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Augmented Data Quality

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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.

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Numerical Data

Making Analytics decisions for statistical data and Fact data requires ML model training and operational accuracy management of Data and its flow.

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Higher Computing Cost

A lot of computing (Cost) is required for keeping Data Quality Metrics updated and in Augmented shape.

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Augmented Data Quality Solutions for Enterprises

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Better visualize the Data Profiling with Observability to improve Data Quality.

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Having an organized and centralized Data platform shapes Data Quality by publishing the common data understanding required for Data Analytics.

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With Automation overcome the manual Handling of Data Quality metrics.

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Discrete knowledge workflow from Connecting the data source up to visually representing the data brings reliability on Quality for Business Teams.

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Features of the Augmented Data Quality Solutions

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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.

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Augmented way of identifying Categorical data from Numerical Data and irrelevant data reduces the strain of Manual workflow management.

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Hindcasting and forecasting data Quality Checks can be automated under the common roof of Augments and brings in the space of Experimentation on Data.

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AI and ML teams will no longer have to maintain manual documentation for the steps of Data Quality while automating such workloads.

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Having the reduced human interventions gives the ability to bring more capability on Data to deliver Quick production releases which improve the customer experience.

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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.

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What are the values added by Solution?

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Relying less on manual efforts and human interventions helps in cost-saving which can bring more openness to new techniques.

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Enhanced business intelligence when shapes the augments of Data Quality, it brings a common understanding of the data across the organization.

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Automated Data Quality Metrics prevent unintended data pass through the Quality Layer and give more reliability to monitoring.

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Observability and Monitoring help add Business value to Data Quality as more robust automated rules can be applied to Data.

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Transform your
Enterprise With XS
Capabilities

  • Adapt to new evolving tech stack solutions to ensure informed business decisions.

  • Achieve Unified Customer Experience with efficient and intelligent insight-driven solutions.

  • Leverage the True potential of AI-driven implementation to streamline the development of applications.

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