Data mapping is the lifeblood of any data integration process. Without a proper data mapping strategy, data transformation logic and filtration errors can occur that can lead to poor quality data. This directly impacts business analysis, forecasting, and business decision-making. Therefore, it is crucial to maintain integrity throughout the data mapping process.
Enterprise data is getting more dispersed and voluminous by the day, and at the same time, it has become more important than ever for businesses to leverage data and transform it into actionable insights. However, enterprises today collect information from an array of data points, and they may not always speak the same language. With the use of an efficient data mapping tool, the data mapping process is used to integrate all the disparate data sources and make sense of them.
In summary, data mapping is defined as the process of establishing relationships between separate data models from disparate sources or systems.
What is Data Mapping? Definition of Data Mapping and Examples
Data mapping is the process of extracting data fields from one or multiple source files and matching them to their related target fields in the destination. Data integration or ETL mapping helps consolidate data by extracting, transforming, and loading it to a data warehouse. The initial step of ETL is data mapping. This mapped data can then be used for producing relevant insights that can improve business efficiency.
Basically, data mapping is about creating a map where source data is directed to the targeted database. The target database can be a relational database or it can be a CSV document – it depends on the choice of the user. In most cases, a data mapping template is used to match fields from one database system to the other using an appropriate database mapping software. Now let’s look at a data mapping example.
Here is a data mapping template example to clarify how does the data mapping process work. In Figure 1, ‘Name,’ ‘Email,’ and ‘Phone’ fields from an Excel source are mapped to the relevant fields in a Delimited file, which is our destination.
Source to target mapping in Astera Centerprise using a graphical data mapping UI
Source to target mapping integration tasks vary in complexity, depending on the hierarchy of the data being mapped, as well as the disparity between the data structure of the source and the target. Every business application, whether on-premise or cloud, uses metadata to explain the data fields and attributes that constitute the data, as well as semantic rules that govern how data is stored within that application or repository. However, data movement controls ensure that data is transferred from source to destination without any data loss.
For example, Microsoft Dynamics CRM contains several data sets that comprise of 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. Therefore, if a new record is to be added to the schema of a data object, a data map needs to be created from the source fields to the Microsoft Dynamics CRM account.
So, what is a good data mapping example? Let’s have a look at a data mapping example in the healthcare industry.
Data Mapping in Healthcare
Healthcare is a prominent data mapping example. Data mapping helps the healthcare industry achieve its goal of interoperability for its EHR (electronic health record) specifically by the source to target mapping which matches the data between a source and target. Based on a data mapping template, the process of data mapping helps healthcare professionals share critical patient information and combine healthcare data from the various databases, data sources and systems, such as EHR and EMR. However, later the mapped data is used for other critical processes, such as data migration and data integration.
Data Mapping Templates Explained
Most data mapping tools offer pre-built dataset matching templates. Users can utilize the data mapping document templates to create database matches from source to target systems. A simple data mapping template document looks like an Entity-Relationship (ER) diagram but with structured data available in sourced entities.
Data mapping template example: A comparison of ER diagram with data mapping template. (Source: Stackoverflow)
The only difference between a data mapping template offered by a data mapping solution and an ER diagram is that data mapping templates can be turned into readymade processes. These can then be added to workflow and automated, making an automated data mapping solution.
A good data mapping template example would be: Let’s say you get an excel sheet in an email every day and your intent is to turn that CSV file into a database and move it to your data warehouse. While manual efforts will take around 30 minutes to get this done each day. But with a data mapping tool compatible with excel, you can create an automated data mapping process that includes the source to target mapping in a few easy steps. This would allow you to:
- Move the emailed file to a folder
- The file is then added to a workflow and filtered according to a pre-set rule in a staging environment.
- The filtered document is then transformed into a database file
- Finally, it is moved into a data warehouse.
Data mapping and transformation: Adding transformations in a data map using Centerprise data mapping tool.
A business data mapping software will do this all within seconds and without human intervention. Now imagine getting 100s of such files every day. Think about how much time your organization will save when a simple, graphical data mapping tool in place to automate the process of copying CSV data to a database. That’s what a database mapping solution can do for businesses.
How is Data Mapping Done? Data Mapping Examples in Action
Depending on the number, schema, and primary keys, and foreign keys of the relational databases data sources, database mapping can have a varying degree of complexity. For instance, in the following example, data from three different database tables are joined and mapped to an Excel destination.
Mapping documents in ETL: The image shows the ETL mapping feature of Astera Centerprise data integrator in action
Data mapping is used to accomplish a range of data integration and transformation tasks depending on the data management needs of an enterprise and the capabilities of data conversion mapping software it uses. Some common known use cases of mapping business data include database schema mapping for pre-integration, data cleansing from disparate data stores, and data conversion from legacy systems.
Before starting a data mapping process, data mapping teams need to make sure that they have all the information available from all stakeholders involved. If there is any sensitive information that should be mapped separately, it should be informed to the data mapping team prior to the start of the process. In most cases, data quality checks will be added to the data map to minimize the risk of data leak or access control sabotage.
Common Data Mapping Techniques
There are three main data mapping techniques:
- Manual Data Mapping: This requires IT professionals to hand-code or manually map data source to the target schema without the use of automating the process using a database mapping tool
- Schema Mapping: This is a semi-automated data mapping strategy in which a data mapping software establishes a relation between a data source and the target schema. Later, an IT professional checks the connections made and make any required adjustments.
- Fully-Automated Data Mapping: The most convenient, simple, and efficient data mapping technique is to use a code-free, drag and drop interface data mapping tool for tasks. A fully automated database mapping software can easily be used by non-technical business professionals.
Why is Data Mapping Important in the ETL Process?
Data mapping in ETL helps leverage data and extract business value out of it as the information collected from various external and internal sources must be unified and transformed into a format suitable for the operational and analytical processes. This is accomplished through data modeling, which is an important step in various data management processes, including:
Automated Data Mapping & Data Integration
For successful data integration, the source and target data repositories must have the same data model, which can be achieved using a data modeling tool. However, it is rare for any two data repositories to have the same schema. In a data warehouse, data mapping tools help bridge the differences in the schemas of data source and destination. Therefore, data mapping becomes important for allowing businesses to consolidate information from different data points easily.
Data Mapping Assists in Data Migration
Data migration is the process of moving data from one database to another. Using a database mapping tool this process takes place smoothly. While there are various steps involved in the process, creating mappings between source and target is one of the most difficult and time-consuming tasks, particularly when done manually. Inaccurate and invalid mappings at this stage not only impact the accuracy and completeness of data being migrated but can even lead to the failure of the data migration project. Therefore, using code-free mapping software that can automate the process is important to migrate data to the destination successfully.
Automated Data Mapping and Transformation
Since enterprise data resides in a variety of locations and formats, data mapping and data transformation are essential to break information silos and draw insights. Data modeling is the first step in data transformation. It is done to create a framework of what changes will be made to data before it is loaded to the target database or data warehouse using the data conversion mapping feature offered by a data mapping tool.
You will need to first bring the data to a staging area, where it will be converted to the desired format and then moved to the final destination i.e. the database or CSV file. You can also send it back to the user through email – all through a data mapping tool like Astera Centerprise.
Data Mapping & 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 data mapping 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. This helps perform seamless B2B data exchange.
What Are Data Mapping Tools: Which Data Modeling Tool Can Streamline Your Business?
Let’s first start with understanding what are data mapping tools? The purpose of a data mapping tool is to easily map data from the source to the destination through a GUI. These database mapping tools can be classified into three broad types:
- On-Premise data mapping tools:
Such tools are hosted on a company’s server and native computing infrastructure. Many on-premise database mapping tools eliminate the need for hand-coding to create complex mappings and automate repetitive tasks in the data conversion mapping process.
- Cloud-Based data mapping tools:
Cloud-based data mapping tools allow legacy to modern and on-premise to cloud data integration using a cloud-based integration platform. In summary, these tools leverage cloud technology to help a business perform its data binding projects.
- Open-Source data mapping tools:
Open-source business mapping tools provide a low-cost alternative to on-premise data mapping solutions. These tools work better for small businesses with lower data volumes and simpler use-cases.
Best Data Mapping Software to Improve ETL Performance
Selecting the right data mapping tool that’s the best fit for the enterprise is critical to the success of any data integration, enterprise data transformation, and data warehousing project. The process involves identifying the unique data modeling requirements of the business and must-have features.
The key to choosing the right data mapping software is research. Online reviews on websites like Capterra, G2 Crowd, and Software Advice can be a good starting point to shortlist automated data mapping software that offers the maximum number of features. The next step would be to classify the features of data mapping tools depending on the unique data management needs of your business.
Some of the key features that an automated data mapping tool must have include:
- Support for a Diverse Systems for Source to Target Mapping: Connectivity to a range of structured, unstructured, and semi-structured data sources, including databases, web services, REST APIs, and flat file formats, such as delimited, XML, JSON, EDI, Excel, and text files are the basic staples of all information mapping tools.
- Graphical, Drag-and-Drop, Code-Free User Interface: It is important to select a data mapping software that offers you a code-free way to create data maps and process data using built-in transformations using a graphical, drag-and-drop UI.
- Ability to Schedule and Automate Database Mapping Jobs: Choose a data integration software with the ability to orchestrate a complete database workflow mapping using a time-based mapping function and event-triggered job scheduling. These tools include reusable data mapping templates based on text mining patterns and extract desired data from unstructured reports. They can automate data modeling and enterprise transformation processes, thereby delivering analytics-ready data faster. Moreover, you can also customize the pre-built data mapping templates to use them according to your requirements.
- Instant Data Integration Mapping Preview Feature for Real-Time Testing: Prevent application mapping errors at the design time using intuitive features like Instant Data Preview, which lets the user view the processed and raw data at any step of the data modeling process.
- SmartMatch Data Conversion Mapping for Resolving Naming Conflicts: Resolve discrepancies in field names by using a synonym-driven file reading and business data lineage feature to address the challenges of naming conflicts. This can be done by defining synonyms for a word in the synonym dictionary of a particular project.
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. With an industrial-strength ETL engine, data virtualization functionality, support for workflow automation, out-of-the-box connectivity to a range of data sources, drag-and-drop graphical UI, and a complete code-free environment, Astera Centerprise automated the entire data journey, from extraction to warehousing.
Download a free 14-day trial and find out how you can build any-to-any data mappings without writing a single line of code with Astera Centerprise data mapping solution.