Data trials and triumphs

This reading focuses on why accurate interpretation of data is key to data-driven decisions. You have been learning why data is such a powerful business tool and how data analysts help their companies make data-driven decisions for great results. As a quick reminder, the goal of all data analysts is to use data to draw accurate conclusions and make good recommendations. That all starts with having complete, correct, and relevant data.

But keep in mind, it is possible to have solid data and still make the wrong choices. It is up to data analysts to interpret the data accurately. When data is interpreted incorrectly, it can lead to huge losses. Consider the examples below.

Coke launch failure

In 1985, New Coke was launched, replacing the classic Coke formula. The company had done taste tests with 200,000 people and found that test subjects preferred the taste of New Coke over Pepsi, which had become a tough competitor. Based on this data alone, classic Coke was taken off the market and replaced with New Coke. This was seen as the solution to take back the market share that had been lost to Pepsi.

But as it turns out, New Coke was a massive flop and the company ended up losing tens of millions of dollars. How could this have happened with data that seemed correct? It is because the data wasn’t complete, which made it inaccurate. The data didn't consider how customers would feel about New Coke replacing classic Coke. The company’s decision to retire classic Coke was a data-driven decision based on incomplete data.

Mars orbiter loss

In 1999, NASA lost the $125 million Mars Climate Orbiter, even though it had good data. The spacecraft burned to pieces because of poor collaboration and communication. The Orbiter’s navigation team was using the SI or metric system (newtons) for their force calculations, but the engineers who built the spacecraft used the English Engineering Units system (pounds) for force calculations.

No one realized a problem even existed until the Orbiter burst into flames in the Martian atmosphere. Later, a NASA review board investigating the root cause of the problem figured out that the issue was isolated to the software that controlled the thrusters. One program calculated the thrusters’ force in pounds; another program looking at the data assumed it was in newtons. The software controllers were making data-driven decisions to adjust the thrust based on 100% accurate data, but these decisions were wrong because of inaccurate assumptions when interpreting it. A conversion of the data from one system of measurement to the other could have prevented the loss.

When data is used strategically, businesses can transform and grow their revenue. Consider the examples below.

Crate and Barrel

At Crate and Barrel, online sales jumped more than 40% during stay-at-home orders to combat the global pandemic. Currently, online sales make up more than 65% of their overall business. They are using data insights to accelerate their digital transformation and bring the best of online and offline experiences together for customers.

BigQuery enables Crate and Barrel to "draw on ten times [as many] information sources (compared to a few years ago) which are then analyzed and transformed into actionable insights that can be used to influence the customer’s next interaction. And this, in turn, drives revenue."

Read more about Crate and Barrel's data strategy in How one retailer’s data strategy powers seamless customer experiences.

PepsiCo

Since the days of the New Coke launch, things have changed dramatically for beverage and other consumer packaged goods (CPG) companies.

PepsiCo "hired analytical talent and established cross-functional workflows around an infrastructure designed to put consumers’ needs first. Then [they] set up the right processes to make critical decisions based on data and technology use cases. Finally, [they] invested in the right technology stack and platforms so that data could flow into a central cloud-based hub. This is critical. When data comes together, [they] develop a holistic understanding of the consumer and their journeys."

Read about how PepsiCo is delivering a more personal and valuable experience to customers using data in How one of the world’s biggest marketers ripped up its playbook and learned to anticipate intent.

Key skills for triumphant results

As a data analyst, your own skills and knowledge will be the most important part of any analysis project. It is important for you to keep a data-driven mindset, ask lots of questions, experiment with many different possibilities, and use both logic and creativity along the way. You will then be prepared to interpret your data with the highest levels of care and accuracy. Note that there is a difference between making a decision with incomplete data and making a decision with a small amount of data. You learned that making a decision with incomplete data is dangerous. But sometimes accurate data from a small test can help you make a good decision. Stay tuned. You will learn about how much data to collect later in the program.

Using data in everyday life

The average adult makes thousands of conscious decisions each day. Think about how many of those are data-driven decisions, such as the ones you’ve been learning about. Data surrounds your everyday activities. The daily weather report contains data, and so does a sign listing your local convenience store’s hours. If you think about it, you base your decisions on available data all the time, whether you're checking the weather report to decide what to wear or looking up store hours to know what time to shop.

For each task listed below, what data is available to help make decisions related to the task? For example, item prices are pieces of data available for deciding how to spend money.

1. When to wake up
2. Whether to go out to eat
3. What to spend money on
4. What to listen to on the radio
5. Who to call on the phone

For this discussion prompt, first come up with three data sources for each of the five tasks. Then, in a post of two or more paragraphs (100-200 words), describe the data sources for two of the tasks. Finally, visit the discussion forum to read what other learners have written, and engage in discussion with at least two posts.

Qualitative and quantitative data in business

This reading further elaborates on the meaning of qualitative versus quantitative.

As you have learned, there are two types of data: qualitative and quantitative.

We can take a closer look at the data types and data collection tools. Imagine that you are a data analyst for a chain of movie theaters. Your manager wants you to track trends in:

• Movie attendance over time
• Profitability of the concession stand
• Evening audience preferences

In our scenario, we assume quantitative data already exists to monitor all three trends.

Movie attendance over time

Starting with the historical data the theater has through its loyalty and rewards program, your first step is to investigate what insights you can gain from that data. You look at attendance over the last 3 months. But, because the last 3 months didn’t include a major holiday, you decide it is better to look at a full year’s worth of data. As you suspected, the quantitative data confirmed that average attendance was 550 per month but then rose to an average of 1,600 per month for the months with holidays.

The historical data serves your needs for the project, but you also decide that you will resume the analysis again in a few months after the theater increases ticket prices for evening showtimes.

Profitability of the concession stand

Profit is calculated by subtracting cost from sales revenue. The historical data shows that while the concession stand was profitable, profit margins were razor thin at less than 5%. You saw that average purchases totaled $20 or less. You decide that you will keep monitoring this on an ongoing basis.

Based on your understanding of data collection tools, you will suggest an online survey of customers so they can comment on the food at the concession stand. This will enable you to gather even more quantitative data to revamp the menu and potentially increase profits.

Evening audience preferences

Your analysis of the historical data shows that the 7:30 PM showtime was the most popular and had the greatest attendance, followed by the 7:15 PM and 9:00 PM showtimes. You may suggest replacing the current 8:00 PM showtime that has lower attendance with an 8:30 PM showtime. But you need more data to back up your hunch that people would be more likely to attend the later show.

Evening movie-goers are the largest source of revenue for the theater. Therefore, you also decide to include a question in your online survey to gain more insight.

Qualitative data for all three trends plus ticket pricing

Since you know that the theater is planning to raise ticket prices for evening showtimes in a few months, you will also include a question in the survey to get an idea of customers’ price sensitivity.

Your final online survey might include these questions for qualitative data:

1. What went into your decision to see a movie in our theater today? (movie attendance)
2. What do you think about the quality and value of your purchases at the concession stand? (concession stand profitability)
3. Which show time do you prefer, 8:00 PM or 8:30 PM, and why do you prefer that time? (evening movie-goer preferences)
4. Under what circumstances would you choose a matinee over a nighttime showing? (ticket price increase)

Summing it up

Data analysts will generally use both types of data in their work. Usually, qualitative data can help analysts better understand their quantitative data by providing a reason or more thorough explanation. In other words, quantitative data generally gives you the what, and qualitative data generally gives you the why. By using both quantitative and qualitative data, you can learn when people like to go to the movies and why they chose the theater. Maybe they really like the reclining chairs, so your manager can purchase more recliners. Maybe the theater is the only one that serves root beer. Maybe a later show time gives them more time to drive to the theater from where popular restaurants are located. Maybe they go to matinees because they have kids and want to save money. You wouldn’t have discovered this information by analyzing only the quantitative data for attendance, profit, and showtimes.

Learning Log: Ask SMART questions about real-life data sources

Overview

In a previous self-reflection, you prepared for a “data conversation” with someone in your life by creating SMART questions to help you understand more about the data they usually interact with, the limitations of the data they have, and their business goals. Now, you’ll complete an entry in your learning log to reflect on that conversation and how you might approach this data for a real project. By the time you complete this log entry, you will have a stronger understanding of how to use the SMART framework to craft effective questions about real life data. This will be a key skill as you begin to develop your own data analysis projects.

Review your notes

Before you begin your new entry, take a moment to locate and read the notes you took during your data conversation. Based on the answers to your well-prepared SMART questions, you should have a better context for your target audience now. Review those answers and start thinking about the following:

• Stakeholder’s business goals; in this case, the person you had a conversation with
• Identifying the data needed to answer the SMART questions
• Exploring what data the stakeholder already has
• Determining the data that you don’t have, but need in order to answer the questions

You’ll reflect on how your data conversation went and what you learned in your learning log template which is linked below.

Access your learning log

To use the template for this course item, click the link below and select “Use Template.”

Link to learning log template: Ask SMART questions about real life data sources

OR

If you don’t have a Google account, you can download the template directly from the attachment below.

Test your knowledge on the power of data

Question 1

What is the difference between qualitative and quantitative data?

A. Qualitative data is specific. Quantitative data is subjective.

B. Qualitative data can be used to measure qualities and characteristics. Quantitative data can be used to measure numerical facts.

C. Qualitative data is about the quality of a product or service. Quantitative data is about how much of that product or service is available.

D. Qualitative data describes the kind of data being analyzed. Quantitative data describes how much data is being analyzed.

The correct answer is B. Qualitative data can be used to measure qualities and characteristics. Quantitative data can be used to measure numerical facts. Explain: Qualitative data can be used to measure qualities and characteristics. Quantitative data can be used to measure numerical facts.

Question 2

Fill in the blank: Data-inspired decision-making can discover _____ when exploring different data sources.

A. if a decision was properly made

B. where the largest amount of data is

C. what the data has in common

D. which experts can give advice

Explain: Data-inspired decision-making deals with exploring different data sources to discover what they have in common.

Question 3

Which of the following examples describes using data to achieve business results? Select all that apply.

• A grocery chain collects data on sale items and pricing from each store
• A movie theater tracks the number of weekend movie goers for three months.
• A video streaming service analyzes user preferences to customize movie recommendations.

Explain: Analyzing user preferences to customize movie recommendations and analyzing product purchases to create better promotions are examples of using data to achieve business results. These examples demonstrate putting analysis to work to achieve business results.

• A large retailer perfoms data analysis on product purchases to create better promotions.

Explain: Analyzing user preferences to customize movie recommendations and analyzing product purchases to create better promotions are examples of using data to achieve business results. These examples demonstrate putting analysis to work to achieve business results.