Why Should I Maintain My Data?

Posted: 02/18/2021 - 03:32
data protection

So, you’ve just shelled out big money to have it classified and your data will almost certainly be correct when you receive it, but it will only stay accurate 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, typos, and cut and paste errors, to name but a few. That is why, and I cannot emphasize this enough, it is crucial to maintain your data. Specifically, it is important that you continue to check and maintain your data for any errors that can have a knock-on effect to your bottom line.

What’s the Big Deal?

Let’s say you use IBM for IT services that cost around £50k and you accidentally misclassify this spend as cleaning services. At the next refresh it's picked up and classified again, and it becomes £100k and then the next refresh it becomes £150k. You now have a major issue on your hands where you are counting your cleaning services spend as £150k plus whatever you are actually spending on it. The problem worsens because you're now not counting the £150k of IT spend with IBM.

What does that mean? Well, you may have agreed to a contract with a cleaning supplier for “£X” amount of spend based on that data. In reality it's maybe half of that. Then you might not be able to honor it or you find yourself in a situation where you have to pay it despite possibly not needing that service at all. 

On the flip side, you could be negotiating better rates with IBM based on usage of the product. However, that’s impossible to realize when it's sitting under the wrong bucket, but that's something I can help with. 

How Do I Maintain My Data?

The secret to keeping your data clean isn’t really a huge secret. It’s a case of good housekeeping.

You need to check and maintain it regularly. In the same way you give your carpets a regular once-over with the vacuum, regularly checking in with your data makes life easier in the long run.

Just like a weekly or twice weekly vacuum round the house, it’s a smart idea to check in on your data on a monthly or quarterly basis. If you have a lot of data and a lot of inputs, you’ll need to review it more frequently.

This is still just as important even if you have a third-party supplier checking over your data. Spot-check your data occasionally to ensure your supplier is fulfilling their obligations. If your team is running the checks for you, make sure you check in with their progress once in a while to verify everyone is on board with the same standards and cleaning the data in the same way. It’s also a good opportunity for highlighting development areas for your team.

How Frequently Should You Check Your Data?

Let’s go back to that carpet analogy. Leaving your carpet for a week doesn’t matter. It doesn’t really matter if you leave it for a month (as long as you’re OK living with dirty carpets). But leave that once fresh and spotless carpet too long and by the time you pull out your vacuum cleaner, your carpet will be beyond saving.

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 are corrupted. Since 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 business 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.
  • An informal opportunity to stay in touch with the work your third-party supplier is doing.
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About The Author

Susan Walsh's picture

With nearly a decade of experience fixing your dirty data, Susan Walsh is The Classification Guru.
She brings clarity and accuracy to data and procurement; helps teams work more effectively and efficiently; and cuts through the jargon to address the issues of dirty data and its consequences in an entertaining and engaging way.

Susan is a specialist in data classification, supplier normalisation, taxonomy customisation, and data cleansing and can help your business find cost savings through spend and time management - supporting better, more informed business decisions.
Susan has developed a methodology to accurately and efficiently classify, cleanse and check data for errors which will help prevent costly mistakes and could save days, if not weeks of laborious cleansing and classifying.

Susan is passionate about helping you find the value in cleaning your ‘dirty data’ and raises awareness of the consequences of ignoring issues through her blogs, vlogs, webinars and speaking engagements.