Data integration methods or tools have undergone a major overhaul in the last few years. Not so long ago, traditional manual methods were employed to integrate data. But as the volume of data increased, these methods became outdated due to their labor-intensive, time-consuming, and error-prone nature.
Companies now require in-depth business knowledge, a strong understanding of a diverse set of data schemas, and cognizance of underlying data relationships to perform data integration.
With time, organizations have shifted their reliance to newer techniques to bolster data integration. One such technique is machine learning.
While building a data integration flow, developers or designers specify a mediated schema that helps in capturing myriad aspects of the domain of interest to supply a source description that defines the semantic mapping between the schema of source data and the mediated schema.
Machine learning can support developers and help them map these source schemas and mediated schemas automatically, eliminating the need to put development effort to build data mapping flows.
Consider a data integration framework that allows home buyers to search for real-estate to understand how machine learning automatically computes semantic mappings between schemas.
A mediated schema, in this case, may contain elements such as house_address, price, contact_phone, listing the address of a house, its price, and respective phone number of the real estate agent. The source description may contain source elements such as house_location, list_price, and agent_phone.
Now, the mapping mechanism needs to map the source elements to the mediated schema elements respectively.
ML equipped data integration platforms looks at the sources of home prices, home addresses along with phone numbers to form identifiers for these elements. Using the patterns and inferences of the elements, the platform know that the source element agent_phone matches contact-phone, which concludes that the agent_phone is the same as contact_phone.
Further, the machine learning program can learn from data properties. For instance, a small number may indicate information like the number of bedrooms, instead of, say, the price of a house. The program can also learn from the closeness or proximity of elements. Say, for instance, a long text file (like a phone number) at the starting of a row present near to the real-estate agency name would be an agent’s phone and not the resident’s phone.
Modern data integration solutions leverage machine learning and natural language processing to unleash the knowledge held by underlying entities, and model those entity relationships with minimal human intervention.
They can perform a large portion of work (almost 90%) with medium to high confidence, including entity discovery and defining entity relationships in real-time. Rest 10% can be handled manually. Therefore, ML-equipped data integration platforms greatly accelerate schema modeling and remodeling in a way that can be optimized based on query or data access pattern.
Machine learning associated integration platforms are also capable of recommending next-best-action or propose data mappings, rules, and transformations. These intelligent data recommendations play a major role in providing aid to the citizen integration model. Data scientists can use these recommendations to cultivate a strong understanding of domain data as well as business in a short period of time.
Machine learning features can also solve high-impact problems like structure discovery and anomaly detection. It can reduced the effort required to create data mappings for data integration while increasing accuracy and speed, thus reducing the customer data onboarding time by up to 80%.
Know how Adeptia’s AI-based data mapping tool can help organizations simplify integration to accelerate partner data onboarding, fast-forward revenues, and make you easy to do business with.