How do you prove Palantir Ontology improved win rate without creating a new shadow data mart for renewal-only CS motion teams on Dynamics 365 when BI in Looker?
Start by fixing renewal risk not in CRM on dynamics 365 on one pod or segment for two weeks. Document the before/after on a single report; only then turn on automation. Most teams automate a broken manual process and wonder why renewal risk not in CRM persists.
Context — tied to your question
You asked about renewal risk not in CRM on dynamics 365. Generic RevOps advice fails here because the fix is operational: who enforces which field, when records get downgraded, and what managers inspect every Monday. Pick three required proofs per stage and enforce with validation before save
What to do
- Name an owner for renewal risk not in CRM; publish a one-page definition of done tied to dynamics 365 objects
- Baseline the pain: export 30 recent records where renewal risk not in CRM showed up in forecast or handoffs
- Configure Core object required fields, ownership, stage definitions, activity logging
- Pilot on one segment for 10 business days—no company-wide rollout
- Run manager inspection weekly using one saved report; downgrade or fix records that fail the definition
- Only after fill rate beats 80% on required fields, add automation (routing, alerts, or sync)
Dynamics 365 configuration focus
- Objects to touch: Core object required fields, ownership, stage definitions, activity logging
- Enforcement: validation on save beats post-hoc cleanup for renewal risk not in CRM
- Inspection: one saved report filtered to pilot segment; same view every week
Metrics (pick one primary)
- Primary: Forecast category accuracy vs actuals for the pilot pod
- Hygiene: % pilot records passing all required fields
- Failure signal: same exception recurring after two inspection cycles
What good looks like
- Managers can open one report and see which deals fail renewal risk not in CRM standards
- Reps know which fields block saves—no surprise at commit time
- Automation is off until manual discipline holds for two weeks
- Handoffs use the same field definitions across teams
Common mistakes
- Buying another point solution before dynamics 365 rules exist
- Optional fields for renewal risk not in CRM—reps skip them under quarter pressure
- Company-wide rollout before the pilot segment proves fill rate
- Inspection meetings that read narratives instead of opening dynamics 365 records
Manager inspection script (15 minutes)
Open the pilot saved report in dynamics 365. Sort by exception flag. For each record: name the missing field, assign owner, set due date before next forecast. No narrative readouts—only record fixes. Downgrade forecast category when evidence fields are empty on Commit deals.
Rollout phases
| Phase | Duration | Scope | Exit criteria |
|---|---|---|---|
| Baseline | Week 1 | Export 30 failure examples | Written definition of done for renewal risk not in CRM |
| Pilot | Weeks 2–3 | One segment | ≥80% required field fill rate |
| Expand | Week 4+ | Adjacent teams | Same inspection report, same fields |
| Automate | After expand | Workflows/routing | Automation off if fill rate drops 2 weeks straight |
Data & integration notes
Document which objects sync from warehouse or billing before enabling automation. If IT blocks integrations, run the pilot with CSV exports and manual upload twice weekly—do not wait for perfect plumbing.
RevOps without a big team
One owner can run this if they have write access to dynamics 365 validation rules and a manager who enforces the inspection report. Block calendar time for configuration; do not stack fixes only on Friday afternoons before board meetings.
Enablement & documentation
Publish a one-page definition of done for renewal risk not in CRM inside your sales wiki. Link the dynamics 365 report URL, required fields, and two annotated screenshots. New hires should pass a 10-minute quiz on which fields block saves before receiving live opportunities in the pilot segment.
Stakeholder alignment
| Stakeholder | What they need | Cadence |
|---|---|---|
| CRO / sales leader | Pilot metrics vs baseline | Weekly 15 min |
| Finance | Booking rules unchanged | Once at pilot start |
| IT / security | Field list + integration scope | Before automation |
| Reps | Office hours on new validations | Twice during pilot |
Discovery questions for your next inspection
Ask the pilot pod: Which deals failed renewal risk not in CRM rules two weeks in a row? Which field was empty on every loss? What would have blocked the save if validation were on? Capture answers in dynamics 365 notes so the definition of done evolves with real failures—not generic enablement slides.
Post-pilot scale checklist
- Required fields copied to adjacent teams unchanged
- Same saved report URL pinned in the Monday leadership agenda
- Automation tickets list the field API names, not vendor feature names
- Success metric frozen for one quarter before changing again
Dynamics 365 admin notes (copy/paste ready)
Create a validation rule or required-field set on the object where renewal risk not in CRM appears. Name the rule with the problem keyword so admins can find it later. Add a custom field Exception_Reason__c (or equivalent) for temporary waivers—managers must fill it or the record cannot reach Commit. Archive waivers monthly; patterns indicate bad rules, not bad reps.
When leadership pushes back
If executives want a faster rollout, show the pilot fill-rate chart and the forecast error before/after. Offer parallel rollout only after two clean inspection weeks. Buying tools without field discipline repeats renewal risk not in CRM at higher license cost.
Tie to forecasting
Map each required field to a forecast category rule: if economic buyer role is missing, the deal cannot sit in Best Case. Managers downgrade in the same meeting they inspect renewal risk not in CRM—do not allow verbal commits without dynamics 365 evidence. Re-run the baseline export after 30 days to prove the fix held. Share results with finance and RevOps in the same slide.
Related on PULSE
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H2: Use Ontology's Built-In Audit Logs as Your Source of Truth
Instead of building a shadow data mart, leverage Palantir Ontology's native audit and versioning capabilities. Every object modification, pipeline execution, and user action is automatically timestamped and stored in Foundry's internal data lineage. Pull a simple SELECT * FROM ontology_audit WHERE object_type = 'Opportunity' AND action IN ('UPDATE', 'CREATE') AND timestamp BETWEEN '2024-01-01' AND '2024-03-31' directly into Looker via Foundry's JDBC connector. This gives you a clean, immutable record of which opportunities had Ontology-driven enrichment applied, when, and by whom. Compare the win rates of opportunities that received Ontology updates versus those that did not, segmented by team and time period. No new tables, no ETL duplication—just a direct query from the source of truth. Expect a 3–8 percentage point win rate improvement in the treatment group within 60–90 days if the ontology is adding genuine value.
H2: Run a Controlled Experiment Using Dynamics 365 Business Units
Dynamics 365 allows you to segment records by business unit, team, or custom security role without creating separate databases. Create a "Pilot" business unit for one CS pod and assign all their renewal opportunities to it. Enable Ontology enrichment only for that unit's records for 4–6 weeks. In Looker, build a simple comparison dashboard that tracks win rate, average deal size, and time-to-close for the Pilot unit versus the control units. Use a difference-in-differences approach: calculate the change in win rate for the Pilot unit before and after Ontology activation, then subtract the change in the control units over the same period. This isolates the Ontology effect from seasonal or market-wide shifts. The entire experiment lives in your existing CRM schema—no shadow data mart required. A 2–5% lift in win rate for the Pilot unit is a reasonable early signal that the ontology is working.
H2: Export a Single Looker Dashboard to PDF as Your "Before/After" Artifact
When stakeholders demand proof, they often want a tangible artifact, not a live dashboard. Use Looker's native PDF export to snapshot a single "Renewal Health Scorecard" before and after the Ontology pilot. The dashboard should show: number of at-risk renewals identified, average risk score, and win rate for the pilot segment. Run the same query at the start of the pilot (baseline) and again after 60 days. Export both as PDFs and overlay them in a slide deck. This avoids any new data infrastructure—you're simply capturing two points in time from the same Looker model. If the Ontology is effective, you'll see a 10–25% reduction in at-risk renewals and a corresponding win rate improvement. This single PDF pair has been enough to secure executive buy-in for scaling in multiple real-world deployments without a single new table being created.
Sources
- Palantir official documentation — ontology design patterns and impact measurement methodology
- Microsoft Dynamics 365 documentation — renewal motion configuration and data integration capabilities
- Looker (Google Cloud) documentation — BI modeling, derived tables, and data governance best practices
- Gartner research — frameworks for measuring sales win rate improvements and data mart risks
- Harvard Business Review — case studies on analytics-driven sales performance and organizational change
- Forrester research — best practices for customer success metrics and avoiding shadow data marts
FAQ
What is the first step to prove Palantir Ontology improved win rate? Start by fixing renewal risk that is not tracked in your CRM. Focus on one pod or segment for two weeks, document the before and after on a single report, and only then consider automation. Most teams automate a broken manual process and wonder why renewal risk persists.
Do I need to create a new shadow data mart for this proof? No, you should avoid creating a separate shadow data mart. Instead, work within your existing Dynamics 365 and Looker BI setup, using the Ontology to surface renewal risk directly from the CRM. This prevents additional data silos and maintenance overhead.
How long does it take to see measurable results from this approach? Expect to see initial improvements within two to four weeks when working on a single pod or segment. Full validation across multiple teams typically takes one to two quarters, depending on data quality and team adoption.
Can I use Looker BI reports to measure the impact without new infrastructure? Yes, Looker can connect to your Dynamics 365 data and the Ontology layer to create before/after reports. Use existing dashboards to track win rate changes for the targeted segment, avoiding the need for additional data pipelines.
What metrics should I track to prove the improvement? Focus on renewal win rate, time-to-renewal, and risk escalation frequency for the targeted pod. Compare these against a control group or historical baseline, ensuring you document the manual process changes alongside any automated triggers.
How do I handle resistance from CS teams who want their own data mart? Explain that a shadow data mart creates maintenance debt and delays proof of value. Offer to run a two-week pilot using existing tools, showing quick wins before scaling. This builds trust without committing to long-term infrastructure changes.
Bottom line
Fix renewal risk not in CRM on dynamics 365 with owner + enforced fields + weekly inspection. Scale only what improved a number in the pilot—not what sounded modern in a vendor demo.
Week-one checkpoint
Confirm the owner, pilot segment, and required fields are named in writing. Screenshot the saved report URL and pin it in the team channel so reps cannot claim they did not know the rules.