I am a fan of those big blockbuster superhero movies. I’m actually one of those nerds that stores action figures in original packaging on the shelves of their office. The human dynamics of getting a bunch of superheroes together is what keeps me watching and it doesn’t hurt that the blockbuster hits have seriously upped their female-empowerment game in recent years.
I am not a fan of your data superheroes though. I continue to be concerned about the prominence of this concept across the data industry landscape. Here’s why it’s problematic. When you ascribe a different set of capabilities to only a specific set of people (i.e., superheroes) you in turn remove the accountability of those of us that are just…people. This means that your average data consumer is not only told they are off the hook but encouraged to be off the hook.
Let me be clear, there is no “data-driven culture” if you don’t imbue a sense of responsibility for the data across the organization, it’s like creating a foundation where half of it is cement with rebar and the other half is foam. You need people using the data, all the people, to create the foundation for a data-driven culture.
Even if you had superheroes (spoiler, you don’t, but that’s another post) they still couldn’t keep up with the demand of your organization. When you put them in that position, they will turn themselves inside out to get the job done, and while it might be fun in the beginning it will lead to burnout. Because without the help from everyone there is no stopping that tidal wave of bad data quality, data requests, or questions.
Reinforcing the data superhero mentality breaks down the relationship between your data consumers and your data “superheroes”, puts both in impossible positions, and eventually will lead to burnout, frustration, shadow IT functions, and a whole host of other things you really should try to avoid. #knockitoff #sayitwithmoxy
Addressing A Data Superhero Mentality
If you think your org might be reinforcing a data superhero mentality, here are three things you can do:
1. Amp up your data literacy efforts. Train more people to be more comfortable with your data.
2. Encourage questions about the data. Don’t roll your eyes and vent about how dumb people are with data. Even if you never do it in front of people, they pick up the vibe. Don’t be that guy.
3. When something goes wrong (and it will), such as someone makes an inaccurate assumption about the data, be willing to learn from the situation and pivot as necessary.
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