When a company is in the process of moving all its data from one source to another, this process needs to be managed properly. Data management consists of important steps such as data migration, data integration, data transformation, and data warehousing. Data mapping is the process that binds all the other processes together.
Data mapping ensures that the source and target data match properly. The outcome of a successful mapping process helps the developers, testers, and project architects. Data mapping tools help to easily carry out the process of mapping.
What is Data Mapping?
Companies collect data from different sources such as websites, customer accounts, employee databases, and vendor databases. These data points are used to chalk out a clear view of the current condition of the business. Analyzing this data uncovers hidden patterns that help in making important business decisions.
Data mapping is the process of maintaining the accuracy of the data when it is moved from one source to another source. It matches the data fields of the source databases to the target databases. For example, Name, Address, and Phone number from data Source A are matched to the same data fields of data source B.
As database/data storage systems may differ in nature, data mapping creates a proper roadmap to ensure that the data reaches its destination safely.
Steps of Data Mapping
- Data definition – The first step of data mapping is to define the data to be moved, including the data tables, the various fields, the format of the fields, and data types. For data integration, data transfer frequency is also specified.
- Data mapping – Then, the source and the target data fields are matched. If data transformation is required in any field, a transformation formula or some code is written.
- Testing – Sample data is taken from the source database and used against a test system. The data transformation process is checked and appropriate changes are made.
- Deployment – Once it is ensured that the transformation is working, data migration or data integration go-live event is organized.
- Maintenance – The data map needs to be maintained and it will need upgrades and modifications. This will be done whenever new data sources are added, data sources change and any requirement in the data destination is altered.
Here are the benefits of data mapping –
- It enhances data management, data security, data retention, and taking data backups.
- It improves the data sharing between departments within a company and external stakeholders.
- It improves the efficiency of business operations that are associated with data.
- Proper data mapping leads to better quality data. This helps in data analysis which in turn helps in making better business decisions.
- It helps in integrating data sources properly and understands data trends.
Learn about: Data Architect Salary in India
Top Data Mapping tools
Data mapping tools help the programmers to create data mapping rules and apply them to the data. These tools have metadata that give information about data objects, attributes, and fields.
The most popular data mapping tools are mentioned below –
This is an enterprise software used for data mapping and integration. It can be used for on-premise and cloud data stores. You can use the cloud-based data preparation tools to clean, access, transform, and enrich data easily. Without coding, Talend lets users handle data, files, applications APIs, and events between any locations.
It offers more than 900 pre-built components and is scalable. A free trial version is available for Talend.
2. Clover DX
Among the most popular data mapping tools, Clover DX has an open architecture that allows you to program data jobs. It has several in-built components for data mapping and transformation. It can handle any number of jobs and is efficient while working with complicated data tasks. You can orchestrate multiple data workloads and multiple systems.
The tool can deploy data workloads in a cloud or on-premise environment. Event triggers, APIs, message queues, and file watchers are available to connect it to external systems for data mapping. Clover DX is flexible, user-friendly, and processes data fast.
A free version is available for 45 days. The paid version ranges from $4000 to $5000.
This tool can map and integrate data on the cloud, on-premise, and hybrid environments. Simple and complex integration patterns are supported easily. Data transformation for unstructured data and complicated hierarchical documents is seamless with Informatica. It accelerates on-premise data mapping and integration with its Power Center.
This is an integrated agile data platform and can carry out B2B data exchange easily. The paid version of the product starts at $2000. A free version is also offered.
Salesforce is one of the most trusted data mapping tools that allow users to connect the legacy systems, back-office systems, and extend their applications using APIs. The platform lets you connect to any external data source within minutes, using simple point-and-click features.
You can integrate applications and devices using the SOAP API and access Salesforce data using the REST API. The platform has a rich set of tools for developing applications to connect the data with other platforms such as Microsoft, SAP, and Oracle. This is a very scalable and flexible platform.
It has three pricing plans – Gold, Platinum, and Titanium.
The best data mapping tools have been discussed here. If you are a part of a small or medium company, you can try data mapping platforms that have a free version such as Clover DX. But bigger firms, choose data mapping tools such as Salesforce that are paid and have a rich set of useful features.
If you are curious to learn about data science, check out IIIT-B & upGrad’s PG Diploma in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.