Five things you need to know about how to enrich data ETL:

  1. Data enrichment refers to the process of enhancing data for better real-time data analysis and business intelligence. 

  2. Enriched data can help e-commerce organizations make better decisions, solve problems, and grow their businesses.

  3. ETL is one of the most effective ways to enrich data. E-commerce companies that know how to enrich data ETL can cleanse data, remove inconsistencies, and transform data into the correct format for analytics. 

  4. ETL typically involves lots of code and programming, making the process difficult for e-commerce companies that lack data engineering experience.

  5. is a data warehouse integration solution that automates ETL. It lets e-commerce enterprises move data between a source and a data warehouse or lake without advanced coding knowledge.

All business decisions happen based on the data that’s available. It makes sense, then, that the more detailed that data is, the more effective those business decisions can be. That’s where data enrichment comes in. When e-commerce companies enrich data, they can improve data analysis and business intelligence and make smarter, more informed decisions. In this guide, learn how to enrich data ETL and discover a data warehouse integration solution that improves the data enrichment process.

Table of Contents is a data warehouse integration solution built for e-commerce. It enriches data by cleansing it, enhancing it, and converting it into the correct format for data analysis. Unlike other tools, removes all the jargon associated with data integration, making it a valuable tool for e-commerce companies that lack coding and data engineering skills. Email now to learn how to enrich data ETL. 

Recommended reading: Salesforce Data Enrichment: What You Need to Know

How to Enrich Data ETL: What is Data Enrichment?

The most widely accepted data enrichment definition involves taking data from external, third-party sources and merging it with an existing database in order to make that data pool more useful. Keeping data clean and accurate is an important aspect of data quality assurance (DQA), which requires the cleansing and profiling of data to remove inaccuracies and obsolete information for better data analysis.

Data can come from a variety of sources, including:

  • Customer interaction with advertisements

  • Emails

  • Contact forms from websites

  • Social media interactions

  • Text messages

  • External databases

Many of these data types are not compatible, and many contain large amounts of unstructured data such as the text section of an email or social media post. E-commerce businesses may store data of this type in a data lake, a type of holding pen for unsorted data. Enrichment requires taking this data and making it compatible with existing databases, ready to support activities like producing reports or providing actionable business insights. E-commerce retailers can generate reports and insights by learning how to enrich data ETL. 

Data enrichment also allows for fuller and more complete customer profiles. In addition to contact details, data enrichment could include insight from product reviews based on keyword analysis, shopping habits, browsing habits, and more. This allows e-commerce businesses to interact with their customers in exactly the right way to give them an edge against their competitors. It also enables online retailers to send customers customized product recommendations for increased engagement and sales.

Another benefit of data enrichment is that it improves data governance. By cleansing and profiling data, e-commerce retailers can adhere to legislation such as GDPR, CCPA, and HIPAA when moving data sets between a source and a target system like a warehouse or lake. That reduces the likelihood of penalties for data governance non-compliance.

With data enrichment, records are more fleshed out, more holistic, and better suited to conducting business across a variety of platforms.

Recommended reading: Top Data Cleansing Tools for 2022

Common Types of Data Enrichment

There are many types of data enrichment, but two stand out when looking at customer records in particular:

  • Demographic enrichment means enhancing your e-commerce data set by learning more about customers. Are they married? Do they travel? Do they have kids? Depending on your business focus, these could be key insights that alter the way you do business with this customer. The insights from that could improve the way you deal with all customers in that demographic.

  • Geographical enrichment normally means adding enhanced e-commerce data about the customer’s location. Businesses can see if they have massive amounts of customers in a single location and plan sales, marketing, logistics, and inventory accordingly. You could also tailor products and services to a specific area. The more you know, the better service you can provide for your customers.

Want to learn how to enrich data ETL? automates the data enrichment process with its low-code/no-code ETL tool, helping you move data between locations without lots of code or data engineering. also lets you integrate data via ELT, ReverseETL, and super-fast CDC. Email to learn how to enrich data ETL in your e-commerce enterprise. 

Use Cases for Data Enrichment

Data enrichment services are all about finding a wealth of data from a range of sources such as relational databases, transactional databases, social media platforms, customer relationship management (CRM) systems, enterprise resource planning (ERP) systems, and other e-commerce data platforms. A good set of business intelligence (BI) tools will be able to enrich data by picking out the salient points from data sets, leading to better, more efficient insights about customers, sales, inventory, and logistics.

Data enrichment can help e-commerce businesses pick out which types of customers regularly purchase additional products and services from their online stores, and that information can then help promote those products to customers who will be genuinely interested. This not only boosts profits but increases customer satisfaction by removing the hard sell.

Should Enriching Data be Automated?

In the past, all data analysis was a manual process, performed only if and when someone was available to assess it. Now, with fierce competition across the e-commerce sector, that method simply isn’t viable. Data needs to be available the moment a customer presents it to a company, whether that’s via interaction on a product page or a sudden surge of click-throughs on a particular advertisement on social media.

Automating data enrichment allows e-commerce businesses to have confidence that their data is being updated and enhanced 24/7 with minimal effort. So, how do you enrich your data in a fully automated or scheduled way that keeps business insights pouring in? Read on to learn how to enrich data ETL. 

How to Enrich Data ETL

ETL stands for extract, transform and load and is a process that gathers data from multiple sources and routed to a single destination, such as a data warehouse.

With a cloud-based ETL solution like, e-commerce retailers are able to easily set up their own data pipelines that funnel whatever types of data they want directly into an existing destination. This final destination could be Amazon (AWS) Redshift, Google BigQuery, or Salesforce. Of course, all of these options (and more) can serve as sources of data as well. It all depends on your business’s requirements. That’s why it’s important to learn how to enrich data ETL.

ETL makes data enrichment simpler because it cleanses data and converts it into a common format, regardless of the originating source. Sometimes this might include removing duplicate or erroneous entries to make the dataset as valuable as possible. Data enrichment tools also include BI tools which can give a more thorough analysis of data.

There are many challenges e-commerce retailers encounter when learning how to enrich data ETL. That’s because the ETL process requires the creation of complicated big data pipelines that involve lots of code and data engineering experience—skills that many e-commerce retailers lack, especially smaller businesses. Enterprises also need to be wary of data quality issues and even data loss when enriching data from an e-commerce platform and moving it to a third-party target system. 

ETL tools can solve these challenges by automating and operationalizing the ETL process. Many of these tools, such as, offer out-of-the-box native data connectors that sync with popular e-commerce systems and move data to a warehouse without the need for code.

Recommended reading: What is ETL?

How Can Help You Enrich Data ETL is a no-code/low-code data warehouse integration solution that solves all sorts of data management challenges, helping you learn how to enrich data ETL. The visual interface makes appealing to users with little coding experience, thanks to the drag-and-drop click-and-point system, which is very intuitive to learn. The ability to move information from data sources, manage data flows, schedule jobs, monitor the progress and status of data movement, and add code where necessary makes a leading choice for e-commerce companies that want to generate deeper insights and improve customer experiences. also integrates data via ELT, ReverseETL, and super-fast CDC, providing you with multiple data integration options depending on the type of data that exists in your systems and long-term data management objectives. Other benefits include excellent customer service, a simple pricing model, enhanced data security, and compliance with all major data governance legislation. is the data warehouse integration solution with deep e-commerce capabilities. It improves data enrichment by cleansing, enhancing, and profiling data, even if you lack advanced coding skills. Email or schedule an intro call and learn how to enrich data ETL in your organization.