Everything You Need To Know About Data Mapping

Employ data mapping to combine/migrate all of your desperate data into a single destination. Learn how!

Everything You Need To Know About Data Mapping

If you run a business, you have data. Maybe you don't realize it, but you have quite a lot of data. The question is, are you using it? If so, are you using it the right way? Perhaps not, and the reason could be that the data are located in different places and stored in different ways. Maybe you need to migrate your data from a legacy system to something more modern, or on-premises to cloud?

To get a real picture of what's happening across your business, and to use the data correctly, you should combine or migrate all of your desperate data into a single destination. A critical piece of this process is know as data mapping.

Start with data mapping to migrate all of your desperate data into a single destination.

What is Data Mapping?

Data mapping involves extracting data fields from multiple sources and then matching them to their target fields in a destination.

Data mapping helps condense data by extracting, transforming, and loading it to a new data file, table, schema, etc. It can make data movement and migration work more seamlessly. This mapped data can then be utilized for producing relevant insights that can improve business efficiency. Modeling or transforming data into something usable for analytics is key for any business. Otherwise, they are missing valuable insights that could significantly impact the business.

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Why Data Mapping Is Important

The whole point of mapping data is to be able to move and combine the data. This allows a business to leverage its data fully and extract value from it. This could help analyze things like buying habits of a business's best customers, or it could be information about those website visitors that don't spend more than a specific amount of money. But the data collected from various sources must be transformed into a format that is suitable for the business's operational and analytical needs. This is done through data modeling, which is an integral step in various data management processes.

Helping With Data Integration

For successful data integration, the source and target data must have similar data models. However, it is a rarity for any two data repositories to have the same schema. This is where data mapping comes in to show where the discrepancies are between two sets of data. Different data mapping tools can help with this process. Specifically, they can help bridge the differences in data source schemas and destinations, allowing businesses to quickly consolidate information from desperate or different sources.

data source schemas and destinations to quickly consolidate information from multiple sources

Helping With Data Migration

Data migration involves moving data from one database to another. This can only happen once the data mapping has been done. Sometimes the data mapping process has to be done manually, but there are data mapping tools to help make this a more automated process. Then you can execute the data migration process so that you have a combined database. There may be issues flagged during the migration, so this step isn't always as straightforward as you might think.

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Makes Data Transformation Simple

Because enterprise data resides in various locations and formats, data transformation is essential to break information silos and draw insights. Once you've done the data mapping to understand what you need at the end of the migration, you may need to transform the data to conform to the map properly. This, again, isn't always a straightforward process, but there are transformation tools that can help read the map and adjust the data to conform to that map without losing the integrity of the data.

It Improves EDI Exchange

Data mapping plays an essential role in the file conversion of EDI files by changing the files into different formats, such as XML or Excel. An intuitive data mapping tool allows users to extract data from multiple sources and utilize built-in functions to map data to EDI formats without writing any code. Whether you run a bank, a retail operation, a warehouse, or other business types, you have EDI data that needs to be transformed into usable data, and vice versa.

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Data Mapping Solution Tools

Choosing the right data mapping tool for the organization is vital to any data integration, data transformation, and data warehousing project. The process involves identifying the unique data modeling requirements of the business and must-have features. It is essential to classify data mapping tools' characteristics, depending on your business's individual data management needs. There are several key features that a data mapping solution must have.

Data Engineer Planning ETL, classify data mapping tools' characteristics
  1. A useful data mapping solution can handle a wide variety of source inputs. Connecting to a range of differently structured data sources, including databases and REST APIs, and flat file formats, such as XML, EDI, Excel, and text files are the basic staples of all information mapping tools.
  2. It should offer a low-code or code-free way to generate data maps, and it should process data using built-in transformations. Ease of use is an important feature depending on who will be using the tool. Business users and analysts are probably more used to a code-free, Graphical User Interface (GUI), while Data Scientists and Engineers are likely more used to low-code or code based extensibility.
  3. The data mapping solution needs to be able to schedule data mapping and automate the process. These tools include data mapping templates to extract required data from unstructured reports. These tools can automate data modeling and enterprise transformation processes, thereby delivering analytics-ready data faster while saving data teams time and resources.
  4. Being able to instantly view the data anywhere during the process to validate things that are happening as designed.
  5. Resolve discrepancies in field names by synonyms and business data lineage feature to address the challenges of naming conflicts. Users can develop synonyms for various fields, which can further automate and accelerate the data mapping and transformation process.
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Wrap Up

Data mapping is an essential part of moving and migrating data, which enables the process of analyzing and discovering data driven insights. Some data is generated because of the various relationships you have with other companies, and some data is generated from your business's operations. Getting all of these different data sets to play nice together, and provide you with insights, is the goal of data mapping.

If you are needing a tool to help with this process, we have you covered. Zuar has developed Runner, which can help with data movement, mapping, and migration.

Are you ready to take your data to the next level?  Schedule a Runner demo today to learn how this robust solution can help transform your data pipeline.

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