Map Data Faster and Easier with Artificial Intelligence
To stay competitive, companies must deal with massive amounts of information coming from a variety of sources in a variety of formats. That data can be impossible to integrate into a unified database without the process of data mapping.
Data mapping, in which source fields are mapped to their related target fields, is necessary for exchanging information between companies. A company’s partners and customers often use different versions of applications, and each of those versions requires data in a unique format. It’s through data mapping that companies transfer this data, regardless of its initial format.
The problem is that though mapping is a vital part of the data integration process, it is also time-consuming and expensive. The transformation step of the data mapping process, where data is mapped from one format to another while business rules and validations are applied, is not only the most important step, it’s also the most tedious, difficult, complicated, and error-prone. That’s because transformation requires a deep understanding of all the data fields in both the source and destination sides as well as the perfect application of all the data rules for all the fields. All of that requires extensive work from IT data integration experts, which delays the data integration process, slowing a company’s revenue stream and increasing operational costs.
Also, because manual data mapping has been the approach companies have taken to enable B2B data integration for decades, those maps are getting older and must be constantly updated. When a company has tens of thousands of data maps in use, as many do, expensive IT resources must be deployed to keep them viable. At many companies, the software supporting data mapping is nearing the end of its life, if it hasn’t already reached (or passed) it. Those companies must devote expensive and scarce IT resources to re-authoring their portfolio of data maps so they’re compatible with newer software.
What all those problems add up to is this: The process of data mapping hasn’t changed in decades, and a different process is needed.
Adeptia has that process: AIMap.
About Adeptia AIMap
AIMap is Adeptia’s AI-guided data mapping system. Using artificial intelligence and machine learning, AIMap analyzes existing data mapping to learn mapping patterns, then trains a neural network to suggest future data mappings, eliminating the need for complicated, time-consuming data mapping on the part of scarce and expensive IT data integration experts.
Adeptia’s goal in creating an AI-driven mapping solution was to make this step in the data transformation process as easy and intuitive as possible, eliminating the need for complicated mapping and coding on the part of IT professionals and instead empowering non-technical business users to create the necessary data integrations with just a few clicks, completing the process in hours instead of weeks or months.
How AIMap works
Step One: Training
Data map objects are tested in a business environment and confirmed to contain validated and correct data transformation rules. Once this is established, Adeptia’s AIMap uses these data maps as examples to train the neural net so it can learn from their mapping rules.
Step Two: Suggesting
After the training step, AIMap is then activated by a user who is creating a new data map. AIMap scans the source and destination schemas, then uses its previously trained algorithms and neural net to find mapping rules for each target field. When the most appropriate rules are found, AIMap ranks them in order of decreasing probability. Then it selects the suggestions with highest probabilities and displays them by High, Medium, and Low confidence levels.
Step Three: Reviewing:
The user then views the suggestions and is guided by the confidence levels that were predicted by AIMap. The accept/reject options in the review process allow the user to work through the suggestions sequentially. Finally, the user can load and run a data file to see the actual results of the mapping and, if necessary, make corrections.
By using AI to enable learning from existing mappings then using that knowledge to suggest mapping to support new connections, AIMap accelerates the mapping process while removing the burden from IT data integration experts, freeing them up to work on other priority projects.
What’s more, because AIMap significantly reduces manual mapping effort and accelerates deployment timeframe, companies can accelerate their revenue cycle and deliver a more satisfying CX where partners are onboarded faster and easier, increasing customer retention and reducing costly churn. What’s more, because AIMap enables companies to build a reusable mapping blueprint that leverages the intelligence of each data map for subsequent integrations, companies can make smaller incremental investments as the number of data connections increase. This leads to improved productivity, reduced costs, and more streamlined integrations.
Key features of AIMap
- Classifies mapping suggestions into High, Medium, and Low confidence (High and Medium confidence suggestions include complex rules, such as Math, String, or Conditional functions; Low confidence suggestions show one-to-one maps from source to target fields)
- Provides a review and accept/reject options for mapping suggestions
- Works with complex, hierarchical data structures, not just flat formats
- Works with all data formats, including Text, CSV, Fixed Length, Database tables, XML, EDI X12, JSON, etc.
- Is designed for high-performance so suggestions for even large schemas can be made in seconds
Contact Adeptia today to learn how AIMap can transform your data integration process.