Google Data Analytics Professional Certificate - Course 2 : Ask Questions to Make Data-Driven Decisions - week 1

Week 1 - Problem-solving and effective questioning

Structured thinking is the process of recognizing the current problem or situation, organizing available information, revealing gaps and opportunities, and identifying the options. In this process, you address a vague, complex problem by breaking it down into smaller steps, and then those steps lead you to a logical solution.

1. Asking effective questions: To do the job of a data analyst, you need to ask questions and problem-solve. In this part of the course, you’ll check out some common analysis problems and how analysts solve them. You’ll also learn about effective questioning techniques that can help guide your analysis.

2. Making data-driven decisions: In analytics, data drives decision making. In this part of the course, you’ll explore data of all kinds and its impact on decision making. You’ll also learn how to share your data through reports and dashboards.

3. Mastering spreadsheet basics: Spreadsheets are an important data analytics tool. In this part of the course, you’ll learn both why and how data analysts use spreadsheets in their work. You’ll also explore how structured thinking can help analysts better understand problems and come up with solutions.

4. Always remembering the stakeholder: Successful data analysts learn to balance needs and expectations. In this part of the course, you’ll learn strategies for managing the expectations of stakeholders while establishing clear communication with your team to achieve your objectives.

5. Completing the Course Challenge: At the end of this course, you will be able to put everything you have learned into practice with the Course Challenge. The Course Challenge will ask you questions about key principles you have been learning about and then give you an opportunity to apply those principles in three scenarios.

Week 1 - Solve problems with data

6 basic problem types:
making predictions, categorizing things, spotting something unusual, identifying themes, discovering connections, and finding patterns.

• Making predictions: This problem type involves using data to make an informed decision about how things may be in the future.
  • 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.
  • For example, a hospital system might use a remote patient monitoring to predict health events for chronically ill patients. The patients would take their health vitals at home every day, and that information combined with data about their age, risk factors, and other important details could enable the hospital’s algorithm to predict future health problems and even reduce future hospitalizations.
• Categorizing things: This means assigning information to different groups or clusters based on common features.
  • 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.
  • An example of this problem type is a manufacturer that reviews data on shop floor employee performance. An analyst may create a group for employees who are most and least effective at engineering. A group for employees who are most and least effective at repair and maintenance, most and least effective at assembly, and many more groups or clusters.
• Spotting something unusual: In this problem type, data analysts identify data that is different from the norm.
  • 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 analyzed aggregated health data can help product developers determine the right algorithms to spot and set off alarms when certain data doesn’t trend normally.
  • An instance of spotting something unusual in the real world is a school system that has a sudden increase in the number of students registered, maybe as big as a 30 percent jump in the number of students. A data analyst might look into this upswing and discover that several new apartment complexes had been built in the school district earlier that year. They could use this analysis to make sure the school has enough resources to handle the additional students.
• Identifying themes: This takes categorization as a step further by grouping information into broader concepts.
  • User experience (UX) designers might rely on analysts to analyze 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.
  • Going back to our manufacturer that has just reviewed data on the shop floor employees. First, these people are grouped by types and tasks. But now a data analyst could take those categories and group them into the broader concept of low productivity and high productivity. This would make it possible for the business to see who is most and least productive, in order to reward top performers and provide additional support to those workers who need more training.
• Discovering connections: This enables data analysts to find similar challenges faced by different entities, and then combine data and insights to address them.
  • Here’s what I mean; say a scooter company is experiencing an issue with the wheels it gets from its wheel supplier. That company would have to stop production until it could get safe, quality wheels back in stock. But meanwhile, the wheel companies encountering the problem with the rubber it uses to make wheels, turns out its rubber supplier could not find the right materials either. If all of these entities could talk about the problems they’re facing and share data openly, they would find a lot of similar challenges and better yet, be able to collaborate to find a solution.
  • 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: Data analysts use data to find patterns by using historical data to understand what happened in the past and is therefore likely to happen again. Ecommerce companies use data to find patterns all the time. Data analysts look at transaction data to understand customer buying habits at certain points in time throughout the year. They may find that customers buy more canned goods right before a hurricane, or they purchase fewer cold-weather accessories like hats and gloves during warmer months. The ecommerce companies can use these insights to make sure they stock the right amount of products at these key times.
  • 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.

Week 1 - Craft effective questions

Effective questions follow the SMART methodology. That means they’re specific, measurable, action-oriented, relevant and time-bound.

• Specific: Specific questions are simple, significant and focused on a single topic or a few closely related ideas. This helps us collect information that’s relevant to what we’re investigating.
• Measurable: Measurable questions can be quantified and assessed.
• Action-oriented: Action-oriented questions encourage change.
• Relevant : Relevant questions matter, are important and have significance to the problem you’re trying to solve.
• Time-bound: Time-bound questions specify the time to be studied.

Example 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?

Things to avoid when asking questions

• Leading questions: questions that only have a particular response
  • Example: This product is too expensive, isn’t it?
• Closed-ended questions: questions that ask for a one-word or brief response only
  • Example: Were you satisfied with the customer trial?
• Vague questions: questions that aren’t specific or don’t provide context
  • Example: Does the tool work for you?

Appendix

Glossary: Terms and definitions

Reference