Data analysis :

• reveals important patterns and insights about that data
• can help us make more informed decisions

Data-inspired decision-making :

• explores different data sources to find out what they have in common

Algorithm :

• a process or set of rules to be followed for a specific task

Question

Fill in the blank: Data-inspired decision-making explores different data sources to find _____.

• outlier
• problems
• commonalities
• predictions

Correct. Data-inspired decision-making explores different data sources to find commonalities.

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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 quantitative data wasn’t paired with qualitative data that would show the company 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 were using the British Imperial system (pounds) for their 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.

When accurate data and success meet

Now, let’s hear some good news. The following are two real-life examples of businesses using data to do some really impressive things.

Video streaming success

Video-streaming services sometimes collect data on customer preferences. They use this data to create recommendations for what kinds of movies and TV shows people will most enjoy. More and more, these methods turn out to be highly successful at predicting what customers want to watch. Taking that a step further, a lot of streaming services even use customer data to decide what content to create. This is a win-win because people get content they like, and the streaming services profit from a boost in viewership and subscription revenues.

Fast food on the go

Some fast-food companies are combining the power of a mobile app with consumer data. After downloading the fast-food chain’s app, customers are able to order food and pay right on their phones. Then, for an even better experience, a fast-food chain can use data they have collected from previous orders to send those customers on-the-spot recommendations for items to add to their order, prompt them to repeat a previous order, or encourage loyalty with exclusive deals for future orders. The data collected is ongoing information about their customers; companies can develop more effective and personalized promotions to keep customers coming back.

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.

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Qualitative and quantitative data

Quantitative data - all about the specific and objective measures of numerical facts.

• What?
• How many?
• How often?

Qualitative data - describes subjective or explanatory measures of qualities and characteristics or things that can't be measured with numerical data

• Why?

Question

Which of the following examples would be determined using qualitative data?

• The most well-liked make and model of car in Puerto Rico
• The annual rainfall in Costa Rica
• The number of commuters who take the train to work
• The frequency of hurricanes per year in Louisiana

Correct. The most well-liked make and model of car in Puerto Rico would be determined using qualitative data. Qualitative data is a subjective and explanatory measure of a quality or characteristic.

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 showtime do you prefer, 8:00 PM or 8:25 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, we 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. We wouldn’t have discovered this information by analyzing only the quantitative data for attendance, profit, and showtimes.

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Test your knowledge on the power of data

TOTAL POINTS 4

Question 1

What is the difference between qualitative and quantitative data?

• Qualitative data is about the quality of a product or service. Quantitative data is about how much of that product or service is available.
• Qualitative data is specific. Quantitative data is subjective.
• Qualitative data can be used to measure qualities and characteristics. Quantitative data can be used to measure numerical facts.
• Qualitative data describes the kind of data being analyzed. Quantitative data describes how much data is being analyzed.

Correct. 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 deals with exploring different data sources to discover _____.

• which experts can give advice
• what the data has in common
• if a decision was properly made
• where the largest amount of data is

Correct. 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 large retailer performs data analysis on product purchases to create better promotions.
• A video streaming service analyzes user preferences to customize movie recommendations.

Correct. 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 movie theater tracks the number of weekend movie goers for three months.
• A grocery chain collects data on sale items and pricing from each store.

Question 4

If someone is subjectively describing their feelings or emotions, it is qualitative data.

• True
• False

Correct. Qualitative data is descriptive, subjective, and explanatory.