Skip to main content

Data mapping and transformation

Let's dive a bit deeper into Data mapping and data transformation.

Imagine you have two distinct systems, System A and System B, and they both deal with similar information, but they store and interpret that information in fundamentally different ways.

Data mapping​

Data mapping is the foundational process of defining the relationships between distinct data models. It's akin to creating a translation dictionary or a blueprint that specifies exactly how elements from a source data structure correspond to elements in a destination data structure.

  • Source data model: This is how data is organized and represented in its original form (e.g., an Excel spreadsheet with specific column headers, an XML document with a defined schema, or a database table with particular column names and data types).
  • Destination data model: This is the required organization and representation of the data for the system that will consume it (e.g., a JSON object with specific key names, a different database table, or a specialized API payload).

Key aspects of data mapping:​

  • Field-to-field mapping: The most basic form, where a column in the source (e.g., Customer_Name in an Excel file) is directly linked to a field in the destination (e.g., firstName in a JSON object).
  • Hierarchical mapping: For complex, nested data structures (like XML or JSON), mapping defines how nested elements in the source relate to nested elements in the destination.
  • Data type alignment: Identifying if a string in the source needs to map to an integer or date in the destination (before transformation occurs).
  • Identification of gaps/conflicts: During mapping, you identify if certain required fields in the destination have no equivalent in the source, or if there are conflicts in how data is represented.

This is about understanding your data and defining the rules for what you want to do with it, even before you start the process.

How would you map nutrients information ?

Data transformation​

Once you have your data map (your blueprint), Data Transformation is the active process of applying those rules to convert the actual data instances from the source format into the destination format. It's the computation or manipulation of data values and structures.

This isn't just a simple copy-paste; it often involves sophisticated operations:

  • Data type conversion: Changing a string like "2023-10-26" from the source into a proper DATE or DATETIME object for the destination. Or converting a text field "TRUE" to a boolean true.
  • Data aggregation: Combining multiple source fields into one destination field (e.g., concatenating FirstName and LastName to form FullName). Or summing up sales figures from multiple rows.
  • Data derivation/calculation: Creating new data values based on existing ones (e.g., calculating TotalPrice from Quantity * UnitPrice).
  • Data normalization/denormalization: Restructuring data to fit a specific schema design, perhaps splitting a single source field (FullAddress) into multiple destination fields (Street, City, State, Zip).
  • Data filtering/validation: Only including data that meets certain criteria, or rejecting data that doesn't conform to expected patterns (e.g., only processing orders above a certain value, or ensuring an email address is valid).
  • Format conversion: Changing the literal encoding or syntax of the data (e.g., converting an XML document's syntax into JSON syntax, or parsing a CSV into a relational table structure).

Why are both crucial?

In modern data ecosystems, information rarely lives in isolation. Businesses use a multitude of applications: CRM systems, ERPs, marketing automation platforms, analytics tools, external APIs, etc. These systems rarely use identical data structures.

Data mapping and transformation are fundamental to:

  • Integration: Enabling different software applications to exchange data seamlessly.
  • Migration: Moving data from an old system to a new one.
  • Data warehousing/analytics: Preparing disparate operational data for consistent reporting and analysis.
  • API interoperability: Ensuring data sent to or received from an API conforms to its required specifications.

In essence, Data mapping provides the "what" (the relationships), and data transformation provides the "how" (the actual processing) to make data usable across diverse systems.