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. To integrate this data and make sense of it, data mapping is used which is the process of establishing relationships between separate data models.
What is Data Mapping?
In simple words, data mapping is the process of mapping data fields from a source file to their related target fields. For example, 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.
Mapping tasks vary in complexity, depending on the hierarchy of the data being mapped, as well as the disparity between the 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.
For example, Microsoft Dynamics CRM contains several data sets which 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 data source to the Microsoft Dynamics CRM account.
Depending on the number, schema, and primary keys and foreign keys of the relational databases data sources, database mappings can have a varying degree of complexity. For example, in the following example, data from three different databases tables are joined and mapped to an Excel destination.
Depending on the data management needs of an enterprise and the capabilities of the data mapping software, data mapping is used to accomplish a range of data integration and transformation tasks.
The Importance of Data Mapping
To leverage data and extract business value out of it, 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 mapping, which is an integral step in various data management processes, including:
For successful data integration, the source and target data repositories must have the same data model. However, it is rare for any two data repositories to have the same schema. Data mapping tools help bridge the differences in the schemas of data source and destination, allowing businesses to consolidate information from different data points easily.
Data migration is the process of moving data from one database to another. 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 a code-free mapping solution that can automate the process is important to migrate data to the destination successfully.
Data mapping in a data warehouse is the process of creating a connection between the source and target tables or attributes. Using data mapping, businesses can build a logical data model and define how data will be structured and stored in the data warehouse. The process begins with collecting all the required information and understanding the source data. Once that has been done and a data mapping document created, building the transformation rules and creating mappings is a simple process with a data mapping solution.
Because enterprise data resides in a variety of locations and formats, data transformation is essential to break information silos and draw insights. Data mapping 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.
Electronic Data Interchange
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.
Data Mapping Techniques
Although an essential step in any data management process, data mapping can be complex and time-consuming. The process of connecting data sources, building mappings for data transformation and integration, and validating the transformed data can take up significant developer resources, particularly when the entire process is done manually.
Based on the level of automation, data mapping techniques can be divided into three types:
1. Manual Data Mapping
Manual data mapping involves hand-coding the mappings between the data source and target database. Although hand-coded, manual data mapping process offers unlimited flexibility for unique mapping scenarios initially, it can become challenging to maintain and scale as the mapping needs of the business grow complex.
2. Semi-Automated Data Mapping
Schema mapping is often classified as a semi-automated data mapping technique. The process involves identifying two data objects that are semantically related and then building mappings between them. For example, to build mappings between the schemas of Database 1 and Database 2, the possible matches that a developer can use include Database1:StudentName≈Database2:Name and Database1:ID≈Database2:SSN.
|Database 1||Database 2|
Once schema mapping has been done, Java, C++, or C# code is generated to achieve the required data conversion tasks. The programming language used may vary depending on the data mapping tool used.
3. Automated Data Mapping
Automated data mapping tools feature a complete code-free environment for data mapping tasks of any complexity. Mappings are created between the data source and target database in a simple drag-and-drop manner. An automated data mapping tool also has built-in transformations to convert data from XML to JSON, EDI to XML, XML to XLS, hierarchical to flat files, or any format without writing a single line of code. Some enterprise-grade data mapping software also offer process orchestration and job scheduling features to automate database mapping.
Types of Data Mapping Tools
Data mapping tools can be divided into three broad types:
- On-Premise: Such tools are hosted on a company’s server and native computing infrastructure. Many on-premise data mapping tools eliminate the need for hand-coding to create complex mappings, and automate repetitive tasks in the data mapping process.
- Cloud-Based: These tools leverage cloud technology to help a business perform its data mapping projects.
- Open-Source: Open-source 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.
How to Evaluate and Select the Best Data Mapping Software
Selecting the right data mapping tool that’s the best fit for the enterprise is critical to the success of any data integration, data transformation, and data warehousing project. The process involves identifying the unique data mapping 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 data mapping software that offer the maximum number of features. The next step would be to classify the features of data mapping tools into three different categories, including must-haves, good-to-haves, and will-not-use, depending on the unique data management needs of the business.
Some of the key features that a data mapping solution must have include:
Support for a Diverse Set of Source Systems
Support for various databases and hierarchical and flat file formats, such as delimited, XML, JSON, EDI, Excel, and text files are the basic staples of all data mapping tools. In addition, for businesses that need to integrate structured data with semi-structured and unstructured data sources, support for PDF, PDF forms, RTF, weblogs, etc., is also a key feature.
If your business uses a cloud-based CRM application, such as Salesforce or Microsoft Dynamics CRM, look for a data mapping tool that offers out-of-the-box connectivity to this enterprise applications.
Graphical, Drag-and-Drop, Code-Free User Interface
To break down information silos and allow both data professionals and business users access to enterprise data, it is important to select a data mapping solution that offers you a code-free way to create data maps. From built-in transformations to join, filter, and sort data to a range of expressions and functions, user-friendly data mapping tools feature an extensive library of transformations to fulfill the data conversion needs of an enterprise.
Ability to Schedule and Automate Database Mapping Jobs
Since data mapping jobs, if not automated, can take up a significant amount of developer resources and time, opting for data mapping software with process orchestration capabilities can bring cost-savings to a business. With the ability to orchestration a complete database mapping workflow and time-based and event-triggered job scheduling, these data mapping solutions automate data mapping and transformation process, thereby delivering analytics-ready data faster.
Instant Data Preview Feature for Real-Time Testing and Validation of Mappings
Mapping data to and from formats such as JSON, XML, and EDI can be complex due to the diversity in data structures. However, to prevent mapping errors at the design-time, an effective data mapping tool should feature an Instant Data Preview engine which lets the user view the processed data, as well as raw data at any step of the data management process.
Smart Match Functionality for Resolving Naming Conflicts
Often, companies are required to leverage incoming data from business partners, such as resellers and suppliers. Mapping and integrating data from third parties can be challenging due to difference in data representation. For example, one vendor might name the Order ID field as ‘Order No.’ while another vendor might name it as ‘Order #’. Hence, an agile data mapping solution should possess a synonym-driven file reading and mapping feature to address the challenge 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, 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.