Data Analysis Phases: The Ask Phase
Tags: datascience, dataanalysis, dataanalytics
Published: 2022-06-22 10:14:06

Data Analysis Phases: The Ask Phase
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Technology has been embedded in every sector, and as a result, generated huge amounts of data. This data has the potential of yielding valuable insights about each sector and which has led to a boom in the data industry in the last ten years. However, the availability and collection of data needs to be complemented with its analysis to gain accurate and deep understanding of it. Data analytics helps businesses and industries control all the data so they can use it to improve processes, identify opportunities and hidden trends, launch new products, serve customers and make thoughtful decisions for further growth and development.
According to Tony, a program manager and data analyst at Google, data analytics is the collection, transformation and organization of data in order to draw conclusions, make predictions and drive informed decision making. Therefore, an individual saddled with these responsibilities is known as a Data Analyst. Well, depending on the type of organization and the extent to which a business has adopted data-driven decision-making practices, it’s clear that data is a major part of a data analyst’s job but it’s only part of the big picture. The other part is problem solving. Being a successful data analyst means understanding that each problem is unique.  Most new problems data analysts face start in unknown territory. It’s up to the data analyst and their problem-solving skills to ask good questions, think strategically and use data to come up with solutions to these problems.
As a Data Analyst, problems are at the center of what you do but that's a good thing.  Think of problems as opportunities to put your skills to work and find creative and insightful solutions. In order to successfully analyze data and come up with solutions. There are six data analysis phases that will help you make seamless decisions: ask, prepare, process, analyze, share, and act. Combining the mentioned phases with anal skills can help get you on track, fill in any gaps and let you know exactly what you need.

In this article, we’ll concentrate on the very first phase, the Ask phase. No matter the problem, the first and most important step is understanding the problem. In the ask step, we define the problem we're solving and make sure that we fully understand stakeholder expectations. Asking questions is a major part of the job. The more questions you ask, the more you’ll learn about your data and the more powerful your insights will be. 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. Questions like, what are my stakeholders saying their problems are? What are their expectations? What do you suspect? Now that I’ve identified the issues, how can I help the stakeholders resolve their problem? Are examples of the type of questions you should start asking when you intend to start a project or solve a problem for your stakeholders. Asking effective questions is so important that it helps you make the most of all the other phases.  For instance,
**Prepare Phase**
Here, you decide what data you need to collect in order to answer your questions and how to organize it so that it is useful.
Questions to ask yourself in this phase:
(i) What do I need to figure out how to solve this problem?
(ii) What research do I need to do?
![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/03o4zdmvao32ufcwqmty.png)
 
 Prepare Phase

**Process phase**
Clean data is the best data and you will need to clean up your data to get rid of any possible errors, inaccuracies, inconsistencies, or missing values.
Questions to ask yourself in this phase:
(i) What data errors or inaccuracies might get in my way of getting the best possible answer to the problem I am trying to solve?
(ii) How can I clean my data so the information I have is more consistent?
(iii)  How can I deal with missing values?
![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/dprzbfwwx2g7r5252v2j.png)
 
 Process Phase

**Analyze phase**
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 and create tables with your results.
Questions to ask yourself in this phase:
(i) What story is my data telling me?
(ii) How will my data help me solve this problem?
(iii) Who needs my company’s product or service? 
(iv) What type of person is most likely to use it?  
![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/c12dtiqrxdni0k3ahawz.png)
 
Analyze Phase

**Share phase**
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.
Questions to ask yourself in this step:
(i) How can I make what I present to the stakeholders engaging and easy to understand?
(ii) What would help me understand this if I were the listener?
![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/1qciv9nc9r11f8hpwf2g.png)
 
 
Share Phase

**Act phase**
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:
(i) How can I use the feedback I received during the share phase (step 5) to meet the stakeholder’s needs and expectations?
We have now established that as a data analyst, you must ask lots of questions. You must constantly ask questions like a detective. It’s not just about asking questions but asking the right questions. Some questions are more effective than others which will lead to key answers you can use to solve various kinds of problems. These effective questions tackle different problems in data analytics like, making predictions, categorizing things, spotting anomalies, identifying themes, discovering connections and finding patterns.  Knowing the difference between effective and ineffective questions is essential for a successful career in data analysis. There is also something else to keep in mind when crafting questions, fairness. Fairness means making sure your questions don’t create or reinforce bias. Let’s also look at things we should avoid when asking questions. These include. 
(i) Leading questions: questions that only have a particular response
(ii) Closed-ended questions: questions that ask for a one-word or brief response only
(iii) Vague questions: questions that aren’t specific or don’t provide context
Poorly phrased, ill-conceived and misguided questions can lead to expensive and time-consuming voyage into the data that won’t yield any actionable insights. Sometimes take a step back and ask, are you asking the right questions? Think creatively and critically to find out the right questions to ask.
As inventor Charles Kettering said, “A problem well defined is a problem half-solved.” Also, the French philosopher Voltaire said, “Judge a man by his questions rather than his answers.” This same standard can be applied to Data analysis. Data analysts that struggle to get meaningful insights from their data are often not asking the right questions. Data analysts need to develop a sharp eye for problems and think through the problem types as you begin analysis. The better the quality of your questions, the more valuable your insights you will get.