Why You Should Take This Course

This course is the second in the Google Data Analytics Certificate program. It builds on the foundation you learned in the first course and dives deeper into the world of data analysis. Here are some reasons why you should take this course:

• Learn how to ask effective questions: This is a critical skill for any data analyst. By learning how to ask the right questions, you can ensure that you are collecting the right data and solving the right problems.
• Make data-driven decisions: This course will teach you how to use data to make informed decisions. You will learn how to analyse data, interpret results, and communicate your findings to others.
• Explore spreadsheet magic: Spreadsheets are a powerful tool for data analysis. This course will teach you how to use spreadsheets to clean, organize, and analyse data.
• Discover the power of structured thinking: Structured thinking is a process for breaking down complex problems into smaller, more manageable pieces. This course will teach you how to use structured thinking to solve data analysis problems.
• Learn how to work with stakeholders: Data analysts need to be able to communicate effectively with stakeholders. This course will teach you how to manage stakeholder expectations and communicate your findings in a way that is clear and concise.

In addition to the above, this course will also help you to:

• Develop your critical thinking skills: Data analysis is all about thinking critically about data. This course will help you to develop the skills you need to analyse data effectively.
• Improve your problem-solving skills: Data analysis is a problem-solving process. This course will help you to develop the skills you need to solve data analysis problems effectively.
• Become a more valuable employee: Data analysis is a valuable skill in today’s job market. This course will help you to become a more valuable employee by giving you the skills you need to succeed in a data-driven world.

If you are interested in a career in data analysis, or if you simply want to learn more about how to use data to make informed decisions, then this course is for you.

Learning Objectives

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• Explain the characteristics of effective questions with reference to the SMART framework
• Discuss the common types of problems addressed by a data analyst
• Explain how each step of the problem-solving roadmap contributes to common analysis scenarios
• Explain the data analysis process, making specific reference to the ask, prepare, process, analyse, share, and act phases
• Describe the key ideas associated with structured thinking including the problem domain, scope of work, and context

Enough chat, let’s dive in..

From issue to action: The six data analysis phases

There are six data analysis phases that will help you make seamless decisions: ask, prepare, process, analyse, share, and act. Keep in mind, these are different from the data life cycle, which describes the changes data goes through over its lifetime. Going through the steps will help you solve all kinds of business problems that you might face on the job.

Step 1: Ask

It’s impossible to solve a problem if you don’t know what it is. These are some things to consider:

• Define the problem you’re trying to solve
• Make sure you fully understand the stakeholder’s expectations
• Focus on the actual problem and avoid any distractions
• Collaborate with stakeholders and keep an open line of communication
• Take a step back and see the whole situation in context

Questions to ask yourself in this step:

1. What are my stakeholders saying their problems are?
2. Now that I’ve identified the issues, how can I help the stakeholders resolve their questions?

Step 2: Prepare 

You will decide what data you need to collect in order to answer your questions and how to organize it so that it is useful. You might use your business task to decide: 

• What metrics to measure
• Locate data in your database
• Create security measures to protect that data

Questions to ask yourself in this step: 

1. What do I need to figure out how to solve this problem?
2. What research do I need to do?

Step 3: Process

Clean data is the best data, and you will need to clean up your data to get rid of any possible errors, inaccuracies, or inconsistencies. This might mean:

• Using spreadsheet functions to find incorrectly entered data
• Using SQL functions to check for extra spaces
• Removing repeated entries
• Checking as much as possible for bias in the data

Questions to ask yourself in this step: 

1. What data errors or inaccuracies might get in my way of getting the best possible answer to the problem I am trying to solve?
2. How can I clean my data so the information I have is more consistent?

Step 4: Analyse 

You will want to think analytically about your data. At this stage, you might sort and format your data to make it easier to: 

• Perform calculations
• Combine data from multiple sources
• Create tables with your results

Questions to ask yourself in this step:

1. What story is my data telling me?
2. How will my data help me solve this problem?
3. Who needs my company’s product or service? What type of person is most likely to use it?

Step 5: Share

Everyone shares their results differently, so be sure to summarize your results with clear and enticing visuals of your analysis using data via tools like graphs or dashboards. This is your chance to show the stakeholders you have solved their problem and how you got there. Sharing will certainly help your team: 

• Make better decisions
• Make more informed decisions
• Lead to stronger outcomes
• Successfully communicate your findings

Questions to ask yourself in this step:

1. How can I make what I present to the stakeholders engaging and easy to understand?
2. What would help me understand this if I were the listener?

Step 6: Act

Now it’s time to act on your data. You will take everything you have learned from your data analysis and put it to use. This could mean providing your stakeholders with recommendations based on your findings so they can make data-driven decisions.

Questions to ask yourself in this step:

1. How can I use the feedback I received during the share phase (step 5) to actually meet the stakeholder’s needs and expectations?

These six steps can help you to break the data analysis process into smaller, manageable parts, which is called structured thinking. This process involves four basic activities:

1. Recognizing the current problem or situation
2. Organizing available information 
3. Revealing gaps and opportunities
4. Identifying your options

When you are starting out in your career as a data analyst, it is normal to feel pulled in a few different directions with your role and expectations. Following processes like the ones outlined here and using structured thinking skills can help get you back on track, fill in any gaps and let you know exactly what you need.

Six common problem types

Data analytics is so much more than just plugging information into a platform to find insights. It is about solving problems. To get to the root of these problems and find practical solutions, there are lots of opportunities for creative thinking. No matter the problem, the first and most important step is understanding it. From there, it is good to take a problem-solver approach to your analysis to help you decide what information needs to be included, how you can transform the data, and how the data will be used. 

Data analysts typically work with six problem types:

Making predictions

A company that wants to know the best advertising method to bring in new customers is an example of a problem requiring analysts to make predictions. Analysts with data on location, type of media, and number of new customers acquired as a result of past ads can’t guarantee future results, but they can help predict the best placement of advertising to reach the target audience.

Categorizing things

An example of a problem requiring analysts to categorize things is a company’s goal to improve customer satisfaction. Analysts might classify customer service calls based on certain keywords or scores. This could help identify top-performing customer service representatives or help correlate certain actions taken with higher customer satisfaction scores.

Spotting something unusual

A company that sells smart watches that help people monitor their health would be interested in designing their software to spot something unusual. Analysts who have analysed aggregated health data can help product developers determine the right algorithms to spot and set off alarms when certain data doesn’t trend normally.

Identifying themes

User experience (UX) designers might rely on analysts to analyse user interaction data. Similar to problems that require analysts to categorize things, usability improvement projects might require analysts to identify themes to help prioritize the right product features for improvement. Themes are most often used to help researchers explore certain aspects of data. In a user study, user beliefs, practices, and needs are examples of themes.

By now you might be wondering if there is a difference between categorizing things and identifying themes. The best way to think about it is: categorizing things involves assigning items to categories; identifying themes takes those categories a step further by grouping them into broader themes.

Discovering connections

A third-party logistics company working with another company to get shipments delivered to customers on time is a problem requiring analysts to discover connections. By analyzing the wait times at shipping hubs, analysts can determine the appropriate schedule changes to increase the number of on-time deliveries.

Finding patterns

Minimizing downtime caused by machine failure is an example of a problem requiring analysts to find patterns in data. For example, by analyzing maintenance data, they might discover that most failures happen if regular maintenance is delayed by more than a 15-day window.

Asking SMART questions

Companies in lots of industries today are dealing with rapid change and rising uncertainty. Even well-established businesses are under pressure to keep up with what is new and figure out what is next. To do that, they need to ask questions. Asking the right questions can help spark the innovative ideas that so many businesses are hungry for these days.

The same goes for data analytics. No matter how much information you have or how advanced your tools are, your data won’t tell you much if you don’t start with the right questions. Think of it like a detective with tons of evidence who doesn’t ask a key suspect about it.  Coming up, you will learn more about how to ask highly effective questions, along with certain practices you want to avoid.

Highly effective questions are SMART questions:

Specific: Is the question specific? Does it address the problem? Does it have context? Will it uncover a lot of the information you need?

Measurable: Will the question give you answers that you can measure?

Action-oriented: Will the answers provide information that helps you devise some type of plan?

Relevant: Is the question about the particular problem you are trying to solve?

Time-bound: Are the answers relevant to the specific time being studied?

Examples of SMART questions

Here’s an example that breaks down the thought process of turning a problem question into one or more SMART questions using the SMART method: What features do people look for when buying a new car?

• Specific: Does the question focus on a particular car feature?
• Measurable: Does the question include a feature rating system?
• Action-oriented: Does the question influence creation of different or new feature packages?
• Relevant: Does the question identify which features make or break a potential car purchase?
• Time-bound: Does the question validate data on the most popular features from the last three years? 

Questions should be open-ended. This is the best way to get responses that will help you accurately qualify or disqualify potential solutions to your specific problem. So, based on the thought process, possible SMART questions might be:

• On a scale of 1-10 (with 10 being the most important) how important is your car having four-wheel drive? Explain.
• What are the top five features you would like to see in a car package?
• What features, if included with four-wheel drive, would make you more inclined to buy the car?
• How does a car having four-wheel drive contribute to its value, in your opinion?

Things to avoid when asking questions

Leading questions: questions that only have a particular response

• Example: This product is too expensive, isn’t it?

This is a leading question because it suggests an answer as part of the question. A better question might be, “What is your opinion of this product?” There are tons of answers to that question, and they could include information about usability, features, accessories, color, reliability, and popularity, on top of price. Now, if your problem is actually focused on pricing, you could ask a question like “What price (or price range) would make you consider purchasing this product?” This question would provide a lot of different measurable responses.

Closed-ended questions: questions that ask for a one-word or brief response only

• Example: Were you satisfied with the customer trial?

This is a closed-ended question because it doesn’t encourage people to expand on their answer. It is really easy for them to give one-word responses that aren’t very informative. A better question might be, “What did you learn about customer experience from the trial.” This encourages people to provide more detail besides “It went well.”

Vague questions: questions that aren’t specific or don’t provide context

• Example: Does the tool work for you?

This question is too vague because there is no context. Is it about comparing the new tool to the one it replaces? You just don’t know. A better inquiry might be, “When it comes to data entry, is the new tool faster, slower, or about the same as the old tool? If faster, how much time is saved? If slower, how much time is lost?” These questions give context (data entry) and help frame responses that are measurable (time).

Example Scenario / Case Study

The scenario

You are three weeks into your new job as a junior data analyst. The company you work for has just collected data on their weekend sales. Your manager asks you to perform a thorough exploration of this data. To get this project started, you must ask some questions and get some information.

Ask the right type of questions

You can apply the SMART framework to all types of questions. The type of questions you ask can help you explore deeper with your data. Consider the ways your questions help you examine objectives, audience, time, security, and resources.

Some common topics for questions include: 

• Objectives
• Audience
• Time
• Resources
• Security

Think about how you can ask SMART questions about each of these topics.

Analysis of the scenario

Here are a few questions you might want to ask:

• When is the project due?
• Are there any specific challenges to keep in mind? 
• Who are the major stakeholders for this project, and what do they expect this project to do for them?
• Who am I presenting the results to?

Here are some examples of questions you might ask based on the suggested topics:

• Objectives: What are the goals of the deep dive? What, if any, questions are expected to be answered by this deep dive?
• Audience: Who are the stakeholders? Who is interested or concerned about the results of this deep dive? Who is the audience for the presentation?
• Time: What is the time frame for completion? By what date does this need to be done?
• Resources: What resources are available to accomplish the deep dive’s goals?
• Security: Who should have access to the information?

These questions can help you focus on techniques and analyses that produce results of interest to stakeholders. They also clarify the deliverable’s due date, which is important to know so you can manage your time effectively. When you start work on a project, you need to ask questions that align with the plan and the goals and help you explore the data. The more questions you ask, the more you learn about your data, and the more powerful your insights will be.

Asking thorough and specific questions means clarifying details until you get to concrete requirements. With clear requirements and goals, it’s much easier to plan and execute a successful data analysis project and avoid time-consuming problems down the road.

Glossary terms from this module

Terms and definitions for Course 2, Module 1

Action-oriented question: A question whose answers lead to change 

Cloud: A place to keep data online, rather than a computer hard drive

Data analysis process: The six phases of ask, prepare, process, analyze, share, and act whose purpose is to gain insights that drive informed decision-making

Data life cycle: The sequence of stages that data experiences, which include plan, capture, manage, analyze, archive, and destroy

Leading question: A question that steers people toward a certain response 

Measurable question: A question whose answers can be quantified and assessed

Problem types: The various problems that data analysts encounter, including categorizing things, discovering connections, finding patterns, identifying themes, making predictions, and spotting something unusual

Relevant question: A question that has significance to the problem to be solved

SMART methodology: A tool for determining a question’s effectiveness based on whether it is specific, measurable, action-oriented, relevant, and time-bound 

Specific question: A question that is simple, significant, and focused on a single topic or a few closely related ideas

Structured thinking: The process of recognizing the current problem or situation, organizing available information, revealing gaps and opportunities, and identifying options 

Time-bound question: A question that specifies a timeframe to be studied 

Series Navigation

<< Foundation Course: GlossaryCourse 2: Ask questions to make data driven decisions, Module 2: Make data-driven decisions >>

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