Data Mapping: Definition, Meaning & Techniques
Quite often, businesses are confronted with a large volume of data that is dispersed across multiple sources.
To manage these data effectively and avoid potential errors, the process of data mapping is deemed essential.
In today's guide, we will explore:
What data mapping is
Why it is significant
How it functions
Some fundamental data mapping techniques
Let's begin with a basic definition.
What Is Data Mapping, and Why Is It Important?
Data mapping is the process of defining the relationship and connection between data from a specific source and the corresponding data elements in a target dataset.
It is used to ensure that data is transferred accurately, consistently, and without errors from one system to another or from one format to another.
Data mapping is the initial step in facilitating data migration, data integration, and other data management tasks.
It is crucial, especially when dealing with large, complex datasets that may be dispersed and at risk of loss or errors during consolidation and transformation.
Moreover, in the realm of business intelligence and data analysis, data mapping plays a vital role.
It is utilized to connect data sources with analytics tools, enabling businesses to make strategic decisions based on data.
In recent years, data mapping has become more intricate, primarily due to the advent of big data.
Therefore, automated data mapping tools have become essential.
Next, let's delve a bit deeper into how data mapping works.
How Data Mapping Works
Data mapping typically involves the following steps.
First, the source of the data is identified, and an understanding of their structure and format is established.
The data source can be a database, a file, an API, and so on.
Next, the destination or target where the data will be transferred to is determined. This could be, for instance, a database, a data warehouse, an application, or another system.
The data is then mapped, which involves matching the source fields with the target fields. Additionally, the method for transforming the data is defined if necessary.
Following this, planned tests and quality assurance are conducted.
This includes a thorough check of the data mapping process, and appropriate adjustments are made.
Moving to the deployment phase, once it's confirmed that data transformation works smoothly, a live data integration event can be scheduled, for example.
Finally, necessary updates and changes are made to the data mapping process as needed.
Over time, new data sources may be added.
It's worth mentioning that the data mapping process can be automated using data integration tools and ETL (Extract, Transform, Load) procedures to ensure consistency.
Among the tools used for data mapping and greater automation are Tableau, Bloomi, and Astera.
Now, let's explore the three basic data mapping techniques.
3 Basic Data Mapping Techniques
The 3 basic data mapping techniques are the following:
Technique #1: Manual Data Mapping
Non-automated data mapping requires data analysts with coding knowledge to connect data sources and document the process.
Techniques like ETL may be used to move data between databases.
This technique is more time-consuming but offers full control and adaptability during data mapping.
Technique #2: Semi-Automated Data Mapping
Semi-automated data mapping includes both manual and automated data mapping processes, often involving some coding.
The advantage of this technique lies in the balance it offers between flexibility and efficiency.
Technique #3: Automated Data Mapping
Automated data mapping means that a tool handles the entire data mapping process.
Anyone, even those with little data-related experience, can complete the data mapping process without writing code.
Additionally, some data mapping platforms employ Natural Language Processing (NLP), which can help teams within a company better understand what the data is telling them, reducing the likelihood of incorrect assumptions.
In this comprehensive guide, we have explored what data mapping is, how it works, and why it's important.
Data mapping is a fundamental aspect of a data analyst's work, a profession with many prospects and career opportunities.
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