Stumbling into an Unfamiliar Dataset
Elon Musk gutting Social Security because he found millions of vampires over 300 years old collecting Social Security benefits is an example of how a stakeholder can make horribly incorrect decisions if they don’t understand the data they’re looking at (or lying that they do understand).
The problem with Elon’s conclusion is the database he was looking at, Numident (which contains a record of every person who has ever been assigned a Social Security number), has people in the system who have died but don't have a date of death recorded because they lived long before electronic records were established. This data set has nothing to do with the monthly benefits payments but shows that there are millions of people ages 100 to 369 in the system.
He didn’t understand or pay attention to the context in which the dataset lived.
How does this relate to marketing?
Diving into a dataset without fully grasping its context or limitations can lead to wildly inaccurate conclusions that guide poor decision-making.
For example, you might look at your conversion rates, see a sudden dip, and assume your entire strategy is failing—without considering that a recent change in Google’s algorithm, or a technical error with tracking, is skewing the data. Or you might look at customer behavior data and assume your target audience is shifting, only to realize that a single campaign you ran disproportionately influenced the results, rather than representing a true trend.
In marketing, understanding the context behind your data is just as important as the numbers themselves. This means not only knowing where the data comes from but also how it’s collected, how it’s impacted by external factors, and how to interpret it correctly. Without this foundation, decisions made from the data may result in misguided strategies, wasted budgets, and missed opportunities.
So, the next time you look at your marketing data, make sure you're asking the right questions:
What does this data really tell me?
What’s missing?
And, importantly, what’s the context in which this data lives?