During which phase of data analysis would a data analyst use spreadsheets?

ASK - In this phase, we do two things. We define the problem to be solved and we make sure that we fully understand stakeholder expectations. Stakeholders hold a stake in the project. They are people who have invested time and resources into a project and are interested in the outcome. Let's break that down. First, defining a problem means you look at the current state and identify how it's different from the ideal state. Usually there's an obstacle we need to get rid of or something wrong that needs to be fixed. For instance, a sports arena might want to reduce the time fans spend waiting in the ticket line. The obstacle is figuring out how to get the customers to their seats more quickly. Another important part of the ask phase is understanding stakeholder expectations. The first step here is to determine who the stakeholders are. That may include your manager, an executive sponsor, or your sales partners. There can be lots of stakeholders. But what they all have in common is that they help make decisions, influence actions and strategies, and have specific goals they want to meet. They also care about the project and that's why it's so important to understand their expectations. For instance, if your manager assigns you a data analysis project related to business risk, it would be smart to confirm whether they want to include all types of risks that could affect the company, or just risks related to weather such as hurricanes and tornadoes. Communicating with your stakeholders is key in making sure you stay engaged and on track throughout the project. So as a data analyst, developing strong communication strategies is very important. This part of the ask phase helps you keep focused on the problem itself, not just its symptoms. As you learned earlier, the five whys are extremely helpful here.

Example of the data process

1. Ask - "You want to ask all of the right questions at the beginning of the engagement so that you better understand what your leaders and stakeholders need from this analysis." what is the problem that we're trying to solve? What is the purpose of this analysis? What are we hoping to learn from it?

2. Prepare - "We need to be thinking about the type of data we need in order to answer the questions that we've set out to answer based on what we learned when we asked the right questions." We also need to be thinking about how we're going to collect that data or if we need to collect that data. It may be the case that we need to collect this data brand-new. So we need to think about what type of data we're going to be collecting and how.

3. Process - It begins with cleaning. "This is where you get a chance to understand its structure, its quirks, its nuances, and you really get a chance to understand deeply what type of data you're going to be working with and understanding what potential that data has to answer all of your questions." Do we have all of the data that we anticipated we would have? Are we missing data at random or is it missing in a systematic way such that maybe something went wrong with our data collection effort? If needed, did we code all of our data the right way? Are there any outliers that we need to treat differently?

4. Analyze - the first thing we do is run through a series of analyses that we've already planned ahead of time based on the questions that we know we want to answer from the very, very beginning of the process. One thing that's probably the hardest about this particular process, the hardest thing about analyzing data, is that we as analysts are trained to look for patterns. Over time as we become better and better at our jobs, what we'll often find is that we can start to intuit what we might see in the data. "This is the point where we have to take a step back and let the data speak for itself." As data analysts, we are storytellers, but we also have to keep in mind that it is not our story to tell. That story belongs to the data, and it is our job as analysts to amplify and tell that story in as unbiased and objective a way as possible.

5. Share - "All of this work from asking the right questions to collecting your data, to analyzing and sharing, doesn't mean much of anything if we aren't taking action on what we've just learned." This is where we use all of those data-driven insights to decide what types of interventions we want to introduce, not only at the organizational level, but also at the team level as well.

The data analysis process is rigorous, but it is lengthy. I can completely appreciate that we as data analysts, get so excited about just diving right into the data and doing what we do best

Test your knowledge on the data analysis process

Question 1

The data analysis process phases are ask, prepare, process, analyze, share, and act. What do data analysts do during the ask phase?

• Clean the data

• Create data visualizations

• Collect and store data

• Define the problem to be solved

Correct. During the ask phase, data analysts define the problem by looking at the current state and identifying how it’s different from the ideal state.

Question 2

During the process phase of data analysis, a data analyst cleans data to ensure it’s complete and correct.

• True

• False

Correct. The process phase is all about getting the details right, so data analysts clean data by fixing typos, inconsistencies, and missing or inaccurate data.

Question 3

During which phase of data analysis would a data analyst use spreadsheets or query languages to transform data in order to draw conclusions?

• Process

• Analyze

• Act

• Prepare

Correct. The analyze phase involves using data analytics tools such as spreadsheets and query languages to transform data in order to draw conclusions and make informed decisions.

Question 4

In which data analysis phase would a data analyst use visuals such as charts or graphs to simplify complex data for better understanding?

• Process

• Prepare

• Act

• Share

Correct. The share phase involves how results are interpreted and shared with others, often through data visualization.

Question 5

A data analyst shares insights from their analysis during a formal presentation to stakeholders. In a slideshow, they make a data-driven recommendation for how to solve a business problem. What phase of the data analysis process would come next?

• Ask

• Process

• Prepare

• Act

In this scenario, the data analyst has just shared insights. So, the next phase would be to act and put those insights to work in order to solve the business problem.

What are the phases of data analysis?

What are the 7 stages of data analysis?