Integrate AI into Data Mapping to Drive Business Decision Making

Prior to analyzing large chunks of data, enterprises must homogenize them in a way that makes them available and accessible to decision-makers. Presently, data comes from many sources, and every particular source can define similar data points in different ways. Say for example, the state field in a source system may exhibit “Illinois” but the destination keeps it is as “IL”. 

In such cases, data mapping plays an important role. It helps business users bridge the difference between two systems or data models, so that when data is moved from a source, it remains highly accurate and usable at the destination. Essentially, data mapping is the process of connecting a data field (from one source) to a data field (another source). It alleviates the potential for errors, enables users to standardize data, and makes it easier to comprehend data by correlating it. 

For quite long, data mapping has been a common business function for most enterprises. But as the amount of data and sources rise, the process of data mapping has turned extremely complex, needing highly automated tools to make it feasible for large data sets. 

Data mapping allows companies to establish a single source of truth for highly sensitive data. It provides visibility into a lot of details – what records companies hold, how these records are related, and which systems hold the records, and more. Comprehending this data at a granular level helps business users achieve insightful information that can be used to make decisions. 

Being an important segment of data management, data mapping, if not properly mapped, can corrupt data as it moves to the destination. It helps users get most of the other processes such as data migrations, transformations, and integrations. Let’s explore each process in detail. 

This is the process that involves data movement from one system to another. Data mapping supports migration by mapping source to destination fields. 

Data integration is an ongoing process of regularly moving data from the system to another. Data mapping helps users map data faster so that it can be transformed and integrated into a database without difficulty. 

The process of converting data from a particular source format to a destination format is called data transformation. It includes the process of cleansing wherein, data types are changed and nulls are deleted to make data more enriched. For example, “Illinois” can easily be transformed into “IL”, which matches the destination format. All these rules of transformation are part of the data map. As data is moved, the data map employs transformation formulas to get the data for analysis. 

Data warehousing is a technique in which companies can pool data into one source for analysis. Data is fetched from the warehouse to run a query, a report, or perform an analysis. In a warehouse, data is already migrated, integrated, and transformed. Data mapping allows users ensure that as soon as data enters in a warehouse, it reaches the destination the way it is intended. 

Though existing data mapping proves extremely helpful for businesses, applying technologies such as AI can improve the results further. Artificial intelligence data mapping can help users map data automatically with speed and precision. It uses machine learning algorithms to deliver predictions that can turn the process of mapping faster.

Moreover, AI Map mechanisms recognize errors such as duplicities, missing values, etc. and deliver accurate results. Ergo, artificial intelligence-powered data mapping mechanisms help users map data sources to the target fields, but also maintains data integrity. This saves a lot of time when machine learning is used to clean and unify data. 

In short, transformative technologies such as AI improves the quality of data mapping, making it more precise and accurate. Such tools enable user map data faster without compromising quality. Much different from the manual tools that were error-prone and slow too. Simply put, companies that want to leverage data from different sources to make accurate decisions must make use of AI-powered data mapping tools. Not only such tools will improve the data quality and speed of processing, but they will also enhance the quality of outcomes.