Decoding the Unknown Facts About Data Mapping Software

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Decoding the Unknown Facts About Data Mapping Software

What you really need to know about data mapping.

· Big Data Zone ·
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Demystify data mapping

The amount of data utilized by enterprises today has increased substantially. At the same time, the need for businesses to leverage data and transform it into valuable insights has increased. However, using such humongous, highly complex data to extract insights and make better business decisions can be a mammoth of a task.

Organizations working with such myriad data must adhere to robust data mapping techniques that help to develop relationships between distinct data models. With easy access to information, companies can use data mapping to improve customer relations and increase business efficiency. Moreover, it helps businesses identify any emerging trends and act on them proactively.

You may also like How the Data Transformation Process Works.

Let us understand data mapping in detail.

What Is Data Mapping?

Data mapping involves the process of mapping data fields from data sources (system A) to their target fields in a storage repository or a data warehouse (system B). Here fields can be emails, financial information, phone numbers, etc.

Depending on the hierarchy of data streams being mapped, along with the disparity between source and target structure, the data mapping process can vary.

Why Is Data Mapping Essential?

Data mapping is used to leverage data gathered from a variety of sources and extract business value from the acquired insights by unifying and transforming it into a digestible format suitable for analytical and operational processes. It plays a central role in managing a wide range of processes, some of which we cover below. 

Data Migration

It involves the process of transferring data from one database to another. In data migration, one of the most tedious and time-consuming steps includes the creation of mappings between source and target. Inaccurate and invalid mappings at this stage can lead to the failure of a project. Data mapping software can be successfully used to migrate data to the target destination.

Data Warehousing

Data mapping is a crucial part of data warehousing. It helps businesses build a logical data model and define ways of how data will be structured as well as stored in a data warehouse

Data Integration

Companies rely on the source and target repositories that share the same data model to allow successful data integration. But, two data storehouses that have the same schema are rare to find. In such a scenario, data mapping tools serve extremely beneficial. They help bridge the differences between the schemas of data source and destination, enabling organizations to consolidate information from various data points respectively.

Data Transformation

Business data is present in an array of formats and locations. Hence, companies need to employ data transformation techniques to disintegrate information silos and extract insights. Data mapping, data transformation’s first step helps to create a framework before data is loaded to the target database.

What Are Different Data Mapping Techniques?

Data mapping can be divided into three techniques.

Manual Data Mapping

Data mapping can be done manually wherein, hand-coding methods are used to map a data source to the target database. It offers supreme flexibility, especially in unique mapping scenarios. However, manual data mapping can pose problems, as the needs of a business grow more complex.

Schema Data Mapping

Schema data mapping is a type of semi-automated data mapping technique that involves identifying two distinct, semantically related data objects and building data mappings between them.

Automated Data Mapping

Manual data mappings are useful. However, they tend to lose their significance and utility as complexity increases. In such cases, automated data mappings prove to a better option. With a complete code-free mechanism, automated data mappings allow the data source to be mapped to the target database in a drag and drop manner.

It also comprises built-in transformations that help developers to convert data from one format to another. For example, an automated data mapping technique can be used to convert data from XML to JSON, XML to XLS, EDI to XML, hierarchical to flat files, or any format without any manual intervention. Apart from that, automated data mapping software provides the capability to orchestrate processes and schedule features for simplifying the technique further.

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Note: Prior to choosing a data mapping tool, organizations need to evaluate their needs and requirements as well as business objectives. Analyzing these factors will help businesses make the right decision.

data integration ,data mapper ,data mapping ,data mapping tools ,data migration ,data transformation

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