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Data Mapping 101: A Complete Guide

April 1st, 2024

Imagine you are an analyst working for a large E-commerce store. You want to track your customer journey to find out where your customers come from and what actions they take before they make a purchase. To identify this, you collect data from various sources such as Facebook, YouTube, your website, store purchases, and apps.

Now, to analyze this data, you need to combine it, transform it, and then send it to a data analysis tool such as Tableau, PowerBI, or a data warehouse. So, how do you do that? Through data mapping, you ensure that correct, consistent data reaches your destination.

In this blog, we will discuss what is data mapping, its importance in business intelligence, data mapping techniques, and the process in detail.

What is Data Mapping? 

Data mapping is the process of matching data fields from one source to data fields in another source. It helps ensure that data from one source can be accurately and effectively transformed or transferred to another destination while maintaining its integrity, consistency, and meaning. Data mapping is the initial step of any data process, including ETL and data integration.

Data Mapping Process

Now that you have understood what data mapping exactly is, let’s walk you through the data mapping process step by step.

  1. Define the Scope of the Project: Clearly define the objectives of the data mapping process, including what data needs to be mapped and why. Some of the common data mapping objectives include:
    • Data Integration: You may have data from various sources, and you might need to consolidate it into a unified and consistent format. For example, you may want to combine customer data from your website, mobile app, and physical stores to gain a holistic view of your customers.
    • Data Migration: In cases where you are transitioning to a new system or database, the objective could be to migrate existing data from the old system to the new one while ensuring data integrity and compatibility.
    • Data Quality Improvement: Sometimes, the primary goal is to improve the quality of data, and you can use data mapping to identify and rectify data errors, inconsistencies, or duplicates during the process.
  1. Identify the Data Sources: Next, determine the various types of data sources that are relevant to your project. These can include, relational databases, flat files, APIs, external systems, legacy systems or data warehouses. Then, examine the schema or structure of each data source. Understand the tables, columns, data types, and relationships within the source data as it will help you in planning the data mapping process effectively.
  1. Profile Data: This is one of the most important steps in the data mapping process. Data profiling will help you understand how the data is organized, such as whether it’s stored in tables, files, or other formats. In relational databases, data profiling involves identifying relationships between tables using foreign keys, primary keys, or other links. Understanding these relationships is crucial for correctly mapping and joining data. It will also help you understand the quality of your data in case there are any missing values, inconsistencies etc.
  1. Match the Fields: Matching fields in the data mapping process is a crucial step that involves identifying and establishing relationships between data elements (fields) in the source and target datasets. Fields that have a one-to-one relationship between source and target are the easiest to map. For example, if the source has a “Customer Name” field, and the target also expects a “Customer Name” field, you’d map them directly. If the source field needs to be transformed before it’s loaded into the target, you would specify the transformation logic. For instance, if the source has a “Birthdate” field in a different date format than the target, you’d map it with a transformation rule to convert the date format.
  2. Transform Data: It is highly likely that your data is not standardized or in the same format, so you might need to transform it. Most tools come with built-in data transformations and functions, which makes it easier to manipulate data. You can easily drag and drop, sort, merge, and join transformations. You can also use complex transformations such as normalize, denormalize, tree join, etc.
    6. Test and Automate: Once you have done all the prerequisites and set up your data maps, it is time to start testing before you can finalize it. Start by mapping a small amount of data and test to see if any issues crop up. If there are no issues, you can simply automate the entire process. Data mapping tools offer trigger-based job schedulers, so you can schedule your process according to time or an event.

Data Mapping Example

Usually, source data is directed to either a relational database, data warehouse, or any file, like a CSV document — depending on the use case. In most cases, companies use a data mapping template to match fields from one database system to the other using a data mapping solution.

Source-to-target mapping integration tasks vary in complexity depending on the data hierarchy and the disparity between the structure of source and target data. Whether on-premise or cloud, every business application uses metadata to explain the data fields and attributes that constitute the data and semantic rules. These rules govern how data is stored within that application or repository. The goal is to ensure a seamless transfer from source to destination without any data loss, and the data mapping process plays a key role in this aspect.

For example, Microsoft Dynamics CRM contains several data sets with different objects, such as Leads, Opportunities, and Competitors. Each of these data sets has several fields like Name, Account Owner, City, Country, Job Title, and more. The application also has a defined schema along with attributes, enumerations, and mapping rules. To add a new record to the schema of the data object, the user will have to create a data map from the source fields to the Microsoft Dynamics CRM account.

Data Mapping Use Cases

Companies often employ tools to collect data from various external and internal sources and then transform it into a format suitable for the operational and analytical processes. Here are some use cases where tools can help:

Data Integration

Successful integration requires source and target data repositories to have the same structure. However, that is a rare occurrence. Data mapping software helps bridge the differences in the source and destination schemas through data transformation and conversion. This allows businesses to consolidate information from different data points efficiently.

Data Migration

Data migration is the process of moving data from one system to another and a database mapping tool can do that seamlessly. While various steps are involved in the process, creating mappings between source and target is one of the most complex and time-consuming tasks, particularly when done manually. Inaccurate and invalid mappings at this stage can adversely affect the accuracy and completeness of data, leading to the failure of the data migration project. Code-free software, with automation features, is a safer alternative to successfully migrate data to any destination, such as a data warehouse.

Data Transformation

Since enterprise data resides in various locations and formats, data mapping and data transformation are essential to break information silos and draw insights. Mapping is the first step in the data transformation process that brings data to a staging area to be converted to the desired format. After transformation, it is then moved to the final destination, i.e., the database.

Electronic Data Interchange (EDI) Exchange

Data mapping plays a significant role in EDI file conversion by converting the files into various formats, such as XML, JSON, and Excel. An intuitive tool allows the user to extract data from different sources and utilize built-in transformations and functions to map data to EDI formats without writing a single line of code. It helps perform seamless B2B data exchange.

Want to load data from multiple sources (databases, files, web applications) into a data warehouse?

Common Data Mapping Techniques

The technique that you choose depends on your requirements, which mostly include the type of data and the volume of data. There are three types of techniques there:

Automated

Most companies are now shifting towards automation, given the high volume and variety of data involved. Automated data mapping utilizes a specialized, often zero-code tool that seamlessly extracts data from multiple sources, transforms it, and then loads it into a destination without any manual effort. There are various advantages of using automation:

  • Instant data preview
  • Less chances of error
  • Faster time to insight
  • Allows business users to take charge

This is why when Mario Ferrer, a Senior data architect used Astera Centerprise, it cut down their data integration time from 2 weeks to just five minutes. As a result, the company experienced immense cost savings and significantly increased developer productivity.

Manual

If you are hesitant to implement a data mapping tool, then the next option is you do it manually. In this, you have to hand-code and manually map each field from the data source to the target destination.  The manual process is not only time-consuming, it is also prone to errors, and it places a huge burden on the IT team. Given the amount of data that companies generate today, manual is not the best option.

Schema Mapping 

It is a semi-automated strategy where a solution establishes a relationship between a data source and the target schema. IT professionals check the connections made by the schema mapping tool and make any required adjustments.

Once you have made your data flows, you can automate the process.

Importance of Data Mapping Across Industries

Healthcare

In the healthcare industry, mapping helps achieve interoperability for EHR (electronic health record) by matching the data between the source and target. This enables healthcare professionals to share critical patient information and combine healthcare data from various databases, applications, and data warehouses such as EHR and EMR.  The mapped data is further used for other critical processes, such as data migration and data integration.

Retail

Data mapping plays a pivotal role in retail by bridging the gap between online and offline shopping experiences. It allows retailers to bring together data from various sources like e-commerce platforms, websites, mobile apps, and social media to create a customer 360 view. With a complete customer profile, retailers can personalize marketing campaigns effectively. For instance, if a customer browses certain products on the website or app, mapping data enables the retailer to send tailored recommendations or promotions via email, social media, or even in-store notifications.

Finance

Data mapping also facilitates efficient and accurate management of financial information. It enables organizations to comprehensively understand the flow of financial data within their systems, such as transaction records, income statements, balance sheets, and investment portfolios. This structured representation helps identify critical data relationships, ensuring data consistency, transparency, and compliance with regulatory standards.

Data Mapping Challenges

  1. Data Heterogeneity: Data often comes from diverse sources. Each source has its own data formats, structures, and conventions. Mapping data from heterogeneous sources can be challenging due to inconsistencies and differences in data representations.
  2. Data Volume: Large volumes can be difficult to handle in terms of storage, processing, and performance optimization during data mapping. Not only does large volume of data take more time, it also requires substantial computing resources, such as memory and processing power.
  3. Data Quality: Poor quality data ridden with missing values, duplicates, inaccuracies, and inconsistencies makes it difficult to accurately match fields between source and target systems.
  4. Complex Transformations: Some data mapping tasks require complex transformations, such as aggregations, calculations, and conditional logic. You need to be very careful with mapping when implementing these transformations as too many transformations can quickly make mapping quite complex. It is better to use a visual tool, which makes it easier to see data mappings in real time.
  5. Data Governance: Data mapping can become more complex when you need to ensure compliance with data governance policies, including privacy regulations like GDPR or HIPAA. In such cases, it is important manage data access, security, and consent while mapping data.
  6. Schema Evolution: The sources and target destination schema can evolve over time. In case you have a long-term project, you need to keep track of any changes in the structure and ensure backward compatibility.

Data Mapping Best Practices

Even if you are using a tool, it is important to follow the best practices to ensure that you avoid any complexities and map data accurately. Here are some factors you should keep in mind:

1.  Clarify the Business Requirements

Determine why mapping your data is necessary. Is it for data migration, integration, reporting, analytics, or another purpose? Clearly articulate the end goals. Consider collaborating with business unit leaders and managers as they can provide insights into how data will be used.

2. Identify and Map Data

Many sophisticated solutions allow you to map objects in a flow simply by dragging and dropping fields of one object to the corresponding fields of the other. These solutions are designed to work with large volumes of intricate data while complying with all General Data Protection Regulation (GDPR) guidelines.

3. Ensure Data Security

Advanced tools are enriched with many security features that allow you to secure your integration projects by allowing access to restricted users based on user roles. Simultaneously, these solutions also allow businesses to carry out a risk analysis of their data.

4. Automate the Process

To resolve inconsistencies in naming conventions, some tools offer automated capabilities to map data. For instance, in Astera Centerprise, users can utilize this feature by creating a synonym file dictionary that includes the current and alternate names of a header field in a table. On runtime, Centerprise automatically matches these dissimilar fields and extracts data from them.

The automated feature is especially helpful when dealing with large datasets, as it reduces the error probability during mappings.

5. Maintain Periodically

To ensure an effortlessly running system, you must frame and execute a consistent maintenance schedule. To address the distinctive challenges of a use case, you may have to update or modify a flow at any given point. These alterations can disrupt the mappings and, by extension, your data flow. Therefore, it’s best to ensure the accuracy of your mappings before executing the process.

Features like Instant Data Preview offer transparency into the mappings of any integration flow.  This feature allows you to debug complex mappings in real-time and prevent errors before executing the flow.

6. Keep Data Sharing Records

You need data maps to identify all the entities with whom you have shared the data. This helps during personal information tracking by utilizing requests and at the time of deletion. Take account of use cases for each mapping, classify applications that use the maps, and document the source-to-target convention of mapping and how it is used in the workflow.

Working with Data Mapping Tool 

A data mapping tool makes it considerably easier to perform the data mapping process. These tools provide a visual and automated interface for mapping data from source to target systems, allowing users of all sorts to take charge of their data-driven initiatives without relying on the IT team.

You will find various tools in the market. Here are some factors that you need to keep in mind before choosing one for your business:

  1. Visual Interface: An intuitive, visual interface that allows you to create, edit, and visualize data mappings.
  2. Connectivity: Support for a wide range of data sources and targets, including databases, file formats, APIs, and cloud services, to enable seamless data integration.
  3. Data Transformation: Advanced transformation functions, scripting capabilities, and data manipulation features to transform data during mapping, ensuring compatibility and consistency.
  4. Data Validation: Validation rules and data quality checks to verify data accuracy and compliance with predefined criteria.
  5. Complex Transformations: Support for complex data transformations, aggregations, calculations, and conditional logic to handle various data mapping scenarios.
  6. Error Handling: Robust error handling mechanisms for capturing, managing, and resolving errors during data mapping processes.
  7. Version Control: Versioning capabilities for managing changes to data mapping logic and maintaining a history of revisions.
  8. Automation: Automation and job scheduling features to accelerate data mapping and eliminate any manual intervention

Astera Centerprise – An Enterprise-Grade Data Mapping Tool 

Designed to offer the same level of usability and performance to both developers and business users, Astera Centerprise is a complete data management solution used by several Fortune 1000 companies. The following picture illustrates database mapping in Astera Centerprise. Data from three different database tables, Lead, Lead History, and Lead Status is joined and loaded into an Excel destination.

ETL data mapping

Mapping can have a varying degree of complexity, depending on the number, data types, schema, primary keys, and foreign keys of the data sources. However, Astera Centerprise significantly simplifies data mapping: Here are some noteworthy features of Astera Centerprise that make it an ideal tool to invest in 2023:

  • Drag and drop graphical UI
  • Industrial strength ETL engine
  • Out-of-the-box connectivity for various sources and destinations
  • In-built advanced transformations and functions to manipulate data
  • Advanced data quality management
  • Workflow automation and job scheduling
  • API integration

Conclusion

By employing a tool, you can accelerate data analysis and get real-time insights. Download a free 14-day trial and find out how to build source-to-destination data mappings without writing a single line of code with Astera Centerprise.

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