Enterprise
Portfolio
Analytics
As Make scales from workflow builder to enterprise automation platform, the challenge shifts from building automations to understanding and managing automation portfolios.
Make has crossed the
enterprise threshold
Make started as a visual integration builder. For many enterprise customers, it has since become the connective tissue of their automation programs — running revenue-critical workflows across teams and functions.
The assignment
Identify one critical friction point preventing large organizations from running business-critical workflows with confidence on Make.
Enterprise customers now operate complex automation ecosystems — hundreds or thousands of scenarios, AI agents, cross-functional programs. The scale they've reached looks very different from the "connect two apps" use case Make was originally built around.
At that scale, a new class of challenge emerges. And it's not about building more automations.
"As Make scales from workflow builder to enterprise automation platform, the challenge shifts from building automations to understanding and managing automation portfolios."
This single observation frames the case study. The challenge isn't whether individual automations work — it's whether the organization can understand and manage them as a whole.
iPaaS is becoming
something bigger
The market is no longer buying integration tools. It is buying enterprise automation control planes — platforms that provide operational confidence at scale.
API glue code
No-code / low-code
Operational control plane
From Connectors to Control
The future platform winner is not just the one with the most connectors. Enterprises increasingly expect operational confidence at scale — governance, visibility, and accountability as first-class capabilities.
Enterprise Orchestration
Analyst research identifies the emerging category as enterprise orchestration systems — not automation canvases. The distinction matters: buyers are now looking for a platform to run their automation program, not just build workflows.
AI Adds Urgency
As AI agents become part of automation portfolios, the need for a control plane intensifies. Nondeterministic AI flows running alongside deterministic automations require clearer visibility, not less.
The Market Signal
Gartner and Forrester both identify "business process orchestration" and "automation governance" as the fastest-growing enterprise requirements in the iPaaS space. Vendors who deliver on operational confidence — not just workflow creation — will define the next generation of the category.
The window to own
enterprise confidence is open
Automation adoption is accelerating. The bottleneck is no longer creation — it is management. The platform that solves this first wins enterprise retention.
The challenge is shifting
Why enterprises are at risk
The highest-value opportunities in enterprise automation are not basic no-code capabilities.
They are enterprise-operating capabilities — the systems that help organizations run automation safely and confidently at scale. Visibility, governance, accountability, portfolio management, and business reporting. This is the category Make must win.
Three roles,
one shared frustration
Portfolio visibility failures manifest differently at each level of the organization. Understanding each lens — and where they converge — is critical to designing the right intervention.
- Operational resilience across all business-critical workflows
- ROI visibility — total value generated by automation
- Governance assurance without micromanaging teams
- A single source of truth for automation health
- Executive-ready reporting for quarterly business reviews
- Full visibility across the entire scenario portfolio
- Clear ownership: who is responsible for each automation
- Health overview: which scenarios are at risk right now
- Cost tracking and operation budget management
- Business-structure-aware filtering and prioritization
- Operational awareness of their team's scenarios at a glance
- Fast troubleshooting when business-critical flows fail
- Visibility into usage trends and error patterns over time
- Ability to report team automation impact to leadership
- Longer history than current Make Analytics provides
Enterprise pain clusters around
control, not capability
13 Make-specific enterprise-relevant quotes were manually coded from G2, Capterra, GetApp, and the Make Community. The dominant themes are not missing connectors — they are debugging opacity, change management, but ultimately reliability and control as deployments scale.
Call
The user finds execution log search to be poor — results are not persistent and the matching logic is unclear, making root-cause analysis harder than it should be. Duplicating scenarios and sub-scenarios is tedious and unintuitive, creating friction when scaling automation patterns across teams. Across the conversation, a consistent theme emerged: the user wants more visibility into logs and variables, and better filtering and search capabilities to understand what their automations are actually doing.
"The error reporting is a bit technical… You have to either look up the issues or learn how to read the technical output. Credit use data… you can only view a month's previous data/credit usage. This can be troublesome when you are on pro or non-enterprise plans and you need to 'budget' your credits."
"The 30-day log retention and full search are available only in the highest tier."
"Keep in mind that this will make debugging of the scenario very limited."
"I would like to modify a scenario without taking it into use. A way to do this is to have different versions of a scenario and then determine which one is active. Then you also have history to go back and check how the scenario has changed. If you have version control, then it would also be possible to have auto save as you are not modifying the live scenario."
"I have lost countless hours on scenario builds because Make doesn't auto-save and only saves when you click save."
"I made a ton of edits and then came back to my computer the next morning, it asks if I want to save or discard changes, THEN it times out and erases all of them!!"
"We do not find ANY way to connect a MS Teams User that is not an Admin. We tried many different approaches, none of them worked."
"Yes, we got the redirect URL from your docs: Microsoft Teams" — [the docs URL was wrong; Make confirmed and issued a correction]
"As our automation usage grew, the cost per operation started to add up. Once you exceed the free tier (1,000 operations/month), costs escalate quickly — especially for scenarios that run frequently or process large volumes of data."
The core pattern
Make is liked for its flexibility and visual builder. Enterprise pain begins when scenarios become complex, shared, or mission-critical. The unmet needs are not about connectors — they are about the operating model: observability, safe editing, auth governance, and cost predictability. That is a classic sign of a platform that has grown into enterprise use cases faster than its enterprise operating model has matured.
While the individual requests vary, they share a common pattern: customers are building increasingly large automation estates and lack the enterprise-grade visibility and controls required to operate them confidently.
Strong signals
from the community
One Idea Exchange thread — requesting a detailed scenario dashboard with longer history — captures the depth of unmet demand. It is not an isolated feature request. It is multi-year evidence of a structural gap.
List of scenarios: Detailed usage dashboard with a longer history
"We can start with a graph/dashboard to view which scenarios have used what percentage of the operations/data allowance for that month." — Miko Garrido · Created by Tereza Klobouckova · July 9, 2022
How to read these signals
The Idea Exchange thread validates breadth of demand (220 votes, 4 years, now Reviewed). John Sultant's post validates depth: a user with 200+ scenarios has built a shadow analytics layer in Notion and is spending a fifth of their operations to maintain it. Together they show the same gap from two angles — many users feel it, and the users who feel it most are doing the most work to compensate.
Make has a strong
enterprise foundation
This is not a platform starting from scratch. Make already provides meaningful enterprise-grade capabilities — the gap is specific and surgical, not foundational.
SSO & Identity
Enterprise-grade single sign-on, SAML integration, and identity management. Enterprise security requirements are met at the authentication layer.
Audit Logs
Full audit trail of platform actions. Compliance and security teams can track who changed what, when, and why across the organization.
Teams
Collaborative workspaces with role-based access. Teams enable ownership and permissions management across the organization. Note: not hierarchical.
Make Grid
Organization-level topology map providing visual orchestration of automation landscapes. Provides high-level oversight of what's running where.
Enterprise Security
SOC 2 Type II, GDPR compliance, encryption at rest and in transit, IP allowlisting, and dedicated enterprise infrastructure options.
Custom Scenario Properties ★
Enterprise customers can define and populate custom properties on scenarios: Business Owner, Department, Cost Center, Criticality, Environment, Business Process, Revenue Impact. These can be displayed, filtered, and sorted in the Scenario list. This is the critical foundation the solution builds on.
The missing layer
Customers can classify their automations using Scenario Properties. But they cannot operationalize that business context inside Analytics and Dashboards. The metadata exists. The analytics do not know it exists.
Custom Scenario Properties are an enterprise-only feature that lets organizations add structured metadata — dropdowns, booleans, numbers, dates — to every scenario. From Make's own documentation:
Custom Scenario Properties are fully supported in the Scenarios module — for filtering, sorting, and table views. They do not exist in the Analytics Dashboard. There is no way to filter analytics by Importance, Owner, or any other custom dimension, nor to aggregate execution data by business structure. The metadata exists. The analytics do not know it exists.
The Problem Statement
Organizations can classify automations
using Scenario Properties.
They cannot operationalize that context
inside Analytics and Dashboards.
As organizations scale from dozens to hundreds of automations, Make provides workflow visibility but lacks portfolio visibility. Enterprise customers already provide business context through Scenario Properties, but Analytics cannot leverage that context. As a result, organizations that have invested in classifying their automations by department, criticality, and business process cannot use that classification for anything beyond filtering a list.
This prevents large organizations from operating automation portfolios with confidence — forcing them to rebuild the analytics layer externally in spreadsheets and Notion databases.
It blocks CIOs from running automation-as-infrastructure programs with board-level accountability — making Make feel like a departmental tool rather than an enterprise platform.
Left unaddressed, this gap becomes a competitive vulnerability as enterprise-focused iPaaS vendors add governance and portfolio management as core platform capabilities.
Enterprise Portfolio
Analytics
Make already provides workflow visibility. Enterprise Portfolio Analytics extends that visibility into business context — helping enterprises understand, manage, and scale automation portfolios today, while laying the foundation for broader capabilities the market is moving toward.
💡 This proposal does not introduce a new enterprise platform. It unlocks value from capabilities Make already owns: Scenario Properties, Analytics, Teams, and Folders.
Deliberately Focused Scope
Enterprise Portfolio Analytics introduces two capabilities: Subfolders and a Custom Analytics Widget Builder. It is not a workflow execution platform, a governance engine, or an observability platform. It is a prerequisite capability — portfolio visibility through business context — that enterprises need before they can govern or orchestrate automation at scale.
How This Complements Make Grid
These three layers are complementary, not competing. Grid answers topology questions. Analytics answers operational questions. Enterprise Portfolio Analytics answers business questions.
Long-Term Direction
The industry is moving toward enterprise automation control planes. But reaching that vision requires foundational capabilities first. Before organizations can govern automation portfolios, they must be able to understand them. Before they can orchestrate automation at scale, they need visibility into ownership, criticality, cost, business impact, and organizational structure.
🧭 Enterprise Portfolio Analytics is a foundational step toward the broader enterprise control plane vision emerging across the iPaaS market.
Teams provide ownership and permissions, but they are not hierarchical. Make currently lacks a way to mirror the organizational hierarchy of the enterprise. Subfolders introduce a simple, purpose-built structure for this — separate from permissions.
Administrators build analytics widgets from system fields and custom Scenario Properties — in a single unified builder. No artificial distinction between system metrics and business data. The platform provides primitives; customers create the views they need.
The widget builder experience
A five-step flow that feels native to Make Analytics — because it is. System fields and custom properties are treated identically throughout.
What enterprise teams actually build
Five illustrative dashboards — each answering a different business question. All built from Make's native analytics data (operations, executions, errors, status, team, folder) plus Custom Scenario Properties. None exist today. All become possible with the widget builder.
Reliability Dashboard
Otázka: Jsou naše business-critical automace zdravé?
| Scenario | Department | Errors (30d) | Error Rate | Last Failed |
|---|---|---|---|---|
| Revenue Sync | Finance | 891 | 10.68% | 3hr ago |
| Lead Enrichment | Marketing | 143 | 6.2% | Yesterday |
| Invoice Processing | Finance | 142 | 0.57% | 2min ago |
Cost & Consumption Dashboard
Otázka: Kde spotřebováváme nejvíce automatizačních zdrojů?
| Scenario | Team | Operations (12M) | Usage Change | Business Process |
|---|---|---|---|---|
| Payment Reconciliation | Finance | 312,480 | ↑ 34% | Finance Ops |
| ERP Order Sync | Operations | 198,760 | ↑ 8% | Order Management |
| CRM Lead Push | Marketing | 156,320 | ↑ 62% | Lead Generation |
Governance Dashboard
Otázka: Kdo vlastní naši automatizační krajinu?
| Team | Critical | No Owner | Inactive |
|---|---|---|---|
| Finance | 14 | 4 | 1 |
| Operations | 11 | 2 | 0 |
| Marketing | 6 | 1 | 0 |
| IT | 9 | 0 | 3 |
Executive Portfolio Dashboard
Otázka: Můžeme se na naši automatizační platformu spolehnout?
ROI & Business Impact Dashboard
Otázka: Jakou hodnotu automatizace přináší? (requires Custom Properties: Revenue Impact, Hours Saved, Cost Avoided)
The foundation for what comes next
Enterprise Portfolio Analytics is not the destination — it is the prerequisite. The platform provides primitives; each enterprise creates the views that match how they operate. As organizations build Reliability, Cost, Governance, and Executive dashboards, they develop the organizational data model that future capabilities — policy enforcement, resource governance, orchestration — will depend on. Portfolio visibility today. Control plane capabilities tomorrow.
How we know
it's working
Four measurement layers. The first two confirm the feature landed. The third — Value Realization — is the most important: it validates the core hypothesis that enterprises will manage automation through business context, not just technical metrics.
These are the most important metrics — they directly validate the core hypothesis.
Treat these as directional outcomes rather than launch KPIs.
The strongest success signal
This feature is not trying to improve workflow execution. It is trying to help enterprises organize, understand, and manage their automation portfolios through business context. Therefore the strongest signal is not error reduction or retention itself — it is Enterprise customers actively using Scenario Properties inside Analytics to create business-level views of their automation landscape.
Phased delivery,
immediate value
Three focused phases. Each ships independently and builds on the previous without requiring rework. Phase 1 alone solves the most-voted customer pain point.
Every claim,
traceable to a source
All major claims in this case study are traceable to one of the following sources. Primary research was collected in 2026.
The problem is not that enterprises cannot build automations in Make.
The problem is that once they build hundreds of them, they lack a way to understand them through the lens of their business.
Enterprise Portfolio Analytics solves that gap by turning existing business metadata — already defined, already populated, already trusted by the organization — into actionable portfolio visibility. It helps enterprises understand, manage, and scale automation portfolios today, while laying the foundation for the broader control-plane capabilities the market is moving toward.
The investment is manageable. The leverage is enormous. The timing is now.