Demo data, toggle to recompute from your own activity.
Files in pipelineThe total number of files submitted for preparation this quarter, across every connected source system. It counts everything that entered the pipeline, whether or not it was eventually approved. It captures the raw workload the data team is being asked to handle.
2,140
+18%
Q2 submissions
Volume up as three new source systems onboarded.
Ingestion-readyThe share of submitted files that cleared the ingestion gate on the first pass, against an 80% target. It is files approved without rework divided by total files submitted. It captures how clean your incoming data is before any manual fixing, the single best read on data readiness.
71%
+6 pts
target ≥ 80%
Most files pass; PII redaction is the main gap.
Avg prep effortThe average number of analyst hours spent preparing one file before it is approved. It is total hands on prep time divided by files processed. It captures the human cost of data prep, the number automation is trying to drive down.
2.4 hrs
−33%
per file · was 3.6
Automated profiling cut hands on cleanup time.
Blocked / rejectedThe number of files held or rejected at the ingestion gate this quarter, shown as a share of intake. A file is blocked when it fails a policy check, most often unredacted PII or unverified provenance. It captures the data you cannot safely use yet, and why.
214
−4%
10% of intake
Mostly sensitive data and licensing holds.
Ingestion funnel
Files received this quarter and where they end up
Received2,140 · 100.0%
Raw files submitted from all sources
Profiled2,086 · 97.5%
Readable & structure detected
Cleaned1,842 · 86.1%
Dedup, missing value & format fixes
Guideline-cleared1,597 · 74.6%
PII, provenance & metadata resolved
Approved1,521 · 71.1%
Passed the ingestion gate
Handed to RAG1,521 · 71.1%
Embedded into the vector database
Where the effort goes
Avg analyst hours per file
2.4
hrs/file
- Profiling0.3 hrs13%
- Cleaning0.8 hrs33%
- PII review0.6 hrs25%
- Guideline sign off0.4 hrs17%
- Chunk tuning0.3 hrs13%
Readiness trend
% of files approved on first pass vs. target
What the numbers mean
Effort is paying off
Automated profiling dropped average prep time from 3.6 to 2.4 hours per file while volume grew 18%.
One gap remains
Readiness sits at 71% against an 80% target. The bottleneck is PII redaction and licensing sign off, not data quality.
Recommended focus
Standardize redaction and pre clear common source licenses to lift first pass approval above target, which means fewer conflicting answers downstream in the RAG Evaluator.
ROI calculator
What automating data prep is worth at your volume
Effort / file
1.9 hrs
was 3.6 hrs
Hours saved / mo
840
analyst hours
Saved / year
$655k
at $65/hr
Automation here = the share of profiling, cleaning, and PII clearance the Data Lab handles before an analyst touches the file.