From the course: Introduction to Career Skills in Data Analytics

Questioning techniques to collect the right data

From the course: Introduction to Career Skills in Data Analytics

Questioning techniques to collect the right data

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- Have you ever heard of analysis paralysis? It's where you overthink a problem that stops you from moving forward. It's a real thing for some people. It's likely due to stress and anxiety related to making the wrong decision or not knowing exactly what to do next. Building an approach and thinking through standard questions and critical thinking with active listening should help you. Technical skills or hard skills are one thing for the analyst, but the soft skills matter just as much. And if you're stuck, no hard skill matters. To be fair, the more exposure to real problems and solving them with data solutions will help you build your approach, but you can slowly start building your questioning now. There are some common questions you might ask for every data-related project and the questions might be more specific based on actual problems and the data that you have at hand. Our scenario is that we have five of our top products. They're being purchased all the time, but the company is losing money. First, you need to understand that there is data in everything, in it and around it. This will help you start to consider the questions. Our task as the analyst is to try to determine why if the sales are moving, why are we losing? There are some basic questions that you should ask about each of the five products. Have these products ever been profitable? If they were profitable in the past, at what point in time? What is different about this point in time versus that point in time? Did the wholesale cost change? Did the list price change? Did the cost of storing or delivering the product change? Any of these answers will lead you further into data analysis. When we start with these basic questions and begin to answer them, then it will lead to more questions. As an example, let's say that in our initial questioning, we determine that the wholesale costs nor the list price have changed in the last three years. The cost to deliver has not changed enough to drive an impact. The cost of storing the products has been steadily increasing. The next round of questions begin. Is it only these five products that are impacted by the steady increase of storage cost? And what we discover, it's not just impacting these five products but all the products. The company just started to realize it in these five products. What can we do to reduce the storage costs? What type of increase can we justify on the products without overpricing the product? Both these questions lead to very different datasets within the organization and then each round of questions and answers leads to more questions. The goal here is to remember you must start asking questions and then remember they rarely stop. They just drive further investigation. The greatest part of the question process is that the end result is discovery and recommendations that are made to improve outcomes.

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