Business Awareness in Data Analytics
Business awareness is absolutely essential for every data analyst!
One of the most common challenges that early learners of data analytics face is not knowing what to extract from a given dataset. Though they’ve gained good technical knowledge in tools such as Excel, Power BI, and Python, the outputs they extract from the data may not be optimized for the stakeholder.
Let’s assume you receive a dataset containing a few tables related to sales and inventory data for a retail business. You may also be provided with a data dictionary (i.e. meaning of each column in the tables and it’s relationships). The next logical question after looking at the data would be to find what data to visualize and what data to be ignored.
In real-world projects, you will have 15–50 columns in each table, depending on the database architecture. To arrive at meaningful insights, you should be able to ask the right questions to the dataset. This capability to ask the right questions will come only when you can comprehend the business part behind the data.
Coming back to the assumption, let’s say the sales table has categorical values like city, product, customer demography, etc. Then you have their corresponding sales value, and costs involved (shipping, logistics, etc.). A person with a basic business understanding will be able to generate visuals such as revenue, and margin (in YTD, MTD formats) per category (like city, product, etc.).
But since it’s a retail business, there may be some specific KPIs that give a better insight into the dataset, such as %ACV distribution, and TDP (which measures the distribution capacity of the FMCG business). Thus, by using these KPIs you will be able to add more value as a data analyst to the stakeholder.
So, how can we develop the capability to ask the right questions to different datasets?
1. Talk to the person who’s going to consume your Excel/Power BI report (or) dashboard. Understand what KPIs they’re tracking currently. Since most companies are slowly increasing their data infrastructure, they may start adding more KPIs as the project progresses.
2. Do a small google search and understand the industry in which the company is operating. This research will be helpful to find new KPIs which may suit your stakeholders’ needs and you may suggest it be added to the report.
3. As you progress in your career, the experience you gained from past projects will come in handy for future similar projects.
4. Specialize yourself in any of the budding new industries. Fin-tech companies are seeing rapid growth and they’re here to stay for the future. Hence, knowledge in visualizing their dataset might be helpful to get job opportunities in that industry.
Simply put, Dataset → Right questions → KPIs → Data visualization (I’ve ignored data cleaning and modelling steps, as they also depend on the KPIs and data structure).
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