Data Mapping is the process of analyzing data from different sources and matching it in a single schema to later transfer information from one database to another. It allows diverse systems and users to manage and process data efficiently.

Thanks to data mapping, mappers can handle foreign keys, additionals, hierarchies, and more and translate them into a single language. Besides, one or more sources can be used in several source fields, giving organizations the ability to understand and leverage metadata.

Now we mention the three main techniques currently exist regarding data mapping, migrating, and transforming.

Most data mapping begins with a manual approach to migrating and transforming data. It’s a good starting point, especially for novice data mappers, but it requires creating link-related sources. It’s excellent for one-time data procedures. However, manual data mapping is almost impracticable due to the size of current databases.

Also known as schema mapping, it’s a semi-automated system with a strong relationship between different sources and the general target schema. The connections are carried out automatically, but a data mapper checks the mapped data looking for adjustments.

This database mapping uses a fully code-free automated system that quickly drags and drops data, migrating, integrating, and transforming it without human intervention. These automated systems performed by automated tools are the most efficient for building data mapping bridges and ensuring data quality. This mapping can even be used for non-technical users.

It’s the process of moving data from one place to another. In this process, and thanks to the right data mapping tool, you drag and drop data from one database to another. In this regard, correctly creating data mappings is crucial to migrating information between two data fields. 

An accurate data migration necessarily requires intelligent data mapping software to avoid data mapping errors. It helps you to migrate data to any destination, such as a data warehouse.

To successfully achieve a data migration, the data sources and repositories, or the reception field, must have the same structure. In this sense, a data mapping tool helps your organization to equal source and destination formats via the data mapping process, conversion, and transformation.

Since data models are allocated in several locations and have different file formats, data transformation is crucial to break the schema mapping and mind the gap. In this sense, the data management must bring the data from the data source to the new area to carry out the transformation.  

If you desire to analyze data maps or data structures, then data warehousing is the case for you. Thanks to data warehouses, you can get reports since the data is already migrated, integrated, and transformed.

In this section, we’re providing a helpful step-by-step guide you should consider for performing a well-understanding data mapping.  

All data mapping examples you can find on the web start with this step. Identifying the simple or complex data you need to map and which you do not want to include is primordial to performing a good process. In this regard, data hierarchy is essential to identify the most relevant information in mapping. 

It would help if you also defined the data relationships, how it works, and the priority and frequency of the mapping iteration.

Match data from the original source with the target database to determine the required changes. At this phase, you need to ensure that there is no data loss and that semantics are in their rightful place. In this sense, semantic mapping is crucial since it shows how the code can vary from source to source. To extract data, matching is essential.

For data accuracy, it’s fundamental to transform the fields. This way, they can store and utilize the new data. For instance, if your source data reflects sales data in different regions worldwide and currencies, you need to transform that info into a unique common currency to measure results and sum quantities.  

Testing is crucial since it can give you critical information to determine changes or fix some data points. These business processes include manual and automated testing to identify and correct replicated errors.  

Current processes require accurate automated testing due to the volume of data in addition to multiple databases and transformation tasks.

Once you get the testing results, it’s time to deploy the enterprise data. It means transforming and migrating it to a multiple or a single database. After testing, this final destination should be capable of receiving and managing the data from diverse sources.

Since more data has been added to a determined source system, it’s mandatory to continuously review, maintain, and update it. This way, you keep the entire source schema working even if you manage a large volume of data.

You can find three types of data mapping tools on the market. All of them fulfill their mission, but there are relevant differences.

On-Premise Tools

These data mapping tools are hosted on the server of the company. Then, they provide a closed environment when data mappers use the native computing infrastructure for data processes. On-premise tools reduce the necessity of hand-coding and other data management tasks. 

Cloud-Based Tools

As its name suggests, these tools, contrary to the previous ones, are hosted online, on the cloud. This way, companies can access them via Internet browsers. These tools also automate repetitive tasks for data conversion, data migrations, and data integrations. Then, they can integrate data and perform complex mappings without great human effort.   

Open-Source Tools

These tools are an excellent low-cost alternative to on-premise and cloud-based tools. Open-source tools are perfect for small businesses that want to create a data lineage from different data formats. These graphical tools for data mapping work splendidly for organizations converting data in more straightforward cases. 

One of the best tools for data mapping is Excel. This spreadsheet software allows you to map through VLOOKUP and INDEX-MATCH functions. Then, the only thing you have to do is link cells to mapping and applying HLOOKUP. 

On the Internet, there are numerous tutorials on data mapping in Excel.

Data mapping helps organizations to maintain their multiple databases in order and update. Due to the velocity of transfers in the actual world, and the thousands of data types that exist out there, accurate mappings are necessary to achieve a competitive advantage over direct competitors. 

Avoid inaccurate analysis of different systems by implementing a data mapping process. No coding knowledge is required if you use a fully automated tool; we believe that is your choice.