Data mapping is a crucial element of the larger processes of data migration and integration. It’s a mechanism that matches fields from data sources to the target fields in a data warehouse or other storage warehouses. Fields can be names, phone numbers, emails, URLs, financial amounts, or any of the other inputs you need to develop and apprehend for querying and reporting purposes.
At a time when organizations have extensively more data sources, types, and formats to work with than ever before, it’s particularly important to address data mapping as part of your overall data strategy.
Data mapping is a way to emerge and prevent issues ahead of time. Before those issues create bigger problems later on for you and your business. Data mapping negates the potential for data errors and mismatches, aids in the data standardization process, and makes intended data destinations clearer and easier to understand.
Quality control of data is achieved through a data mapping process facilitates beneficial data analysis. And effective data analysis enables your business to make reasonable decisions with the velocity and confidence needed in today’s ever-changing market and ensure compliance.
On-premise: Data processes that happen on site can feel more secure, accessible, and controlled. But unless you need extremely fast access to your own data, on-premise data mapping is often too unwieldy and cost-prohibitive in the long term due to the purchase and upkeep of hardware, software, and other equipment.
Open source: On the other hand, open-source data mapping exercise tools can be quite cost-effective. Using the latest code bases, these tools are both reliable and efficient. But they still require a level of knowledge and hand-coding to be able to use effectively.
Cloud-based: When it comes to meeting the needs of today’s organizations, cloud-based data mapping tools fit the bill since they are built to be fast, flexible, and scalable. These tools can easily adapt to changing schemas without slowing down or losing information and are generally backed up with expert setup and support.
Data-driven mapping: involves evaluating data from two different sources simultaneously using heuristicsand statistics. The analysis is done to discover complex mapping between the two data sets. It is the most common technique since it automatically finds transformations between the two data types.
Transformation logic: technique used to create applications responsible for data mapping.
Semantic – Semantic mapping is similar to the auto-connect feature that is used in graphics mapping. The only difference is that the metadata registry can’t be used to look up data element synonyms. Semantic can only discover exact matches between data columns.
Hand-coded: involves data mapping while using graphical mapping tools, procedural code or by creating XSLT transforms.
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