Scaled adoption
Expanded from an analysis workflow into a national, multi-division capability.
Analytics product
Employee Sentiment Intelligence
A privacy-aware analytics capability that turns employee feedback into topic-level signals, comparisons, and executive-ready evidence.
- Role
- Product lead · analytics engineering · executive storytelling
- Timeframe
- Multi-year product evolution
- Status
- Completed
- Publication
- Sanitized public case study
Productized delivery
Defined Full, Comparative, and Benchmark offerings for repeatable use.
Decision-ready output
Connected thousands of data points to privacy-aware executive reporting.
The challenge
Make complex work decision-ready.
Unstructured feedback is rich in context but difficult to compare, govern, and explain at executive speed. The work needed to preserve nuance while creating a repeatable analytical product.
Miguel combined structured data preparation, explainable NLP signals, privacy-aware reporting, and stakeholder-driven product tiers. The public explorer below demonstrates the interaction model with deterministic synthetic fixtures.
Synthetic explorer
See the product interaction model
All organizations, responses, and results are fictional.
Topic sentiment
Leadership
PositiveNeutralNegative
Context over time
Six-month signal
Jan
Manager forum
FebMar
Tool rollout
AprRecognition pilot
MayJun
Current lens
Leadership is the active topic for all organizations, using all sources across Jan–Jun 2025. The values update deterministically to demonstrate filtering behavior.
Behind the build
System decisions that shaped the outcome
Verified public career history and sanitized local product artifacts.
From workflow to product
The capability evolved beyond one-off analysis into defined offerings with consistent inputs, outputs, and executive narratives.
- Full analysis for deeper organizational discovery
- Comparative views for period and cohort context
- Benchmark views for decision-oriented framing
Privacy by design
Reporting emphasized themes, aggregates, and contextual evidence rather than exposing individual responses or low-volume slices.
What this demo proves
Filtering, topic comparison, trend context, and event overlays can remain useful without live scraping, raw reviews, or private systems.