Visualize data - Deepline

Inspect one exported dataset and produce a small summary that preserves failed and unresolved rows.

Review what the workflow produced before you hand rows to sales, marketing, or ops. The SDK is not only for enrichment. It should help you answer:

Which accounts are new this week?
Which segments have the strongest signal?
Which titles need a different workflow?
Which rows failed and need human review?
Which accounts should go to Salesforce now?

Start with an output CSV

Export a completed file-backed Play before you analyze it. Batch CSV enrichment owns the run and export procedure. This page starts after you have output/accounts-enriched.csv. Open the file first. You are looking for boring things:

Check	Why
row count	Make sure no records disappeared
segment	Know which workflow path each account took
signal columns	See why the account is interesting
owner or next step	Confirm where the record goes next
error/review columns	Separate good output from uncertain output

Make a quick chart in Python

Use this when you want a fast view before building anything permanent.

import pandas as pd

df = pd.read_csv("output/accounts-enriched.csv")
print(df["segment"].value_counts())
print(df["next_step"].value_counts())

That is enough to answer: “Did this workflow find the kind of accounts we wanted?”

Common GTM views

View	Useful when
accounts by segment	You route enterprise and mid-market differently
titles by seniority	VPs and directors get different plays
signal by source	You want to know which signal found the account
output by owner	You are sending tasks to Salesforce
failed rows	You need a human review queue

Keep visualization close to the workflow

Recommended folder shape:

workflows/
  weekly-new-accounts.play.ts
  input/accounts.csv
  output/accounts-enriched.csv
  output/accounts-summary.csv

The workflow creates the enriched list. The summary tells your team what changed.

Verify this task

Check source IDs and required columns before grouping. Count unknown values explicitly rather than allowing chart defaults to hide them. Ask your agent to explain a failed check before repeating work or expanding the scope.

​Start with an output CSV

​Make a quick chart in Python

​Common GTM views

​Keep visualization close to the workflow

​Verify this task

Start with an output CSV

Make a quick chart in Python

Common GTM views

Keep visualization close to the workflow

Verify this task