Big Data

A Detailed Guide to Data Mapping

Data Mapping Guide

Data mapping underpins a companies’ data-driven processes. Without a robust data mapping strategy, data transformation logic, filtration errors become obvious which leads to poor quality data and ultimately decision-making. Therefore, it is important for organizations to maintain integrity throughout the data mapping process. 

The big data revolution has disrupted the market environment. It has shifted the way customer think and function. For any organization to meet the changing demands and improve its ease of doing business, it’s important to use transient big data and transform it into valuable insights. But this is not as easy as it sounds. 

Enterprises these days collect information from multiple data points, and it’s not necessary that they speak the same language. With an effective data mapping tool, business users can easily integrate myriad data sources and comprehend them. 

Ergo, what is data mapping?

In short, data mapping is known as the process of building relationships between different data models and sources. In this blog post, you’ll explore how data mapping can help users map data at the speed of business. 

What is Data Mapping?

In a typical data mapping process, source data is directed to the target database. The target database is usually a relational database. Or it can be a CSV document. In a majority of cases, data mapping templates are used to match fields from one database to another. Here is an example for detailed understanding. 

In a company, employee details like name, email, phone fields from excel source are mapped to the relevant fields in a delimited file, which is the target destination.  

Usually, when it comes to mapping source to target, complexity is almost unavoidable. It primarily depends on the hierarchy of data being mapped, as well as the disparity between different data structures of source and target. 

Every business application, whether on-premise or cloud, utilizes metadata to illustrate data fields and attributes that encompass data as well as semantic rules that govern how data is stored within the target repository. Business users can employ data movement controls to ensure streamlined data flow across without delays or losses. 

What Are the Most Common Data Mapping Techniques?

Business users can rely on three different types of techniques of data mapping. Here are they:

Manual Data Mapping: In this method, IT teams need to manually hand-code or map source data to the target schema. In doing so, EDI mapping and custom coding are used. Since coding is done manually, errors and delays become apparent. 

Schema Mapping: In this data mapping method, a semi-automated data mapping strategy is used wherein, a relation between a source and target schema is createdThen, IT professionals monitor connections made by schema mapping tool and make the required changes. 

Automated Data Mapping: This is highly advanced as it uses a code-free, drag-and-drop data mapping UI to execute data mapping tasks easily in just a few clicks. Non-technical business users can easily rely on this technique to map data without excessive IT intervention. 

Why is Data Mapping Important?

Data mapping is an essential segment of the ETL process. It allows business users to use data and extract business value out of it as the information garnered from multiple data sources needs to be consolidated into a unified database, where it can be analyzed and stored. 

For creating successful data mappings, the source and target data repositories must have the same model. This can be achieved using a data modelling tool. After this is done, data is mapped using a data mapping solution. Data mapping tools can help users bridge the differences in the schemas of data source and destination in a target repository. And so, data mapping is undoubtedly essential for enabling businesses to combine information across. 

Data mapping also helps users break down data silos and garner insights. It is, in fact, the first step of data integration. Once the data is mapped, it can be integrated. Real-time insights can then be extracted from the integrated data, which allows users to make improved decisions. 

Modern data mapping solutions use AI-driven suggestions to map data faster. It enables business users to accurately maps data from source to target, meanwhile, IT can focus on more high-value tasks. 

It’s time you reinvent your data mapping strategy and enable your business users and IT users to become more productive. 

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