A Complete Guide to Data Mapping
Data mapping is the process of matching fields from one database to another. It’s the first step to facilitate data migration, data integration, and other data management tasks. Before data can be analyzed for business insights, it must be homogenized in a way that makes it accessible to decision makers.
You can’t have a successful data migration if you don’t map it out from the start. Learn what data mapping is and why it’s an important early step in data migration and transformation projects. Data mapping is an essential step in uncovering key insights from data. It helps establish relationships between different data entities while reducing data redundancies for more reliable data analysis.
Data mapping is also an important early step in several data movement and transformation projects. In this guide, learn about the different types of data mapping, how it works and some of the best data mapping tools on the market.
What is data mapping?
Data mapping is the process of connecting or matching fields from one source to another source in the data field. The objective behind data mapping is to minimize errors in data, make data more accessible and simplify data management.
In addition, data mapping allows data from various sources to be merged into one, which could be a central repository. The most common reasons for data mapping include data migration, electronic data interchange, data transformation and data integration.
To understand the need for data mapping better, imagine having a customer’s information in multiple databases. In order to prevent software platforms from counting the same customer multiple times, data mapping is used to map out all entries of that customer to avoid data duplication and maximize data efficiency and effectiveness. In practice, data mapping is usually much more complex, requiring sophisticated software to complete the mapping process.
Types of data mapping techniques
The complexity and vastness of modern business data make it challenging to perform manual data mapping, as manual coding is time-consuming, resource-intensive and prone to errors. However, manual data mapping offers the benefit of completely customized maps that adhere to the organization’s exact needs.
Automated data mapping is a strategy that requires less technical knowledge, offers deployment flexibility and is less time-consuming than manual data mapping. However, it requires users to have data mapping tools, which sometimes come with a hefty price tag.
If your budget allows, a fully automated data mapping process might be worth it, because it takes care of the entire data mapping process without any need to code.
In semi-automated data mapping, organizations benefit from a balance between flexibility and effectiveness. Some parts of the data mapping process are automated, while others are manually coded. It is common for semi-automated data mapping to use graphic or visual representations of data.
Despite its many benefits, it’s important to note that semi-automated data mapping requires coding knowledge and technical skills to navigate between automated and manual data mapping.
Steps in the data mapping process
Identify data for transfer, migration and transformation
The first step in most data mapping processes is determining which data needs to be transferred to a new location. This is a simple yet vital step in the process, as it helps define which tables and fields need to be moved and what format data needs to follow after the move. The frequency of data transfer and the technique used for mapping can also be defined in this step.
Map the data
Performing data mapping includes establishing the data flow, mapping out data from source to destination and maintaining process logs. For higher frequencies or volumes of data, an automated or semi-automated approach is more efficient than manual data mapping.
Transform the data
In this step, the data format can be changed to match the standards and format limitations of the destination. Transforming data at this point makes it easier to store and use the data field effectively.
Test and deploy
Once data is transformed, it must undergo validation and testing through manual, automated or semi-automated methods. With larger data volumes, most organizations prefer to follow automated testing procedures and/or use automated testing tools.
After testing is complete, data can be deployed to its application. For example, this might involve moving data to a database for business intelligence or analytics.
Maintain data maps over time
Data maps will require periodic maintenance and updates to keep up with the evolving needs of the business. As datasets and related apps and source systems change over time, these changes should be notated and incorporated into the data map.
Data mapping tools and templates
Advanced data mapping tools and templates can help businesses get the most from their data without allocating many resources. Paper-based data mapping still exists; however, with the enormous amount of data and increased frequency of data changes in most businesses, the majority of people rely on data mapping tools to complete the process.
Data mapping software offers better transparency, efficiency and reliability than paper-based or manual methods. It also allows businesses to work with real-time data while assessing data flow, content and transformation goals.
Another key advantage of using data mapping tools and templates is they allow you to optimize all kinds of data, even in more complex formats. Advanced data mapping tools can help streamline data mapping while minimizing human error, saving time and maximizing data accuracy.
Why is data mapping important to data migration projects?
The process of moving data from one system to another is known as data migration. Data mapping supports migration by mapping data source tables and fields within the tables to destination fields and tables.
Data migration is one of the most common use cases for data mapping and in most cases, it is the first step of data migration. As data mapping bridges the gap between source and destination fields, it helps prepare data for the migration process and minimizes the risk of data inaccuracy.