XenonStack Recommends
Challenges Faced
Categorical Data
Manual validations of distinct data in tables, relationships, and data mapping to a valid value take lots of manual effort for Data Stewards.
Numerical Data
Making Analytics decisions for statistical data and Fact data requires ML model training and operational accuracy management of Data and its flow.
Higher Computing Cost
A lot of computing (Cost) is required for keeping Data Quality Metrics updated and in Augmented shape.
Augmented Data Quality Solutions for Enterprises
Better visualize the Data Profiling with Observability to improve Data Quality.
Having an organized and centralized Data platform shapes Data Quality by publishing the common data understanding required for Data Analytics.
With Automation overcome the manual Handling of Data Quality metrics.
Discrete knowledge workflow from Connecting the data source up to visually representing the data brings reliability on Quality for Business Teams.
Features of the Augmented Data Quality Solutions
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 while automating 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.
Keep reliability at the core while Augment of Data Quality makes it easy to achieve by fundamental categorization of Categorical and Numerical Data.
Augmented Data Quality Implementation Strategy
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Profile: When data is in the system, Get a better view of all the structures, relations, and components of data.
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Organize: The users must organize the data based on classifications, tags for easy future reference, or object mapping.
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Govern: Once Data is organized, automate the Policies and Rules on data and users can define the rules as per organization requirements as well.
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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.
What are the values added by Solution?
Relying less on manual efforts and human interventions helps in cost-saving which can bring more openness to new techniques.
Enhanced business intelligence when shapes the augments of Data Quality, it brings a common understanding of the data across the organization.
Automated Data Quality Metrics prevent unintended data pass through the Quality Layer and give more reliability to monitoring.
Observability and Monitoring help add Business value to Data Quality as more robust automated rules can be applied to Data.
Transform your
Enterprise With XS
Capabilities
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Adapt to new evolving tech stack solutions to ensure informed business decisions.
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Achieve Unified Customer Experience with efficient and intelligent insight-driven solutions.
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Leverage the True potential of AI-driven implementation to streamline the development of applications.
