A data analyst at a shoe retailer uses data to inform the marketing plan for an upcoming summer sale. A data analyst at a gas company uses historical data to analyze which time of
year customers use the most gas. A data analyst at a technology company uses data to identify a unique drop in social media engagement. A data analyst at a school system uses data to make a connection between home sales and new student enrollment. Correct: A data analyst at a shoe retailer using data to inform the marketing plan for an upcoming
summer sale is an example of making predictions. How satisfied were you with our customer representative? How did you learn about our company? In what ways did our product meet your needs? What do you enjoy most about our
service? True False Correct: The question, “How could we improve our website to simplify the returns process for our online customers?” is action-oriented because it’s likely to result in specific answers that would lead to change. True False
It is closed-ended It is vague It makes assumptions It uses slang words that not everyone can understand
Blog By Sisense Team See Sisense in action Sign up for a Free Trial to build visual, interactive experiences. Work Email * Full Name * Company * Phone Number * By checking this box, I agree that my contact details may be used by Sisense and its affiliates to send me news about Sisense’s products and services and other marketing communications. By clicking the Submit button below I confirm that I have read and understand Sisense's Privacy Policy and Terms of Service.  Get the latest in analytics right in your inbox. Work Email * Full Name * Company * Phone Number * By checking this box, I agree that my contact details may be used by Sisense and its affiliates to send me news about Sisense’s products and services and other marketing communications. By clicking the Submit button below I confirm that I have read and understand Sisense's Privacy Policy and Terms of Service. Data Analysis is never easy, yet we are all Data Analysts. Remember the time you opened the Excel file and compared two options for purchasing a new laptop? Or the time you looked at the stock market to understand where we are heading? Congratulations, you are a Data Analyst. But analysis is a skill that is refined over years and years of hard work. Which does not mean, that there are no ways to make it a bit easier to start. I have been looking closely at how Google is doing things and I really like their framework, let me use this article to reflect on that. According to Google, there are six data analysis phases or steps: ask, prepare, process, analyze, share, and act. Following them should result in a frame that makes decision-making and problem solving a little easier. Please let us not mix them with the data life cycle, let's keep that for another time. But to understand how the six phases would help in decision-making, let's review them. Step 1: Ask - Understand the problemIt is always important to understand what even seems to be the problem or the question. Making an assumption or not understanding fully the problem will lead to wrong conclusions and will result in wrong actions. Identifying the problem is naturally also one of the hardest tasks. Like A. Einstein stated: 'If I had an hour to solve a problem I'd spend 55 minutes thinking about the problem and 5 minutes thinking about solutions.' So what would help to identify the problem? The following actions should help:
*Chinese whispers (Commonwealth English) or telephone (North American English) is an internationally popular children's game. Players form a line or circle, and the first player comes up with a message and whispers it to the ear of the second person in the line.
Step 2: Prepare - What do I need?Once there is an understanding of the problem, one can think about how to solve this. Time to decide what data needs to be collected in order to answer the questions and how to organize it so that it is useful. One should think about the following aspects:
Questions to ask yourself in this step:
Step 3: Process - Make it usable!When we start using the data, it might be a combination from different sources or it might not be of the highest quality. A process known as data cleaning is the fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset. What we aim to achieve is clean data. And to tell the truth, that is a science on its own. There are plenty of tools, theories, and methods to use, but let's keep everything basic here. Data Cleaning does not require fancy tools or words, a simple Spreadsheet program (yes, that is Excel) will suffice. Although my preference lies with others (Pandas!👌). So during this step one might:
Questions to ask yourself in this step:
Step 4: Analyze - Tell me the story!Next up is to make some conclusions based on the trustable data. Data Analyses is a skill that takes time to master, but over time the patterns will emerge faster and methods one uses will develop. Main concept is to think analytically about your data, be critical and be creative. There might be a need to sort and format the data to make it easier to process, make a Pivot table, or create awesome graphs! Remember it is a story that must unfold. Further processing might include:
Questions to ask yourself in this step:
Step 5: Share - Get different viewsOne thing still to remember, whatever we do, we are biased. So as the next step, get additional opinions about the findings. This will significantly help to improve the results and ensure that main aspects were taken into account. As the are many ways to share the finding each person has their preference and so does each company. However, many studies reassure that with clear and enticing visuals of the analysis results, the story is better understood. (A Good article on this https://hdsr.mitpress.mit.edu/pub/zok97i7p/release/3). The tools do not really matter here, it can be Tableau, Excel or even good old paper and pencil! But take this as a chance to show the stakeholders how their problem was solved. Sharing will certainly help with:
Questions to ask yourself in this step:
Step 6: Act - We know the problem, Let's solve it!No analysis conclusion should remain to collect dust on a shelf! Rather some action should be taken. Taken the results and depending on the problem statement, recommendations for further actions can be made. And once the recommendations are ready, the actual decision can be made! Not necesarrily is the conductor of the analysis the one to make a decision, it could also mean providing the decision-makers(stakeholders) with recommendations based on the findings so they can make data-driven decisions. But the key here is data-driven decisions. Questions to ask yourself in this step:
And done you are!
But with that, let's go and make some data-driven decisions! What are the 5 steps to the data analysis process?article Data Analysis in 5 Steps. STEP 1: DEFINE QUESTIONS & GOALS.. STEP 2: COLLECT DATA.. STEP 3: DATA WRANGLING.. STEP 4: DETERMINE ANALYSIS.. STEP 5: INTERPRET RESULTS.. What is the Ask phase of the data analysis process?The first phase of the data analysis process is asking the right questions. You should clearly understand why you are doing this analysis and what kind of problem you are solving. You define the problem by understanding the stakeholder's expectations.
What do data analysts do during the ASK phase?What do data analysts do during the ask phase? 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.
Which of the following questions do data analysts ask to make sure they will engage their audience select all that apply?Solution. To engage their audience, data analysts ask about what roles the people in the audience play, their stake in the project, and what they hope to do with the data insights.
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In which step of the data analysis process would an analyst ask questions such as what data errors might?
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