Same label, different logic
Paid customer means one thing in sales and another in finance.
DataGlue for Data & analytics leads
I can build the chart. I need a rule everyone agrees on.
Your team can query the data. The harder work is agreeing what a result means, linking the right records and keeping that meaning useful outside a dashboard.
A dedicated team builds it with you. Start with one decision.
WarehouseWebsite eventsCRMBilling
DataGlue
Show the formula.
Which events. In what order.
Example tagSigned work paid within 30 days
To your CRM, ads, BI, email or chosen LLM.
Connections and destinations agreed in the build.
Sound familiar?
You could write the query. You just can’t settle the definition.
IDs do not line up
Website profiles, CRM contacts and account records need evidence to join.
Answers stay in a dashboard
The team that needs the next action cannot use the result in its own tool.
Every new source starts again
Mapping, matching and documenting consume the time meant for analysis.
A joined picture
Build the meaning alongside the data.
DataGlue’s team maps one outcome with you. The formula shows events, order, time conditions and match keys. Tags can use your existing warehouse and delivery routes.
Systems usually joined
- Warehouse
- Website events
- CRM
- Billing
- Support
- BI
Example 01
- CRM: deal signed
- Billing: invoice issued
- Billing: payment received
In this order, within 30 days, on the same confirmed deal and invoice IDs.
Events → tagSigned work paid within 30 days
Example 02
- Website: enquiry with email
- CRM: call booked with the same email
In this order, within 7 days, with a confirmed person link.
Events → tagEnquired, then booked
Example 03
- Warehouse: order placed
- Logistics: delivery confirmed
- Support: issue resolved
In this order, within 10 days, on the same order ID.
Events → tagDelivered order with issue resolved
Example rules, not customer results. We agree source access, match keys, time windows and tag destinations in your build.
How we work
One question. Five steps with your team.
The same build process, shaped around your decision and its sources.
Book a call
Bring one question.
Talk it through with the people who would build it. We find where its data lives today.
Working session
A working session to map your data.
We map every source behind the decision, then show you what it looks like glued together. You get the scope and plan before work begins.
Build together
We connect the pieces.
A dedicated team finds the signals, glues them into one living model and matches records on evidence. Your systems stay where they are.
Go live
Your first result, ready to use.
We take the first result live in your Glue Console. Ask in plain words, see what each step read, and open the counts to their rows.
Keep building
Your team keeps the Console.
Then the next outcome builds on the same model. The rollout runs in phases, each measured against the outcome.
What you get
Useful context. In the tools you use.
Inspectable definitions
Check the event formula behind each result.
Match evidence
See confirmed links, candidates and conflicts.
Source context
Keep original records tied to the answer.
Warehouse routes
Stream evidence where the build sets that up.
Useful output
Send tags beyond BI to the team’s chosen tools.
Extra builders
A dedicated team works with your data team.
See the working
Check the approach before the call.
Read the published outcome examples, source details and comparisons behind this approach. They explain the method; they are not measured results for your role.
Trusted by
Fair questions
What you might be wondering.
Couldn’t we just build this in our warehouse?
Yes. A warehouse and reverse ETL can support this when your team builds and maintains the definitions, matches and sequences. DataGlue brings a dedicated team to that work. Keep your warehouse and the delivery routes you trust.
Can our team inspect and export the records?
Outcome counts open to their rows. Sessions, events and identity links can be exported on request. Evidence can stream to your own Postgres warehouse where the build sets that up; source changes may need new mapping.
Do we need a data team or clean data first?
No. Bring one question and the names of the systems behind it. A dedicated team maps the sources, joins the records and builds the result with you. Your team sets ownership, privacy and quality rules.
What does it cost, and what happens on the call?
The first call checks the outcome, the sources and the fit. No system access is needed for that conversation. If it fits, we map the data in a working session. The build is a separate engagement, scoped and priced before work begins; there is no public rate card.
Your next step
Bring the question you keep coming back to.
We’ll talk through the outcome, where its data lives and whether a build fits. No system access needed for the first call.
Book a Call
Free first call. Scope and price agreed before a build begins.
Is it a fit?
An honest fit check.
A good fit
This may suit you if…
- A repeated business question needs several sources.
- You want help building the matching and event rules.
- Your team wants to keep its warehouse and BI tools.
Probably not a fit
This may not suit you if…
- You only need a chart over an agreed data model.
- Your team already owns and maintains the full outcome pipeline.
- You want uncertain record links treated as confirmed.