Jason Zigelbaum’s Post

Day 2&3: I gained knowledgeable tips on series of process that raw data undergo before becoming a valuable information. Data go through series of steps that includes collection, documentation, sorting, cleaning,analyzing and visualising. I also understood different kinds of errors that can be encountered such as measurement error, input error in case of questionnaires or personal error, duplicates or presence outliers. During the lecture, we practised questions based on real life scenario and made right decisions to help predict future outcomes. For instance, treating different products with varying prices and qualities and answering questions based on the product that will yield higher sales and those that are good for promotion. We were also able to predict future happenings and reasons why other products were not good choices.

Today I'll be travelling to Boston to catch up with some lovely friends from all over the world over the next two days. While I'm busy with that, here's some top spend classification tips to keep your data nice and accurate. ✔️ Normalise your suppliers first ✔️ Search for common words ✔️ Use values as a guide ✔️ Look for patterns and abbreviations ✔️ Use context ✔️ Don't assume you know what something is Of course there are many more, these are just a few, but what would you add to keep your data in tip top shape?

Quantitative data: Any metric that can be counted, measured, or quantified, such as length in inches, distance in miles, or time in seconds, minutes, hours, or days. This data is strictly numerical in nature. Quantitative data offers the assurance of fact and evidence, but it will only get you so far if you don't know how to put it in the right context. Don't just count on your data — make it count by checking out our guide to data-driven success: https://bit.ly/3XEyX1R

🎥 Outliers: The Hidden Saboteurs of Your Data Outliers are the rogue agents - skewing your results and making the mean (represented here by the red line) less reliable. But fear not! The median (shown in green) stands strong, unaffected by these outliers, offering a more dependable measure of central tendency in skewed datasets. So, next time you’re working with data, remember this: the mean may flatter, but the median tells the truth. 👉 Outliers distort. The median restores. Always be mindful of outliers when interpreting results - it could make all the difference. --------- ♻️ Repost this to help your network learn these lessons. 📌 Follow me for more content like this

I recently met w/a few leaders regarding collecting & disaggregating data & using it to determine next steps. One challenge with analyzing data is it often leads us to adapt a known, traditional (familiar) linear path for a solution. Meaning, once data reveals the problem, then we figure out something we should 'start' doing to solve it (i.e. have a training, increase the funding, hire more people). However, there are times when it's not what you need to start, but knowing what you need to 'stop!' We don't often consider (or have the time to consider) the processes & practices that must absolutely end. And by continuing them & trying new known efforts, we compete against the problems we're trying to solve! If I want to lose weight, the known effort for me is to go out & walk or change eating habits. When in reality, it's reducing & monitoring my sugar & carb intake AND walking (true story). So, contributing factors to solutions is not always about addition, but sometimes about subtraction along with addition!

📈 Quality data powers pricing success. But what exactly should you be looking at during the data collection phase? 1/ Are you talking to the right people? If you ask an audience that hasn’t ever seen or used your product, you may be wasting your time (and money). 2/ Are people who they say they are? It’s perhaps unsurprising that many people lie to get paid for a survey. 3/ Do people understand the questions you’re asking? You can have the smartest, most qualified respondents, but if they don’t understand the questions, they won’t answer correctly.

Let’s face it – We’re lazy.  Doing nothing is always easier than doing something.  That’s why convincing customers to take action is such an art form.  It’s essential to show your customers just what you can do for them through:  - Data - Statistics - Case studies But that’s not all.  You also need to demonstrate to your customers what will happen if they don’t take action.  This could be wasted time, lost clients, or even a damaged reputation.  The cost of inaction is much higher than the cost of taking action.  Always.

An interesting observation about surveys: The more specific questions you ask… …the less meaningful the data becomes. When you ask customers to rate an aspect of your product or service, you get highly scripted answers. Turns out this is the worst way to gauge how customers actually feel about you. Instead, I’d recommend added free text responses to all your surveys. Have you ever made this change, and noticed a difference?

We’ve all heard about the 80/20 rule, but applying it can be tricky. It’s fascinating because it suggests that 20% of your efforts can lead to 80% of your results—but figuring out that 20% is the hard part. Here’s a simple way to use it: 1. Pick a report you’re proud of. 2. Think about the steps you took to create it. 3. Identify the key actions or ideas that made the biggest impact. Now, use these steps: 1. Analyze: Find the trends that really matter. 2. Segment: Group your data to see what’s most important. 3. Prioritize: Focus on what makes the biggest difference. 4. Communicate: Share your findings clearly and simply. 5. Monitor: Keep an eye on things to adjust as needed. By focusing on the most impactful actions, you can get better results with less effort. Give it a try!

More Data = Better Results Myth: Collecting extensive data (e.g., 20+ tests) gives better insights. Reality: Overloading a client with tests can lead to confusion and fatigue. A focused, intentional evaluation (like identifying key weaknesses or asymmetries) delivers better actionable results.