Build to scale

Smartsheet Bridge beginner tips: What I wish I had been told when I started

Adrien Leduc — Mon, 25 May 2026 13:32:37 GMT

1. Use the logs to grab reference paths for dynamic data

It sounds obvious in hindsight, but when you're starting from scratch it isn't. The logs are your best friend for finding the exact reference path you need. Copy it from there and paste it wherever you need to reference data dynamically elsewhere in your workflow.

2. Loops are built with child workflows, not a "For Each" module

If you've been asking an AI assistant for help with loops, it may have pointed you toward a "For Each" module, but that doesn't exist in Bridge. Instead, you build two workflows: a parent and a child. In the parent, once you have an array, pass its reference (typically shown with a number) into the Number of Runs field of your child workflow module. Bridge will then iterate over every item in that array.

3. For bulk data operations, use scripts and the Smartsheet API

If you need to add or clear large amounts of data, don't try to do it through a loop. You'll hit two problems: it's slow, and it will eventually fail mid-run. The Smartsheet community has solid resources for this:

4. You can merge branches

Select your source item, hold Alt, then click the target item. Click again to cancel. This is genuinely buried in the documentation, I even struggled finding it again for this article.

5. Use Match instead of Equal in conditional junctions

Equal only works with numbers. Match works with any string, making it the safer default in almost every case.

6. Junctions don't have to be binary

You're not limited to two-path conditional junctions. Multipath junctions are supported. They're more complex to configure, but worth knowing about: Routing workflows with junctions.

7. Use a dedicated error sheet for API error notifications

There are two categories of errors to be aware of:

API errors are manageable. You get error codes you can act on. The simplest approach is to write each error to a dedicated "errors" sheet, then configure Smartsheet's native email notifications to alert you from there.

Bridge errors are trickier. They halt the entire workflow with no outbound notification. You'll need to catch and manage these from within Smartsheet itself, not Bridge. For example if you expect a sheet to be regularly filled with 2000 lines and you only get 1500 that could be a trigger for you to check, which you can automate.

Building with Bridge is not easy if you're not familiar with the tool. If you want help for a Smartsheet bridge project, please contact me using the form below.

Why the Stacked Bar Chart is My Go-To in Smartsheet Dashboards

Adrien Leduc — Mon, 18 May 2026 09:40:50 GMT

Making the Most of Smartsheet's Stacked Bar Chart

Let's be honest, Smartsheet dashboards offer a fairly limited chart library. You get your classics: pies, bars, lines, and scatter plots, with a few variations of each so creativity quickly becomes a necessity.

There are clever workarounds out there, like this community-built speedometer chart, but as you can probably tell, it's not exactly a quick build.

Given those constraints, I think the better approach is to master what is already available and squeeze as much value as possible out of the native chart types.

My favourite is the stacked bar chart. It's the most versatile chart in the toolkit, and it works across a surprising range of use cases. Here are four of my favorites:

1. Positive and negative counts

This is the stacked bar chart in its most traditional form: positive values stacked above the axis, negative values below. It's ideal for showing revenues and costs by month, for example. You can pair it with a simple bar chart showing the monthly balance, or a cumulative version that tracks the running total throughout the year.

2. Count by category (vertical or horizontal)

Another classic use, breaking down a count of items by category. Clean, readable, and instantly familiar to any stakeholder. The horizontal variant works particularly well when you have long category labels.

3. RAG status (Red, Amber, Green)

Most people would use the half-donut chart for RAG indicators, and I do too sometimes. But I find the arc shape can distort perception. The middle segment appears taller and therefore more prominent than it really is. A stacked bar keeps all three statuses visually comparable and on equal footing.

It's also a practical space-saver on a crowded dashboard.

4. Data quality and completeness tracking

This is my personal favorite application of the stacked bar chart, and the one I find most underused.

When managing a portfolio of projects, it's easy for updates to slip through. Project managers miss their deadlines, fields get left blank, or entries contain errors. Conditional formatting on your portfolio sheet can flag these issues, but how do you surface that information quickly to leadership or PM managers?

The principle is straightforward (elegant even, if you allow me), every row in your chart must total the same value: the total number of items in your list (projects, in this case). You then divide each row into three segments:

Correct: entries that are complete and accurate
Issues: entries with errors or inconsistencies
Missing: entries with no update at all

(Those are the best labels I've found, let me know if you have better taxonomy.)

This is typically built through an intermediary metrics sheet that calculates these three counts per project manager or time period. The result is a chart that makes gaps and errors visible at a glance, no digging required.

If you'd like help building charts like these in Smartsheet or need support designing your dashboards, use the form below to get in touch.

Smartsheet won't fix your data

Adrien Leduc — Thu, 07 May 2026 14:13:39 GMT

Your dashboards are only as good as your data

Which means that, most of the time, they are not that good.

Every organisation wants dashboards. They want visibility, better decisions, and a quick way to gauge the health of their operations.

So people build dashboards, and at first, everyone is excited. The charts look clean, the metrics are live, and leadership finally has a central place to look at performance. Then, a few months later, someone opens the dashboard and says:

"This doesn’t make sense."

Cue the dramatic music.

So what went wrong?

Usually, it is one of these:

Nobody, or almost nobody, is updating the data
The data was incomplete from the start
The format changed and broke the reporting logic
The business stopped using one data point and quietly replaced it with another
The data point being used was misunderstood from day one
The dates are a complete disaster

If you have started building dashboards in Smartsheet, chances are you have already run into at least one of these problems, and the important thing is this: Smartsheet is not the problem.

Smartsheet reflects the quality of your operational data. If the data is inconsistent, incomplete, or poorly defined, your dashboards will reflect that too. The good news is that a lot of these issues are preventable.

Here are some of the most common problems I see, and how I usually deal with them.

1. Nobody is updating the data

This is probably the most common issue. People often expect data collection to happen naturally. It does not. You know the classic:

"Have you opened a ticket?"

Everyone hates hearing it, but there is a reason organisations rely on these processes. That is how work gets tracked, prioritised, and reported on.

The key is to collect information at the point where people still have an incentive to provide it. If someone needs support, approval, access, or resources, that is when the information should be captured. Not three weeks later when nobody remembers what happened.

For CRM-style processes, this becomes harder because there is often no immediate leverage. Sales teams, operational teams, or managers need to genuinely buy into the importance of maintaining data quality.

A few things help:

Keep forms and fields as short as possible
Use dropdowns instead of free text wherever possible
Standardise field definitions
Remove ambiguity completely

If two people can interpret a field differently, they eventually will.

2. The data was incomplete from the start

This one is painful. Sometimes the data simply does not exist. Maybe people skipped fields for years. Maybe an old process never captured the information properly. Maybe half the historical records are unusable.

At that point, you need to decide whether fixing the past is actually worth it.

Sometimes you can reconcile missing information from another system or database. Sometimes you cannot, and even when you technically can, the effort required may not justify the value. Sometimes you may need to accept the loss and move forward with cleaner processes.

You can also compromise. For example, maybe last year’s data only exists as totals, while this year includes categories and breakdowns. That is still usable, as long as everyone understands the difference. This might happen anyway as you're improving the data with time.

The format changed and now everything is broken

This happens constantly.

One day the system uses: TRUE / FALSE

Then suddenly it becomes: 1 / 0

Or someone renames statuses, or a department starts using a different workflow, or a field quietly changes meaning and suddenly the dashboard logic no longer works.

One thing I recommend is avoiding direct edits to historical data whenever possible. If existing data has already been used in reports, workflows, or exports, changing it retroactively can create even bigger problems.

Instead, I usually create a new standardised column and use formulas to reconcile the old and new formats into a single clean output.

That way:

the original data stays intact,
reporting becomes consistent,
and the transition is easier to audit.

The data point is not what you thought it was

This is far more common than people realise.

A column called “Completion Date” sounds straightforward until you discover:

one team uses it as a submission date,
another uses it as an approval date,
and a third only fills it in when finance validates something.

This usually happens because nobody documented the fields properly.

Before building reporting logic, always confirm:

what each field actually means,
who updates it,
when it gets updated,
and what business process it represents.

And do not rely on a single person’s explanation.

Different teams often have completely different interpretations of the same data.

Dates are chaos

Dates deserve their own category because they fail in spectacular ways.

You get:

start dates that are actually end dates,
text pretending to be dates,
mixed formats,
timezone confusion,
and the eternal USA versus rest-of-the-world format war.

Thankfully, Smartsheet handles dates relatively well as long as the columns are configured correctly.

A few practices help a lot.

- For system and reporting data, use the YYYY-MM-DD which sorts correctly everywhere and removes ambiguity.

- For user-facing displays, use month names instead of numbers. For example: 15 June 2027. Nobody will confuse that format.

- When linking sheets together, always make sure both columns are configured as Date columns. If one side is treated as text, you eventually end up with broken formatting, failed formulas, or reporting inconsistencies.

Final thoughts

Most dashboard problems are not dashboard problems. Smartsheet can help structure, automate, and surface information extremely well, but it cannot magically fix broken operational habits. If you need help to get a handle on you data and structure it in beautiful dashboards, please use the form below to contact me:

How to Build Client-Level Dashboards in Smartsheet Control Center

Adrien Leduc — Wed, 22 Apr 2026 10:31:11 GMT

Managing Client Portfolios in Smartsheet Control Center

Last time, we looked at how to automate project portfolios in Smartsheet using Control Center.

But as your portfolio grows, a new challenge appears:
how do you give teams a clear, client-level view across multiple projects?

You can build reports filtered by “Current User” so teams only see their own projects. That works, until someone needs to focus on a single client. At that point, things start to break down.

This is where Control Center can go a step further.

The Goal: A Client-Level Dashboard

What you’re really aiming for is a dedicated dashboard per client, showing:

Key client information
Assigned team
All active and archived projects
Project value and progress
Visual summaries (charts)

To get there, you need to structure your data differently.

Step 1: Build an Account Portfolio

If you’re using the Smartsheet Salesforce connector, this is straightforward—just configure a new pipeline.

If not, it is common to build your account portfolio manually. Otherwise you could populate it via:

API
Bridge
Data Shuttle

As with project portfolios, you can control which accounts are processed by the control center using a filter field (e.g., only include accounts with at least one active project).

Step 2: Create Client-Level Project Sheets

This is the key piece and where things get more advanced.

Because reports can’t dynamically group data by client in the way you need, you’ll provision dedicated sheets per client thanks to the control center.

Typically:

One sheet for active projects
One sheet for archived projects

These should mirror your project portfolio structure, but only include relevant columns.

Step 3: Structure the Sheet for Automation

To make this work:

Add an auto-number column starting at 1
→ This defines the order of projects per client
Pre-create enough rows to handle the maximum expected projects per client
Add a Client Name column
→ Populate it via the metadata sheet using a column formula

Step 4: Pull Project Data Dynamically

Use an INDEX/COLLECT column formula to bring in the projects unique reference number:

=INDEX(
  COLLECT(
    {Project Unique ref},
    {Account Name Column},
    [Account Name]@row
  ),
  [AutoNumber]@row
)

This effectively builds a client-specific project list, ordered by your auto-number column.

For the remaining fields, you can use Smartsheet’s new table interface + reference feature to quickly generate the corresponding formulas (instead of writing INDEX/MATCH manually for each column).

Step 5: Build Reports

Create reports based on your client-level sheets:

Active projects report
Archived projects report
Any additional views relevant to your workflows

Step 6: Create the Dashboard

Finally, assemble everything into a dashboard:

Embed your reports
Add charts (e.g., project value, progress)
Include key client information

Once deployed, each account in your portfolio links directly to its dashboard—giving teams a clean, focused view ahead of client reviews, QBRs, or OGSM sessions.

A Quick Reality Check

This is not a beginner setup.

What I’ve outlined here covers the core mechanics, but in practice:

There’s significant configuration behind Control Center
Dashboard design varies heavily by use case
Data structure decisions early on matter a lot

If You’re Considering This

If your teams are struggling to get a clear client-level view across projects, this approach can make a big difference—but it needs to be set up properly.

If you want help designing or implementing it, feel free to reach out via the form below.

Smartsheet Control Center: Automating Multiple Portfolios at Scale

Adrien Leduc — Wed, 15 Apr 2026 10:14:52 GMT

How Control Center Works (Without the Headache)

At its core, Control Center creates new items from templates and automatically provisions them when a new request is submitted through an intake sheet.

Here’s how it works in practice:

1. Intake: where everything starts

A new request lands on your intake sheet. This can happen via a form, or (especially for projects) through the Smartsheet Salesforce Connector. You could do this through an API as well.

You can choose to trigger provisioning immediately, or only after an approval step. This gives you flexibility depending on how controlled your process needs to be.

2. Provisioning: turning requests into structured work

Once approved, Control Center provisions a workspace or folder using your pre-built templates.

These templates can include anything you’ve set up in advance:

Sheets
Reports
Dashboards

The powerful part is that the data captured in your intake sheet is automatically pushed into the newly created assets. This ensures consistency and removes manual setup work.

3. Portfolio tracking: keeping everything visible

After provisioning, Control Center can automatically add a row to a separate sheet, commonly called a summary sheet.

In simple terms:

Each provisioned project = one row in the summary sheet
Intake sheet = all requests (both processed and unprocessed)

Your summary sheet becomes a high-level portfolio view. However, for operational teams, it’s often better to use reports of the summary sheet so each team only sees the columns relevant to them.

4. Archiving: closing the loop

When projects are completed, Control Center can archive them.

This step isn’t fully automatic, but it’s quick, you typically just need a couple of clicks. Most teams handle archiving on a weekly basis.

What to Watch Out For

Control Center is extremely powerful, but setting it up can feel overwhelming at first.

A successful setup requires:

Well-structured templates (sheets, reports, dashboards)
A properly configured intake sheet
Careful configuration of Control Center settings (some of which aren’t very obvious)

In short: the tool works brilliantly but only if everything is set up correctly from the start.

Do you need help getting started?

If you have access to Control Center and want to automate your project creation but aren’t sure where to begin, I can help you design and build the whole setup.

How We Streamlined Project Assignment and Resource Allocation

Adrien Leduc — Tue, 07 Apr 2026 15:33:46 GMT

Efficient project assignment is critical for maintaining balanced workloads and delivering projects on time. Yet many organisations struggle with informal processes, leading to inconsistent outcomes.

In this post, I’ll walk through a common problem in project assignment and how we solved it with a structured, automated approach.

The Problem: Inconsistent and Unbalanced Work Allocation

Before implementing a structured system, project assignment was largely informal. This created several challenges:

No standardised workflow or clear ownership for assigning projects
Some consultants were overloaded, while others had capacity
Limited visibility into upcoming availability and future work

The result was inefficiency, uneven utilisation, and unnecessary delays in getting projects started.

What We Built

The solution combined resource visibility with a centralised workflow:

Resource Management per Project
Clear tracking of who is assigned to what
Provisional Resource Planning
The ability to tentatively allocate consultants to upcoming opportunities
Centralised Assignment Process
A single, transparent system for managing all project assignments

The Outcome: Faster Decisions, Better Utilisation

The impact was immediate and measurable:

95% of projects assigned within one business day
(with an average turnaround of less than one day)
Reduced variance in consultant utilisation
Workloads became more balanced across the team
Improved visibility and planning
Managers could make proactive decisions rather than reactive ones

By combining clear criteria with automation and centralised control, we transformed a fragmented process into a scalable system.

If your organisation is still relying on informal project assignment, introducing even a lightweight structure can unlock significant efficiency gains. Contact me via the form below if you'd like to talk about it.

Stop Chasing Projects: How We Automated the Entire Project Portfolio

Adrien Leduc — Wed, 01 Apr 2026 09:21:55 GMT

Most teams don't have a project problem. They have a visibility problem.

Are you aware of all the projects your teams are delivering? Do you know if they're all being paid for? Do you keep chasing for updates on their delivery? I've seen it: we start a project early, or with a budget we think we have, and when it's pulled, no one is informed, because platforms aren't talking to each other and people forget.

The root cause is almost always the same: no centralised project portfolio, manual data entry nobody trusts, and custom work slipping through unpaid because there was no written validation before delivery began.

Here's what that actually costs you:

Revenue leaked on custom projects delivered without sign-off
Time wasted on double entries between your CRM and project tools
Stakeholders making decisions on outdated or incomplete portfolio data

What we built

We connected Salesforce directly to a structured intake sheet, so the moment a deal closes or confirmed, a project folder and template is created and the project portfolio gets provisioned. No copy-paste, no manual line creation.

From that intake sheet, project toolkits (all you need to manage the project) are created from templates through a control centre, and a live summary sheet becomes the single source of truth for project assignment and portfolio visibility. Reports pull directly from it, so every stakeholder sees exactly what they need, filtered to their role, without anyone having to maintain a separate deck or chase a status email.

The outcome

No more unpaid custom projects. Written validation is now baked into the workflow before delivery begins.
Portfolio visibility without admin overhead. Relevant stakeholders have a real-time view, scoped to their profile.
Project completion updates itself. It flows from the project sheet directly to the project portfolio automatically.

If any of this sounds familiar, let's talk.

If you have access to Smartsheet premium apps: Smartsheet Salesforce Connector and Control Center, we can set this up together. Use the form below to let me know about your case and I'll reach back.