Smartsheet won't fix your data

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: