Data mapping is the process of matching fields from multiple datasets into a single dataset. It is used to merge the data from different data sets into a single one to extract usable information.
The datasets are connected through unique identifiers called keys. These keys can be used to connect different datasets together based on their relationships. An example of a dataset keys are employee numbers and social security numbers.
These keys allow the user to map the data from the source database to the destination database make the data readily consumable by analytical and business processes.
Why do you need to map the data ?
Data mapping is used to define the data connections and relationships which can be used to visualize the data and produce the required results.
Data mapping is needed when consolidating data to be used in a workflow or a data warehouse. This is especially important if you need to use data from many data sets.
How do you map Data?
To properly map the data, you have to identify the databases that contain the datasets, the tables with the required data and the required fields in those tables.
Once you have that information, then you need to check the data from the source tables to confirm if they are in the right format, if they are not in the required format, then you would need to transform and/or cleanse the data.
Transformation is the conversion of the data from one data format to another while data cleansing is the removal of erroneous or duplicate data from the records.
Once the data is ready, then you would need to map the fields in the source table to the fields in the destination table.
What are the different types of Data Mapping techniques?
There are 2 different ways in which you can map data and they are manually and automatically.
- Manual Data Mapping: with this technique the data is mapped manually to create a new data set. While this might be good for simple datasets, it can be quite time consuming and prone to errors and when you have big datasets to merge, it can get really complicated and difficult to do.
- Automated Data Mapping: this is the preferred method of data mapping for big datasets. This can done with the help of various software applications which can be used to upload the data to the software application and then it would automatically match the data from the source tables to the destination tables. If there are any encountered errors, they can be displayed in a report, which the user can use to correct those errors and try the data mapping process again. The automated data mapping tool uses a graphic user interface (GUI) which allows is easy to understand and can be used to view the stages in the process, encountered errors, warnings and final results. Examples of data mapping tools are Altova MapForce Platform, Talend Data Integration, IBM InfoSphere DataStage and Adeptia Integration Suite (AIS).