Global Executive Talent Intelligence Professional

Below is a step‑by‑step process framework you can use (or adapt) for the Global Executive Talent Intelligence (GETI) function.
It is organized around the three core pillars of the role — Data & Insights, Talent Sourcing, and Business Partnering — and includes recommended tools, key deliverables, and metrics to keep the work measurable and aligned with the broader GETA strategy.

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1️⃣ Data & Insights Pipeline

Phase	Goal	Typical Activities	Tools & Sources	Output / Artefact	Success Metric
1.1 Define Intelligence Requirements	Align on what the business needs to know (e.g., market‑size, competitor moves, talent pool health).	• Conduct kickoff calls with hiring managers, business unit leads, and DEI partners. • Capture questions in a “Talent Intelligence Brief” (TI‑Brief).	Confluence / SharePoint for brief docs; JIRA/Asana for task tracking.	TI‑Brief (one‑page) with priority questions, timeline, owners.	% of brief items delivered on time (target ≥ 90%).
1.2 Data Acquisition	Pull raw data from internal & external sources.	• Pull internal HR data (headcount, turnover, promotion rates). • Subscribe to external executive databases (e.g., BoardEx, PitchBook, Capital IQ, LinkedIn Recruiter Premium). • Scrape public sources (SEC filings, press releases, news). • Leverage AI‑enabled web‑crawlers for niche markets.	• SQL/Redshift data warehouse (internal). • APIs (LinkedIn, Crunchbase). • Python/BeautifulSoup or Octoparse for web scraping. • Data‑cleaning in Alteryx, Trifacta, or Pandas.	Raw data repository (structured CSV/Parquet files) stored in a secure cloud bucket (e.g., S3).	Data completeness score (≥ 95% of required fields populated).
1.3 Data Enrichment & Normalization	Make disparate data comparable.	• Map titles to a standardized taxonomy (e.g., C‑suite, SVP, EVP). • Geocode locations, assign industry codes (NAICS/SIC). • Append compensation benchmarks (salary surveys, public comps).	• Title‑normalization scripts (Python, R). • Look‑up tables in Snowflake. • Compensation APIs (e.g., Payscale, Mercer).	Cleaned, enriched dataset ready for analysis.	Data quality audit (error rate < 2%).
1.4 Analytics & Visualization	Turn data into actionable insights.	• Trend analysis (growth of C‑suite talent pool, churn rates). • Competitive landscape mapping (who’s hiring where). • Gap analysis (skill/experience gaps vs. target profile). • Predictive modeling (likelihood of a candidate to move within 12 mo).	• Power BI / Tableau for dashboards. • R/Python for statistical models (logistic regression, survival analysis). • Looker for self‑serve reporting.	• Executive Talent Market Dashboard (real‑time). • Monthly “Talent Landscape” briefing deck.	Dashboard adoption (≥ 70% of hiring managers view at least once per month).
1.5 Insight Delivery & Recommendations	Communicate findings in a decision‑ready format.	• Host “Intelligence Review” calls (30 min) with stakeholders. • Provide a one‑page “Actionable Insight Sheet” (AIS) with recommendations (e.g., target companies, talent pools, timing).	• Email templates, Confluence pages for version control.	AIS + recorded meeting minutes.	Stakeholder satisfaction survey (target ≥ 4/5).

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2️⃣ Executive Talent Sourcing Process

Step	Objective	Core Activities	Tools	Deliverable	KPI
2.1 Target Profile Finalization	Translate intelligence into a concrete candidate persona.	• Consolidate TI‑Brief insights with hiring manager’s “Ideal Candidate Profile”. • Define mandatory vs. nice‑to‑have attributes (experience, functional expertise, cultural fit).	–	Candidate Persona Document (PDF/Confluence).	Time to finalize persona (≤ 5 business days).
2.2 Market Mapping	Build a pipeline of potential candidates before a role opens.	• Identify “high‑potential” companies (competitors, adjacent industries). • Use Boolean strings, LinkedIn Recruiter, and proprietary databases to pull profiles. • Tag each prospect with source, seniority, and fit score.	LinkedIn Recruiter, Hiretual, SeekOut, Lusha, ZoomInfo, internal ATS (Greenhouse/Workday).	Market‑Map Spreadsheet (company, candidate, fit score, contact status).	Number of qualified prospects per target (≥ 30).
2.3 Outreach Strategy Design	Maximize response rates while respecting DEI principles.	• Choose outreach channel mix (InMail, email, phone, referral). • Draft personalized messaging templates (role‑specific, market‑insight‑driven). • Set cadence (Day 0, Day 3, Day 7).	Outreach automation (Gem, Beamery, Outreach.io).	Outreach Playbook (template library + cadence).	Response rate (≥ 20%).
2.4 Candidate Engagement & Assessment	Qualify interest and fit quickly.	• Conduct “Discovery Calls” (15‑30 min) to gauge motivation, cultural alignment, and compensation expectations. • Use a structured scorecard (experience, leadership style, DEI alignment).	Calendly, Zoom, Scorecard in Greenhouse/Lever.	Completed Scorecards (digital).	Time from first contact to qualified status (≤ 10 days).
2.5 Pipeline Management & Reporting	Keep the talent pool fresh and visible.	• Update ATS with stage, notes, and next steps. • Refresh market‑map quarterly. • Provide weekly pipeline health reports to hiring managers.	ATS dashboards, Power BI pipeline report.	Weekly Pipeline Health Snapshot.	Pipeline velocity (average days per stage).
2.6 Offer & Onboarding Handoff	Ensure smooth transition to hiring team.	• Share candidate dossier (resume, scorecard, market insights). • Participate in offer strategy calls (compensation benchmarking). • Conduct “stay‑interview” after 30 days to capture early feedback.	DocuSign, SharePoint, HRIS.	Candidate Dossier Package.	Offer acceptance rate (≥ 85%).

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3️⃣ Business Partnering & DEI Integration

Activity	How It Connects to Data/Intelligence	Practical Steps	Tools	Metric
Strategic Talent Forecasting	Use market‑trend models to predict future executive headcount needs (e.g., M&A, new business units).	• Run scenario analysis (growth vs. contraction). • Present forecasts in quarterly business reviews.	Anaplan, Excel Monte‑Carlo models.	Forecast accuracy (± 10% variance).
Competitor Benchmarking	Compare your organization’s executive talent density, diversity ratios, and compensation against peers.	• Pull competitor data from public filings, ESG reports. • Visualize gaps in a “Competitive Talent Index”.	Tableau, ESG data platforms (MSCI, Sustainalytics).	Improvement in index score YoY.
DEI Talent Intelligence	Overlay demographic data on talent pools to identify under‑represented groups.	• Tag candidates with gender, ethnicity (self‑identified or inferred via voluntary data). • Build “Diverse Executive Pipeline” dashboards.	Diversity‑focused sourcing tools (Entelo, Jopwell), Power BI.	% of pipeline that is diverse (target ≥ 30%).
Stakeholder Education	Translate analytics into “Talent Literacy” for hiring managers.	• Run quarterly “Data‑Driven Hiring” workshops. • Provide cheat‑sheets on interpreting market dashboards.	LMS (Cornerstone), Zoom.	Attendance rate (≥ 80%).
Feedback Loop & Continuous Improvement	Capture outcomes (time‑to‑fill, quality‑of‑hire) to refine intelligence models.	• Post‑hire surveys (hiring manager, candidate). • Feed results back into predictive models.	SurveyMonkey, Qualtrics, ATS analytics.	Quality‑of‑Hire score (target ≥ 4/5).

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4️⃣ Sample End‑to‑End Workflow (Illustrated)

[Week 0]   TI‑Brief kickoff → Define questions → Assign data owners
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[Week 1‑2] Data acquisition (internal + external) → Clean & enrich
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[Week 2‑3] Analytics (trend, gap, predictive) → Build dashboard
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[Week 3]   Insight delivery meeting → AIS + recommendations
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[Week 4]   Profile finalization → Market‑map creation
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[Week 5‑6] Outreach campaign launch → Track responses
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[Week 7]   Discovery calls → Scorecards → Move to “Qualified”
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[Week 8‑9] Pipeline health report → Adjust sourcing tactics
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[Week 10]  Offer hand‑off → DEI check‑list review
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[Week 12]  On‑boarding hand‑off → Stay‑interview → Feed back into model

The timeline can be compressed for “high‑priority” roles (e.g., 4‑week sprint) by overlapping data acquisition and outreach phases.

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5️⃣ Recommended Technology Stack (2026)

Category	Primary Tool(s)	Why It Fits a Global Executive Role
Data Warehouse	Snowflake (multi‑region, secure)	Handles large, heterogeneous data sets; easy to share across regions.
ETL / Data Prep	dbt + Fivetran	dbt for transformation logic (SQL‑based, version‑controlled); Fivetran for automated connectors to LinkedIn, PitchBook, internal HRIS.
Analytics & Modeling	Python (pandas, scikit‑learn) + R (tidymodels)	Advanced predictive models (e.g., churn probability, move‑likelihood).
Visualization	Power BI (enterprise) + Tableau (self‑serve)	Interactive dashboards with role‑based security.
Sourcing & CRM	Beamery (talent CRM) + LinkedIn Recruiter Premium + SeekOut	Centralizes candidate profiles, outreach cadence, and DEI tagging.
ATS Integration	Greenhouse + Workday Recruiting	Seamless hand‑off to hiring managers; built‑in scorecard fields.
Collaboration	Microsoft Teams + Confluence + JIRA	Global team coordination, documentation, and task tracking.
Compliance & Privacy	OneTrust (data‑privacy) + Vanta (security)	Ensures GDPR, CCPA, and other jurisdictional requirements for executive data.

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6️⃣ Key Performance Indicators (KPIs) Dashboard

KPI	Definition	Target	Frequency
Time‑to‑Insight	Days from TI‑Brief sign‑off to first dashboard release.	≤ 10 days	Weekly
Data Quality Score	% of records passing validation rules (completeness, accuracy).	≥ 95 %	Monthly
Pipeline Diversity Ratio	% of qualified candidates who self‑identify as under‑represented.	≥ 30 %	Quarterly
Response Rate (Outreach)	% of contacted executives who reply.	≥ 20 %	Weekly
Qualified Candidate Rate	% of contacts that move to “Qualified” stage.	≥ 15 %	Weekly
Offer Acceptance	% of offers accepted by executive candidates.	≥ 85 %	Per hire
Hiring Manager Satisfaction	Survey score on relevance of intelligence & candidate quality.	≥ 4/5	Per hire
Predictive Model Accuracy	% of hires that stay > 12 months vs. model forecast.	≥ 80 %	Quarterly
Compliance Audits Passed	Number of privacy/compliance checks passed without findings.	100 %	Quarterly

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7️⃣ Quick‑Start Checklist (First 30 Days)

Day	Action	Owner
1‑3	Review existing GETA strategy, DEI goals, and current talent intelligence assets.	You + GETA Lead
4‑7	Conduct stakeholder interviews (HRBP, Business Unit Heads, Diversity Council).	You
8‑10	Draft TI‑Brief template & get sign‑off on first priority market (e.g., FinTech C‑suite).	You
11‑15	Set up data pipelines (Fivetran → Snowflake) for internal HR data and external executive databases.	Data Engineer
16‑20	Run first “Talent Landscape” analysis (headcount growth, churn, compensation).	You + Analyst
21‑23	Build and share the first dashboard; collect feedback.	You
24‑27	Create candidate persona & market‑map for the priority role.	You + Sourcing Lead
28‑30	Launch outreach pilot (10 executives) and schedule first discovery calls.	You + Recruiter

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TL;DR

1. Define intelligence needs → collect & clean data → analyze → deliver actionable insights.
2. Translate insights into a candidate persona, build a market map, and run a structured outreach cadence.
3. Integrate DEI metrics at every step and keep hiring managers in the loop with regular reporting.
4. Leverage a modern tech stack (Snowflake, dbt, Power BI, Beamery, LinkedIn Recruiter) to automate and scale.
5. Measure success with a KPI dashboard that balances speed, quality, diversity, and compliance.

Feel free to let me know which part you’d like to dive deeper into—e.g., a sample dashboard mock‑up, a Boolean string library for executive sourcing, or a DEI‑focused talent‑pipeline report template. I’m happy to provide concrete examples or help you build a customized playbook for your organization.