Dataset to Executive Report

Store Positioning

Store title: Dataset to Executive Report

Short description: Turn structured JSON, CSV, or Apify dataset rows into deterministic executive summaries, KPI rollups, anomalies, recommendations, and Markdown report artifacts.

SEO title: Dataset to Executive Report — evidence-backed report and intelligence workflow

SEO description: Turn structured JSON, CSV, or Apify dataset rows into deterministic executive summaries, KPI rollups, anomalies, recommendations, and Markdown report artifacts. Use it to turn sources into decision-ready rollups, executive summaries, and evidence-backed recommendations.

Categories: DEVELOPER_TOOLS

Keywords: dataset, executive, report, apify actor, structured data, csv qa, data qa, dataset qa, api testing, premium report/intelligence actor

Fixed-Inclusive PPE Pricing

This actor uses pay-per-event pricing. Event prices include Apify platform usage; users are not expected to pay a separate platform-usage pass-through charge for the configured pricing model.

• Tier: R1 — Premium report/intelligence actor
• Primary event: row-analyzed at $0.00892 base
• Default max charge: $25.00
• Store discounts: FREE/BRONZE base, SILVER discounted, GOLD deepest approved discount

Event set:

• actor-start: base $0.02000, GOLD $0.01600. Dataset To Executive Report: charged when actor start is completed. The price includes Apify platform usage; no separate usage pass-through is intended.
• row-analyzed: base $0.00892, GOLD $0.00714. Dataset To Executive Report: charged when row analyzed is completed. The price includes Apify platform usage; no separate usage pass-through is intended.
• finding-emitted: base $0.00892, GOLD $0.00714. Dataset To Executive Report: charged when finding emitted is completed. The price includes Apify platform usage; no separate usage pass-through is intended.
• report-section-generated: base $0.03600, GOLD $0.02880. Dataset To Executive Report: charged when report section generated is completed. The price includes Apify platform usage; no separate usage pass-through is intended.
• executive-report-generated: base $0.25000, GOLD $0.20000. Dataset To Executive Report: charged when executive report generated is completed. The price includes Apify platform usage; no separate usage pass-through is intended.

Public Task Concepts

• Build a Dataset to Executive stakeholder brief from source rows
• Prioritize Dataset to Executive findings with evidence and confidence
• Summarize Dataset to Executive metrics into recommended actions
• Prepare a Dataset to Executive source table with uncertainty notes
• Export Dataset to Executive decision rows for review

Turn structured JSON, CSV, or Apify dataset rows into an executive-ready report: KPI rollups, grouped performance tables, anomaly signals, template-based recommendations, and Markdown/JSON artifacts.

What does Dataset To Executive Report do?

Dataset To Executive Report converts rows you already have into a decision-ready summary without calling paid LLMs or external APIs. It is built for operators, agencies, founders, and internal automation workflows that need a repeatable report from exports, scraper output, sales tables, support queues, or monitoring datasets.

It produces:

• A single structured dataset row with the complete report.
• Overall KPI rollups with target and previous-period comparisons.
• Ranked group rollups by fields such as region, segment, team, source, or category.
• Rule-based anomaly detection for target misses, adverse changes, and group outliers.
• Deterministic executive summary text, key findings, and recommendations.
• Optional Markdown and JSON report artifacts in the default key-value store.

Why Use This Actor

Option	Best For	Output	Setup	Cost Control
Dataset To Executive Report	Repeatable executive summaries from structured rows	Dataset row plus Markdown/JSON report	Zero-config sample, JSON, CSV, or dataset ID	L1 PPE: $6.50 per 1K processed rows
Spreadsheet pivot tables	Manual one-off analysis	Charts or sheets	Manual formulas and formatting	Free, but manual time grows
BI dashboard setup	Long-lived dashboards	Interactive dashboard	Requires modeling, permissions, and maintenance	Usually subscription-based
Ad-hoc LLM summaries	Narrative drafts	Text summary	Requires prompt design and data handling	Can become expensive and inconsistent

This actor is intentionally deterministic. The same input produces the same style of report, with no hidden model call, no vendor key, and no scraped external data.

How to Use

Run it with the default input to see a small revenue report, or provide your own rows.

{
  "reportTitle": "Monthly Revenue Executive Brief",
  "records": [
    {
      "region": "North America",
      "segment": "Enterprise",
      "revenue": 184000,
      "previousRevenue": 166000,
      "pipeline": 312000,
      "conversionRate": 0.18,
      "churnRisk": 0.06
    }
  ],
  "groupBy": ["region", "segment"],
  "includeReport": true,
  "llmMode": "off"
}

1. Open the actor input.
2. Paste JSON records, paste CSV text, or provide an Apify dataset ID.
3. Configure KPI metrics if the inferred numeric fields are not enough.
4. Choose grouping fields such as region, segment, team, source, or category.
5. Run the actor and use the default dataset row or KVS report artifacts.

Revenue Brief

Use revenue, previousRevenue, pipeline, conversionRate, and churnRisk metrics to create a monthly leadership brief.

Support Operations Brief

Use fields such as team, priority, ticketsClosed, slaRate, backlog, and csat to summarize service performance.

Dataset ID Brief

Provide datasetId and a low maxItems cap to summarize an existing Apify dataset sample before creating a scheduled reporting task.

Input Configuration

Field	Type	Default	Description
records	array	sample revenue rows	Inline JSON object rows.
csvText	string	empty	CSV-like text with a header row. Used before records when provided.
datasetId	string	empty	Optional Apify dataset ID. Used before inline data when provided.
reportTitle	string	Monthly Revenue Executive Brief	Title for dataset and Markdown output.
reportContext	string	empty	Optional metadata for your own workflow. No LLM call is made.
audience	string	executive	Audience label. Supported values: executive, operator, sales, investor, board.
groupBy	string[]	["region", "segment"]	Fields used for group rollups.
metrics	array	revenue sample metrics	KPI definitions: field, label, aggregation, format, target, previous field, good direction.
maxItems	integer	50	Maximum source rows to process. Hard cap: 10,000.
topGroups	integer	8	Maximum ranked group rows included.
anomalyThresholdPct	number	0.25	Relative anomaly threshold. 0.25 and 25 both mean 25%.
includeReport	boolean	true	Write Markdown and JSON artifacts after charges succeed.
llmMode	string	off	Only off is supported. External LLM calls are disabled.
maxChargeUsd	number	10	Hard run-level PPE cap; stops before paid work or withholds uncharged reports.
debug	boolean	false	Enables additional logs.

Output Format

The actor pushes one report row to the default dataset.

{
  "reportId": "7b8f8f899b6df84c",
  "reportTitle": "Monthly Revenue Executive Brief",
  "sourceType": "inline-json",
  "sourceId": "inline-records",
  "rowsProcessed": 5,
  "status": "watch",
  "headline": "Revenue is $489,000, with watch-list items that should be reviewed before decisions are finalized.",
  "executiveSummary": "Processed 5 structured rows into a deterministic executive report...",
  "keyFindings": ["Revenue is $489,000 (+4.5% vs previous) and is classified as watch."],
  "kpis": [],
  "groupRollups": [],
  "anomalies": [],
  "recommendations": [],
  "dataQualityWarnings": [],
  "markdownReport": "# Monthly Revenue Executive Brief\n...",
  "artifactKeys": ["EXECUTIVE_REPORT.md", "EXECUTIVE_REPORT.json"],
  "pricingTemplate": "L1",
  "pricingEventName": "row-analyzed",
  "llmUsed": false
}

When includeReport is true, the key-value store also contains:

• EXECUTIVE_REPORT.md: Human-readable Markdown report.
• EXECUTIVE_REPORT.json: Full structured report.
• EXECUTIVE_REPORT_SUMMARY.json: Compact run summary.

Integration Examples

Python

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("junipr/dataset-to-executive-report").call(run_input={
    "records": [{"region": "EMEA", "revenue": 128000, "previousRevenue": 121000}],
    "metrics": [{
        "field": "revenue",
        "label": "Revenue",
        "aggregation": "sum",
        "format": "currency",
        "previousField": "previousRevenue",
        "goodDirection": "up"
    }],
    "groupBy": ["region"]
})

items = list(client.dataset(run["defaultDatasetId"]).iterate_items())
print(items[0]["headline"])

Node.js

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('junipr/dataset-to-executive-report').call({
  csvText: 'region,revenue,previousRevenue\nNorth America,184000,166000\nEMEA,128000,121000',
  groupBy: ['region'],
  includeReport: true,
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items[0].executiveSummary);

Tips and Advanced Usage

Metric Configuration

Use sum for totals such as revenue, pipeline, leads, tickets, or cost. Use average for rates, ratios, percentages, scores, or cycle times. Add target and previousField when you want target misses and period-over-period change signals.

Grouping

Good group fields are low-cardinality dimensions: region, segment, team, priority, source, owner, category, plan, or product line. Avoid grouping by unique IDs because every row will become its own group.

LLM Mode

llmMode is fixed to off. The actor does not call OpenAI, Anthropic, Gemini, or any other external model. If future LLM enhancement is added, it should be separately priced and disabled by default.

Data Privacy

This actor does not scrape or enrich data. It only processes rows you provide or datasets your run can access. Do not upload sensitive data unless you are authorized to process it in Apify.

Limitations

• The report is deterministic and does not call an LLM, so narrative analysis is based only on supplied rows, configured metrics, and local scoring rules.
• It summarizes structured datasets; it does not clean unstructured documents or infer facts not present in the input.
• Large datasets should be sampled or capped with maxItems before running because each processed row can be billable in production.
• Recommendations are operational prompts for review, not financial, legal, medical, or compliance advice.

FAQ

Does this actor call an LLM?

No. It is deterministic and returns llmUsed: false.

Can I use CSV instead of JSON?

Yes. Paste CSV with a header row into csvText. Numbers, currency-like values, booleans, and percentages are normalized where possible.

What happens if my metrics are not configured?

The actor tries the default revenue-style metrics when those fields exist. Otherwise it infers up to six numeric fields and chooses reasonable formats and aggregations.

Are report artifacts written before charges?

No. In production, row-processing charges must succeed before the dataset report row or KVS report artifacts are written.

Can this summarize large datasets?

Does this modify my source dataset?

No. It reads rows, generates a report, and writes output to the run dataset and key-value store.

Related Actors

• Dataset QA Auditor
• CSV Deduper Normalizer
• Actor Pricing Simulator
• URL Canonicalizer
• Text Cleaner For RAG

Changelog

• 1.0.0: Initial local-first package with deterministic KPI rollups, grouping, anomaly detection, recommendations, Markdown/JSON reports, L1 PPE billing guards, examples, and fixture tests.