# Use Case 3: Demo data
Use this use case when you need realistic demo data without sensitive information. The focus is stable, shareable demo scenarios.
### What problem this use case solves
Sales and pre-sales teams need high-quality datasets to demonstrate workflows. Data must be safe to share in demo environments.
Classic anonymization can degrade the demo experience. It also requires careful handling of indirect identifiers.
### When to choose this use case
Pick this when you need shareable demo data that still “feels real”.
If you’re unsure, start with **Mock or mask all** and only enable [Consistent mapping](/configure-a-data-generation-job/configure-column-settings/consistent-mapping.md) for a small set of key entities.
* You demo workflows and UI journeys with realistic values.
* You need zero real identifiers in the demo environment.
* You need stable “hero customers” across tables and refreshes.
* You need realistic formats for validators and UI flows.
* Use [Mock](/configure-a-data-generation-job/configure-column-settings/mockers.md) for narrative fields.
* Use [Mask](/configure-a-data-generation-job/configure-column-settings/mask.md) for format-critical fields.
### When to avoid this use case
Skip this when demos are not the goal.
* You need external sharing with documented privacy evidence and low linkability. Use [Use Case 9: Data Sharing & Monetization](/overview/get-started/use-cases-and-configuration/use-case-9-data-sharing-and-monetization.md).
* You need DTAP-style test data for apps and APIs (not demo narratives). Use [Use Case 1: Application & API Testing](/overview/get-started/use-cases-and-configuration/use-case-1-application-and-api-testing.md).
* You need upsampling or load generation at scale. Use [Use Case 2: Load & Stress](/overview/get-started/use-cases-and-configuration/use-case-2-load-and-stress-testing.md).
### Recommended Syntho configuration
This setup is optimized for **stable demo narratives**. You want repeatable “storytelling” values. You want zero real identifiers, even indirectly.
{% stepper %}
{% step %}
#### Prerequisites
* Use the [Prerequisites](/overview/get-started/prerequisites.md) checklist.
**Checklist**
* [ ] Demo journeys listed (happy path + 1–2 alternates).
* [ ] “Hero entities” defined (small, intentional set).
* [ ] Destination is isolated and easy to reset.
{% hint style="warning" %}
Treat demo data as shareable by default. Avoid any real identifiers.
{% endhint %}
{% endstep %}
{% step %}
#### Source & destination management
Create one workspace per demo dataset or audience. Examples: `sales-basic`, `partner-demo`, `industry-demo`.
* Duplicate a working workspace before big changes. This gives you a rollback point.
* Use simple versioned names like `v1`, `v2`, `baseline`, or `pilot-partner-x`.
#### Baseline rules
* Keep the **source stable**. Prefer snapshots or back-ups.
* Avoid a **live production** source for iterative work.
* Keep the **destination isolated**. Never write into production.
* Keep **schemas aligned** between source, workspace and destination.
* Use **views** when you need only a subset of the original database.
#### Lifecycle rule of thumb
* Keep the source connection when you expect schema changes.
* Remove the source connection when you expect a new run only much later.
* Revalidate after schema changes. Use [Validate and synchronize workspace](/configure-a-data-generation-job/generation-and-validation/validate-and-synchronize-workspace.md).
**Nuances for this use case**
* Use a stable seed when you can. It makes storytelling predictable.
* Treat demos as shareable by default.
* Keep “hero entities” stable intentionally. Use consistent mapping sparingly.
* Don’t generate into a schema that already contains old demo data. You may get inconsistent data across sessions.
{% endstep %}
{% step %}
#### Configure generators
**Workspace initialization mode**
Choose a [workspace mode](/setup-workspaces/create-a-workspace/workspace-modes.md). It applies baseline generator suggestions during workspace creation.
Recommended modes for this use case:
* **Mock or mask all** when you want “looks real” data with zero identifiers.
* **Mock all** when you have little to no source data and you’re building a demo dataset from scratch.
* **De-identify** when you start from a production-like schema and want to keep non-PII columns mostly intact.
**AI-generated synthesis**
Use this when your demo must feel **statistically realistic** (distributions, correlations). It’s useful when you have an single entity table or demo view.
**Example (realistic churn patterns):** create a `demo_customer_features_view` (tenure, orders, segment), then use **AI synthesize** so churn-related features look realistic while still being unlinkable.
**Rule-based generation**
Use this when a demo needs **guaranteed scenarios**. Use [Calculated columns](/configure-a-data-generation-job/configure-column-settings/calculated-columns.md) to enforce “must exist” states.
**Example (always-have VIP customers):** create a tier that drives demo flows (pricing, permissions, entitlements).
```excel-formula
// New column: demo_tier (simple, stable ratios)
SWITCH(TRUE,
RAND() < 0.05, "VIP",
RAND() < 0.25, "PREMIUM",
"STANDARD"
)
```
**Masking**
Use this when UI flows require **valid formats** (email, phone, IBAN) and you want stable data across tables.
**Example (stable hero customer):** mask `email` and `phone_number` to valid formats, then enable **Consistent mapping** only for `customer_name` and `company_name` so the same “ACME” appears across orders and invoices.
**Hybrid**
Use this when you need both **narrative control** and **realistic variation**.
It maps well to the “deterministic relations” and “absolute calculations” patterns in [Example data generation scenarios](/overview/get-started/syntho-bootcamp/example-data-generation-scenarios.md).
**Example (order totals and margin always consistent):** if your demo shows “margin” or “savings”, make the math hold.
1. AI synthesize or mock `order_total` and `fulfillment_cost`.
2. Use a calculated column for `gross_margin` so it’s always correct.
```excel-formula
// New column: gross_margin (absolute calculation)
[order_total] - [fulfillment_cost]
```
**Minimal configuration steps**
1. Run a [PII scan](/configure-a-data-generation-job/privacy-dashboard/automatic-pii-discovery-with-pii-scanner.md).
2. Use **Mock** for narrative fields. Use **Mask** for validator formats.
3. Enable consistent mapping only for the direct identifiers.
{% hint style="info" %}
Use consistent mapping sparingly. It helps storytelling, but increases linkability.
{% endhint %}
Concrete example: a “customer journey” demo narrative
Goal: a dataset that always has a few customers in each lifecycle stage so demo flows never get stuck.
Example workspace name: `sales-basic`.
Example “story columns” you can enforce with calculated columns:
```excel-formula
// New column: journey_stage
IFS(
[last_order_date] >= DATEADD(TODAY(), -30, "day"), "ACTIVE",
[last_order_date] >= DATEADD(TODAY(), -180, "day"), "AT_RISK",
TRUE, "CHURNED"
)
```
```excel-formula
// New column: is_premium (ensure some premium customers exist)
IFS(
[journey_stage] = "ACTIVE", RAND() < 0.20,
[journey_stage] = "AT_RISK", RAND() < 0.10,
TRUE, RAND() < 0.05
)
```
Tip: only apply consistent mapping to the “hero entities” you showcase (top accounts, key products). This keeps the story stable while limiting linkability.
{% endstep %}
{% step %}
#### Handle keys and relationships (relational schemas)
If your demo is **single-table** (or you share one flattened table), you can skip this step.
Demo flows break on missing relationships. Validate FKs before polishing the data.
Use [Manage foreign keys](/configure-a-data-generation-job/manage-foreign-keys.md) and add [virtual foreign keys](/configure-a-data-generation-job/manage-foreign-keys/add-virtual-foreign-keys/add-virtual-foreign-keys.md) if the source schema is incomplete.
* [Key generators](/configure-a-data-generation-job/configure-column-settings/key-generators.md)
{% endstep %}
{% step %}
#### Validate and sync
Run the demo journeys against the generated dataset. Fix the exact tables and columns that break flows.
Resync whenever the product schema changes. Use [Validate and synchronize workspace](/configure-a-data-generation-job/generation-and-validation/validate-and-synchronize-workspace.md). Demos go stale fast without validation.
* [Validate and synchronize workspace](/configure-a-data-generation-job/generation-and-validation/validate-and-synchronize-workspace.md)
{% endstep %}
{% step %}
#### Tune generation settings
Prioritize fast reset times. Demo environments get rebuilt often.
Use [View and adjust generation settings](/configure-a-data-generation-job/generation-and-validation/view-and-adjust-generation-settings.md) once the storytelling rules are stable.
* [View and adjust generation settings](/configure-a-data-generation-job/generation-and-validation/view-and-adjust-generation-settings.md)
{% endstep %}
{% endstepper %}
### Common pitfalls & misconfigurations
#### Use case-specific pitfalls
* Treating demo datasets as “internal-only” and skipping privacy review steps.
* Applying consistent mapping broadly instead of only on direct identifiers
General pitfalls
These pitfalls show up in most projects:
* Running full-scale jobs before a small validation run.
* Skipping workspace validation/sync after schema changes. Use [Validate and synchronize workspace](/configure-a-data-generation-job/generation-and-validation/validate-and-synchronize-workspace.md).
* Breaking relational integrity (missing PK/FK setup, missing foreign keys, missing virtual foreign keys). Start with [Manage foreign keys](/configure-a-data-generation-job/manage-foreign-keys.md) and [virtual foreign keys](/configure-a-data-generation-job/manage-foreign-keys/add-virtual-foreign-keys/add-virtual-foreign-keys.md).
* Leaving sensitive columns on [**Duplicate**](/configure-a-data-generation-job/configure-column-settings/duplicate.md), or trusting the [PII scan](/configure-a-data-generation-job/privacy-dashboard/automatic-pii-discovery-with-pii-scanner.md) without reviewing false positives/negatives.
* Overusing [**Consistent mapping**](/configure-a-data-generation-job/configure-column-settings/consistent-mapping.md) (it slows down data generation and increases linkability).
### Governance, compliance, and automation
#### Use case-specific recommendations
* Maintain a list of approved tables/fields. Don’t add columns ad-hoc right before a demo.
* Separate internal sales demos from partner demos. Use separate workspaces and destinations to prevent accidental sharing.
* Automate a pre-demo reset: regenerate → run 2–3 scripted demo journeys → confirm no PII columns remain on **Duplicate**.
General recommendations
Use these recommendations for most workspaces.
#### Ownership and change control
* Assign a single **workspace owner** (data steward / privacy lead / DBA).
* Require a ticket or change request for generator changes.
* Duplicate a workspace before large edits. Keep the previous version as rollback.
#### Access control
* Default to **read-only** access for source connections.
* Restrict **who can view source data** in the UI.
* Use separate workspaces per environment or audience.
#### Automation (baseline)
* Use the [Syntho REST API](/syntho-api/syntho-rest-api.md) to standardize scans and runs.
* Automate data generation not workspace configuration.
* Keep job logs for failed runs. This reduces back-and-forth during support.
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