The challenges of data mapping
Mapping source columns with a data destination is arduous and time-consuming. Data fields can come from many source types and formats. Even though you are the expert on your datasets, you may require the assistance of IT to set up the mapping. However, the more sources, the more handoffs, the higher the possibility of errors.
As organizational data has become more dispersed and voluminous across organizations and applications, it's more important than ever to ensure that you understand your data. When data from different applications is formatted in different ways, it’s hard to extract meaningful insights or create growth opportunities.
To maintain data integrity, you need to ensure your data goes to the right place when transferring or moving it from one system to another. So how can you get the most from your data — and avoid the biggest challenges of data mapping?
Data mapping as the foundation of collaboration
Proper data mapping ensures that the consistency and integrity of data is maintained across systems. It is a crucial step to facilitate any integration task. For example, when moving data from a database into a data warehouse or data lake.
Data mapping is the process of creating relationships between source and destination data assets by assigning, or “mapping,” specific fields in the source data to corresponding fields in the destination. It could be something as simple as mapping a source field called “name_last” with a destination field called “customer-last-name”.
Data mapping includes transforming the data as necessary to comply with the destination constraints. An example of data transformation is the process of concatenating multiple fields into one (e.g., mapping “first_name” and “last_name” to “full_name”) or changing data from one type or format to another. It may also include changing the values within the data, such as when reformatting a date.
Although data mapping is usually an activity performed by IT (integration specialists or data engineers), mapping requirements come from the line of business. Indeed, making sure that the right source fields go to the right destination fields is mainly a business concern, as a bad mapping would have direct repercussions on the quality of data used as insights.
Collaboration across teams is how business gets done. Data mapping allows connecting the dots to provide valuable insights to others throughout your organization. But as many IT departments are overburdened, requests can become bottlenecked.
What if you could handle the mapping yourself — as self-service, without having to wait for IT — to get to those shareable datasets faster?
How Talend makes data mapping easier
Talend’s cloud data mapping is a no-code/low-code solution, designed to create flat-to-flat correspondences between two schemas in a matter of minutes. Its user-friendly interface provides you with an at-a-glance visualization of the mapping between one dataset and another. You can find fields from the input or output schemas, easily link fields using drag and drop, or map using your keyboard — which is particularly useful if you are working with extensive schemas.
Here are some of the key advantages that Talend offers:
- Auto-mapping will help you find the best matches between two schemas to save precious time.
- Talend Data Preparation enables full self-service. Execute data mapping on your own, without the help of IT or data engineer as you export your preparation to a dataset. The result of your work becomes available to anyone in your organization, directly into Talend Data Inventory.
- Using Talend Pipeline Designer, you can link your pipeline fields with fields of the destination dataset. Data mapping allows the creation of graphical relationships between the pipeline schema and a destination dataset with its own schema constraints.
- Already comfortable with data mapping? Using the expression builder in Pipeline Designer, you can write your own transformation functions within the data mapping interface.
When can you get started?
With cloud data mapping coming to Data Preparation and Pipeline Designer, you will be able to map your fields with a target dataset in Data Inventory, fostering collaboration throughout your organization using reliable shared datasets.
Data mapping will be available in the coming weeks as BETA for all existing users of Pipeline Designer — watch for in-product notification in your regular user account to try it out.
Not a Talend customer? Contact sales today to try our data mapping for yourself.