{"id":1732,"date":"2024-05-06T20:46:21","date_gmt":"2024-05-06T20:46:21","guid":{"rendered":"https:\/\/solutionsreview.com\/thought-leaders\/?p=1732"},"modified":"2024-05-06T20:46:21","modified_gmt":"2024-05-06T20:46:21","slug":"look-after-your-data-coat","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/thought-leaders\/look-after-your-data-coat\/","title":{"rendered":"Look after your data COAT!"},"content":{"rendered":"

You should hopefully know by now that your data\u00a0needs<\/em><\/strong>\u00a0to have its COAT on. It\u2019s a simple and effective method and this principle is applied to all The Classification Guru\u2019s services and your data. But what you might not know is this: your data COAT also needs to be maintained.<\/p>\n

Let\u2019s put it this way\u2026 you wouldn\u2019t invest tens or even hundreds of pounds in a warm winter jacket to protect you from the elements and then leave it in a heap to get dirty or let it come apart at the seams. So, why would you do that with your data? If you\u2019ve just spent thousands on expert data classification, then you need to maintain it, so it stays expertly classified!<\/p>\n

Why do I need to look after my COAT?<\/strong><\/p>\n

Your data will almost certainly be correct when you receive it. It\u2019ll have its COAT on. It\u2019ll be Consistent, Organised, Accurate and Trustworthy. \u00a0But, unless you look after it, it will only stay that way for a short period of time.<\/p>\n

Updates and changes mean that before you know it, your once neat & tidy data sets will contain unclassified data, data that\u2019s been incorrectly classified by your team or AI, typos, and cut & paste errors to name but a few.<\/p>\n

Specifically, it is really, really important that you continue to check and maintain your data for any errors that can have a knock-on effect.<\/p>\n

For example, let\u2019s say you use IBM for IT services and you accidently misclassify this spend, say it\u2019s \u00a350k, as cleaning services. Then at the next refresh it\u2019s picked up and classified again, and it comes to \u00a3100k and then the next refresh, it becomes \u00a3150k\u2026 You then have a major issue where you are counting your cleaning services spend as \u00a3150k plus whatever you are actually spending it on, and you\u2019re not counting the \u00a3150k of IT spend with IBM.<\/p>\n

So, what does that mean? Well, you may have agreed to a contract with a cleaning supplier for \u2018x\u2019 amount of value based on that data. But, in reality it\u2019s maybe half of that, so then you might not be able to honour that price, or you find yourself in a situation where you have to pay it, but you don\u2019t actually need that service at all.<\/p>\n

On the flip side, you could be negotiating better rates with IBM based on usage of the product, but you don\u2019t realise because it\u2019s sitting under the wrong bucket at the moment, and that\u2019s something that I can help with.<\/p>\n

How do I maintain my data COAT?<\/strong><\/p>\n

The secret to keeping your data clean isn\u2019t really a huge secret, it\u2019s really a case of good housekeeping \u2013 you need to check and maintain it on a regular basis.<\/p>\n

In the same way 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\u2019s a good idea to check in on your data on a monthly or quarterly basis. Remember, if you have a lot of data and a lot of inputs, you\u2019ll need to check it more frequently.<\/p>\n

Perhaps you have a 3rd party supplier checking your data for you. This is a great idea! But, it\u2019s still really important you spot-check your data once in a while to ensure your supplier is fulfilling their obligations.<\/p>\n

And, if your team is running the checks for you, make sure you check in with their progress once in a while to make sure everyone\u2019s on board with the same standards and cleaning the data in the same way. It\u2019s also a good opportunity for highlighting development areas for your team.<\/p>\n

So, how frequently should you maintain your data COAT?<\/strong><\/p>\n

Well, let\u2019s go back to that winter jacket analogy.<\/p>\n

Leaving your jacket or COAT hanging up for a week doesn\u2019t matter. It probably doesn\u2019t matter if you leave it for a month. But leave that once clean and cosy COAT too long and by the time you decide you want to wear it again it\u2019ll smell fusty; it\u2019ll be dusty, the moths might even have had a go\u2026<\/p>\n

It\u2019s the same with your data. Data that\u2019s not maintained will slowly become unusable over time. Incorrect or conflicting information will build up. AI outputs become corrupted. And because you can\u2019t afford to use bad data, you end up spending significant time or money to fix the problem. Ouch.<\/p>\n

Regular data maintenance means:<\/p>\n