Manual validations of distinct data in tables, relationships, and data mapping to a valid value take lots of manual effort for Data Stewards.
Making Analytics decisions for statistical data and Fact data requires ML model training and operational accuracy management of Data and its flow.
A lot of computing (Cost) is required for keeping Data Quality Metrics updated and in Augmented shape.
Having a reliable platform where computing costs can be reduced just by an intelligent understanding of the platform gives more scope for building complex Data Quality metrics.
Augmented way of identifying Categorical data from Numerical Data and irrelevant data reduces the strain of Manual workflow management.
Hindcasting and forecasting data Quality Checks can be automated under the common roof of Augments and brings in the space of Experimentation on Data.
AI and ML teams will no longer have to maintain manual documentation for the steps of Data Quality as ElixirData has the capacity to automate such workloads.
Having the reduced human interventions gives the ability to bring more capability on Data to deliver Quick production releases which improve the customer experience.
Reliability is at the core of ElixirData as Augment of Data Quality is easy to achieve by fundamental categorization of Categorical and Numerical Data.
Profile: When data is in the system, ElixirData provides a better view of all the structures, relations, and components of data.
Organize: ElixirData lets the user organize the data based on classifications, tags for easy future reference, or object mapping.
Govern: Once Data is organized, ElixirData automatically suggests some Policies and Rules on data and users can define the rules as per organization requirements as well.
Transform: Data Lineage processes bundling brings the ease to see how the Quality data is transformed across the system and how it can be managed in the future.