Recipe to become a Data Analyst — Start Asking Everything

Welcome back to Episode 2 of becoming a data analyst

Recap: We have discovered the secret recipe of data analysis and how to build a data mindset, now without any further delay, let’s take a deep dive into learning the phases of data analysis.

As a reminder 6 phases of data analysis are:

These six steps can help you to break the data analysis process into smaller, manageable parts, through structured thinking.

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 blog, we are going to discuss in brief about “Ask” Phase.

Every Data Analytics project starts with a problem statement alias business requirement. Most of the new problems data analysts face starts in unknown territory. It’s up to the data analyst and their problem-solving skills to think strategically, ask good questions, and use data to come up with solutions to these problems.

The nitty-gritty of problem-solving is defining & understanding the problem and target audience thoroughly. It’s impossible to solve a problem if you don’t know what it is. Below are a few pointers to consider before creating an ask checklist.

• 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:

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

Asking a million questions

As data analysts, we’ll find that problems are at the centre of what we do every single day, problems can be small or large, simple or complex, no problem is like another and they all require a slightly different approach but the first step is always the same: Understanding what kind of problem you’re trying to solve. Let’s check out the common variety of problems that data analysts typically face:

1. Making Predictions — using data to make informed decisions on how things may be in future
2. Categorizing things — assigning information to different groups or clusters based on common features
3. Spotting something unusual — identifying data that is different from the norm
4. Identifying themes — grouping categorized info into broader concepts
5. Discovering connections — finding similar challenges faced by diff entities and combining data and insights to address them
6. Finding patterns — using historical data to understand what happened in the past and is therefore likely to happen again

Common types of problems

To solve such types of problems, data analysts must ask effective & right questions. We shall make use of the proven and famous SMART methodology for curating our effective questions.

SMART is mnemonic acronym for “Specific Measurable Action-Oriented Relevant Time-bound”

• Specific questions are simple, significant & focused on a single topic or a few closely related ideas
• Measurable questions can be quantified & assessed
• Action-Oriented questions encourage change
• Relevant questions matter, are important & have significance to a problem we’re trying to solve
• Time-bound questions specify the time to study

With the general understanding of effective questions, we should remember one more important factor while crafting our question is Fairness.

Fairness means ensuring that your questions don’t create or reinforce bias and that makes sense to everyone.

It’s important for questions to be clear and have straightforward wording that anyone can easily understand. Unfair questions also can make your job as a data analyst more difficult. They lead to unreliable feedback and missed opportunities to gain some truly valuable insights. 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?

In response, to the data we receive, it is very important to interpret the data accurately because when data is interpreted incorrectly, it can lead to huge losses below are a few examples of Coca-cola and Pepsico on how their interpretation reflected their market.

Data is a powerful tool for decision-making, and you can help provide businesses with the information they need to solve problems and make new decisions. There are a lot of different kinds of questions that data might help us answer, and these different questions make different kinds of data. Two kinds of data that we will come across most often are quantitative and qualitative.

• Qualitative data are subjective or explanatory measures such as why questions
• Quantitative data are specific & objective measures of numerical data such as the what, how many and how often

Data analysts will generally use both types of data in their work. Usually, qualitative data can help analysts better understand their quantitative data by providing a reason or a more thorough explanation. In other words, quantitative data generally gives you the what, and qualitative data generally gives you the why.

It’s our job as data detectives to know what questions need to be asked to find the right solution. Then we can start thinking about cool and creative ways to help stakeholders better understand the data. Focus on questions like what, who, where, when, & how.

Finally few tips when communicating with stakeholders

• Communicate clearly, establish trust, and deliver your findings across groups
• Always discuss goals (ask for results/goal or next plan)
• Feel empowered to say “No”
• Plan for unexpected
• Know your project
• Start with words and visuals
• Communicate often

Hoping this blog gave you a basic idea to frame questions, now, here is a fun challenge, pick up a problem from your daily life, try to understand what is the actual issue and prepare your checklist of questions using the SMART methodology and structural thinking and let me know your experience in the comments section. Coming up we will jump into Prepare Phase.