dvsum lp dq monitoring high quality data consistently

Solutions

Deliver high quality data for analytics
on-time, every-time

Don't let bad data take you by surprise. Get alerts, root-cause and resolve data issues impacting Analytics faster.

Challenge

Everyone understands importance of Data Quality but no one wants to own it. When data changes unexpectedly, data pipelines fail, reports are delayed, inaccurate data shows up on reports, analysts spend valuable root-causing source of bad data.  It results in  loss of trust in data, slower decision making, and productivity loss.
You need an easier way to manage and monitor data quality. DvSum can help.

Solution Highlights

connect

Automated
Data Quality Checks

anallytics

Data Quality Monitoring and integration to Data Pipelines

monitor

Self-service Root-cause analysis of data issues

scan

Impact Analysis of data model changes

How DvSum helps you deliver high quality data
on-time, every-time

Features

AI-generated
Data Quality Checks

Using a combination of statistical anomaly detection and rule-based algorithms, DvSum automatically recommends and can set data quality monitoring checks. These checks can track variances to data types, empty values, volume, shift in data distribution.

solution2 automated ai generated data quality checks
solution2 automated data quality monitoring data drifts schema drifts
Features

Automated Monitoring
and Alerts

As new data lands in your data lake or data warehouse, DvSum checks for unexpected variances in data formats, quality, volume and distribution. Checks can run automatically or can be integrated into your Data Pipelines. Relevant teams are notified for unexpected variances. Alerts are also available in the Data Catalog.

Features

Self-service Root-cause analysis of data issues in Reports

DQ Alerts are highlighted in the Data Catalog. Combined with Data Lineage, Data Analysts can quickly find source of bad data impacting their reports and get issues resolved fast.

Features

Impact Analysis of data model changes

Data requirements constantly evolve and requires changing data models. Data Engineering teams and Data Architects can easily and comprehensively identify and manage downstream impact of source data model changes. Deliver new capabilities without breaking existing data pipelines and reports.

Monitor Data Quality.

High quality data for Analytics on-time, every-time