PiConnect
When your data is not ready yet.
Optional data preparation for organisations whose analytics ambitions are ahead of their data infrastructure, standalone or embedded.
The problem PiConnect solves
Panintelligence queries your data where it lives. For most organisations that works immediately, connect and you’re live.
But some aren’t there yet: data is fragmented, there’s no warehouse, and the roadmap has outpaced data engineering.
That’s what PiConnect solves: a structured layer that prepares data so Panintelligence can run on a reliable foundation.
What PiConnect does
Is PiConnect right for you?
You probably need PiConnect if…
Your data is fragmented across multiple systems with no single source of truth. You don’t have a data warehouse, or can’t justify building one.
Your analytics ambitions are ahead of your data engineering capacity. You need to onboard new data sources quickly, without building bespoke pipelines each time.
You probably do not need it if…
You already have a mature data warehouse such as Snowflake, Databricks, BigQuery, Postgres or SQL Server, with an established in-house data engineering team managing your pipelines.
Panintelligence already connects cleanly to your data source and performs well in practice, and your data governance framework is already in place.
For internal teams
Increase
- A repeatable onboarding process for customers whose data never looks quite the same
- Confidence that a messy customer source system will not break their dashboard
- One data layer that holds steady as your customer base diversifies
- Capacity to say yes to a customer’s unusual data setup instead of quietly avoiding it
Decrease
- The same metric calculated three different ways in three different spreadsheets
- A new acquisition or system migration that stalls the entire analytics roadmap
- Dashboards quietly rebuilt by hand whenever an upstream system changes its export
- Analyst hours spent reconciling numbers before anyone can act on them
For SaaS vendors
Increase
- A repeatable onboarding process for customers whose data never looks quite the same
- Confidence that a messy customer source system will not break their dashboard
- One data layer that holds steady as your customer base diversifies
- Capacity to say yes to a customer’s unusual data setup instead of quietly avoiding it
Decrease
- A bespoke data mapping exercise for every new customer you onboard
- Inconsistent dashboard experiences depending on how clean a customer’s data happens to be
- Implementation delays caused by a single customer’s non-standard data source
- Engineering time spent writing one-off scripts that nobody documents afterwards
The architecture is intentionally honest
Panintelligence, PiDashboard, PiReport and PiPredict, queries data in place and moves nothing. PiConnect does: it extracts, transforms and prepares data into an analytics-ready layer. That’s a different architectural decision, and you should understand it before you buy it.
If a vendor is vague about whether their platform moves your data, that’s worth investigating. We’ll always tell you exactly what PiConnect does, and if you don’t need it, we’ll say so.
Deployment and delivery
- Available as a fully managed service by Panintelligence, or configured collaboratively with your team
- Hybrid approaches supported: combine live queries, cached data, and prepared data
- Professional services support available for design and initial data flow setup
- Priced separately from the core platform, scoped on data sources, volume, and deployment model
- No mandatory data migration or enforced architecture change
Questions worth asking before you buy anything
The foundation everything else builds on
PiReport and PiPredict both build on PiDashboard’s governed data model, whichever way you deploy it. Add a module and you activate new capability on the foundation you already have. You do not re-implement, re-connect, or re-govern anything.
See PiDashboard on your data
Standalone or embedded, live on day one of your PoC.
Add automated reporting
PiReport runs on the same data, no rebuild needed.
