These industry-specific common models also include various metrics and business objects Automating the in terms end-users can understand and clarify how data is used for different purposes. For example ? if a pharmaceutical company were to get accelerated approval by the FDA for a certain drug ? it could quickly use a pre-built commercial data model to prepare the data for a more successful commercial launch. Without this quick and painless method ? they’re stuck hiring data teams to do this costly work or building their own infrastructure to do it themselves.
Automated Data Mapping
Regardless of the usefulness of a uniform data model ? business users gambling database would still need to outsource their IT needs for wrangling data without an automated means of mapping it into that model. Competitive options in this space leverage multiple machine learning approaches to automate this critical step ? truly making this approach self-service. Those algorithms enable end-users to map their data to the proper metrics in a no-code manner based on declarative language. Instead of writing SQL code ? for example ? users black creators and artists simply write a declarative statement about which data they want to be mapped to which object in the model.
Those statements correlate with
The underlying data assets linked together in a knowledge graph that understands element germany cell number from what those assets mean in business terms. AI does the work of actually mapping the data into the model. When integrating data for clinical trials ? for example ? biopharmaceutical companies look at an assortment of data from their own databases ? healthcare providers ? and insurance companies to understand a patient’s end-to-end journey. That knowledge becomes the basis for getting the drugs in the right hands at the right time ? thus improving patient outcomes.  into a common model for this goal makes the need for service teams obsolete — which dramatically reduces your overhead and decreases the risk of involving outside parties.