Monitor your database for data quality issues and get a daily report

Every day the system checks your database for missing data, duplicates, and outliers, then emails a clear pass or fail report.

How the work actually flows

It branches. Every path runs; runs once per each data quality check type.

Pattern: Multiple Instances with a priori Design-Time Knowledge (13) ยท Parallel Split (2)

flowchart TD trig(["daily schedule"]):::trigtime s0[["run quality checks on table"]]:::mi s1["score each check result"]:::task s2["compile html report"]:::task trig --> s0 s0 --> s1 s1 --> s2 gx{"+ where to send report"}:::gate s2 --> gx p00["send report by email"]:::task gx -->|"email report"| p00 p10["log results to sheets"]:::task gx -->|"log to sheets"| p10 p00 --> out p10 --> out out[/"daily quality report delivered"/]:::out pay{{"bad data caught before it causes damage"}}:::pay out --> pay classDef task fill:#e7f6fe,stroke:#34b8f0,color:#2c2a29 classDef svc fill:#f6f8fa,stroke:#7c8795,color:#2c2a29 classDef mi fill:#e7f6fe,stroke:#0079a8,color:#2c2a29,stroke-width:2px classDef human fill:#fff,stroke:#0079a8,color:#0079a8 classDef store fill:#f6f8fa,stroke:#0079a8,color:#2c2a29 classDef trig fill:#00a4eb,stroke:#0079a8,color:#fff,font-weight:bold classDef trigtime fill:#00a4eb,stroke:#0079a8,color:#fff,font-weight:bold classDef trigdata fill:#8ad4f5,stroke:#0079a8,color:#06314c,font-weight:bold classDef gate fill:#fff,stroke:#e8a23d,color:#6b4708,font-weight:bold classDef out fill:#1f9d6b,stroke:#167a53,color:#fff,font-weight:bold classDef pay fill:#06314c,stroke:#021f33,color:#fff
Starts itA stepRuns once per itemEvery pathResultPayoff
Build size
Advanced

A larger build with multiple systems, AI reasoning, and custom rules.

Business functions
Email AutomationSpreadsheet & Database OpsReporting & Analytics
Connects
PostgreSQLGmailGoogle Sheets
Featured in

The problem it solves

Bad data quietly creeps into your database, duplicate records, missing fields, weird outliers, and nobody notices until a report is wrong or a customer complains. You need a way to catch these problems before they cause real damage.

Who it fits

Data analysts and analytics engineers responsible for keeping business data trustworthy.

How it works

  1. Every day, the system runs a set of quality checks against your database table
  2. It checks for null values, duplicate records, unusual row counts, and outliers
  3. Each check is scored as pass, warn, or fail
  4. Results are compiled into a clear HTML report
  5. The report is emailed out and logged to Google Sheets for tracking over time
What you get

Data problems caught before they spread

You get a daily pass or fail report on missing data, duplicates, and outliers across your database.

What you get

A daily data quality report by email, plus a historical log in Google Sheets.

What you need

A SQL database such as Postgres or MySQL, a Gmail account, and a Google Sheets account.

We can build this. But should you?

The hard question is not how to build it. It is whether this is the right thing to build first.

That is what a Fractional Chief AI Officer figures out with you, before anyone writes a line of code.

Let's Talk Strategy

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