For an organization seeking to establish a robust, data analytics capability, the process of building and launching such a system can be a daunting and time-consuming task. However, Inzata’s ML-assisted Data Modeler, the first of its kind in the market, known as InModeler, has emerged as a game-changer in this domain. With its innovative utilization of machine learning, InModeler streamlines the data modeling process, making it faster, more efficient, and accessible to both technical and non-technical users.
Comprehensive Functionalities of InModeler: Streamlining Data Modeling from Loading to Business Layer Generation
At its core, InModeler goes beyond being just another data modeling tool. It excels in supporting the entire lifecycle of the data modeling exercises, from data loading logical modeling, logical integration, and data enrichment to the final, fully automated integration of data on the physical level. Let’s delve into the key functionalities of InModeler:
InModeler supports the loading of physical files into the PDL format, with each data file processed separately. Leveraging its intelligent guessing logic, InModeler automatically determines whether a column represents an attribute or a fact. Users can verify and adjust these initial guesses. The system seamlessly accommodates data from various sources, including third-party applications like SalesForce or internal systems through many data source connectors.
Logical Data Modeling
InModeler automates the integration of individual source data files into a comprehensive enterprise data model. It intelligently handles the complexities of combining all the column combinations of disparate data sources, finding corresponding integration columns, and thus creating a unified structure that serves as the foundation for advanced analytics.
InModeler facilitates the fully automated deployments and integration of dimensions (e.g., geographical information), hierarchies (e.g., time and diagnoses), and BI packages (such as CRM or Laboratory & Exams BI systems). These enrichments are automatically recommended based on the compatibility of data clusters’ columns with the core bottom dimensional attributes available from the Inzata Object Marketplace. Inzata ensures that only fully compatible packages for integration are presented to users during the data enrichment process.
Physical Layer Data Integration
Building upon the logical model, InModeler processes the physical integration of selected data files. It identifies common attributes and performs denormalization to create a unified, multidimensional enterprise-level physical data structure. Additionally, InModeler incorporates higher-level hierarchies from enriched dimensions into the integrated model.
Business Layer Generation
In the final step, InModeler generates the business layer metadata, encompassing attributes, attribute hierarchies, facts, and metrics. This metadata provides the foundation for generating reports automatically, enabling users to start leveraging the data analytics capability swiftly. Remarkably, users can complete the entire process in as little as several tens of minutes, even when dealing with complex scenarios involving hundreds of attributes from multiple files.
Machine Learning Empowerment in Data Modeling
The power of Inzata’s ML-assisted Data Modeler lies in its ability to combine machine learning with traditional data modeling techniques. It streamlines operations by automating the mapping, cleansing, transformation, and relationship creation processes. This eliminates the need for manual coding or extensive involvement from IT resources. This accelerates the development and launch of data analytics capabilities within organizations.
Furthermore, InModeler offers an intuitive and user-friendly interface, allowing technical and non-technical users to navigate and utilize the tool effectively. It empowers users to harness the power of data analytics without being burdened by complex technicalities.
In summary, Inzata’s ML-assisted data modeler (InModeler), represents a revolutionary advancement in data modeling, integration, and enrichment. By leveraging machine learning, it seamlessly integrates disparate data sources into unified, integrated data models. The automation, speed, and user-friendly nature of InModeler enable organizations to expedite the deployment of their data analytics capabilities. With InModeler, organizations can embark on their data analytics journey with confidence and efficiency.