What is Data Mapping?

Think data mapping as the guide for how data flow between different sources and systems. It helps in defining the relation between different data elements connects, which ensures information flows smoothly, is reliable, and organized. By understanding how your data flow, you can enable analytics and improve your decisions.

It serves three main purposes-

Understanding Data Flow.
Ensuring Data Quality.
Supporting Data Governance.

What are the Data Mapping steps?

To start data mapping, you must follow these steps-

1. Define the Scope: Clarify the goal of your mapping project- whether it's for integration, migration, or improving quality.

2. Identify Data Sources: Explore and locate every relevant data sources such as databases, APIs, or legacy systems to understand where the information is.

3. Profile Your Data: Analyze your data quality to identify inconsistencies.

4. Match Your Fields: Establish connections between source and target datasets. Also include any transformations needed for compatibility.

5. Standardize Your Data: Convert your data format to align with the target system before loading.

6. Test and Automate: Use test data to validate your mapping and identify issues before automating the workflow.

Data Mapping Techniques

Identify the techniques that’s best suited for you-

Direct Mapping: Matches fields directly with no modifications, perfect when source and target structures are similar.
Transformation Mapping: Use rules to transform data, which ensures flexibility in handling diverse scenarios.
Lookup Mapping: Enrich the primary dataset by adding content from external data sources.
Schema Mapping: This focuses on aligning the structure of different schemas to ensure compatibility between systems.
Automated Mapping: Use tools or algorithms to automatically map data without manual intervention.

Effective Data Mapping Best Practices

Test data regularly to maintain quality and accuracy.
Establish guidelines to ensure integrity and security.
Keep detailed records of your mapping processes for transparency and accountability.
Align mapping activities with business needs by involving relevant teams

How Can We Help

Implementing the data mapping process can complex and sensitive, but we can take care of data mapping challenges that you will face. We can help you in handling multiple pre-built connectors, set up data pipelines quickly ensuring your data flows smoothly. We also embed analytics services that can be based on your newly mapped data.