Data Analysis: How to ask the best data analytics questions

I often recommend a couple ways to get the get the ball rolling when it comes to getting people in your organization to start asking compelling questions. However, getting people to ask better data questions is not always as simple as creating the right environment. Even a highly skilled data science team often needs more guidance.

To stimulate questions, it is often helpful to focus on specific areas that are fertile grounds for questions. In this newsletter, I highlight three key areas that are not only the places you’ll find great questions, but also are a good place to start. These are questions that:

• Clarify the definitions of terms
• Identify "facts" that are really assumptions
• Reveal errors in reasoning

Note: These three areas are intended to initiate the process or get your team moving if it's stuck. Don't let these areas limit the scope of your exploration. If you address these three areas, you’re bound to come up with at least a few data analysis questions to improve the gears. When the team develops some momentum, team members will naturally ask more questions.

Fundamentals of Data Analytics Questions

Data analytics examines raw data to find important patterns that help us understand and boost business performance. By learning to read data, you turn random numbers into useful insights. This process lets you see trends and behaviors that affect your business, harnessing data visualization to make these insights more accessible.

For example, understanding what customers like or how the market behaves helps you make smart choices that lead to growth. You'll keep an eye on important business metrics and also guess future trends from past data. With this knowledge, you can improve your strategies, make your operations better, and increase your business's success. So, dive into data reading and use it to lift your business to a higher level.

In short, by focusing on data, you can make better decisions that help your business grow through data visualization and google analytics. Make sure to keep learning and applying data analysis to stay ahead.

Clarify Key Terms

George Carlin once joked that he put a dollar in a change machine and nothing changed. Jokes like this are possible because many words in the English language have different meanings based on the context in which they're used and on different individual's understanding of the words. While jokes are funny, however, people often get into heated arguments when they don't have a shared understanding of what certain words or phrases mean. Just look at how different people define "success." For some, it's spending time with family, for others it's financial security, and for some knowledge or power.

The world of business is not immune to ambiguity inherent in certain terms; for example, ask two people to define "custom satisfaction." Does it simply mean that the person is a return customer? Is a customer who never complains satisfied? Can a customer who returns a product for a refund be satisfied? If a customer never buys another product, can we assume that customer was not satisfied?

Your data science team needs to be sensitive to ambiguous terms and nail down their intended meanings. Here's a short list of ambiguous terms commonly used in various organizations:

• Acceptable or adequate
• Agile
• Better, faster, bigger, etc.
• Happy
• Normally
• Reasonable
• Satisfied
• Sufficient
• Often, frequently, or rarely

Identify "Facts" That Are Really Assumptions: Analyzing Data

People often accept assumptions as facts. A company's leadership, for example, may believe that the company has such a unique manufacturing process that nobody can compete with it on price or quality even when that's not true. The truth may be that some other company has yet to develop something better or that there is an entirely new product being developed somewhere that will make the company's existing product obsolete — leadership just doesn't know about it yet.

In general, assumptions have four characteristics:

1. They are often hidden or unstated. Very few people start sentences by saying, “If we assume this to be true, this other thing must be right.” They simply make the assumption and present it as a fact.
2. Assumptions are usually taken for granted or accepted as “common sense.”
3. They’re essential in determining your reasoning or conclusion. Your reasoning might even depend on the assumption.
4. They can be deceptive. Often, flawed reasoning is hidden by a common sense assumption. Something like, “Sugar is unhealthy, so artificial sweeteners must be healthy.”

Data science teams must remain on the lookout for false or questionable assumptions. Not all assumptions are bad. If the assumption reflects reality and facilitates positive or productive decisions and activity, it can be helpful. However, false assumptions can create blind spots and introduce misinformation into the decision-making process.

Reveal Errors in Reasoning: Data Science

Data science teams need to be aware of the possibility of errors in data and errors in reasoning, which are even worse. Additionally, they must ensure the use of right data to mitigate these risks. A data error may result in a minor setback or a series of false reports. On the other hand, an error in reasoning can lead the team down the wrong path or result in completely wrong conclusions. Watch out for the following types of logical fallacies(reasoning that results in invalid arguments):

• Ad hominem: Attacking the person who made the claim instead of the claim itself. For example, a team member dismisses a statement on the basis that the person who made it lacked the expertise or credentials to be believed.
• Ad populum: Accepting a claim as fact simply because it is a popular belief. In other words, if everyone agrees, then it must be right.
• Appeal to authority: Assuming a claim is true because an authority on the topic says it's true, without providing other evidence to support the claim.
• Appeal to ignorance: Assuming a claim is true because no evidence suggests it is untrue. For example, "Nobody has actually proven that God exists, so God does not exist" is a fallacy. So too is "You can't prove that God doesn't exist, so God does exist."
• Question dismissal often overlooks the potential insights that can be gained from data analysis questions to improve understanding.: Avoiding a question because it may result in an uncomfortable situation. For example, a team member shelves a question because he or she thinks that it might reveal an issue that makes the organization's leadership uncomfortable.
• Circular reasoning: Using the conclusion of an argument to support the premise on which it is based; for example, “We are a data-driven company so our data must be correct.”
• Straw man argument: Distorting someone else's position in order to weaken it and then attacking that position instead of what the person truly believes and claiming victory. For example, stating "If you accept Bill’s argument that the data is terrible, we have to start from scratch,” when Bill never said or meant "the data is terrible," is a straw man argument.
• False dichotomy or either/or fallacy: Presenting an issue as though there are only two possibilities when there may be more; for example, “If the data is right, it means we’re all wrong.”

Importance of Asking Analytics Questions

Asking the right questions in data analysis is important. It helps you focus your efforts to match the goals of your business and find useful insights. When you ask sharp questions, you know exactly what you're looking for. This makes your data analysis direct and effective. Without clear questions, you might just look through data without finding important information.

By improving how you ask questions, you do more than just collect data. You turn it into valuable knowledge. This method helps you choose the best data sets and guides your analysis towards useful results.

Structured Data Analysis Approach: Asking questions

Start with clear, direct questions. They will help you guide a structured analysis. This method ensures your efforts are focused and tied to your business goals. It makes understanding data easier and lets you choose the best tools for analysis.

When you're well-organized, your analysis can be more effective. Here are the key steps:

• Define Objectives: Clearly state your goals.
• Select Analytical Techniques: Pick methods that fit your data and goals well.
• Data Collection: Collect relevant data in an organized way.
• Data Interpretation: Look at patterns and trends to find useful insights.

Each of these steps builds on the last. First, knowing what you want to achieve helps you pick the right tools. Then, gathering data in a planned way makes sure you have what you need. Finally, looking at the data helps you make good decisions. This process keeps your analysis clear and helpful.

Translating Objectives Into Questions

When turning business goals into clear questions for analysis, it's important to know exactly what you want to achieve. This helps in making questions and answers that can be answered clearly, leading to useful results through utilizing data sources effectively. Here's how you can do it:

• Identify key metrics: Look for metrics that are closely linked to your goals. Metrics are just numbers that tell you how things are going.
• Define success criteria: Decide what success means for each goal. This is like describing what winning looks like for you.
• Break down objectives: Change broad goals into clear questions that you can answer.
• Prioritize questions: Choose to focus on the questions that will make the biggest difference.

Selecting Key Performance Indicators: Data Analyst

Selecting the right Key Performance Indicators (KPIs) is crucial. They help you see if your business is meeting its goals and give you clear steps to take.

Here's how to pick the right ones:

Match KPIs to Your Business Goals: Each KPI should help you reach your business goals. Think of KPIs as tools that show you how well you're doing in areas that matter most to your success.

Choose KPIs You Can Measure: Pick KPIs based on data you can count and trust, ensuring they are derived from big data analytics for accuracy and comprehensiveness. It's like choosing a ruler that you know has accurate markings to measure a table's length.

Pick KPIs That Help You Act: Choose KPIs that help you make decisions and take actions. If a KPI shows sales are down, it should push you to find out why and fix it.

Check and Change KPIs as Needed: Keep an eye on your KPIs to make sure they're still useful. If they're not helping, it might be time to find new ones that do.

Strategy Formulation Process

Starting your strategy formulation means setting clear goals using insights from your data analysis. First, identify specific aims based on what your data tells you. This makes sure your strategies are based on strong evidence.

Then, turn these insights into plans you can act on. Make sure your goals are clear and measurable, so you can check later if the strategy works.

As you build your strategy, keep looking at your data to improve your plan. Use feedback loops to adjust as needed. Remember, creating a strategy isn't just a one-time job; it's a repeating process. Keep checking how you're doing and change things based on new information. This careful method ensures your strategies stay useful and effective, pushing your analytics forward.

Keep the conversation going between these steps. Ask yourself, 'How can we better use new data?' or 'What did we learn from the last review?' This keeps your strategy sharp and responsive.

Conclusion - Data Analysis Questions to Improve

Some of the techniques described in this newsletter boil down to listening and observing closely and being skeptical about what you hear and observe. Whenever you encounter a statement presented as a fact, ask yourself, "Is this really true?"

Whenever you encounter someone presenting a position, ask yourself, "Is the conclusion based on sound reasoning?" Questions like this force you to take a closer look and determine for yourself the truth and validity of a statement or conclusion.

In the world of data analysis, knowing which questions to ask is the key to this. By asking clear and precise questions, you turn simple data into valuable answers that can help improve a business.

Remember, your success depends on setting clear goals, choosing important data points, and always improving your methods. So, keep your focus sharp, and you'll guide your business to do better and achieve its main goals.

When you understand the importance of asking the right questions, you can take raw data and find the key information that will help your business grow. This process is about turning data into knowledge that can lead to better decisions, emblematic of the work done by a data scientist. Make sure you keep your goals in mind and pick the most important information to track.

Q: What are some basic and advanced questions to ask when analyzing data?

Basic questions usually start with understanding the dataset:

• What type of data are we dealing with?
• What is the size of the dataset?

Advanced questions often involve statistical analysis and seeking patterns:

• What trends do we observe?
• How can these trends help in predicting future outcomes?

Q: How can asking the right questions of the data improve business performance?

Do you know how asking the right questions can help your business? It can reveal important details about customer behavior, market trends, and how your business runs. When you focus on the right questions, you can use the answers to:

- Make smart business choices - Improve how things work - Make customers happier

What are some important questions to ask like a data analyst to avoid bad data?

Data analysts check their data to make sure it's good to use.

Here are the steps data analysts follow:

1. Check where the data comes from. 2. Make sure the data is complete and accurate. 3. Clean the data if needed. 4. Look for any errors or strange values.

Q: How can data analysis questions lead to actionable insights?

By asking specific questions, you can find patterns, surprises, or links in your data. Some key questions are:

- What does this trend tell us? - How can we use these findings to fix our problem?

When you use this method, you can turn insights into actions. This shapes plans that help your company succeed.

Q: What are 5 questions to ask when analyzing data to improve your business?

1. What specific business problem are we trying to solve?
2. Which data sets are most relevant to this problem?
3. What patterns or trends should we look for in the data?
4. How can we measure the impact of our findings?
5. What strategic actions can we recommend based on our analysis?

Q: Why is asking the right questions during data cleaning essential?

During data cleaning, you need to ask the right questions. This helps you find and fix errors in the dataset. Questions like these are important:

1. What problems should we look for? 2. Which pieces of data are missing or repeated? 3. How do we handle unusual data points?

These questions help you plan a clear data cleaning strategy and It helps make sure the dataset is good and reliable for later analysis.

Q: How does framing questions to improve your business with data analytics lead to better decision-making?

By asking the right questions, you can make sure your data work matches your goals.

?Ask questions like:

What numbers will help grow our business?

How can we use data to track our progress?

When you focus on these questions, you'll get useful insights. These insights will help you make smart decisions. This way, your business will make better and more targeted choices.

This is my weekly newsletter that I call The Deep End because I want to go deeper than results you’ll see from searches or AI, incorporating insights from the history of data and utilizing data science methods. Each week I’ll go deep to explain a topic that’s relevant to people who work with technology. I’ll be posting about artificial intelligence, data science, and data ethics.?

This newsletter is 100% human written ?? (* aside from a quick run through grammar and spell check).

More Sources:

1. https://www.datapine.com/blog/data-analysis-questions/
2. https://www.pragmaticinstitute.com/resources/articles/data/evaluating-your-data-strategy-7-questions-you-need-to-ask/
3. https://www.newhorizons.com/resources/blog/data-analysis-for-continuous-improvement
4. https://www.polymersearch.com/data-analysis-guide/15-data-analysis-questions-for-efficient-analytics
5. https://www.domo.com/learn/article/how-to-turn-business-questions-into-analytics
6. https://www.theseattledataguy.com/17-questions-you-need-to-ask-about-your-data-strategy/
7. https://hbr.org/2015/10/the-two-questions-you-need-to-ask-your-data-analysts
8. https://databox.com/data-analysis-questions
9. https://thedataliteracyproject.org/asking-the-right-questions-to-gain-true-data-insights/
10. https://rikkeisoft.com/blog/data-analytics-framework/
11. https://www.dhirubhai.net/advice/1/how-can-data-analysis-frameworks-identify-opportunities-gmmde
12. https://www.forbes.com/sites/brentdykes/2020/08/11/a-simple-strategy-for-asking-your-data-the-right-questions/
13. https://hbr.org/2023/09/4-skills-the-next-generation-of-data-scientists-needs-to-develop
14. https://userpilot.com/blog/how-to-analyse-qualitative-data/
15. https://hbr.org/2012/10/what-should-you-tell-customers
16. https://www.geeksforgeeks.org/a-comprehensive-guide-to-data-analytics-framework/
17. https://www.surveymonkey.com/mp/how-to-analyze-survey-data/
18. https://iimskills.com/data-analytics-framework/