Can you trust your data?

If you’re relying on data to make decisions, here’s a question for you:

Can you actually trust your data?

Bad data leads to bad decisions. And if you’ve ever tried to build a Power BI dashboard using unprepared data… you know what I'm talking about. That’s because good analytics starts before the dashboard—with trustworthy data.

So, let’s talk about what makes data trustworthy—and how to get there.

The 3 C’s of trustworthy data

To be useful, your data needs to be:

• Correct

• Current

• Constant

These three qualities are the backbone of any solid reporting system. Miss one, and your insights could be misleading at best—or flat-out wrong.

1. Correct data: accuracy isn’t optional

Data errors creep in more easily than you think. A common name mix-up might credit someone with attending a course they never showed up for. Or a stock item might never be recorded because “we were going to use it straight away.”

Sound familiar?

Then you’ve got things like:

• Null values

• Duplicates

• Outliers

• Inconsistent fields between systems

All of these distort the truth your analytics are supposed to reveal. Cleaning and validating your data isn't optional—it's foundational.

Question for you:

What’s the most unexpected data error you've ever uncovered?

2. Current data: how fresh is “fresh enough”?

Everyone has a different definition of “up-to-date.”

• For a factory floor, real-time data might be essential.

• For HR reports, yesterday’s numbers might do just fine.

But what matters most is transparency—do you know how current your data is, and can you trust that timestamp?

3. Constant data: reliable, available, and secure

Once your data is cleaned and verified, you need to keep it:

• Securely stored

• Regularly backed up

• Available wherever it’s needed

You don’t want your cleaned dataset disappearing on a lost laptop or overwritten by mistake. Constancy means your data is dependable and accessible, day in and day out.

Choosing the right data platform: why Azure?

The Azure data platform gives you flexible, scalable ways to store your analytics-ready data, depending on your requirements:

Azure SQL Database

• Great for datasets up to a few terabytes (TB)

• Geo-redundant and cost-effective

• Easily scalable up or down

• Supports Medallion architecture using schemas or databases

(If you’re curious about that approach, I wrote more about it here.)

Microsoft Fabric

• Ideal for high-performance analytics at scale

• Better suited for large volumes of data

• Higher cost, but better performance

• Also supports the Medallion architecture.

Once your data is in the cloud, everything gets easier—from sharing semantic models to boosting Power BI performance.

Don’t skip the foundations

Data visualisation tools like Power BI are only as strong as the data underneath them. Trustworthy data isn't just clean—it's correct, current, and constant.

So, here's a challenge:

What’s your biggest headache when it comes to data quality or reporting?

Drop it in the comments—I’d love to hear what you're wrestling with (and maybe swap ideas on how to fix it). And as always, if you want to talk about data quality, you can get in touch here.

Anatec AI has worked with data quality issues for many years. We focus on helping companies make better use of their data to improve their performance and resilience.

Key words: reporting, analytics, data quality, Power BI, Microsoft Fabric, dashboard design