Enterprise data is getting more dispersed and voluminous by the day. 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 various data points, and they may not always speak the same language. The data mapping process is used to integrate all the disparate data sources and make sense of them. And for that, you need an efficient data mapping tool.
What is Data Mapping?
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 mapping also helps consolidate data by extracting, transforming, and loading it to a destination system. The initial step of any data process, including ETL, is data mapping. Businesses can use the mapped data for producing relevant insights to improve business efficiency.
During the data mapping process, the source data is directed to the targeted database. The target database can be a relational database or a CSV document — depending on the use case. In most instances, companies use a data mapping template to match fields from one database system to the other.
Here is a data mapping template example to clarify how the mapping process works from an excel source. 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 integration tasks vary in complexity. The level of intricacy depends on the data hierarchy and the disparity between the data structure of source and target. 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 process from source to destination without any data loss.
For example, Microsoft Dynamics CRM contains several data sets that comprise 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 will need to be created from the source fields to the Microsoft Dynamics CRM account.
Data mapping is used in a range of use cases and industries to streamline data processes. For example, in the healthcare industry, source-to-target mapping helps achieve interoperability for EHR (electronic health record) by matching the data between a source and target. It also helps healthcare professionals share critical patient information and combine healthcare data from the various databases, data sources, and systems, such as EHR and EMR. The mapped data is further used for other critical processes, such as data migration and data integration.
Data Mapping in Action
Mapping can have a varying degree of complexity, depending on the number, schema, primary keys, and foreign keys of the data sources. For instance, in the following example of database mapping, data from three different database tables, Lead, Lead History, and Lead Status are joined and mapped to an Excel destination.
Database 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 the data conversion mapping software. 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 the data mapping process, data mapping teams need to ensure that they have all the information available from all stakeholders involved. If any sensitive information needs to be mapped separately, they should be informed before starting the process. In most cases, they add data quality checks 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: It requires IT professionals to hand-code or manually map the data source to the target schema.
- Schema Mapping: It is a semi-automated strategy. A data mapping software 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.
- Fully-Automated Data Mapping: The most convenient, simple, and efficient data mapping technique uses a code-free, drag-and-drop data mapping UI. Even non-technical users can carry out mapping tasks in just a few clicks.
Data Mapping Use Cases
Mapping allows companies to extract business value out of data 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. Here are some of use cases that widely utilize the mapping process:
For successful integration, the source and target data repositories must have the same structure, which is a rare occurrence. Data mapping tools help bridge the differences in the schemas of source and destination systems through data transformation and conversion. This allows businesses to consolidate information from different data points efficiently. This is why data integration tools available in the market include the code-free mapping feature.
Data migration is the process of moving data from one database to another, which can be performed smoothly using a database mapping tool. 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 mapping software, with automation features, is a safer alternative to successfully migrate data to the destination.
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 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. It helps perform seamless B2B data exchange.
Types of Data Mapping Software
There are different types of solutions available in the market that simplify the data mapping process. They can be classified into three broad types:
On-Premise mapping tools are hosted on a company’s server and use native computing infrastructure. Many on-premise tools eliminate the need for hand-coding to create complex mappings and automate repetitive tasks in the data process.
These tools are hosted on the cloud and can be accessed via web browser. Cloud-based tools also have automation features that can simplify the mapping process.
Open-source tools provide a low-cost alternative to on-premise data mapping solutions. These graphical database mapping tools work better for small businesses with lower data volumes and simpler use-cases.
How to Find the Right Data Mapping Tool?
Selecting a data mapping software is critical to the success of any data integration, transformation, and warehousing project. The process involves identifying the unique business use-case and must-have features.
The key to choosing the right software for your needs is research. Online reviews on websites like Capterra, G2 Crowd, and Software Advice can be a good starting point to shortlist your selections. Some of the key features you would want in an automated data mapping tool 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 and CSV is the basic staple of all information mapping tools.
- Graphical, Drag-and-Drop, Code-Free User Interface: A code-free environment to create mappings and a graphical, drag-and-drop UI to process data using built-in transformations.
- Ability to Schedule and Automate Jobs: The ability to orchestrate a complete workflow using time and event-triggered job scheduling is a valuable feature in a tool. This automation cuts down the manual work, improving productivity and saving time.
- Instant Preview Feature for Real-Time Testing: Intuitive features like Instant Data Preview help prevent application mapping errors at the design time. This functionality lets the user view the processed and raw data at any step of the data process.
- SmartMatch Data Conversion Mapping for Resolving Naming Conflicts: Synonym-driven file reading to resolve discrepancies in field names and business data lineage function to address the challenges of naming conflicts. It 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 warehousing 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 automates the entire data journey, from extraction to loading.
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.