How a Data COAT Supports Your Data Governance Principles

It’s time to talk about one of my favourite topics – making sure your data is wearing its COAT.

I developed the COAT methodology to help people stay engaged with maintaining clean data – and quality is one of the essential data governance principles. After all, although we all work with data of some type, not everyone is a data professional. And that’s why we need a simple and memorable framework to encourage consistency and accuracy that everyone can understand, use and get on board with.

What does COAT stand for?

1. Consistent

Consistency is key. Data should be uniform across systems and sources, this means applying standardised formats, definitions, and validation rules. Put documentation and training in place so everyone who inputs data does so in the same way, using the same terminology business-wide. This standardised process will help prevent errors, mismatches and, God forbid, dirty data. To ensure consistency, you and your team should consider agreeing on things such as:

• the required fields and data points to be populated – should there always be a unit of measure or weight for products? Do you always need an email for a customer record?
• using the same units of measurement – for example, centimetres or inches, pounds or kilos
• using identical formatting conventions
  • addresses – agree whether to use abbreviations such as St or Street, New York or NY, United Kingdom or UK
  • dates – working with global dates can be tricky, agree which format everyone will use
  • phone numbers – these can get very messy. Do you need country dialling codes?  What about the use of spaces or brackets?
  • currency – it might not be clear there are multiple currencies in the data, which could impact the numbers, reporting and decision making
  • language – we often see multiple languages which can cause issues. Agree on a common language and define business terminology to make sure everyone shares the same understanding.

Without consistency, your records won’t align. This will make it difficult to find and use your information effectively, could cause a lot of errors, and cost you a lot of time and money to fix.

2. Organised

Let’s change the subject for a moment and think of a messy wardrobe. If you randomly throw a top into that wardrobe, you’ll struggle to find it later. But if you organise your closet by category, colour, or style, you can grab what you need instantly, making it far easier to get dressed.

Implementing proper metadata management, categorisation, and governance processes ensure that high-quality data is easily accessible.

To keep your data organised, you can categorise it by:

• region or country
• business unit or division
• department or product.

Doing this allows you to gather important information quickly and reliably. For example, if someone asks, “How many screws did we buy in Germany last year?” and your data is well-organised, you can generate a report in seconds – you’ll save days of work.

3. Accurate

Did you realise that the term ‘accuracy’ may mean different things in different contexts? Therefore, you need to define what accurate means in your organisation. For example:

• in finance, accuracy means numbers must be 100% correct for reporting
• in sales and marketing, accuracy might mean having a full name and email address for each prospect.

To avoid confusion, create an agreed list or data glossary so all teams use the same definitions and standards.

4. Trustworthy

Only when your data is consistent, organised, and accurate, does it become trustworthy. And trust, as you’ll know, is everything in data governance principles and business.

Reliable, trustworthy data means you can:

• report to management with confidence
• improve visibility on spending, forecasting, inventory, and sales
• make better, more informed business decisions
• drive strategic decisions
• enhance decision-making.

Data should be reliable, unbiased, and come from reputable sources, ensuring confidence in its use across the organisation. It takes a team to manage data governance, and trust in your team is just as important. Everyone must be engaged, take ownership and responsibility and using a data COAT will help greatly with all of this.

Maintaining your data COAT – a key part of upholding data governance principles

Putting your COAT on is not enough – you must keep wearing it.  Implementing regular validation, deduplication, and cleansing processes help ensure high data accuracy. This means it’s critical to maintain your data.

Wondering how to maintain your data COAT? Here’s how:

• Regularly check, update, and clean your data.  Don’t just check new data, go back and re-check existing data, as cut & paste errors can happen.
• Refresh and update your data either monthly, quarterly, or at least every six months. Leave it longer and you risk starting from scratch.

Regular data maintenance means:

• enhanced data accuracy for more informed business decisions
• preventing time-consuming and costly data clean-up by maintaining data integrity over time
• a well-trained, agile data team. Consistently handling smaller tasks is more efficient than addressing large, infrequent ones

As with almost everything, caring for your data COAT little and often is the best approach. Just like cleaning your house, maintaining your car, or having a massage, self-care and maintenance are essential.

Finally, wear your data COAT or you risk leaving your data out in the cold.

You wouldn’t go outside in sub-zero temperatures without protection, so why leave your data unprotected? Poor data quality leads to bad business decisions, wasted money, and even job losses.

Just like a cheap coat won’t protect you properly, poor data management will cost more to fix later. However, if you invest in good quality data services from the start, you’ll save time, money, and prevent needless frustration.

If you need expert help with your business data, get in touch with The Classification Guru, for more information.

Susan Walsh, The Classification Guru, will be speaking at the Data Governance, AI Governance & Master Data Management Conference Europe 2025 this March. As the moderator for the panel “Navigating the Intersection of Data Governance and AI Governance”, she will be joined by industry leaders to explore how organisations can align data governance with AI governance for better decision-making and compliance. Don’t miss this insightful session on Monday, 17 March 2025, from 11:20 AM – 12:00 PM.