Data Analytics Questions to Ask for Better Data Analysis

The success of any data science initiative hinges on the team's ability to ask interesting data analytics questions that are relevant to the organization's success and its ability and willingness to challenge assumptions and beliefs. After all, without questions, you can have no answers. However, asking compelling questions and challenging long-held beliefs can be difficult, especially in organizations with strict hierarchies that discourage questioning and the challenging of authority.

If your data science team is struggling to come up with compelling questions and hesitates to challenge assumptions, the suggestions I present in this post can get the ball rolling. Getting started is the most difficult part. As soon as the team gets into the swing of asking questions and questioning beliefs, it will have no shortage of follow-up questions.

Conduct Question Meetings For Data Analysis

One of the best ways to encourage data science team members to ask questions and challenge beliefs is to build an environment that's conducive to the free exchange of ideas. The research lead is ultimately responsible and can start to nurture the free exchange of questions and answers by modeling the desired behavior — listening and learning without judging. Everyone on the team should engage in deep listening— focused listening that enables them to hear and understand what others are saying, ignoring any initial impulse to judge what they hear. Team members need to recognize that they have plenty of time later to analyze what they hear, but the first step is to fully understand what the other people are getting at.

A good way to encourage questions and reinforce deep listening is to conduct question meetings. In these meetings, the research lead should encourage participants to ask questions before making statements. This technique is sometimes called a "question first" approach. These meetings are about eliciting the maximum number of questions. They’re focused on everyone asking their questions and listening, a practice often done by a data scientist. Ban smartphones, laptops, and other electronic devices from these meetings. Everyone should focus on listening, with one person taking notes.

Although question meetings are mostly unstructured, consider starting the meeting like this:

1. Set the tone by starting with a question, such as “Does everybody know why we are having this meeting?” and then wait for a response. This will help you make sure everyone is on the same page. A good research lead is not afraid of short periods of silence. Don’t try to answer your own questions. Give everyone in the room time to think about their answer, ensuring they can ask specific questions.
2. When you’re satisfied that everybody understands the meeting's purpose, present the challenge. For example, you may say something like, "The CEO wants to know why we're losing market share to XYZ Corporation." Don't share what you think; instead, focus on asking specific questions to gather insights. Leave the topic open for the rest of the team to weigh in on. Sit down and wait to see if anyone starts asking questions.
3. If, after a few minutes, no one says anything, you could ask something like, “Does everyone understand why this is a challenge?” What you’re hoping to get from the team is something like, “How do we know we're losing market share?” or "What is XYZ Corporation doing different or better than us?" or "When did this start?" These types of questions can help to guide the team's analysis.

Avoid quick statements that are likely to limit the scope of the discussion, such as "The CEO suspects that we are losing market share due to the recent reorganization of our marketing department." Such statements keep people from coming up with their best ideas. Remember that it’s the discussion that gives your team the greatest value. You want the team to consider all possibilities.

Solicit Questions to Improve Your business Performance

If you’re a fan of detective shows, you’ve probably seen a crime wall plastered with maps, photos, names, clues, sticky notes, and so on. The board functions as a combination collage, story board, and puzzle that provides the detective with a clear visualization of the evidence.

Your data science team can create its own "crime wall" by soliciting questions from across the organization through the use of a question board. Here are some suggestions for hosting an effective question board:

• Go big with your business goals in mind. Use a large whiteboard, so plenty of room is available to post questions and answers.
• Provide a ready supply of large, colorful sticky notes and pens. Consider color-coding questions and responses — red or pink sticky notes for essential questions, yellow for non-essential questions, white or purple for responses or analysis results.
• Above the board, attach a large arrow pointing down to the board with the text "Question Board" or "Ask a Question."
• Place the board off to the side in a well-trafficked area. You want everyone in the organization to know where the board is, but you want it somewhat protected, so three or four people can gather around it to talk without disrupting or being disrupted by others.
• Use string or yarn to illustrate how various questions are connected.

A question board delivers the following benefits:

• Gives the team a shared space for group discussion.
• Shows how questions are interconnected.
• Helps organize your questions by type.
• Helps to tell a story.
• Gives other people in the organization a place to participate.

Hosting question meetings and a question board are only two ways to encourage people in the organization to ask compelling questions. You are likely to come up with your own unique ideas. What's important is that you provide the encouragement and means for people to contribute their questions.

FREQUENTLY ASKED QUESTIONS (FAQ)

How can I ensure that I am asking the right questions in data analysis?

To ensure that you are asking the right data analysis questions to improve outcomes, you need to clearly understand the business objectives, know what data and data sources are available, and consider how the insights will be used.

Consulting with stakeholders and business analysts can also help in knowing which questions to ask to align with business goals.

Why is data cleaning important in data analysis?

?Data cleaning is important in data analysis because errors in data can lead to incorrect insights, which can misguide business goals.

Data cleaning involves correcting or removing errors and inconsistencies in raw data to improve the quality and reliability of the analysis. This step is essential for accurate data visualization and ensuring that the findings are trustworthy.

How can data visualization help in data analysis?

Data visualization helps in data analysis by transforming complex data sets into visual formats like charts, graphs, and dashboards.

This makes it easier to identify trends, outliers, and patterns that are not easily noticeable in raw data, helping you make informed decisions. BI tools are commonly used for creating effective visualizations.

What are important KPIs to consider in business analytics?

Important KPIs in business analytics vary depending on the business’s objectives but common ones include sales metrics, customer acquisition costs, customer lifetime value, churn rate, and operational efficiency.

Monitoring these KPIs helps in making informed business decisions.

How do data analysts collect data for analysis?

Data analysts collect data using various methods such as surveys, transaction records, sensors, social media interactions, and more.

They may also extract data from existing databases and data warehouses to read data accurately. It’s important to ensure that the data collection process is methodical and the data is reliable.

Why is it important to consider past data in analysis?

Considering past data is important because it provides historical context, helping to identify trends, patterns, and anomalies over time.

This makes it easier to make accurate predictions and informed decisions. Past data acts as a benchmark for current and future performance.

What role does business intelligence play in data analysis?

Business intelligence (BI) plays a crucial role in data analysis by providing tools and processes that help in transforming raw data into actionable insights.

BI allows businesses to monitor their KPIs, generate detailed reports, and create interactive dashboards, which aid in strategic planning and decision-making.

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More sources

1. https://www.datapine.com/blog/data-analysis-questions/
2. https://www.indeed.com/career-advice/interviewing/analytics-questions
3. https://www.simplilearn.com/tutorials/data-science-tutorial/data-science-interview-questions