the cognistx blog

How AI finds, Corrects Data Issues, Fueling Powerful Insights and Operational Efficiency

February 1, 2023
Raminder Dhiman

Data is the lifeblood of the modern enterprise. However, bad data can be poisonous, leading your teams off in the wrong direction and causing you to lose thousands of dollars in revenue. That’s why quality is critical. With the Cognistx Data Quality Engine (DQE), you can automate the time-consuming processes that are holding you back and restore trust in the insights your systems can provide.

AI-Powered Anomaly Detection

Can you trust your data? According to our research, 85% of senior managers say they don’t believe their systems use reliable data. One of the main capabilities of our DQE is AI-powered anomaly detection. This means that the platform has the ability to use artificial intelligence models to predicatively and automatically identify inaccuracies, incompleteness, inconsistencies, and unreliability issues with your data.

In data science, inaccuracy issues refer to data that does not correspond to the reality of the situation. Incompleteness errors are gaps in your data that prevent you from accurately extracting insights. Inconsistencies occur when multiple data points describing the same situation are in conflict with each other. Finally, unreliability issues deal with the data as it flows through the pipeline. Data must be protected from any kind of intentional or unintentional tampering as data moves through your organization.

Identifying these issues automatically saves your organization enormous amounts of time. Instead of your staff having to manually search for errors throughout tables and databases, these errors will be automatically identified. Cognistx’s DQE identifies the issues that your team can review and correct.

Data Auto-correction

Cognistx goes beyond just identifying errors automatically; our powerful solution can also fix them.

Data auto-correction provides normalizing, cleaning, and data completion features that can improve the consistency and reliability of your data. Normalization refers to the practice of systematically grouping similar values into common values and formatting your data to be more consistent. Data cleaning is the process of removing problematic data points that could skew your results. All incorrect, corrupted, or improperly formatted data is removed in this process.

Finally, data completion involves locating any missing data to ensure that the analysis can be safely and accurately performed. Cognistx goes beyond just identifying errors automatically; our powerful solution can also fix them.

What's a Data Quality Engine (DQE)

Research has shown that US companies are losing trillions of dollars each year as a direct result of erroneous and poor-quality data. This is not acceptable. In today’s fast-paced, highly-competitive global economy, you cannot afford to be left behind by your competitors as you scramble to sort out your data. High-quality business intelligence can give you the edge you need to succeed.

Cognistx DQE processes more than 51 million data records each year, saving companies, on average, $5 million each year. This is the power of an AI-ready data quality platform.

Ready to learn more?

Contact Raminder Dhiman,, to learn more about how the Data Quality Engine and to set up a demo.

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