichment allows you to gain better insights into your customers’ behaviors and preferences by enriching your customer profiles with additional information such as age, gender, location, occupation, and so on.

This will help you create more accurate segmentations and improve your ability to predict future purchases or other actions taken by customers.

Improved analysis capabilities

In addition to improving analysis capabilities, data enrichment also helps provide better decision-making tools that can help businesses make better decisions about their products and services offerings, marketing campaigns, and customer service strategies.

Increased sales opportunities

With a more comprehensive profile of your customers’ needs, preferences, and behaviors, you will be able to tailor your product offerings more precisely which in turn will lead to increased sales opportunities for your business.

3 key considerations to implement Data Enrichment the right way

The following steps are highly recommended in a data enrichment setup:

  1. Data Collection: This step involves collecting existing data from various sources such as call centers, websites, social media accounts etc.
  2. Data Cleansing: This data cleaning step is essential to be performed on all the collected data by removing duplicates and correcting errors that exist in it for instance.
  3. Data Integration: It integrates all the cleaned up data into one single repository so that it can be easily accessed by other systems in your organization or through data pipelines if you don’t have a central database / data warehouse.

Which Tools to Use for Data Enrichment Processes?

Manual research

Manual data enrichment can be done with manual data entry, spreadsheet uploads, file transfers, and other similar processes.

Manually enriching your data is a popular method for getting more out of your existing data sets.

It’s easy to do and doesn’t require any specialized tools or skills.

However, manual enrichment can also be time-consuming, error-prone and expensive if it’s not done correctly.

Web scraping

Web scraping is the process of extracting data from websites using programming scripts.

These scripts are often written in Python, Ruby, and other programming languages that can run on your computer or internet-connected device.

They’re designed to read the HTML code of a page and pull out the information you need — such as product prices, contact information, or social media posts — for use in another application

So why would you want to do this?

One reason is web scraping can be much faster than other methods of collecting data from websites, such as executing a web API request or doing it manually — especially when dealing with large amounts of content.

The more complex the website (or website architecture), the more likely it will be challenging (or impossible) to get all the data you need using those obsolete methods.

Another reason is that sometimes there isn’t an API available for your needs.

For example, if you have an app that needs access to news headlines but there isn’t an API available, then web scraping is one way around this problem.

Data Enrichment Tools

Clearbit, Dropcontact or ZoomInfo are examples of data enrichment tools.

They did the work to collect, clean and aggregate all the available data online for your needs.

For example, if you have a list of phone numbers, you can use a data enrichment tool to find out the name of the person associated with that number.

Data enrichment tools can be used in many different scenarios: 

  • Marketing

Enrich your customer database with additional information such as email, social media handles, and location information. This will help you deliver personalized messages and offers to your customers.

  • Sales

Enrich your CRM database with additional information about prospects and customers. This will help you qualify leads and make more informed decisions about who should receive an offer.

  • Support

Enrich your support ticketing system with additional information about customers so that agents can provide better service when handling tickets.


Data enrichment, like other data transformation activities, is not something you do once and then forget about.

Customer data, no matter how thorough, is always changing.

People frequently shift from one location to another, from one job to another, causing customers and users’ physical addresses to change for instance.

It is critical that businesses perform a continuous data enrichment process.

Customers receive irrelevant information and offers when brands ignore constant data enrichment. Instead of manually enriching data, technology has made the process quicker and faster using data activation tools.

Effective enrichment requires that you capture data that is value-adding, actionable, easy to understand and generally sensitive data.

Data enrichment is an essential process for data-centric businesses in today’s environment.

At RestApp, we have built the Data Activation Platform for modern data teams and designed our next-gen data modeling editor to be intuitive and easy-to-use.

If you’re interested in starting with your data journey, check out the RestApp website or book a demo.


Subscribe to our newsletter

Brian Laleye
Brian Laleye
Brian is the co-founder of RestApp. He is a technology evangelist and passionate about innovation. He has an extensive experience focusing on modern data stack.
Share this article
Subscribe to our newsletter
Ready to experience data activation
without code?
Activate and combine any data sources without code

Transform your data with our No Code SQL, Python and NoSQL functions

Run automatically your data pipelines and sync modeled data with your favorite tools

Share your pipelines and collaborate smarter with your teammates

Discover how Data Transformation really means

Find out the new data architecture concept of Data Mesh

Learn how Operational Analytics actives your data

Learn how to deliver great customer experience with real-time data


Crunch data at scale with ease

Configure connectors, no build

Save time & efforts for data prep

Save time & efforts for data prep


Stay always up to date on data activation

Get access to tips and tricks to model your data

Discover our always evolving and regularly updated documentation

Find out how we keep your data safe