Data and Stories don't always jive

If you've read any of my articles you know that I am a strong advocate for following the story, not just believing what the data tells you. Behind every data point, every request, every strategy there is a story; probably several. The challenge, sorting out the various branches of the story.

Let me share with you an example. Not too long ago there was a story of a National Football League star player who was upset with his team and wanted out. Apparently he felt the team did not surround him with enough talent to win the championship.

The team's leadership felt as though they had done what they could to surround this player with talent and were handicapped by this player's salary to make any big moves for other star players. Let's look at the data for 2020 for this player:

• YDS 4,299
• TD 48
• INT 5
• QBR 84.4

This was this players stats. He was near first or first in most categories. Obviously he produced at a high level and he wants more from the team. The team on the other hand can look at this data and say that the player is surrounded by a talented team that helped him achieve these numbers. In fact, in most years this player ranks in the top 10 and his team usually wins 11 games or more out of 16 so both sides have history reinforcing their beliefs.

The data does not lie – but it may not tell the story you think it does. The ways in which we collect data can still unconsciously impart human biases. If all we do is look at this data as is, no more research, we are most likely going to land on the player or team's side of the argument.

Now try this one on for size. In 2020 this player was 2-2 versus teams with a winning record and in 2019 he was 3-2. Now this story is getting interesting. In fact, going back over the last decade this player is barely above average against winning teams after five games into the season.

How about a little more? 0-42. That is this players record when a winning team is leading his team by 1 point or more going into the fourth quarter. From a statistical standpoint, this is a significant sample size because this is over the span of several years.

So now you have more stories to explore regarding the players, the team's makeup in each of those years, the team's they played, their records, etc. But before you get lost in all the data, remember that this player has usually been ranked in the top 10 over the last decade.

Now we have a quandary of sorts. Is the player right regarding his wants of better talent? Is the team right in that they have supplied that talent and keeping within budget? Is the statistician right that this player is average against winning teams and cannot win against winning teams when trailing in the 4th quarter?

So why am I sharing this? In the Digital Transformation era, data is playing a much bigger role in strategy and decision making and it will not be uncommon for you to get a request based on the data.

But it's important in our roles not to take it at face value. There's a performance story or two behind that data that may have much different interpretations than the way the data is shared.

Thanks for reading, until next time...