Boost data reliability

Boost data reliability: How DataQuantifyX automates data process monitoring

In today's data-driven world, data reliability and quality are vital factors. However, data processing can be complicated and error-prone, leading to incorrect insights and decisions based on incorrect information. Enter DataQuantifyX, which offers integrated solutions for data observation and testing excellence.

DataQuantifyX provides a full suite of tools to automate data observation and quality control throughout your organization. It gives real-time visibility into your data pipelines, improves data quality, makes this automation smarter, and centralizes management.

Monitor Your Data in Action

DataQuantifyX comprehensively monitors data, alerting users at once to system failures or disconnections in the toolchain and any significant issues with data. Real-time notifications give your team the power to quickly spot and address any data-related mishap.

The platform now automatically generates and executes data quality testing. Intelligent automation automatically generates and runs data quality tests so your team can spend time wisely and efficiently solving issues rather than setting up tests step-by-step. This ensures end-to-end trust and reliability of the data. It provides data integrity across the whole pipeline, from source to end. This builds confidence in your data, tools, and systems for accurate decision-making and trustworthy results.

DataQuantifyX also achieves unified visibility across all your data lakes, making dealing with complex data environments easier. With a single pane of glass that covers observation, troubleshooting, and resolution in depth, your company and teams can work together much more quickly.

How DataQuantifyX Data Observer works

DataQuantifyX's Data Observer continuously observes data from its source to its delivery value, keeping an eye on all tools, teams, and environments that arise in this process to catch any issues immediately. It monitors real-time data from the source through production, monitoring every tool and team in the entire data pipeline and every dataset. When problems arise, it collects trouble alerts from Data Stream through every tool in production and every user, ensuring wide-ranging views with full system coverage. It also promises quick repair time that avoids disruption of production data flows.

Effortless production monitoring is a key benefit, allowing teams to identify and alert to issues before they escalate. A shortage of timely alerts and constant production system errors can lead to frustration. With AI-based SQL execution and complete end-to-end data-point backtracking, the Data Load component at the user site can automatically complete many data quality tests. Should errors occur, they surface appropriately at the user's computer. This eliminates customer-facing bugs, increases data reliability, reduces mistakes, and lets your teams function more freely.

DataQuantifyX also provides tools for significant production changes by data teams, with DataQuantify Designer supporting early-stage QA validation to ensure that code and configuration changes are sound. When these two products are combined as part of functional, unit, and regression tests in development, the time from change to production is drastically reduced and incidence rates are minimized.

What does a DataQuantifyX QA Tester do?

The DataQuantifyX QA Tester has five tasks to protect data quality:

  • Data Profiling: Analyzes datasets to understand their structure, content, and quality.
  • Hygiene Checks on New Datasets: Meticulous checks using 45+ data profiling metrics and 25+ hygiene detectors to ensure datasets are clean and reliable.
  • Automatic Generation of Validation Tests: Generates tests without writing code, automatically finding test cases.
  • Ongoing Testing of Refreshed Data: Continuously tests updated data using 32 AI-powered validation tests for anomalies, schema conformance, and data freshness.
  • Continuous Monitoring for Anomalies: Detects anomalies and alerts users and clients in real-time to ensure high operational efficiency.

By promptly identifying and removing data issues, the DataQuantifyX QA Tester enables fast, automated query execution and supports teams in maintaining confidence in their data assets.

Central Hub for Data Quality Oversight

DataQuantifyX is a central hub for data quality control, allowing you to manage business rules and validation processes across the data lifecycle. It offers direct insight into quality across multiple locations and allows teams to implement meaningful improvements across workflows.

DataQuantifyX Agents

DataQuantifyX Agents integrate with ETL, ELT, BI, data science, visualization, governance, and analytics tools. They gather and log operational data from connected systems, such as run times and start/stop events, and pass it to the DataQuantifyX Observer for continuous monitoring and analysis.

Conclusion

DataQuantifyX boosts the stability of your data and optimizes your company's data processes. Stratilligent offers excellent support to implement and scale your data reliability journey.

For additional information or to schedule your demo, please contact us at contact@stratilligent.com.