Businesses must increase their enterprise data quality to expedite their Digital Transformation. Data quality is critical for any business that uses data for strategic and operational decisions. Data quality issues jeopardize organizational decision-making. Addressing data quality issues should therefore be a top priority.
With successful Digital Transformation, business processes rely more on automation, analytics, and AI-driven systems. You become a more data-driven enterprise and all these systems require high-quality data. As businesses utilize data analytics to guide business decisions more routinely, the emphasis on data quality (DQ) in enterprise systems has intensified.
With data democratization, non-technical users are now empowered to gain insights with easy access to clean data. No longer do business analysts and executives have to go through the IT department to get the needed data; instead, they can independently access contextually relevant high-quality data. This enables agile organizations to compete with real-time critical business insights, allowing them to make more informed decisions quickly.Unlocking the power of data-driven insights
At Kairos, we understand that data quality is critical for digital enterprises. We are committed to helping organizations with their Data Quality management needs. Our visual no-code automated solution, “DQGateway,” is purpose-built for Data and Business Analysts and is a testament to that commitment. “DQGateway,” provides unparalleled insight into your data. Our solution can identify errors, discrepancies, and irregularities in your data that may adversely impact its accuracy. It provides a comprehensive snapshot of data quality to all stakeholders and promotes data democratization.
Our data quality checks validate the accuracy and completeness of the data before it gets into the Data Warehouse or Data Mart, as well as make sure there are no errors in the transformation process from raw datasets to structured ones. This step ensures that any analysis performed on the dataset will produce reliable results.
Data Quality Checks
- Verify the accuracy & completeness of the data
- Automate checks for any errors or inconsistencies
- Analyze structure & characteristics of the data
- Visualize correlations between various data variables
Data Integration Tests
- Verify correctness of ETL processes.
- Check data compatibility & consistency.
Analytics Event Testing
- Verify correctness of analytic event data at source.
- Check event data coverage.
Tools & Expertise
Automated Analytics Testing
Comprehensive Test Coverage
Cost & Time Optimization
Successful Data-driven Outcomes
Let’s create a better tomorrow. The future starts with us.