In God We Trust, All Others Must Bring Data 📊✨

Also, let's understand how we’re influenced by the availability heuristic and the difference between percentile vs percentage

May 07, 2024

Hello, data enthusiasts and curious minds!

Welcome to our 8th edition of DataPulse Weekly, where we unravel the magic behind data and its impact on our daily lives.

Each newsletter promises to be a journey through the fascinating intersections of data, stories, and human experiences. Whether you're an analyst, or simply curious about how data shapes our world, you're in the right place.

Let’s dive straight into today’s Data Menu -

Today’s Data Menu

Data Case Study: Opinions, Assumptions, Hypotheses and Data
Metric: Fill Rate
Meme of the Week
Human Bias Focus: Availability Heuristic
Data Nugget: Understanding Percentile vs Percentage

Data Case Study:

Picture a meeting involving different stakeholders and a data analyst to discuss inventory planning. Each stakeholder offers a distinct perspective:

Supply Chain Lead: “We should hold more buffer stock to ensure uninterrupted delivery during potential supply chain disruptions.”
Procurement Head: “Our suppliers have been consistent lately and seem reliable enough to minimize lead times.”
Finance Manager: “Excessive buffer stock is tying up too much working capital and increasing our holding costs. We need to optimize safety stock levels.”
Sales Manager: “Upcoming promotions will likely boost demand, especially for new product lines.”

In high-stakes environments like supply chain management, it can be challenging to navigate situations where many variables are at play, particularly when statements can be more opinionated than data-backed.

W. Edwards Deming once said, "In God we trust, all others must bring data."

Businesses today realize the importance of data-driven decisions over gut feelings, instincts, or opinions. As the data champion, the analyst plays a vital role in categorizing input and ensuring decisions are based on evidence.

Categorizing Perspectives: The data analyst categorizes the team's input as follows:

Assumptions:
- Suppliers will maintain consistent lead times (Procurement Head).
- Promotions will increase product demand (Sales Manager).
Opinions:
- Holding more buffer stock is crucial to uninterrupted delivery (Supply Chain Lead).
- Current inventory levels are tying up too much working capital (Finance Manager).
Hypotheses:
- If buffer stock levels are increased, delivery performance will improve (Supply Chain Lead).
- If stock levels are optimized, holding costs will decrease without significantly impacting delivery (Finance Manager).
Facts or Historical Data:
- Historical lead times and promotional forecasts will validate assumptions and hypotheses.
Constraints:
- Warehouse capacity and working capital budgets must be considered during analysis.

Actionable Next Steps: The data analyst proposes several actions based on the above information:

Historical Performance: Gather historical lead time data and promotional forecasts to confirm assumptions.
Inventory Simulation: Simulate different inventory scenarios to find an optimal balance between holding costs and service levels.
Demand Forecasting: Validate promotional impact with historical sales data to refine demand forecasts.

Conclusion:

As a data champion, the data analyst excels at categorizing input, validating assumptions, and guiding decisions with concrete analysis. By doing so, they empower the organization to make well-informed decisions that balance service levels, costs, and customer satisfaction, making them indispensable in any high-stakes strategy.

In a world where not everyone has the data readily available or fully understands its implications, the data analyst bridges the gap, providing clarity and ensuring the team moves forward in the right direction.

Supply chain efficiency often depends on tracking key metrics. One crucial metric is the fill rate, which we'll discuss in the next section.

Metric of the Week: Fill Rate

What Is It?

Fill rate measures a company's ability to meet customer demand by fulfilling orders on the first attempt. It represents the percentage of customer demand that is satisfied immediately. The formula is:

Fill Rate (%) = (Total Shipped Orders / Total Customer Orders) × 100

Why It Matters

A high fill rate ensures customer satisfaction, reduces backorders, and builds loyalty. It strengthens brand reputation and optimizes supply chain efficiency by aligning inventory levels with actual demand, making it an essential metric for maintaining profitability and customer relationships.

💡 Remember that building a data mindset is effective only when we focus on solving data-related problems. The below question is designed for exactly this kind of practice. We will address this in the last section of this newsletter.

Food for thought:

Imagine someone is preparing for one of the world's most competitive exams, the JEE, to gain admission to an IIT. They appear for nationwide JEE Main mock tests and score 90 out of a total of 300 marks. Do you think it is a good score? How can they determine if they are doing well in their preparation?

Meme of the Week:

Data >>>> Opinions 😉

Did you know there are more than 180 ways your brain can trick you? These tricks, called cognitive biases, can negatively impact the way humans process information, think critically and perceive reality. They can even change how we see the world. In this section, we'll talk about one of these biases and show you how it pops up in everyday life.

If you're on Earth with an internet connection, you've probably heard about the recent rainfall that devastated much of Dubai. Now, imagine you've received a job offer from a company in Dubai and are asked to relocate there.

Would you consider moving to Dubai for your next job?

Recent news of heavy rains and flooding in Dubai might make you hesitant. Media reports have highlighted waterlogged roads, disrupted businesses, flight cancellations, and chaos in public transportation, painting a grim picture of a city overwhelmed by extreme weather. If you've seen those reports, it's understandable that you might think moving to Dubai is questionable.

However, this impression is shaped by the availability heuristic, a cognitive bias where recent, vivid events dominate our decision-making, clouding a more comprehensive understanding. In reality, Dubai ranks highly in safety, public transportation, infrastructure, and connectivity. The city is strategically located, offering easy access to both Europe and Asia. Its economy is diverse and provides excellent opportunities for career growth. And you know what’s the best part? There's no tax on personal income! That's right—if you heard it from us, don't forget we were the ones who told you.

By letting this media storm dictate your perception of Dubai, you might be overlooking its many strengths. The city is a vibrant hub that values diversity, inclusion, and innovation. Despite its challenges with heavy rains, the city’s futuristic infrastructure and meticulous urban planning ensure a swift recovery.

So, before letting a single event influence your decision, consider how the availability heuristic might be distorting your judgment.

This brings us to our last section where we address the questions asked earlier.

Data Nugget: Percentile vs. Percentage

Diving into data's crucial role in decision-making, let's revisit our previous question: How can a JEE aspirant determine if they are doing well in their JEE Main preparation?

We can understand this better through percentages and percentiles.

Percentage: Percentage measures the score obtained as a proportion of the total score. For instance, if a student scores 100 out of 300 marks in the JEE Mains mock test, their percentage score is:

Percentage = (100/300) × 100 = 33.33%

Percentile: A percentile indicates the percentage of test takers whose scores fall below a given score. Here are some example scores from the 2024 JEE Main and their corresponding percentiles:

A score of 100 marks places a student in the 93rd percentile, meaning they've scored higher than 93% of test takers.

For JEE Main 2024, scoring in the 93rd percentile is the qualifying mark.

Conclusion: Percentiles reveal a student's relative standing compared to others, while percentages quantify individual performance. Using both metrics together provides a comprehensive understanding of a student's progress and competitive position. The goal isn't just to score high marks but to perform better than 93% of other candidates to reach the qualifying mark.

That's it for today's newsletter! We've broken down complex data concepts and will keep doing so in future editions. If you found this helpful, we'd love for you to subscribe and share it with someone who would benefit from it. Your support inspires us to go the extra mile to create even more valuable content for you. And, remember, when presented with opinions, always seek data and remember what W. Edwards Deming said.

DataPulse Weekly