JohnData has always been an interest of mine. Nowadays, everyone understands that data is of high importance. A funny thing about data is that it doesn’t matter whether it is good data, bad data, or no data, there can be some information derived from it. I often hear the joke that it’s not “tell me what the data says”, it’s “what do you want the data to say?” – which can be a good or a bad thing. The bad thing being that the “insights” can be manipulative and provide false information. This can be leveraged for immoral outcomes and to persuade others to do “something”. The good being that maybe you do have important information that can be used for a positive outcome.
Regardless of the type of data, I think there are a few tips that I can provide to help you better understand the presented information: knowing the source of the data, context, and different perspectives.
Source of Data
I have seen many times when articles are shared on social media without the poster investigating the source of data; especially when it involves confirmation bias. It is important that one knows the legitimacy of the source and the data. Without that, someone could be sharing false information such as fake news that could be misleading and create unnecessary uproar in the comment section.
Even if it is good data, it would be prudent to validate the transformation of how the data was converted to insight. At the transformation level, there may have been incorrect equations used, data could’ve been left out to force an idea, or data could’ve been intentionally added to improve metrics. Taking the extra time to look at the low-level details could help with verifying information.
Overall, there is one thing to understand here – garbage in, garbage out.
Context
Context is everything when it comes to conveying something important. Without context, a number is just a number. A recent example of this is when my wife asked how to show that an acceptance rate for communication channel of 38% was a big deal. I then began to probe with a handful of questions. Here are a few of them:
- 38% of how many were sent? (38% of 100 is very different than 38% of 1,000,000)
- What was the acceptance rate for the previous time you used that channel? (4% to 38% is a big deal)
- What is that compared to some other communication channel? (38% being the best while other channels are all under 5% is a big deal)
- Are you able to use time as another metric to show that the 38% in a specific timeframe is a big deal?
Obtaining that information to compare to the 38% will be incredibly helpful for others to see why 38% is important. Otherwise, 38% is just 38% – so what?
Having said that, even when there is context to compare to, you should go back to look at the data; you could be misled by something showing as a huge increase. For instance, if a report is showing a key performance indicator has a 50% increase, amazing right? Not if I sold 2 chocolate bars yesterday and then sold 3 today: that’s a 50% increase. If I sold 2,000 chocolate bars yesterday, a 50% increase from that is 3,000 chocolate bars. Completely different scales.
But wait, let’s take another example. Another way to look at the context of the data is that if I sold 2 houses yesterday, 50% increase of that is 3. If I sold 5 houses in two days, that’s a big deal. (At least I think, I’m not a realtor :))
In short, provide some context around your numbers or ask for more context.
Different Perspectives
This part of the post comes directly from the book titled Factfulness by Hans Rosling. I started to read this book and one section really stood out to me – focusing on the good when there is bad news. Having a different perspective on the data can greatly expand the mind of how information can be used. A great example from the book is that while there are still children dying before their 5th birthday in the 2000s, it is only 4% in comparison to 44% in the 1800s (p. 60).
Another example in today’s world is the covid crisis. Looking at the image below that I grabbed from Google, we can see there was a decrease in the rate of people getting vaccinated from March to April in comparison to the rate from April to June.
Looking only at the decreased rate, one may rush to the idea people are starting to not care about the pandemic and want to get the rate up. From a different perspective, looking at June, there are nearly 5 million vaccinated people in comparison the 1 million people in March. The idea here is that although the slowing vaccination rate may seem bad, looking at the holistic view, that is better than before.
So, taking the time to look at different perspectives, you may realize you’re better off than you think.
Conclusion
To wrap everything up, validate and ask questions about the data, the process, the context. Also, don’t forget to have a different perspective on data, you may find the silver lining.
And I know that I need more pictures in my blog posts. Add that to my 2022 goals.
Thanks for reading!
