# Jenny Tumay (@jennytumay) on tiktok

- id: koinon:creator:tiktok_jennytumay
- url: https://koinonlink.com/kol/tiktok/jennytumay
- schema_version: 1.4

## Identity
- platform: tiktok
- handle: jennytumay
- platform_url: https://www.tiktok.com/@jennytumay
- website: youtube.com/jennytumay
- country: United States
- entity_type: creator
- person_id: Cross-platform identity not resolved for this creator.

## Reach
- followers: 2204
- following: 96
- posts_observed: 1
- verified: false
- size_tier: nano

## Engagement
- status: not_collected
- status_note: Engagement has not been collected for this creator yet. This is a gap in our collection, NOT a statement that the creator has no engagement. Follower count is known, so it is measurable.
- unit_note: engagement_rate and peak_post_engagement_rate are ratios (0-1); avg_interactions_per_post and best_post_interactions are absolute counts. Read status before reading any number: a null rate has several different causes and status says which one applies here.

## Content
- category: software
- subcategory: martech
- category_confidence: 0.85
- category_source: derived_from_recent_content
- topics: Power BI back to basics教程, Microsoft Fabric Lakehouse与Dataflow, Power BI语义模型与报告制作, AI在Power BI/Fabric中的应用, 数据安全与权限管理, AI伦理与Responsible AI
- bio: Big 4 AI & data consultant | ucla mba
💌 [contact removed]

## Audience (inferred)
- status: present
- description: Data analysts, data engineers, and beginners in Power BI/Fabric, primarily working professionals learning Microsoft's data platform and AI integration
- roles: data
- seniority: learner
- pain_points: Unfamiliarity with basic Power BI and Microsoft Fabric operations, Uncertainty about using AI to assist with data modeling and report creation, Frequent mistakes in data security, permissions, and semantic model design, Lack of structured hands-on experience with Fabric Lakehouse and ETL workflows
- posts_analysed: 16
- confidence: 0.6

## Commercial (inferred)
- audience_buyer_fit: 0.9
- promotion_intent: 0.8
- open_to_collaboration: false
- contact: Koinon does not publish creator contact details.

## Quality signals
- authenticity_score: 35
- authenticity_basis: profile_shape_only
- credibility_score: 35
- flagged_for_review: false
- koinon_bd_grade: C — measures Koinon partner-outreach fit, not creator quality
- interpretation: Signals for triage, not verdicts. A flag means the profile is worth a manual check.

## Evidence
- [tiktok_jennytumay#d0aea985] Power BI back to basics episode 6: Download AI skills for Microsoft Fabric (skills.md) files. Focusing specifically on the power bi report authoring skill #enterpriseai #githubcopilot #powerbi (https://www.tiktok.com/@jennytumay)
- [tiktok_jennytumay#7ba2efc9] original sound - jennytumay
- [tiktok_jennytumay#30309b11] It is a becoming a big trend on TikTok now! Click here: enterpriseai
- [tiktok_jennytumay#64f9f1c3] Check out Jenny Tumay’s video! #TikTok >
- [tiktok_jennytumay#68e61430] Create/build a Power BI Project File (PBIP) in Microsoft Power BI for vibecoding reports with AI models #dataanalytics #vibecoding #powerbi (first step for set up)
- [tiktok_jennytumay#6b7e645e] Search:
- [tiktok_jennytumay#9ea4d162] power bi report server
- [tiktok_jennytumay#ccc17dbe] Create a semantic model (dataset) in Microsoft Fabric in ep 4 of power bi back to basics! #dataanalytics #datamodeling

## Provenance
Every inferred field below carries how it was produced. Facts stay facts; inferences stay inferences.

- audience.description: source_type=ai_inferred, confidence=0.6, freshness_days=52, method=llm_persona_from_recent_content
- audience.roles: source_type=ai_inferred, confidence=0.6, freshness_days=52, method=llm_persona_from_recent_content
- audience.seniority: source_type=ai_inferred, confidence=0.6, freshness_days=52, method=llm_persona_from_recent_content
- commercial.audience_buyer_fit: source_type=ai_inferred, confidence=0.6, freshness_days=52, method=llm_scorer — Relative signal within Koinon indexing, not a market-wide score.
- commercial.promotion_intent: source_type=ai_inferred, confidence=0.6, freshness_days=52, method=llm_scorer
- quality.anomaly_flags: source_type=heuristic, confidence=0.5, freshness_days=52, method=profile_snapshot_and_engagement_ratio_heuristic — Means "worth a manual check", NOT confirmed fraud. Flags can be wrong and are not a verdict about the person.
- quality.authenticity_score: source_type=heuristic, confidence=0.25, freshness_days=52, method=Profile-shape heuristic from a single snapshot (follower count, following ratio, account age, posting volume, verification), reduced for engagement red flags where post engagement was collected. Where only a follower count is available the score saturates at 58. — The input set differs by platform; do not compare this score across platforms.
- quality.koinon_bd_grade: source_type=ai_inferred, confidence=0.5, freshness_days=52, method=llm_partner_fit_classifier + fit_score >= 0.6 AND authenticity >= 60 => A; fit_score >= 0.4 AND authenticity >= 45 => B; else C. authenticity < 30 or a promotional-pattern flag => quarantine. — Koinon partner-outreach fit, not creator quality.
- reach.followers: source_type=observed, confidence=0.95, freshness_days=52, method=platform_profile

## Freshness
- profile_checked_days_ago: 52
- content_analysed_days_ago: 52
- audience_updated_days_ago: 52

## AI retrieval
- indexing_requests: 10
- user_triggered_requests: 0
- note: Being indexed in bulk is not the same as being retrieved to answer a question.

## This profile
- Claim or correct: https://koinonlink.com/claim?k=tiktok_jennytumay
- Request removal: https://koinonlink.com/claim?k=tiktok_jennytumay
- Terms: https://koinonlink.com/for-ai