Sanjay Padhiar’s Post

Before you start pulling every bit of data on your com website, stop and think... 👉 What is the data that’s actually going to tell me something? And better yet... 👉 What is the data that’s actually going to tell me something 𝘐 𝘳𝘦𝘢𝘭𝘭𝘺 𝘯𝘦𝘦𝘥 𝘵𝘰 𝘬𝘯𝘰𝘸? Not every metric on your website’s data report matters. Here’s a 3-part test to find the data that does matter ⬇️ 1️⃣ If this metric changed, how big would the impact be on my company? 2️⃣ Will improving this metric contribute to the strategic goals? 3️⃣ Is this a metric that will also improve other metrics? The whole point here is if it's not significant to the bottom line. If it isn't, then it's not going to be worth your effort to monitor it right now.

Ever felt stuck with data that just doesn’t represent the data distribution? StratifiedShuffleSplit can help! Not only can this tool split your data as desired, but it also maintains the proportion of your original distribution across all splits. Want to learn more? Check out this tutorial 📹. https://lnkd.in/gpfkFs5G

Quick Tip for Data Whizzes: Need to spot anomalies fast? Try setting your p-value threshold to 1/N (N = number of data points). This helps focus on the rare events that shouldn’t show up by chance. While it’s more conservative than the usual 5%, it’s a quick-and-dirty way to highlight the biggest outliers when you’re short on time—just keep in mind, it might miss some subtle signals. Ideal when you need to prioritize the most unlikely outcomes!

Making Data Work for You: The Importance of Cleaning** Ever wondered how we make sense of all that data? 📊 It starts with cleaning – a crucial step to ensure our data is accurate and reliable. Here's what data cleaning involves: 1. **Fixing Mistakes**: We spot and correct errors like typos or missing info. 🛠️ 2. **Removing Doubles**: We get rid of duplicate entries to keep things clear and simple. 🔍 3. **Getting Organized**: We standardize formats (like dates or measurements) to make comparisons easy. 📏 4. **Dealing with Messy Bits**: We handle oddities and inconsistencies to make sure our data tells the right story. 🔧 Cleaning data isn't just about tidying up – it's about setting the stage for smart decisions and meaningful insights. Let's dive into the world of data cleaning together! 💪 Share your tips and experiences in the comments. #DataCleaning #DataAnalysis #Insights #DecisionMaking

This year more than ever, we all talked about data. Data, data everywhere but the key information to solve the problem may not be captured by data. When you take the sample, was it warm? Was it pressurized? Was it smelly? Was it foamy? Did it come out steadily? How did it behave when it is swished around in the sample bottle? Any contaminants or stratification? All the things that can only be gained by physically present and collecting samples. and also we ought to be reminded that there is no data if the process is not running. 80% of data is made of all the tedious but hard work of keeping the process running. That won't show up in the spreadsheet, but it is the story worth telling.

Can You Ensure Data Accuracy? 📋🔍 Take the Data Checking Assessment to demonstrate your proficiency in verifying and ensuring data integrity. It's time to put your data skills to the test! Signup Here: https://lnkd.in/dXsN_3mi #DataChecking #AccuracySkills #DataQuality #DataIntegrityTest

Data Model - The Joys Of Late Arriving Data Late arriving data is one of the annoying scenarios about data warehouses. You really just have two approaches; hold the data until everything is available, or post an interim record and adjust when the data is complete. For a lot of people, they will wait, but if you have a financial data warehouse you may have no choice - you will have to post. But, posting data is tricky, you can't post a financial fact and then update it. That will look like magic to any enduser - it was there in one place and then it disappeared (definitely not allowed for financial data). You get around it by posting the interim fact, THEN when you have the updated data you post an offset fact AND the new corrected fact. You can then look at the data as of the original date and the interim shows. If you change to the updated date then you get the new fact AND the trail with the offset. Beauty in data #data #database #datamodel

What’s one of the most valuable lessons I’ve learned when it comes to buying data? Don’t just focus on the data itself—focus on how it aligns with your strategic goals and the outcomes you want to achieve. I’ve seen too many teams get caught up in the allure of big data sets without a clear plan on how they’ll actually use the information. The key is to be intentional with your data buying process so you can get the best quality and best price for the data you need. That’s why Blue Street Data just launched a comprehensive Data Buyer’s Guide that walks you through the 12 critical steps of the process. This isn’t a theoretical guide—it’s shaped by real-world experiences that I and those within my network have had, helping organizations avoid common pitfalls and make smarter decisions. Regardless of whether your team has experience buying 3rd party data or you're just getting started, this guide is for you. If you’re curious about how to apply these steps in your own organization, I’d love to discuss! Access our Data Buyer's Guide at this link: https://lnkd.in/eiJ3MMfb

This is what I talked about in my newsletter this past weekend: Data Collection process. You can access it by subscribing to my newsletter - link in bio.

BAD DATA series: Part 2 Still on my series of “Bad Data”, Here’s a gentle reminder for us: Focus on “QUALITY” over “QUANTITY” – relevant data is key. Don't settle for 5 million data points with 60-70% quality. Instead, aim for 500,000 data points with 95% quality. Quality over quantity is key! But the question is, How do we know how “quality” our data is? I’d talk about this in my next post 😃.