Data mapping underpins the data integration processes of any organization. In the absence of an effective data mapping strategy, data transformation and errors related to filtration can occur that can inhibit data quality. This directly impacts important processes such as business analysis and decision-making. Therefore, it is important to maintain integrity across the data mapping framework.
Organizational data is getting more dispersed and voluminous each day, and at the same time, it has turned out to be important than ever for businesses to leverage data and transform it into actionable insights. However, myriad business ecosystems garner information from an array of data points, and they may not always speak the same language. So, data mapping applications enable users to integrate all different data sources and make sense of them. Simply put, data mapping mechanisms establish relationships between separate data models from myriad sources or systems.
What is Data Mapping?
Data mapping involves the extraction of different data fields from one or multiple sources files and matching them to their target fields in the destination. It enables business users consolidate data by extracting, transforming, and loading it to a data warehouse. The mapped data can then be used for generating insights that can boost business efficiency.
In simple terms, data mapping is about creating a map where source data is directed to the specific database. The target destination can be a CSV document or a relational database – it is contingent on the choice of the user. In a majority of cases, a data mapping template is employed to match fields from a single database system to another.
For example, in a data mapping scenario, different fields such as Name, Phone, Email, etc. from an excel source are mapped to the relevant fields in a delimited file, which is the destination.
Data mapping tasks vary in complexity, and it depends on the hierarchy of the data being mapped, along with the disparity between the data structure of the source and target. For successful implementation, business applications use metadata to explain the data fields as well as attributes and define semantic rules that govern the data storage activities within an application or repository. However, several controls enable users to transfer data from source to destination without any losses or delays.
For example, Microsoft Dynamics CRM encompasses different data sets that contain objects like competitors, leads, and opportunities. Every data set includes entities such as name, city name, job title, and more. The application also defines schema coupled with attributes, mapping rules, and enumerations. Ergo, if a new record is to be appended to a specific schema of a data object, a data map has to be generated from the source fields to a specific database like the Microsoft Dynamics CRM account.
How is Data Mapping Done?
On the basis of the number, primary keys, schema of sources, data mapping can differ depending on the complexity. In relation to the example mentioned, data from multiple databases are merged into a target database such as Excel.
Data mapping is leveraged to execute a variety of data transformation and data integration tasks depending on the data management needs of an organization. As far as use cases are concerned, data mapping has many; for pre-integration, data conversion, data cleansing, and more.
When it comes to how organizations use a lot of data mapping techniques. Some of them include manual data mapping, schema data mapping, and fully automated data mapping. While in the manual method, IT professionals hand-code to map data, schema mapping involves a semi-automated data mapping approach for creating different mapping scenarios. And, finally, automated data mapping encompasses a code-free, drag, and drop interface tool for diverse data mapping tasks. These solutions employ artificial intelligence/machine learning to create intelligent data mappings faster – without the support of IT or technical teams.
How Can AI be a Gamechanger?
Artificial intelligence enables business users to create data mappings faster using AI-driven suggestions. By using machine learning algorithms, they can increase the accuracy and speed of the data mapping process, facilitating improved data transformation and data integrations.
Organizations can do away with the need to execute complex EDI mapping that requires tedious coding which is time-consuming and error-prone. They can use AI-enabled data mapping tools to enable non-technical users to map data while freeing IT for more high-value tasks.