Understanding Data Mapping: The best approach to no-code tools and processes

When we talk about tackling the toughest data mapping challenges, that conversation often occurs in technical circles, leaving the teams interacting with customers out of the loop.

Your organization is likely no stranger to data. We know the problem of messy data isn’t limited to those fluent in Python and SQL. It affects every person who comes in contact with data day in and day out. We aim to broaden the lens and discuss everyday data mapping challenges for frontline teams.

What you need to know about data mapping and database schema

What is data mapping, and why is it important?

Data mapping is the process of matching data fields from one or multiple source files to a data field in another source. The mapping process involves taking an initial set of data (“the source”) and matching or “mapping” it to a destination (“the target”). The target is often a centralized database or system you want to send the source data to.

How does database schema relate to data mapping?

Your database schema is the collection of metadata that describes the relationships between objects and information in a database. It’s the blueprint or architecture of how your data will look.

The success of your data project depends on accurately mapping the source data to the database schema. If data isn’t mapped properly, if it contains inconsistencies or errors, it can quickly become corrupted in transit, leading to project delays and inaccurate analysis. Quality insights depend on accurate data mapping.

Experienced software engineers can hardcode the schemas that map your sources to their targets. But why tap valuable resources to complete day-to-day tasks? Look to fully automated tools with intuitive UIs instead.

No-code data mapping tools are transforming the way we think about data ingestion and data onboarding by putting the power to manipulate data in the hands of the people who need it.  

Let’s compare the traditional data mapping process to an automated no-code data mapping process.

The traditional data mapping process

1. Define and identify the tables and fields you want to map and the desired format of the fields on the destination side.
2. Standardize naming conventions across your sources.
3. Create data transformation rules and hardcode the schema logic.
4. Manually match the source fields to the destination fields.
5. Deploy a test system, sampling data from the source.
6. Run the transfer and make adjustments.
7. Once you know the process is working, schedule your data job.
8. Repeat the process as necessary.

Automated data mapping with no-code tools like Osmos

1. Upload your file (CSV, Excel, or JSON) or import from a source system
2. Deploy AutoMap - Easily match your source and destination fields
3. Quickly clean up any errors using AI and one-click data cleanup, testing as you go
4. Repeat processes as necessary

Sound data management begins with accurate data mapping

Traditional data mapping processes are slow. They require technical expertise and consume valuable resources. Many organizations’ engineers and developers are bogged down by tedious data tasks that could be completed by the people who understand the business context of the data they receive. By this, we mean implementation and operations specialists.

Customer, vendor, and partner data can be the most challenging to map because data from external sources rarely matches internal targets. This is where the business context of data is of critical importance. When you put data ingestion tasks, like data mapping and data cleanup, in the hands of the teams that interact with customers, you accelerate business.

Frontline teams can more effectively troubleshoot errors because they understand how the data will be used. They also have first-hand knowledge of the intricacies of customer data. When given the right tools, your frontline teams can swiftly move data through the onboarding process without entering a single line of code. No manual testing needed. No technical expertise necessary. Just set up your validations, and get right to mapping and cleaning data.

We understand why reallocating processes like data ingestion and mapping away from developers and engineers may initially seem scary. Considering how valuable engineering and developer time is, the decision is clear. Organizations need engineering and developer resources to power business-critical projects. They also need easy-to-use, secure, intuitive data transformation tools. Enter Osmos.

Streamline data onboarding and data ingestion with Osmos

Osmos makes data ingestion easy, fast, and error-free. We empower implementation and operations teams to independently accelerate data ingestion processes while improving data quality. Whether using the Osmos Uploader, Pipelines, or our powerful Data Transformation Engine, your frontline teams can rapidly activate customers and partners by automating the mapping and cleanup of messy data.