If you’re familiar with our work, you may already know that good data governance means your data needs to have its COAT on. It’s a simple and effective data management framework we apply to all The Classification Guru’s services and your data. But did you know this? Your data COAT must be maintained.

Hold on, what’s a data COAT?

New to The Classification Guru? Don’t know what a data COAT is? This quick video explains all.

Let’s put it this way… you wouldn’t spend your hard-earned money on a fancy high-performance winter jacket to protect you from the elements and then leave it in a heap to get dirty. And you wouldn’t treat it roughly, so it falls apart at the seams. So, why do that with your data? If you’ve just spent thousands on expert data classification, you need to maintain it. This way it stays expertly classified!

How long do I need to keep my data COAT on for good data governance? 

Good data governance means you should keep wearing data COAT all year round, regardless of the season. Your data will almost certainly be correct when you receive it. It’ll have its COAT on. It’ll be Consistent, Organised, Accurate and Trustworthy.  But, unless you look after it, it will only stay that way for a short period of time.

Updates and changes mean that before you know it, your once neat and tidy data sets will contain unclassified data, data that’s been incorrectly classified by your team or typos, missing information and cut & paste errors to name but a few issues.

Specifically, it is essential that you continue to check and maintain your data for any errors that can have a knock-on effect.

For example, you might have a supplier that must be classified in a specific category. Even if it seems unusual, you must make sure it’s classified like this. For example, you might usually classify Google as ‘Cloud’ or ‘Software’ under ‘IT’, but your organisation is using Google Ads. In this instance, it should be classified as ‘Advertising’ under ‘Marketing’. By having a data COAT on, you can help ensure that regardless of which team member is classifying the data, it will always be correct. And if you’re using technology or AI, you can add these rules in to make sure the tech classifies correctly too.

It’s also the same for address data; define whether you want to spell out counties or states in full or abbreviate them. And with phone numbers do you want to add in dialling codes? What about the use of spaces, brackets and dashes? Get everyone inputting correctly at the point of entry. This will save you so many hours, days or weeks in the future having to fix everything.

It’s really important to document your COAT and have clear guidelines to make sure everyone is doing the same process the same way. It can be hard to incentivise people to do this, but if their accuracy is tied to performance and you have the ability to track changes and errors, this will be a great motivator.

How do I keep my data COAT on?

There’s no big secret here. The key to keeping your data COAT on is maintenance, it’s really a case of good housekeeping – you need to check and maintain it on a regular basis.

In the same way that you regularly give your jackets and coats a wash and fix-up, regularly checking in with your data makes life easier in the long run. Just like sewing up any loose buttons and keeping your garments fresh, it’s a good idea to check in on your data on a monthly or quarterly basis. Remember, if you have a lot of data and many, many inputs, you’ll need to check it more frequently.

Perhaps you have a 3rd party supplier checking your data for you. This is a great idea! But, it’s still really important you spot-check your data once in a while to ensure your supplier is fulfilling their obligations.

And, if your team is running the checks for you, make sure you check in with their progress now and then. That way you can be sure everyone is following the data COAT standards and they are cleaning, checking and maintaining the data in the correct way.

As part of the maintenance process, you should also spot-check your data to make sure it’s clean. Just because it was accurate last month, doesn’t mean it will be tomorrow.  Cut & paste errors and accidental deletions can all happen and it’s important to check for them so that they don’t become a bigger problem. This is particularly important if your data is being used as training data for AI or GenAI.

So, how frequently should you maintain your data COAT?

Well, let’s go back to that winter jacket analogy.

Leaving your jacket or COAT hanging on a coat stand for a week doesn’t matter. It probably doesn’t matter if you leave it for a month or two. But leave that once clean and cosy COAT too long and by the time you decide you want to wear it again it’ll smell fusty, it’ll be dusty, the moths might even have had a go. It will need a decent wash at the very least.

It’s the same with your data. Data that’s not maintained will slowly become unusable over time. Incorrect or conflicting information will build up. AI outputs become corrupted. And because you can’t afford to use bad data, you end up spending significant time or money to fix the problem. Ouch.

• Regular data maintenance means:
• Better data accuracy for better decisions
• Avoiding a time-consuming and costly data clean-up operation (because your data won’t slowly become corrupted).
• A better-trained and more responsive data team. Doing a little bit of something regularly is always easier than doing a lot of it occasionally.

How The Classification Guru can support your data governance

Need help with your data governance? We’re happy to oblige. The Classification Guru team can review your existing data, run a quick check and flag any errors. Alternatively, we can carry out an in-depth check and fix any mistakes so your data is as perfect as it possibly can be.

Want to be sure you’re always making decisions based on the right data? Outsource your regular data maintenance service to The Classification Guru. Get in touch to tell us more and receive a no-obligation guideline quote.

Enjoyed this post? Read this one next – The Dangers of Dirty Data.