# DataGuru (@learndataanalysis) on tiktok

- id: koinon:creator:tiktok_learndataanalysis
- url: https://koinonlink.com/kol/tiktok/learndataanalysis
- schema_version: 1.4

## Identity
- platform: tiktok
- handle: learndataanalysis
- platform_url: https://www.tiktok.com/@learndataanalysis
- country: Canada
- entity_type: creator
- person_id: Cross-platform identity not resolved for this creator.

## Reach
- followers: 53493
- following: 2901
- posts_total: 593
- posts_observed: 6
- verified: false
- size_tier: koc

## 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: devtools
- category_confidence: 0.95
- category_source: derived_from_recent_content
- topics: Power BI DAX tips and challenges, Power Query data cleaning techniques, Excel interactive dashboards, Data visualization best practices, Financial and statistical reporting in Power BI

## Audience (inferred)
- status: present
- description: Data analysts, BI practitioners and Excel users focused on Power BI reporting, DAX and data visualization
- roles: data
- seniority: practitioner
- pain_points: Manually cleaning messy Excel data row by row, Struggling to write or optimize DAX measures for calculations, Building interactive dashboards without custom visuals, Formatting tables and visuals to match Excel-style reports
- posts_analysed: 40
- confidence: 0.75

## Commercial (inferred)
- audience_buyer_fit: 0.8
- promotion_intent: 0.7
- open_to_collaboration: false
- contact: Koinon does not publish creator contact details.

## Quality signals
- authenticity_score: 61
- authenticity_basis: profile_shape_only
- credibility_score: 61
- flagged_for_review: false
- koinon_bd_grade: A — 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_learndataanalysis#98a4d8be] #powerbi #dax #datavisualization #pareto #fabric (https://www.tiktok.com/@learndataanalysis)
- [tiktok_learndataanalysis#2a5ca0e6] The Champion
- [tiktok_learndataanalysis#632f2785] It is a becoming a big trend on TikTok now! Click here: powerbi
- [tiktok_learndataanalysis#3b336617] Check out DataGuru’s video! #TikTok >
- [tiktok_learndataanalysis#7c04efc1] #powerbi #powerquery #dax #datavisualization #fabric
- [tiktok_learndataanalysis#508af079] I'll Never Let You Go
- [tiktok_learndataanalysis#cfd801c4] Stop filling blank rows manually in Excel. Use Power Query Fill Down and clean your dataset in seconds. #powerbi #powerquery #exceltricks #datacleaning #dataanalytics
- [tiktok_learndataanalysis#b772bd81] This DAX trick highlights bars that perform above the average #powerbi #dax #datavisualization #businessintelligence #powerbitip

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

- audience.description: source_type=ai_inferred, confidence=0.75, freshness_days=61, method=llm_persona_from_recent_content
- audience.roles: source_type=ai_inferred, confidence=0.75, freshness_days=61, method=llm_persona_from_recent_content
- audience.seniority: source_type=ai_inferred, confidence=0.75, freshness_days=61, method=llm_persona_from_recent_content
- commercial.audience_buyer_fit: source_type=ai_inferred, confidence=0.6, freshness_days=61, 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=61, method=llm_scorer
- quality.anomaly_flags: source_type=heuristic, confidence=0.5, freshness_days=90, 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=61, 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=90, 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=90, method=platform_profile

## Freshness
- profile_checked_days_ago: 90
- content_analysed_days_ago: 61
- audience_updated_days_ago: 61

## 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_learndataanalysis
- Request removal: https://koinonlink.com/claim?k=tiktok_learndataanalysis
- Terms: https://koinonlink.com/for-ai