Make vs Zapier for BI Consultancies Managing Client Delivery
A business intelligence or data engineering consultancy has an odd relationship with automation tools like Zapier and Make: the client-facing work is often building serious data pipelines in dedicated tooling, while the firm's own business operations, project handoffs, deliverable tracking, client reporting, run on something much lighter.
Keeping those two layers distinct matters. This guide is about the business operations layer specifically, not about whether Zapier or Make should sit inside a client's actual data pipeline, which is a different and generally inadvisable idea covered in the third section below.
Vendors Covered in this Article
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Turning a scoped engagement into a properly resourced project
A data consulting engagement's scope usually specifies data sources, deliverable types, dashboards, pipelines, models, and expected data volume, and that detail should drive how a new project gets set up rather than a flat template applied regardless of engagement shape. Make's ability to branch project setup based on the specific deliverable types named in a signed scope handles this better than a one-size template.
This matters because a dashboard-only engagement and a full pipeline-build engagement need very different project structures, from different milestone cadences to entirely different technical review checkpoints, and getting that setup right from day one avoids a messy restructure a few weeks into the project.
Alerting internally when a client's dashboard refresh silently fails
A client-facing dashboard that quietly stops refreshing is one of the worse failure modes in this business, since the client often doesn't notice until they're looking at stale numbers in an important meeting. An internal monitoring check that confirms each client dashboard's last successful refresh, alerting the team well before the client would ever notice, is a genuinely valuable use of either automation tool.
Make's ability to check refresh status across a whole portfolio of client dashboards in one scenario, rather than needing separate monitoring per client, scales better as your client roster grows. This is squarely business-operations monitoring, watching the health signal a pipeline tool already reports, not building or running the pipeline itself.
Why the automation tool shouldn't sit inside the actual data pipeline
It's tempting, especially early on, to use Zapier or Make as a quick connector inside an actual client data pipeline, pulling data from one source and pushing it into another as part of the transformation work itself. Resist this for anything beyond a genuinely temporary prototype. These tools weren't built for data engineering workloads, and a client relying on one as a hidden piece of their production pipeline is inheriting a fragile dependency they likely don't know exists.
Keep the line clear in every engagement: dedicated data tooling for the actual pipeline work you're delivering, and Make or Zapier strictly for your own firm's business operations around that delivery. Blurring this line is a common way client work quietly turns into unsupported technical debt.
Tracking deliverable review cycles without losing feedback in email threads
A dashboard or model deliverable usually goes through a review cycle with the client, feedback, revision, re-review, and losing track of open feedback items in an email thread creates the same kind of rework risk seen in other project-based service businesses. Logging each piece of feedback as a discrete tracked item, checked for resolution before a deliverable moves to final, closes that gap.
Either tool can log incoming feedback into a tracker. Make's ability to check that every item from a review round is resolved before the deliverable can be marked final is the more reliable version, catching a missed item rather than trusting that if the file was updated, everything requested was addressed.
A feedback tracker that holds up through a review round does the following:
- Log each piece of client feedback as its own item instead of leaving it buried in an email thread.
- Give every item a status, so open, addressed and confirmed are visible at a glance.
- Check that every item is resolved before a deliverable moves to final.
- Confirm ambiguous or technically nuanced feedback directly with the client rather than relying on the tracker alone.
Deciding how much internal reporting infrastructure to build
Change failure rates vary widely by how disciplined a team's own delivery process is1, and a data consultancy's internal business operations deserve some of that same delivery discipline: clear ownership of each automation, a place to check what broke, and a habit of testing changes before they touch a live client-facing workflow.
Don't over-invest in internal tooling for its own sake, though. The goal is freeing consultants to spend billable hours on client delivery, not building an elaborate internal platform that itself becomes a maintenance burden. Keep the operations layer as lean as it can be while still catching the failures that actually matter, like a silently broken client dashboard.
Assign clear ownership for each internal automation you build, one named person responsible for knowing how it works and noticing when it breaks. A consultancy where every automation was built by whoever had a free afternoon, with no owner tracking whether it's still functioning correctly, tends to accumulate quiet failures that nobody catches until a client asks a pointed question first.
What Good Looks Like
Good consultancy operations automation resources a new engagement correctly for its actual deliverable types, catches a silently broken client dashboard before the client does, and never lets a review-round feedback item get lost between email and the final file.
Building The Capability (5-Stage Skill Ladder)
How to Get Started
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Zapier fits simpler internal notifications, like a basic new-project kickoff checklist, for a consultancy running a small number of similarly shaped engagements.
Make earns its complexity once engagement types genuinely vary, or once monitoring needs to check a growing portfolio of client dashboards in one place.
Workato is worth evaluating for a larger consultancy needing centralized governance across many internal systems, separate from any client-facing data tooling.
Frequently Asked Questions
Is it ever okay to use Zapier or Make inside an actual client data pipeline?
Only as a clearly labeled, genuinely temporary prototype, never as a hidden permanent piece of a production pipeline. If a lightweight connection proves out an approach, replace it with proper data engineering tooling before the client comes to depend on it, and tell the client which parts are prototype versus production.
How do we catch a client dashboard refresh failure before the client notices?
Build a monitoring check across your full portfolio of client dashboards that confirms each one's last successful refresh time, and alert your team when any dashboard goes past its expected refresh window. Catching this internally, even an hour before a client would notice, protects the relationship.
Should deliverable feedback tracking replace direct client conversations?
No, use it to make sure nothing from a review round gets lost, not to replace the conversation itself. A tracked feedback item still benefits from a consultant confirming understanding directly with the client, especially for anything ambiguous or technically nuanced.
Sources
Where we quote a benchmark, we show its source. Other figures in this guide are estimates or general guidance, so check them against your own numbers.
- Change failure rate by DORA performance cluster. DORA Accelerate State of DevOps 2024 (Google Cloud), cluster table via Octopus Deploy analysis, 2024.
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