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Data Quality Agent: continuous monitoring of data feed quality

Business question
Is the data feeding our KPIs and analyses complete, consistent and up to date, and how can we know without asking IT every time there is a doubt?
What's at stake
For a multichannel insurer whose complaint KPIs draw on five data feeds: about 740 to 1,290 hours of business and IT checking freed a year, or $59k to $103k.
Data required
All data feeds ingested into the cockpit (CRM, contracts, billing, phone interactions, emails, chat, complaints, reviews, web and app), with their loading metadata: expected vs received volumes, date and time of last load, fill rate per field, customer and contract identifiers, reference lists of allowed values.
Sample result
An agent that continuously checks every data feed and detects missing fields, inconsistencies, duplicates and feed interruptions. Each anomaly is translated into business language (for example: 3,200 phone channel calls not linked to a customer since Monday) with its impact on the affected KPIs. All sources are harmonized into a single working format, and business teams get a data reliability dashboard they can use without going through IT or the data team.
Demo · 1 min
Case study

A multichannel insurer tracked its complaint rate from five sources (CRM, phone, email, chat, web form). Every month the customer service teams saw gaps between the cockpit figures and internal reporting, and could not trace their origin without opening a ticket with IT, which took several days to answer.

The Data Quality Agent was connected to all incoming feeds. It checked fill rates, daily volumes against history, duplicate customer IDs and consistency between interaction dates and contract dates. Each anomaly was reported in a message business users could read, linked to the feed concerned and the KPIs affected.

Within four weeks, the agent uncovered a silent break in the chat feed that followed a change to the tool (about 18% of conversations with no customer ID), as well as a 6% duplicate rate on phone calls caused by double logging. After the fix, the gap between the cockpit and internal reporting went from 9% to under 1%, and the time to detect a feed break went from several weeks to under 24 hours. The teams stopped asking IT to interpret the data: verification requests fell by more than 70%.

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