Use Cases for Common Data Quality Problems

Your data quality problems, solved.
When was the last time you looked at your data, and thought — this can’t be right? The current state of data quality is manual, reactive, and inherently unscalable.
At Qualytics, we address critical challenges faced by businesses who rely on data integrity by solving the need for a proactive, accessible, and collaborative approach to data quality.

Use Cases for Common Data Quality Problems

Your data quality problems, solved.
When was the last time you looked at your data, and thought — this can’t be right? The current state of data quality is manual, reactive, and inherently unscalable.
At Qualytics, we address critical challenges faced by businesses who rely on data integrity by solving the need for a proactive, accessible, and collaborative approach to data quality.

Here are a few data quality challenges enterprises often face and how Qualytics addresses them.

I want to automatically profile my historic data to create a robust, dynamic profile metadata.

Automatic Profiling of Historical Data

Qualytics automates the generation of profile metadata for every single field from every table and database it has access to, and calculates statistical metadata per datatype automatically.
I want automatic data quality rules generated so that my team doesn’t need to spend time authoring them from scratch.

Automatic Generation of Data Quality Rules

Qualytics utilizes the automated metadata profile and historic data actuals to infer deep, contextual DQ rules. There are often 3-5 rules generated per field, along with additional rules that span inter-column and cross-datastore relationships.
I want my SMEs to be able to write DQ rules directly without needing a data engineer to write code.

Low / No-Code UI for Data Quality Rules

Qualytics provides a low/ no-code UI in which users can interact with the full lifecycle of Data Quality, from reviewing anomalies to writing their own DQ rules easily without having to write code. Or if they choose to, they have the ability to write complex SQL rules directly into the platform
I need my data quality platform to work within the security and privacy constraints that we have.

Security & Privacy Compliance

Qualytics is purely containerized and provides enterprise-level flexibility across SaaS. Hybrid or on-prem deployment models.
I want to automate downstream remediation actions so we don’t need to pull in various teams when anomalies occur.

Automated Downstream Remediation

Qualytics is the only solution in the market that truly enriches a target datastore with anomalies and metadata. This capability, combined with sophisticated tagging and notification workflows enables users to take and automate simple to complex downstream remediation actions.
I am worried about Schema Drift and its downstream impact.

Schema Drift Monitoring

Among many data quality factors, Qualytics actively infers and monitors Expected Schemas. When there is a drift from the original expectation, an anomaly and/or downstream remediation is able to be taken seamlessly.
I want my team to know before a Data Stream is going to fail to function as designed.

Active Observability and Specialized Checks

Active observability ensures that incoming data meets expectations. Specialized checks: Time Series Distribution, Metrics
I need to know when we run ETL or replicate data from sources (databases. files) to targets (warehouses, lakes, databases), data is of expected equivalency and my data pipelines and scripts worked as expected.

Data Equivalency in ETL Processes

Qualytics provides full data diff functionality across datastore technologies. This ruletype ensures that data integrity is maintained when data is expected to be replicas of each other. With or without unique identifiers, Qualytics performs complex diff checks to highlight mismatching or missing fields between a source and a target.
I need actionable feedback and KPIs on the health of my data.

Actionable Data Health Insights with Data Confidence Index™

Qualytics provides full data diff functionality across datastore technologies. This ruletype ensures that data integrity is maintained when data is expected to be replicas of each other. With or without unique identifiers, Qualytics performs complex diff checks to highlight mismatching or missing fields between a source and a target.
I need to centrally manage common rules to be used across my enterprise.

Centralized Rule Management

Qualytics provides Check Templates through the Check Library, enabling centralized management of rules with optional edits of parameters at the edge.
I want to automatically profile my historic data to create a robust, dynamic profile metadata.

Automatic Profiling of Historical Data

Qualytics automates the generation of profile metadata for every single field from every table and database it has access to, and calculates statistical metadata per datatype automatically.

Schema Drift Monitoring

Among many data quality factors, Qualytics actively infers and monitors Expected Schemas. When there is a drift from the original expectation, an anomaly and/or downstream remediation is able to be taken seamlessly.

Automatic Generation of Data Quality Rules

Qualytics utilizes the automated metadata profile and historic data actuals to infer deep, contextual DQ rules. There are often 3-5 rules generated per field, along with additional rules that span inter-column and cross-datastore relationships.

Active Observability and Specialized Checks

Active observability ensures that incoming data meets expectations. Specialized checks: Time Series Distribution, Metrics

Low / No-Code UI for Data Quality Rules

Qualytics provides a low/ no-code UI in which users can interact with the full lifecycle of Data Quality, from reviewing anomalies to writing their own DQ rules easily without having to write code. Or if they choose to, they have the ability to write complex SQL rules directly into the platform

Data Equivalency in ETL Processes

Qualytics provides full data diff functionality across datastore technologies. This ruletype ensures that data integrity is maintained when data is expected to be replicas of each other. With or without unique identifiers, Qualytics performs complex diff checks to highlight mismatching or missing fields between a source and a target.

Security & Privacy Compliance

Qualytics is purely containerized and provides enterprise-level flexibility across SaaS. Hybrid or on-prem deployment models.

Actionable Data Health Insights with Data Confidence Index™

Qualytics provides full data diff functionality across datastore technologies. This ruletype ensures that data integrity is maintained when data is expected to be replicas of each other. With or without unique identifiers, Qualytics performs complex diff checks to highlight mismatching or missing fields between a source and a target.

Automated Downstream Remediation

Qualytics is the only solution in the market that truly enriches a target datastore with anomalies and metadata. This capability, combined with sophisticated tagging and notification workflows enables users to take and automate simple to complex downstream remediation actions.

Centralized Rule Management

Qualytics provides Check Templates through the Check Library, enabling centralized management of rules with optional edits of parameters at the edge.

Ready to join the Data Revolution?

Qualvtics is more than a data quality solution — it’s a game-changer for your business.
Don’t let outdated data processes hold back your company’s potential. Qualytics is here to empower your enterprise with a proactive, comprehensive approach to data quality. Discover the impact of superior data confidence in your operations, decision-making, and innovation capabilities by reaching out to our team to get the conversation started.
Don’t let outdated data processes hold back your company’s potential. Qualytics is here to empower your enterprise with a proactive, comprehensive approach to data quality.
Discover the impact of superior data confidence in your operations, decision-making, and innovation capabilities by reaching out to our team to get the conversation started.