Quantitative, Qualitative, Data-Inspired, Data-Driven

Question 1

What are some good questions to ask about data?

Answer 1

1. Who: The person or organization that created, collected, and/or funded the data collection
2. What: The things in the world that data could have an impact on
3. Where: The origin of the data
4. When: The time when the data was created or collected
5. How: The method used to create or collect it
6. Why: The motivation behind the creation or collection

Question 2

How is there a relationship between why data was collected and possible bias?

Answer 2

Because sometimes, data is collected, or even made up, to serve an agenda.

Question 3

What is data-inspired decision making?

Answer 3

Explores different data sources to find out what they have in common

Question 4

What is an algorithm?

Answer 4

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

Question 5

What does data-driven decision-making mean?

Answer 5

Using facts to guide business strategy

Question 6

What are potential problems when making a data-driven decision

Answer 6

1. The quantity and quality of data may not be sufficient
2. The data may be biased
3. You might overreliy on historical data
4. Qualitative insights may be ignored

Question 7

Data-Driven decision example

Answer 7

A website that sells widgets has an idea for a new website layout they think will result in more people buying widgets. For two weeks, half of their website visitors are directed to the old site; the other half are directed to the new site. After those two weeks, the analyst gathers the data about their website visitors and the number of widgets sold for analysis. This helps the analyst understand which website layout resulted in more widget sales. If the new website performed better in producing widget sales, then the company can confidently make the decision to use the new layout!

Question 8

What is quantitative data?

Answer 8

Specific and objective measures of numerical facts.

Quantitative data is numerical information that can be measured or counted.

• Countable or measurable. relating to numbers
• Tells us how many, how much or how often
• Fixed and universal, “factual”
• Gathered by measuring and counting things
• Analyzed using statistical analysis

Question 9

What are some examples of quantitative data?

Answer 9

• height
• weight
• number of objects
• volume
• temperature
• pressure
• price
• speed
• percentages

Question 10

What are some quantitative data tools?

Answer 10

1. Structured interviews
2. Surveys
3. Polls

Question 11

What is qualitative data?

Answer 11

Subjective or explanatory measures of qualities and characteristics

Descriptive information about characteristics that are difficult to define or measure or are described by words and not numbers.

• Descriptive, relating to words and language
• Describes certain attributes, and helps us to understand the “why” or “how” behind certain behaviors
• Dynamic and subjective, open to interpretation
• Gathered through observations and interviews
• Analyzed by grouping the data into meaningful themes or categories

Question 12

Examples of qualitative data

Answer 12

• feelings and emotions
• texture
• flavor
• color (unless it can be written as a specific wavelength of light)
• expressions of more/less, ugly/beautiful, fat/thin, healthy/sickly
• country of origin
• sex (male or female)

Question 13

What are some qualitative data tools?

Answer 13

1. Focus groups
2. Social media text analysis
3. In-person interviews

Question 14

What is a data-inspired decision?

Answer 14

Data-inspired decisions include the same considerations as data-driven decisions while adding another layer of complexity. They create space for people using data to consider a broader range of ideas: drawing on comparisons to related concepts, giving weight to feelings and experiences, and considering other qualities that may be more difficult to measure. Data-inspired decision-making can avoid some of the pitfalls that data-driven decisions might be prone to.

Question 15

Example of Data-Inspired decision

Answer 15

A customer support center gathers customer satisfaction data (often known as a “CSAT” score). They use a simple 1–10 score along with a qualitative description in which the customer describes their experience. The customer support center manager wants to improve customer experience, so they set a goal to improve the CSAT score. They start by analyzing the CSAT scores and reading each of the descriptions from the customers. Additionally, they interview the people working in the customer support center. From there, the manager formulates a strategy and decides what needs to improve the most in order to raise customer satisfaction scores. While the manager certainly relies on the CSAT data in the decision-making process, input of support center representatives and other qualitative information informs the approach as well.

Question 16

Example 1 of data analysis failure.

Answer 16

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. The company thought this was the solution to take back the market share that had been lost to Pepsi.

But as it turns out, New Coke was very unpopular—and the company ended up losing tens of millions of dollars. The data seemed correct, but it was incomplete: 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.

Question 17

Example 2 of data analysis failure

Answer 17

In 1999, NASA lost the $125 million Mars Climate Orbiter even though the teams had good data. The spacecraft burned to pieces because of poor collaboration and communication. The Orbiter’s navigation team was using the International System of Units (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 there was a problem until the Orbiter burst into flames in the Martian atmosphere. Later, a NASA review board investigating the cause of the problem discovered the issue was in the software that controlled the thrusters. One program calculated the thrusters’ force in pounds; another program working with 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. The two teams might have communicated so they picked a single unit of measure, or so the analysts would have known that conversion was a necessary step in the process to prepare the data. A conversion of the data from one system of measurement to the other could have prevented the loss.

Question 18

Should you use a data-driven or data-inspired approach?

Answer 18

As a data analyst, you’ll rarely need to consider, “Am I being data-driven or data-inspired?” It’s helpful to have some context for these two approaches, though your own skills and knowledge will be the most important parts of any analysis project. So, keep a data-driven mindset and ask lots of questions. Experiment with many different possibilities. And use both logic and creativity along the way. Using this approach, you’ll be prepared to interpret your data with the highest levels of care and accuracy.

Question 19

How are quantitative and qualitative data used together?

Answer 19

Quantitative data allows us to see numbers as charts or graphs. Qualitative data can then give us more a high-level understanding of why the numbers are the way they are.

Question 20

Example of using quantitative and qualitative data together

Answer 20

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.

Question 21

Interesting chart on the level of detail in data

Answer 21