As part of our consulting training at the data school, we’re frequently reminded that we should think about telling a story with our data. But what does that mean?
It’s one thing to point out anomalies, patterns and summarise data, but it’s another to explain why the things you highlight actually matter. Here is an example of explaining data:
The product with the highest sales in 2020 was the photocopier. It generated £300,000 in sales which was 30% higher than the next best selling product, TVs. £50,000 was spent on marketing the photocopiers compared with £33,00 on TVs.
Here’s how you could tell a story with this data:
The product with the highest sales in 2020 was the photocopier. It generated £300,000 in sales which was 30% higher than the next best selling product, TVs. The next best selling product, however, has a much higher profit margin. In fact, 50% more revenue was invested in marketing the photocopiers compared to the TVs. Since revenue invested in marketing corresponds with an increase in sales, it would be advisable to allocate more marketing resources towards TVs to see an overall increase in profits.
Another way to think of this is to imagine a ‘why’ question every time you are tempted to just explain a visual in terms of ‘x category was greatest at point y’ or ‘product q had the most sales in the year w’. Why did the product have the most sales? Can you see a relationship with any other variables in the data? What are the implications of this product selling the most - does it mean more stock should be bought of this product instead of another?
In summary, as a data analyst you are expected to analyse data, but as a consultant you are expected to find actionable insights from which improvements can be made with the data you've analysed.