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ETL vs Data Onboarding: Making an Informed Decision

What Are the Differences Between ETL and Data Onboarding, and Which One Is Right for You?

May 5, 2022

Data migration is a roadblock many companies face while onboarding new customers. The process of moving data from one system to another can be cumbersome and time-consuming. It’s a significant bottleneck that can be the deciding factor that either impresses or disappoints a new customer.

When a customer signs up—or even before signing up—you likely need to incorporate their data into your system. Once the data is in place, your customer can query it, enrich it, and use it in your product. You can run into trouble with your data migration. This can make it impossible to get anything off the ground. Customers can churn even before closing the sale. That's not your desired outcome.

Traditionally, a company would transfer data via ETL (extract, transform, load). But this can be a difficult, friction-laden time and expertise sink. More recently, data onboarding has engaged the shortcomings of ETL head-on. Data onboarding increases success by lightening workflow loads and improving outcomes.

Here, we'll discuss their differences. Then you can decide which data migration method is right for your business.


What Is ETL?

ETL stands for extract, transform, and load. But what does that mean? ETL involves extracting data from one place, like a customer database. Then the data is transformed by cleaning it up. This makes it relevant to a new data environment, (deduplicating, sorting, combining, syntax, etc.). After that, it is loaded into the new data environment. This could be your target database or a data warehouse.

Data migration is necessary for business integration. ETL tools help companies create data integration strategies. They use these to gather data from multiple sources and put it all in one place. ETL data migration needs sophisticated tools and considerable human expertise to pull off, however. In other words, it's expensive, both in terms of financial impact and opportunity cost.


What Is Data Onboarding?

Data onboarding is an automated data-integration process that collects, uploads, matches, and validates customer data. It involves integrating analytics into a product’s workflow. This provides users with a seamless, personalized onboarding experience.

This process can be especially helpful for companies that offer data exports. It allows new users—many of whom are new to the latest software product experiences now available—to start using a product or service immediately after signing up. This avoids the delays often seen when using ETL data-migration methods.


How Does Data Onboarding Differ From ETL?

The first step in customer integration is data migration. Your company extracts customer data from various sources. You then integrate the data into your system. This can be a tedious process. Some kinds of data may have a greater chance for errors or formatting issues.

ETL requires considerable personnel overhead. Companies using ETL for data migration must have data analysts and engineers on staff. Their job is to oversee data integration via ETL. For smaller ETL projects, investing in hiring a data analyst may not be feasible. It certainly isn't efficient.

It’s not unusual for large-scale data migration to take a month or more to complete. The amount of time and effort that goes into these projects makes them very expensive. This is true both of financial outlay and time to implementation. Every day this process takes brings with it risks: customer disappointment, added cost, and pre-onboarding churn.

In a data-onboarding project, most of the process is automated. This includes collecting data from different sources, transforming it, and loading it into the analytics platform. Data onboarding provides a no-code user-friendly interface. Even non-experts can use it. You can assign personnel to the data-migration task in a much more flexible way. Data onboarding sidesteps the personnel overhead of ETL. Instead of spending large sums on data analysts, data onboarding allows you to reduce costs by using technology to onboard your data.

Further, data onboarding is much faster than ETL. It gives your engineering team the freedom to devote time to other important initiatives. Speed means you reduce onboarding risks, like early churn.

Error correction is one of the major shortcomings of ETL. Even with a team of data experts, one of the most common reasons for data inconsistency is human error. ETL involves manual data transformation, which can lead to data discrepancies. One common example is the task of offloading CSV files, which can pose a burden on the less tech-savvy of your customers. This can lead to CSV import errors that can drag down the entire process.

In ETL projects, the data is extracted, transformed, and loaded manually. Large ETL projects handle large amounts of data. You need to migrate this data from other software products, spreadsheets, CSV files, or from offline to online. This is a tedious, messy process, fraught with roadblocks, friction points, and data errors that can be difficult to track. Only a team of engineers with the required expertise can extract and transform data in this way. Even for them, only by using specialized ETL tools can they succeed.

Data onboarding avoids data discrepancies through automation. It automates a part of the ETL process, so even using non-technical personnel for the data migration, error rate is considerably lower. When data errors do arise, they're caught well before the end-user interacts with the product.


Data Variance is a Decision Point

Depending on your business, data can be either low-variance or high-variance.

Because of the manual processes involved in ETL, it works best with low-variance data. Low-variance data fits neatly into specified formats or repeatable rules. It takes time and expertise to sort low-variance data. But once this detailed work is done, the load should be relatively easy.

However, traditional ETL tools don't work well for high-variance data, due to its complexity. High-variance data varies in its file formats, so it needs a lot of cleaning and processing before integration. It's this process that requires the software knowledge and technical expertise of data analysts and software engineers.

That’s where data onboarding comes in handy. Data onboarding allows you to perform data cleaning and preprocessing easily. It's especially efficient when dealing with high-variance data.

This means you'll see more efficient and accurate data migration even if you have predominantly low-variance data. But data onboarding will shine when you deal with high-variance data. Since this can vary from customer to customer, data onboarding acts as a hedge against any possibility. This ensures that your process is friction-free and successful in any data environment.

Efficiency, accuracy, speed, flexibility, and reduced overhead all make data onboarding the right choice for many businesses. If you're looking at traditional ETL, data onboarding deserves serious consideration.


Conclusion

Your customers expect a smooth transition when they sign up for your service. Extensive ETL tools help transfer data. Some businesses prefer ETL, but it is tedious, cumbersome, error-prone, and requires considerable resources.

Data onboarding automates the data-migration process, making customer integration seamless, more accurate, faster, relatively frictionless, and flexible. It reduces onboarding risk and helps build trust with your customer. It cements long-lasting relationships and can help reduce ch‎urn.

If efficiency and customer satisfaction are among your utmost priorities, data onboarding helps you sail smoothly through the onboarding process.

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