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    Tiger Data Observability - Self Service Data Quality Framework

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    A framework for improving data quality on AWS with self-service capabilities
    Listing Thumbnail

    Tiger Data Observability - Self Service Data Quality Framework

     Info

    Overview

    As per Gartner, poor data quality costs Organizations $12.9 million annually. Good data allow organizations to measure the effectiveness of its business strategy and KPIs to make informed decisions and to take right actions. Measuring business KPIs and taking swift actions improve the Organization’s capabilities, products, services and thus driving customer satisfaction. However as Organizations grow, the IT systems and its data sources keep increasing thereby rendering the data estate multi layered and complex. Voluminous data calls for quality control mechanisms that are critical for effective business decisions. Manually measuring data quality against several dimensions like Completeness, Accuracy, Uniqueness, Timeliness, Consitency, Validity in a big data landscape could be time consuming and resource intensive. Manually detecting data quality issues are costly and have serious repercussions to business operations. This mandates the need for a scalable and an automated data quality framework. Following the Fail fast design principle, Tiger built a solution that can quickly deliver business impact by detecting the data quality issues early in the Analytics value chain. The platform has helped us build a configurable metadata driven framework with the follwoing capabilities: ● Self-service UI to quickly profile and automate rule discovery ● Configuration-based backend processing ● AWS cloud native and open-source technologies ● Monitoring and Alerting

    Highlights

    • Data observability with self-service capabilities to operate and manage data quality rules using low cost, no-code approach via react based UI, APIs and metadata repository
    • The framework can be easily integrated with existing data pipelines to prevent bad data flowing to the downstream applications
    • The framework is scalable, maintainable, extensible to integrate to existing data products

    Details

    Delivery method

    Pricing

    Custom pricing options

    Pricing is based on your specific requirements and eligibility. To get a custom quote for your needs, request a private offer.

    Legal

    Content disclaimer

    Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.

    Support

    Vendor support

    Implementation of this framework is managed and executed by Tiger Analytics. The platform is implemented in the client AWS ecosystem by Tiger's Engineering Team and the necessary support is provided through a standard model. Escalation matrix for different ticketing priorities will be agreed and defined in the Services Contract or SOW. For any incidents/service requests/queries, the users can write to dataobserv.support@tigeranalytics.com .