AI & DATA SCIENCE / PEOPLE ANALYTICS
BA feedback analytics
Exploring what a positive feedback log could reveal about recognition, and what it could not fairly measure.
How the use case emerged
Positive feedback was recorded over time, but a static log made it difficult to see recurring themes. I explored whether text analysis could help a team reflect on recognition and learning more consistently.
The prototype
The original prototype used 109 feedback entries, a small and selectively collected dataset. I structured the restricted feedback log, prepared its text and context fields, and explored theme extraction and classification as ways to summarise recognition patterns. I reviewed the data's collection bias alongside model output: a positive-only log is a record of what was submitted, not a full account of everyone's work. No names, quotations, scores or individual-level outputs are shown here.
Positive feedback has a selection boundary
What it could support
A carefully reviewed summary could prompt conversations about recognition, coaching and where evidence is thin. A positive-only log reflects opportunity and recording habits as well as contributions, so it cannot rank people or establish comparative performance.
Responsible boundary
It should never automate appraisal, reward, recruitment or work allocation. Any future use would need a lawful purpose, access controls, fairness review, a route to challenge interpretation and accountable human decisions.
Evidence boundary: Real workplace feedback informed the prototype. The underlying records and model outputs are withheld from this public account.
A CLOSER LOOK
Recognition needs context
EXPLAINABLE AI & DATA / READ THE METHOD
Read the evidence, understand the method.
A guide to the charts and case evidence on this page. Exhibit captions remain the authority for data origin and the claims each visual supports.
Follow the reasoning.
- SourceRead the exhibit’s evidence label
- RepresentInspect units, categories and scales
- CompareDistinguish observed results from demonstrations
- ReviewCheck limitations before taking action
Explore the building blocks.
Number of concepts in this guide, not performance or usage measures.
Explore the explanation matrix · 14 concepts
14 concepts shown
On small screens, scroll the table sideways. “Learning context” explains a related idea; it does not mean that method runs on this page. Deep learning is the standard term for “deep ML”.
| Technical term | Technical description / definition | In plain English | How this page uses it |
|---|---|---|---|
| Visual encodingVisualisation | Mapping values or categories to marks, position, length, colour and labels.Primary reference ↗ | Make the comparison visible, with a scale and a clear label. | Published analysis Read axis labels, denominators and captions before comparing exhibits. |
| Synthetic dataAI/Data Concepts | Generated records used to represent scenarios rather than directly report observed individuals or events.Primary reference ↗ | A practice dataset. Useful for demonstration, not proof of workplace impact. | Demonstration Exhibits labelled synthetic or reconstructed illustrate methods; they are not disclosed workplace records. |
| Descriptive analyticsAI/Data Concepts | Summaries of observed data using counts, rates, distributions or comparisons.Primary reference ↗ | Tell us what the records show; this alone does not predict next week. | Published analysis Counts and trend summaries describe the stated records and period, not future performance. |
| Residuals and prediction errorEvaluation & XAI | A residual compares an observed value with its fitted or predicted value. Error metrics summarise those discrepancies.Primary reference ↗ | How far did the estimate miss, and where does it miss most? | Learning context Prediction exhibits can compare observed and estimated values; a smooth chart alone is not validation. |
| Confusion matrix, precision and recallEvaluation & XAI | A confusion matrix counts predicted versus actual classes. Precision measures positive-result reliability; recall measures positive-case coverage.Primary reference ↗ | Which alarms were right, which were false, and which cases were missed? | Learning context Where a confusion chart is shown, inspect false alarms and missed cases as well as correct predictions. |
| Data science pipelineData Science Pipeline | An ordered, reproducible sequence for acquiring, validating, preparing, analysing and evaluating data.Primary reference ↗ | A chain from a question and source to an answer someone can check. | Published analysis Case studies connect the problem, evidence, preparation, method, evaluation and decision. |
| Explanation versus causal proofEvaluation & XAI | A model explanation describes selected behaviour or contributions; it does not establish causal effects or full reliability.Primary reference ↗ | A reason for this output is useful, but it is not proof that the model is right. | Responsible use A persuasive visual does not prove causation or transferability. |
| Data minimisationResponsible AI | Limit collected, disclosed and retained information to what is needed for the stated purpose.Primary reference ↗ | Use the smallest appropriate amount of information. | Responsible use Restricted source records stay private; approved aggregates and labelled reconstructions support public learning. |
| pandasPython Package | A Python library for labelled tabular data manipulation and analysis.Primary reference ↗ | A programmable workbench for cleaning and comparing tables. | Learning context A common Python option for tabular preparation; its inclusion is not a claim that this browser runs pandas. |
| MatplotlibPython Package | A Python plotting library for static, animated and interactive visualisations.Primary reference ↗ | Turn numerical work into labelled figures that can be inspected and shared. | Learning context A common way to prepare Python figures. This page displays published assets rather than launching Python plotting sessions. |
| Machine learning (ML)AI Capability | Methods that estimate patterns or parameters from examples to support inference on other inputs.Primary reference ↗ | Learn a pattern from examples, then check whether it helps with new ones. | Learning context ML learns from examples. Read the page-specific methods below to see whether any model runs here. |
| Deep learningAI Capability | Machine learning using neural networks with multiple representation-learning layers.Primary reference ↗ | A layered model learns useful representations; more layers do not guarantee a better answer. | Learning context Deep learning is an AI capability, not a label for every chart. No deep model is implied by this explanation panel. |
| Natural language processing (NLP)AI Capability | Computational methods for representing, analysing or generating human language.Primary reference ↗ | Help a computer work with words, while remembering that context matters. | Learning context Text search, translation, thematic classification and generation are different language tasks; their implementation is identified separately. |
| Sentiment analysisAI Capability | A language-analysis task that estimates an expressed attitude, often as polarity or sentiment categories.Primary reference ↗ | Estimate the tone of words—not the private feelings or personality of their author. | Learning context Sentiment estimates expressed tone. It must not be confused with topic classification or a diagnosis of a person. |
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