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Case study · a multi-unit franchise operation

The questions used to wait. Now they get answered while the meeting is still happening.

The operator already owned the data lake. What he did not own was a way to ask the lake anything without routing the question through a vendor and waiting for the answer to come back.

At a glance
Company
A multi-unit franchise operation
Who
The operator running the group
Starting point
A working data lake, and every question about it routed through the vendor
What changed
senti on top of the existing lake, self-service for the people who need answers
Outcomes
Live data validation in owner and peer group meetings, regional directors reaching information they previously could not, dashboards built from a requirements list rather than a development cycle, no new technical headcount 2

1The situation.

He had the data lake. That was not the problem.

The problem was that every data need routed through a vendor. Any query, any fix, any question meant reaching out and waiting. Dashboard access was limited to whatever views already existed, so anything outside them meant going back through the vendor again. Direct work meant querying the warehouse by hand through a database client.

The cost was not only time. Credibility was exposed. Franchisees would catch errors in the numbers, in public, in front of other franchisees.

“Every data question used to come to Rayson and wait. Now I answer them in the meeting.” 1
A franchise operator running senti on an existing data lake

2What he tried first.

An off-the-shelf cloud assistant from the warehouse vendor. It returned nonsensical answers more than half the time, because it had no context and no semantic model underneath it. Beyond that, he found nothing viable.

“We tried the warehouse vendor's own assistant. More than half the time it gave us answers that made no sense, because nothing underneath it understood our data.”
A franchise operator running senti on an existing data lake

3What using it looks like.

senti is the first stop for data questions from across the organization.

Quality assurance
Finding stale datasets, broken dynamic tables, and anomalies quickly.
Query building
Handing over a list of requirements and getting the query back directly.
Dashboard development
Dashboards built from a requirements list in a fraction of the time the previous cycle took. 2

Beyond the operator himself, regional directors now reach a body of information that was previously out of their hands entirely.

“My regional directors can get to information that was completely out of reach for them before.”
A franchise operator running senti on an existing data lake

4What changed.

In the room Data gets validated live, during owner and peer group meetings
Reach Regional directors access information they previously could not get to
Delivery Dashboards get built from a requirements list rather than a development cycle
Headcount All of it without adding an engineer, an architect, or an analyst

What he emphasised: the pace of development, and that contextual accuracy has improved release over release.

“I can validate a number live, in front of owners, instead of promising to follow up.”
A franchise operator running senti on an existing data lake

5Who he says it is for.

Any mid-size organisation, franchise system, or multi-unit operation aggregating data. Especially relevant to anyone standing up a data lake, and to anyone trying to avoid the cost of dedicated technical hires. His framing is that owning your data is the first step and senti is what lets you scale quickly from there.

6Bring us the question you can't currently defend.

One hour, your warehouse, your question. You leave with the answer and the trace behind it.

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1 Wording attributed to the operator is proposed from his account of the deployment and is pending his approval; the source material is a summarised set of notes, not a transcript.  2 Dashboards described here are built by the customer from queries senti produced. senti does not export to BI tools.