Unlock your data’s full potential
In today’s data-driven landscape, overlooking data quality can lead to costly mistakes and missed opportunities. The Data Observability Starter Kit addresses common pitfalls by elevating data monitoring to a top priority for business stakeholders and providing seamless integration into existing data platforms. With built-in quality checks and anomaly detection, businesses can proactively tackle issues before they impact downstream applications, ensuring accurate insights and informed decision-making.
By simplifying data quality onboarding and offering out-of-the-box capabilities, our starter kit accelerates time to market for quality monitoring across all data types, empowering businesses to unleash the full value of their data assets and gain a competitive edge in the market.
Data Observability Starter Kit demo
Data Observability Starter Kit features
Explore data observability quality, integration, and acceleration use cases
DATA QUALITY
Proactively manage data quality
With built-in quality checks and anomaly detection, the starter kit enables businesses to address data issues before they affect downstream applications, reducing the technical and financial burdens of post-impact corrections.
DIVERSE DATA TYPES
Deliver diverse data quality checks
Leverage ready-to-use quality checks for every type of data, whether tabular, structured, or unstructured. These checks cover null/missing values, statistical distribution, data freshness, and volume, ensuring thorough and accurate data quality monitoring.
INTEGRATION
Seamlessly integrate across platforms
Designed for compatibility with cloud data platforms of any scale, including Hadoop-as-a-service, Kubernetes, and modern data warehouses like Snowflake, AWS Redshift, GCP BigQuery, and Azure Synapse Analytics, the starter kit ensures a frictionless setup process.
ONBOARDING
Streamline the onboarding process
Simplifying data observability onboarding, the starter kit streamlines the adoption of existing ETL/ELT pipelines, data ingestion strategies, and upstream/downstream dependencies management, facilitating seamless integration into existing workflows.
RAPID DEPLOYMENT
Accelerate time to market
The starter kit’s implementation blueprint is designed to enable rapid deployment of quality monitoring capabilities, empowering businesses to quickly reap the benefits of improved data integrity, facilitating immediate enhancements to operational decisions and strategies.
Data Observability Starter Kit benefits
Discover the tangible business value of data observability
How the Data Observability Starter Kit works
The Data Observability Starter Kit employs rule-based and unsupervised ML models to monitor data quality across various types, including tabular, structured, and unstructured data. It integrates with ETL/ELT pipelines and data platforms, providing real-time metrics on data ingestion, pass-through rates, and the detection of schema changes, ensuring thorough data quality management and issue identification.
Industries
Digital commerce
Customized for the digital commerce sector, our starter kit swiftly rectifies data discrepancies, ensuring precise customer insights, enhanced sales strategies, and streamlined inventory management processes.
Manufacturing
Tailored for the manufacturing sector, our starter kit optimizes operational efficiency by monitoring production data, facilitating proactive maintenance, and reducing downtime for increased productivity.
Pharma
Ideal for the pharma industry, our starter kit accelerates data transformation, enabling agile responses to regulatory changes, optimized research and development processes, and improved supply chain efficiency for enhanced patient safety and drug development.
Insurance
Designed for the insurance industry, our starter kit streamlines claims processing, detects anomalies, and enhances data integrity, ensuring efficient operations, fraud mitigation, and enhanced customer satisfaction.
Wealth management
Customized for wealth management, our starter kit enhances decision-making processes by accurately monitoring financial data, mitigating risks, and ensuring regulatory compliance for improved client trust and operational efficiency.
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