1 Simple Word that Could Be Damaging Your Data

Keturah Charlesworth Author

As an NDIS provider, the importance of collecting information—whether it's about participant needs, incidents, risks, finances, or service delivery—is that it paints a picture. It tells us where we've been, where we're going, and whether we need to adjust our course to stay on track. We honestly use it all the time: how much profit did we make in July? How many new participants did we onboard last month? How many incidents were related to missed services and how many to falls?

This information gives us incredible insight and can drive meaningful change, growth, and improvement within our organisations. But there's one simple word that can quietly throw a spanner in the works.

And that word is...

“Other”

You know the one. It's the catch-all category tucked at the bottom of dropdown menus, tick boxes, or survey forms. Sometimes, it's paired with an extra little text field where you can explain your ‘other.’ But often, it's just left hanging, undefined and unchecked. It feels like a safety net, but in reality, it's often a trap.

What Does ‘Other’ Really Tell Us?

In many cases, ‘other’ tells us more about our data collection process than the data itself. It signals that we haven’t fully thought through the categories we need. Maybe we were pressed for time, or perhaps we anticipated that there would always be exceptions. But relying on ‘other’ often highlights gaps in our understanding of the data we’re collecting. It’s like admitting, "We’re not sure what else might be out there, so here’s a miscellaneous option."

This lack of clarity can lead to inconsistent data, where one person's ‘other’ means something entirely different from someone else's. Instead of providing clarity, ‘other’ often creates more questions than answers, undermining the purpose of collecting the data in the first place.

What Does It Miss?

When someone selects ‘other,’ valuable context is often lost. Unless there's a follow-up text box (which people don’t always fill out thoughtfully), we’re left with a vague, undefined category that offers no real insight. This gap makes it difficult to identify trends over time, as there's no way to group similar ‘other’ responses together effectively.

Imagine trying to improve your incident reporting system, but discovering a large chunk of reports are simply labeled as ‘other.’ What does that even mean? Was it a slip, a trip, an equipment malfunction, or something entirely different? Without specifics, patterns remain hidden, and opportunities for targeted improvements are missed.

Moreover, it misses the nuances that could have been captured with a more specific category. It can also obscure the root cause of issues because the data isn’t detailed enough to draw clear conclusions. In short, ‘other’ misses the opportunity to tell the full story.

The Risks of Using ‘Other’

While ‘other’ might seem harmless, it can introduce several risks into your data management processes:

1. Skews Information: When respondents choose ‘other’ for answers that could fit into existing categories, it distorts the data. This skew makes it seem like certain categories are less relevant than they actually are.
2. Challenges in Trending: Identifying patterns becomes tricky when data points are lumped into an ambiguous category. It disrupts the consistency needed to track changes over time.
3. Creates More Work: The poor person tasked with analysing the data has to spend extra time dissecting ‘other’ responses, often chasing clarity that could have been captured upfront. This inefficiency drains resources and slows down decision-making.
4. Skews Results of Standard Categories: Important categories may appear underrepresented because respondents opted for ‘other’ instead. This misrepresentation can lead to misguided strategies, responses, or interventions.
5. Reduces Critical Thinking: It can become a lazy default, allowing respondents to avoid considering the most accurate option or reflecting on the root cause. This undermines the quality of the data collected.
6. Delays in Identifying High-Risk or Urgent Issues: Critical information, such as incidents involving injuries or reportable events, may be hidden amongst the  'others.' This can result in delays in necessary or mandatory actions, potentially putting individuals at risk and compromising compliance with legal or regulatory requirements.

Imagine if an incident involving a serious injury was logged under ‘other’ because the existing categories weren’t a perfect fit. That data point could easily be overlooked in routine reviews, delaying urgent follow-up actions or even mandatory reporting. In situations where time is critical, such delays can have serious consequences.

The Impact on Data Integrity

Data integrity is all about maintaining accuracy, consistency, and reliability. Every ‘other’ response chips away at that integrity. When data lacks clarity, it loses its power to inform decisions. Over time, reliance on vague categories can lead to inconsistent data that doesn’t reflect reality.

Inconsistent data makes it difficult to compare results across different reports, time periods, or teams. It creates blind spots in decision-making, where leaders may base strategies on incomplete or misleading information. For example, if ‘other’ hides a recurring issue—like equipment failures—leadership might miss the chance to address a systemic problem, simply because the data wasn’t clear enough to highlight it.

Ultimately, poor data integrity erodes trust in your systems and the reports they produce, making it harder to advocate for changes or improvements based on that data.

When Can ‘Other’ Be Useful?

Despite the risks, there are times when ‘other’ has its place:

• Discovering New Trends: If you're gathering exploratory data, ‘other’ (with a required text field) can reveal emerging categories you hadn’t anticipated. This approach is valuable when you're testing new processes or systems, or entering unfamiliar markets. For instance, an unexpected ‘other’ trend in client feedback might highlight a new service need.
• Highly Individual Responses: In cases where responses are genuinely unique, a free-text field might serve the purpose better than ‘other.’ For example, when collecting personal feedback or qualitative data, open-ended responses provide richer detail. This is especially useful in satisfaction surveys where experiences can vary widely.
• Flexible Data Collection: When you have control over the data management system and can regularly review, update, and refine response options based on ‘other’ entries. This flexibility ensures that categories evolve with your organisation's needs, preventing ‘other’ from becoming a permanent catch-all.

Steps to Avoid the ‘Other’ Trap

1. Evaluate Its Value: Before including ‘other,’ ask yourself if it will offer genuine value. If not, leave it out. Consider whether the potential insights justify the ambiguity it introduces.
2. Brainstorm Thoroughly: Spend time identifying all likely response options. Engage with different stakeholders to ensure comprehensive coverage. This collaborative approach helps uncover perspectives you might have missed on your own.
3. Encourage Feedback: Foster open communication with staff and participants. If a new category emerges repeatedly, consider adding it to your standard options. This practice keeps your data collection tools relevant and comprehensive.
4. Review Regularly: Don’t set and forget. Periodically review data collection tools to update categories and reduce unnecessary reliance on ‘other.’ Regular audits of your data systems help maintain their effectiveness.
5. Use Free Text Where Appropriate: If variability is expected, a well-designed free-text field can provide richer, more specific data than ‘other.’ Ensure that these fields are used purposefully and that there are processes in place to analyse the qualitative data effectively.

Final Thoughts

While ‘other’ might seem like a convenient option, it often creates more problems than it solves. By taking a proactive approach to data collection design, you can improve data integrity, enhance your ability to identify trends, and make more informed decisions. So next time you see ‘other’ lurking in your data tools, ask yourself: is it helping or hiding the insights you really need?

Data should be your NDIS organisation’s compass, guiding you with precision and clarity. Make sure you don't let ‘other’ set you off in the wrong direction.

Do you have any insights or thoughts about using the word 'other' in your data collection endeavours? Share them in the comments section below.

Categories: : Auditing, Data analysis, Root cause