DQGateway

Data Quality is intentional. A conscious and continuous effort to ascertain the
availability of accurate, complete, and reliable data for business decisions at the right time.
Kairo’s DQGateway is AI driven, ‘No Code’ solution which addresses every
aspect of data quality effectively.

Data Accuracy and
Cleansing

  • Extensive data profiling – missing values, inaccuracies, duplication, inconsistencies, outliers, security issues, privacy violations…etc, through pre-configured profiler and/or custom rules.
  • CI/CD integrated and/or scheduled automated checks.

Integration

  • Integrate with over 50 major data platforms and tools, ensuring a 70% faster data quality implementation process.
  • Integrate with all leading DevOps tools to create effective CI/CD pipeline

Real-time Data monitoring
and Reporting

  • Gain insights from detailed data quality reports, leading to a 60% improvement in data governance and strategy formulation.
  • Subscribe to events, tasks, work units to get real-time alerts.
  • Did we mention it goes beyond reporting and provides insights on your data too?

Scalable Data
Quality

  • Scale data quality operations seamlessly, no performance degradation.

Enhanced Data Security &
Compliance

  • Ensure 100% compliance with industry data protection standards, reducing the risk of data breaches.
DQGateway reflects our unwavering commitment to data quality. However, rest
assured, these standards are integral to our Data Quality Testing services, extending beyond DQGateway.

Data Democratization Testing

Making quality data accessible and in a consumable form such
that it helps in decision making.

Objectives set for ‘Data Democratization’ define

  • Access Control, Ease of Use, User Experience, Insights Effectiveness…

Governance

  • Role, Access level, Data Quality, Privacy, Security, Regulatory Compliance

Usability

  • User Interface Effectiveness, Ease of Use, Customizing interface / Self-serving, Facility to store / save

Insights

  • Data summarization / consolidation, dicing-slicing of data, visualization, data staleness

Data tool and platforms

  • Ease of use, performance against Governance, Usability and Insights, Data Virtualization, Data Federation, Self-Serving interface, Data Quality Enforcement…
We understand that organizations are interested in democratizing “insights”
and not data per se. That is why, we go beyond fundamentals to include,

Track user adoption, Analyze user activity, Measure data discovery and sharing

Conduct surveys and interviews, offer training and workshops, Monitor data-driven decision-making

Track data-driven initiatives, Measure the impact of these initiatives, Analyze sentiment about data use etc.

Test Data Management

Artificial but realistic data sets that mimic the characteristics of actual data, organizations can overcome data limitations, ensure compliance, and accelerate their testing and development cycles. Synthetic Data not only mitigates privacy concerns but also enables comprehensive testing in controlled environments, fostering innovation and efficiency across various industries.

However, ensuring the validity of Synthetic Data is important as otherwise it will result in undesired, and erroneous outcomes.

We employ elaborate measures to ensure the validity of
Synthetic Data by checking for,
  • Preservation of statistical properties
  • Preservation of correlations among variable
  • Preservation of data patterns
  • Preserve categorical / ordinal variables representation
  • Identification of outliers
  • Preservation of data dependencies