Turn column metrics into actionable insights
digna Data Analytics calculates higher-level statistics, such as trend, volatility, on core data statistics like record counts, missing values, or averages.
How digna Data Analytics Works
This module performs two types of analysis on your data:
✦ Trend Analysis
✦ Volatility of your data
It helps you understand how these foundational metrics behave over time by applying statistical analysis to reveal patterns, stability, and change.
You can monitor how the number of records, number of missing values, min/max, sum, average, and other column-level statistics evolve - and then analyze their trends, detect fluctuations, and compare periods of relative stability or disruption.
Ideal for long-term monitoring, this module gives you a deeper view into the dynamics of your data beyond raw numbers.
Enterprise Use Cases for digna Data Analytics
The calculated metrics from this module can be used for both Data Quality and Operational/Business KPIs across different industries. Below are examples in the Banking and Retail industry.
Industry | Data Quality | Operational/Business KPIs |
|---|---|---|
Banking | Spot data downtime or volume anomalies across departments | Track fluctuations in financial metrics across time segments |
Retail | Track missing value rates over time to spot degradation. e.g, Identify rising NULLs in CRM data post-migration. | Analyze sales trends by product/region. e.g Highlight top-performing SKUs with absolute/relative growth. |
Outcome: digna not only allows you to do ad-hoc analysis for trend and volatility, both for Data Quality and Business/Operational KPIs, but it also allows you to set alerts so you get notified.
