research-synthesis-data · diff
v0.1.0 to v0.1.1
14 added, 14 removed. Audit A to A.
---
name: research-synthesis-data
- version: 0.1.0
+ version: 0.1.1
description: >
Convert raw discovery notes into structured insights using atomic research methods
adapted for data products. Use when synthesizing findings, reviewing evidence,
summarizing research, writing problem briefs, or when someone asks "what did we
learn?" or "what does the evidence say?"
user-invocable: false
---
## Evidence Hierarchy
- Not all evidence is equal. Rank sources in this order:
+ Assess evidence for the specific question, coverage, independence and recency. These source types offer different signals, not a universal ranking:
- 1. **Observed workarounds** (strongest) — Someone built something to solve this problem. Documented in `data-consumer-discovery` workaround archaeology format. Time invested = validated demand.
- 2. **Usage data** — Query logs, dashboard access patterns, API call frequency. Behavior over opinions.
- 3. **Quality incidents** — Support tickets, data bug reports, escalations. Pain that generated action.
- 4. **Consumer quotes** — Direct statements from interviews. 3+ independent quotes on the same theme = a pattern.
- 5. **Stakeholder requests** (weakest) — What someone asked for. Often a solution masking a different problem.
+ 1. **Observed workarounds**: Record the task, cost and alternatives using `data-consumer-discovery`. Investment shows effort, not validated demand or willingness to buy.
+ 2. **Usage data**: Query logs, dashboard access and API calls show recorded behavior within instrumentation and access limits.
+ 3. **Quality incidents**: Tickets and escalations show reported problems; silence does not establish absence of harm.
+ 4. **Consumer quotes**: Preserve wording, context and independence. Repeated quotes can suggest a pattern, not a fixed validation threshold.
+ 5. **Stakeholder requests**: Separate the requested solution from the decision, constraints and authority behind it.
- CRITICAL: When usage data contradicts interview data, usage data wins. People describe aspirational workflows. Logs show actual ones.
+ When usage and interviews conflict, check identity, coverage, period, permissions and workflow definitions. Logs may miss off-platform work; interviews may describe intended rather than observed behavior. Preserve unresolved contradictions.
## Atomic Research Chain
Build insights from the bottom up. Every level must trace to the one below it.
### Nuggets
Raw observations tagged with source and date. One fact per nugget.
- Example: `[Interview: Sarah, Analytics Lead, 2024-01-15] Spends 4 hours every Monday rebuilding the regional performance report from 3 separate data exports.`
+ Synthetic example: `[Fictional interview: Sarah, Analytics Lead, 2024-01-15] Spends 4 hours every Monday rebuilding the regional performance report from 3 separate data exports.`
Tag each nugget: source type (interview, log, ticket, observation), consumer segment (Explorer, Reporter, Decision-maker, Builder), and topic.
### Patterns
- Three or more nuggets from independent sources pointing to the same conclusion. Less than three is anecdotal.
+ Group observations that support the same explanation. Report the sample, independence and contrary cases. No universal count establishes a pattern; a single well-supported safety event can still require action.
- Example: `3 of 5 analytics leads manually combine data from 3+ sources weekly. Average time: 3.5 hours. All distrust the automated report because "the numbers don't match what I pull manually."`
+ Synthetic example: `3 of 5 analytics leads manually combine data from 3+ sources weekly. Average reported time: 3.5 hours. They report distrust of the automated totals. The sample does not establish demand across the organization.`
### Insights
Patterns interpreted in context. Answers "so what?"
- Example: `Regional reporting is the highest-pain workaround across analytics. The root cause is inconsistent metric definitions across source systems, not missing data. Fixing the semantic layer would eliminate 15+ hours/week of manual reconciliation.`
+ Synthetic interpretation: `Definition differences may explain the regional reporting mismatch. Compare the same population and period under each definition before claiming a cause or forecasting time saved.`
### Recommendations
Insights translated into action. Each recommendation links to a specific skill or command:
- Problem Brief → feeds `data-product-validation` scorecard
- Data Landscape Assessment → feeds `data-quality-assessment` audit
- Consumer Map → feeds `stakeholder-alignment` prioritization
## Three Synthesis Outputs
Every discovery effort produces three artifacts:
**1. Problem Brief** — The top 3 problems ranked by evidence strength. For each: problem statement, evidence summary (nuggets and patterns), affected consumer segments, estimated impact. Feeds directly into `/dpo:write-data-prd`.
**2. Data Landscape Assessment** — Current state of relevant data sources. For each: what exists, quality baseline (cross-ref `data-quality-assessment`), gaps, access constraints. Feeds into `data-product-validation` Data Feasibility scoring.
**3. Consumer Map** — Who needs what, segmented by type (see `data-consumer-discovery` segments). Includes: frequency of need, current workaround, trust level, downstream dependents. Feeds into `stakeholder-alignment` prioritization.
## Contradictory Evidence
When evidence conflicts:
1. Flag the contradiction explicitly. Do not smooth it over or pick the more convenient interpretation.
- 2. Check the evidence hierarchy. Higher-ranked evidence wins.
- 3. If same-rank evidence conflicts, note the split and recommend a targeted follow-up (specific question, specific data pull).
+ 2. Compare scope, timing, source reliability and independence. Do not choose a winner by source type alone.
+ 3. Record competing explanations and a targeted follow-up that could distinguish them.
Example: "Interview data suggests daily demand, but query logs show weekly access. Recommend monitoring actual usage for 2 weeks before committing to real-time refresh SLA."
NEVER present a clean narrative when the evidence is messy. Stakeholders deserve honest uncertainty over false confidence.