As we've seen in the news, dirty data is everywhere; it doesn't discriminate! We can laugh and say, "How could this happen?" but I see this every.single.day in my world. It's way more common than you think. So, how can you help prevent this? Well, first thing I say is bloody check! I live by the principle, "check, check and check again", and we have a triple check system at TCG before anything goes out the door. Is it fool-proof? No, there's no perfect data set, but it does help minimise the errors. Secondly, make sure your data has its COAT on, it should be Consistent, Organised, Accurate and Trustworthy. And if you want to know how to apply this to your data, pick up a copy of Between the Spreadsheets 2nd edition from Facet Publishing or any bookstores or online.

As we've seen in the news, dirty data is everywhere; it doesn't discriminate! We can laugh and say, "How could this happen?" but I see this every.single.day in my world. It's way more common than you think. So, how can you help prevent this? Well, first thing I say is bloody check! I live by the principle, "check, check and check again", and we have a triple check system at TCG before anything goes out the door. Is it fool-proof? No, there's no perfect data set, but it does help minimise the errors. Secondly, make sure your data has its COAT on, it should be Consistent, Organised, Accurate and Trustworthy. And if you want to know how to apply this to your data, pick up a copy of Between the Spreadsheets 2nd edition from Facet Publishing or any bookstores or online.

Data Cleaning Tricks You Probably Don’t Think About We all know the basics: remove nulls, fix typos, standardize formats. But here are a few subtle ways to make your data stronger: ⭐Check Against Real Sources: Validate your data using trusted references. ⭐Think Like the Domain: Ask if the data actually makes sense. ⭐Look at It Visually: Use charts like Scatter plot,box plots to spot unusual patterns. ⭐Follow the Logic: Ensure related fields align correctly. ⭐Check Samples: Inspect a few rows manually for hidden errors. The cleaner your data, the sharper your insights.

The hidden cost of bad data Bad data costs companies more than just errors. It costs: • Time wasted fixing reports • Lost trust in numbers • Missed opportunities for growth Fixing data at the source is always cheaper than fixing it later. 👉 What’s one “bad data” issue you’ve run into at work? #DataQuality #BusinessIntelligence #DataStrategy

Let’s be honest... we’ve all seen it happen. Data is there, but no one’s using it properly. The top 3 culprits I see: 1. Gut-feel decisions instead of data-led: your hunch might be good, but proof is better. 2. No single source of truth: when systems don’t talk, your teams waste hours chasing the right numbers. 3. Reporting without action: pretty charts don’t pay the bills. Your data can be a goldmine. I help businesses hire the people to dig it up. sunnyn@macildowi

Let’s be honest... we’ve all seen it happen. Data is there, but no one’s using it properly. The top 3 culprits I see: 1. Gut-feel decisions instead of data-led: your hunch might be good, but proof is better. 2. No single source of truth: when systems don’t talk, your teams waste hours chasing the right numbers. 3. Reporting without action: pretty charts don’t pay the bills. Your data can be a goldmine. I help businesses hire the people to dig it up. sunnyn@macildowi

Data validation in Excel is a tool that sets rules to control what kind of data can be entered in cells, helping to keep the data correct 🎯

How do you even start looking through +982,000 data points when you’re not sure what you’re supposed to find? That’s what landed on my desk last week. → An Excel file with pressure readings and timestamps → A bunch of machines showing strange behaviour → No clear link between them First thing I did was throw it all into a graph to get the overview. Two time periods jumped out. So I zoomed in. Made two more graphs to narrow it down. That gave me a much better idea of when the pressure drops happened— and how often. When I lined it up with the machine logs, a pattern started to show. That gave us a clear direction. Now it’s just about executing on it. Once the data’s visual, it’s way easier to make sense of it. And when people can actually see the problem, it’s a lot easier to move forward together. How would you have tackled a dataset like this?

📊 One of the most common challenges I hear from people is how much time they spend tracking their data manually. Spreadsheets, formulas, updates… it all takes hours. And by the time everything is collected, there’s often little energy left to actually analyze what the data is saying. The result? Reports get made — but insights get missed. Sometimes the biggest improvement isn’t in collecting more data, but in creating a system that lets you spend less time managing it and more time understanding it. When data becomes easy to access and interpret, that’s when real progress starts.

🧹 Clean Data = Clear Insights Ever noticed how messy data leads to messy decisions? Before analysis, one of the most important (and ignored) steps is data cleaning. Here are 3 simple Excel tools that make your data clean and reliable: 1️⃣ Remove Duplicates Go to Data → Remove Duplicates — in one click, Excel eliminates repeated entries. 2️⃣ TRIM Function Use =TRIM(A2) to remove extra spaces that break your formulas or sorting. 3️⃣ Text to Columns When all your data is stuck in one cell (like “John,Smith,Accounts”), use Data → Text to Columns to split it neatly. 💡 Clean data not only saves time but also builds trust in your analysis. Remember, even the best formulas can’t fix bad data. #ExcelTips #DataCleaning #Productivity #DataAnalysis #ExcelTraining