Data mapping plays a central role in helping companies fuel their data-driven processes. When an enterprise fails to formulate a robust data mapping strategy, data transformation logic and filtration errors become obvious which could lead to poor data quality and insights delivery, and ultimately decision-making and revenue generation. In order to avoid that, companies must maintain data integrity throughout the data mapping process.
The big data revolution has completely disrupted the market environment. It has transformed the way customers think, feels, and function. For any enterprise or business ecosystem to meet or exceed the emerging demands and expectations and therefore improve their ease of doing business, it’s essential to leverage the true potential of complex data of customers or partners and transform it into valuable insights. But that’s a hard row to hoe.
Enterprises, in current times of disruption, garner information from disparate data points, and it’s not necessary that they speak the same language and resides in the same format. With an effective data mapping strategy, business users can effectively integrate myriad data sources and comprehend them.
Data mapping is known as the process of building relationships between multiple data sources and models. It maps different data fields between data sources or data models. As users do that, they can integrate complex data faster - that again helps users extract actionable information and make decisions for the business. Let’s dive into detail and find out how data mapping can help organizations grow and innovate at the speed of business.
Broadly speaking, data mapping is the process of mapping the source data with the target database. The target database is normally a relational database. The target database can be a CSV document. In a plethora of cases, data mapping templates help companies match data fields from one database to another.
In an organization, employee details such as name, email, phone fields from excel sources are mapped to other fields that are relevant in a delimited file, which is the target destination.
Usually, when users map source to target, complexity is almost unavoidable. It mainly is contingent on the hierarchy of data being mapped, as well as the disparity between myriad data structures of the particular source and target.
Whether on-premise or cloud, applications use metadata to describe data fields as well as attributes that comprise data and semantic rules that determine how data is stored within the target database. Business users can utilize data movement controls to facilitate improved data flow without encountering delays or losses.
Business users can utilize the following ways to map customer data:
Manual Data Mapping: IT teams attempt to hand-code to map source data to the target schema. They leverage EDI mapping and custom coding to do that. Since manual coding is done, errors and delays become apparent.
Schema Mapping: It uses a semi-automated data mapping strategy that enables users to create a relation between a source and target schema Later, connections are being monitored by a schema mapping tool.
Automated, AI-Enabled Data Mapping: It uses an automated, AI, ML-enabled approach to creating intelligent data mappings with speed and precision. users can rely on machine learning-powered data mapping to use predictions to quickly map customer data - securely and easily.
Data mapping solutions powered by AI and ML enable users to bridge the differences in the schemas of data source and destination in a target repository. And so, data mapping helps business users speed up integration and insights delivery steps.
Additionally, data mapping breaks down data silos and collects insights. Data mapping is indeed the first step of data integration. Once the data is mapped, it can be integrated into a data lake or target database. Real-time insights can then be extracted from that database, which allows users to make better business decisions and ultimately drive value and experiences.
Modern data mapping tools with AI and ML features can help users quickly map customer data from source to target, meanwhile, IT can focus on more high-value tasks.
So, to drive data value, it’s time that you employ transformative approaches like AI and ML.