Data mapping is the life blood of any data integration process. Without a proper data mapping strategy, data transformation 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. So, data mapping process is used to integrate all the disparate data sources and make sense of them.
In summary, data mapping is the process of establishing relationships between separate data models from disparate sources or systems.
What is Data Mapping? Explained with 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.
Data mapping in simple terms 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.
Here is a data mapping example to clarify the above concept further. 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 tool in Astera Centerprise
Data integration mapping 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.
Data Mapping Templates Explained
Most data mapping tools offer pre-built dataset matching templates. Users can use them to create database matches from source to target systems. A simple data mapping template looks like an Entity-Relationship (ER) diagram but with structured data available in sourced entities.
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 example would be: Let’s say you get an excel sheet in 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 solution, you can create an automated data mapping process. 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.
Adding transformations in a data map using Centerprise data mapping tool.
A 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 data mapping solution is in place to automate the process of copying csv data to a database. That’s what data mapping solutions 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 example, in the following example, data from three different databases tables is joined and mapped to an Excel destination.
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 process. In most cases, data quality checks will be added to the data map to minimize risk of data leak or access control sabotage.
Why Data Mapping is Important in the ETL Process?
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 modeling, which is an integral 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. 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, 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. 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.
Automated Data Mapping & Transformation
Because enterprise data resides in a variety of locations and formats, data transformation is 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?
Data mapping tools help 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 moddeling 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 offer 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 solution 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 solution that offers you a code-free way to create data maps and process data using built-in transformations using drag-and-drop UI.
- Ability to Schedule and Automate Database Mapping Jobs: Opt for data integration software with the ability to orchestrate a complete database workflow mapping using 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, 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.