Data Analytics 101 Series — The ‘Ask’ Phase
The most common method to gather data is by asking someone. This is precisely where the data analytics process starts. The ask phase of the data analytics process is simple yet powerful. Let’s see how it works!
In my previous article, I covered the overview of the data analytics process. In this one, we will cover the first phase of the data analytics process — the Ask Phase.
Let’s get started!
The Objective of the Ask Phase
The purpose of data analytics is to uncover patterns from the data gathered and to make business decisions based on those patterns identified. But, in order to do this, we must define the problem statement.
A problem statement is nothing but the issue we are trying to solve with data.
To define a problem statement, we must ask the right questions. Defining the problem statement is the most crucial part of the data analytics process as there might be multiple factors under consideration for the analysis. A simple way to define the problem statement is to ask yourself a couple of questions
1. What is my current position?
2. What is my goal?
3. What is stopping me from achieving my objective?
By the end of this process, you should be able to define the problem statement for most cases.
One common pitfall during the Ask phase or during problem definition is that it can be overwhelming to choose the right factors for analysis. Keep in mind that considering too many factors can be less efficient and might not yield the best results. For example, A company facing a high attrition rate may have multiple factors contributing to it. These factors can be work-life balance, compensation, work atmosphere, etc. But scoping in the most influential factors that contribute to the problem proves to be the most efficient method.
Post defining the problem statement, the next step is to conduct several interviews with multiple stakeholders starting from the senior management to the process owners.
By conducting these interviews, you get a lot of insights that otherwise cannot be uncovered.
Key Indicators
While most of the ‘Ask’ phase of data analytics deals with conversations, meetings, etc., there are certain parameters that indicate issues preemptively. These are called key performance indicators (KPIs). Monthly KPI dashboards can sometimes help identify minor issues, over a long period of time, become major concerns to the goals of the company.
For example, for an e-commerce company that is looking to expand its business and generate revenue, some KPIs include
— Previous quarters’ revenue
— Previous quarters’ profit
— Product categories that generated the highest revenue
— Product categories that generated the lowest revenue compared to the cost incurred to the company to sell it.
The KPIs mentioned above may not be all, but a few that indicate the performance of the company and the products. By identifying these KPIs, we will be able to get a fair idea of how the business will perform in the future.
Conclusion
The ‘Ask’ phase proves to be a base upon which the other data analytics processes are built. Having a clear vision of the problem makes it way easier to collect the data required to solve it. Hence all influential factors must be considered for the problem statement before moving to the subsequent phases.
Happy Learning!
Check out my other articles on Blockchain and Machine Learning/Deep Learning. Let me know about any other topics to cover in the future!
