At some point in most analytics roles, you realise that the biggest risk to a dashboard isn’t poor visual design—it’s unclear data. Calculated fields that aren’t documented, tables with ambiguous names, and metrics that mean different things to different people can quietly undermine trust and slow delivery.
This article focuses on why clearly defining calculated fields and data points in Tableau dashboards is essential, not just for analysts, but for stakeholders and organisations as a whole.
**When Flexibility Becomes a Risk
**Every organisation has its own way of storing and supplying data. In some environments, analysts and business users are given a high degree of flexibility to create tables, name them freely, and build calculated fields as needed. While this can speed things up in the short term, it introduces long-term risk when governance and documentation don’t keep pace.
Without safeguards, it becomes difficult to distinguish:
- Core, trusted business tables
- Temporary or experimental tables
- Calculated fields with hidden filters or assumptions
Over time, this can lead to dozens—or even thousands—of similarly named tables, each with unclear provenance. What initially feels empowering can quickly turn into a maintenance challenge.
**The Problem With “Obvious” Metrics
**Some of the most dangerous data points are the ones that look obvious.
Columns with simple names often hide complex logic: pre-applied filters, partial channel coverage, or calculations layered on top of other calculated fields. Different users may interpret these fields differently, especially when no clear definition exists.
This is where undocumented calculated fields become particularly risky. A metric that seems self-explanatory may, in reality, represent something far more specific—and unless that definition is captured, inconsistencies are almost inevitable.
**When Documentation Isn’t Designed for the End User
**In one of my previous roles, data column names were extremely technical, especially for time-sensitive analytics. Understanding their meaning often required deep domain knowledge. While some documentation existed, it focused on interpreting trends and correlations rather than explaining what the data points actually represented for non-technical users.
As a result, I developed a close working relationship with a specialist who had the necessary domain knowledge. While this was helpful initially, it created long-term risk. Over time, reliance on a single individual increased—and when they moved to another role, that knowledge gap became immediately apparent.
This highlights a key issue: documentation must be tailored to the end user. Highly technical explanations have their place, but without accessible definitions, data literacy and independence suffer.
**The Trust Problem: “Can We Use This?”
**One of the most frustrating experiences as an analyst is finding a data point in a table and not knowing whether it can be trusted.
In these situations, you end up asking around—only to find that no one is entirely sure. Each new table or calculated field requires additional verification. Without up-to-date documentation, this uncertainty becomes a recurring blocker.
In practice, this can lead to delayed dashboard delivery or difficult conversations with stakeholders when timelines slip—not because the work is complex, but because the meaning of the data is unclear.
**Where Should Documentation Live?
**A recurring question is where documentation should be stored. There’s no perfect answer, and each option comes with trade-offs:
Git repositories offer version control and transparency, but require discipline to keep updated.
- Internal company pages or wikis are accessible, but can quickly become outdated.
- Google Docs are easy to collaborate on, but lack strong governance.
- Within Tableau dashboards (descriptions, tooltips, captions) keeps context close to the data.
- Metadata layers or data catalogues can help, though adoption varies across organisations.
Often, a hybrid approach works best—keeping high-level definitions close to dashboards while maintaining deeper technical documentation elsewhere.
**The Reality of Data Dictionaries
**Data dictionaries are undeniably valuable—but they are also time-consuming to maintain. Even with good tooling or version control, updating documentation during busy periods is challenging. Different teams often adopt different standards, especially in large organisations.
In many ways, data dictionaries are like academic referencing: rarely enjoyed, frequently postponed, but absolutely essential. The key is acknowledging this reality and aiming for good enough and current rather than perfect and outdated.
**Safeguarding, Not Bureaucracy
**Putting safeguards in place doesn’t mean limiting flexibility—it means protecting trust. Clear naming conventions, documented calculated fields, and agreed definitions reduce risk without stifling creativity.
The goal isn’t to stop people experimenting with data. It’s to ensure that when data is used for decision-making, everyone understands what it represents.
**The Bottom Line
**Undocumented calculated fields don’t just create confusion—they create delays, rework, and mistrust. Over time, they can turn dashboards into dead ends rather than decision-support tools.
Clear definitions, accessible documentation, and thoughtful governance are not administrative overheads. They are foundations of reliable analytics.
Dashboards should move organisations forward, not leave analysts and stakeholders second-guessing the data behind them.
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