“Does it spark joy?”
Yes, I’ve been watching Marie Kondo’s Netflix special “Tidying Up” with my wife.
Marie Kondo enters peoples homes and helps them organize their lives by empowering them with the freedom to let go of their material possessions. She removes the feelings of guilt and remorse from saying goodbye to something you own and replaces it with an affirmative appreciation for the service they’ve provided.
So yes, I’ve said, “Thank you for your service” to my favorite pair of Levi 511s that I have planned to fit back in again for the past five years and gave them away. I let them go.
To watch the KonMari method I couldn’t help but draw parallels to data analytics, and the mess of data we deal with every day. We need to be aware of the data that can “spark joy,” meaning be truly useful to the use cases that matter to us, and separate it from the data that is cluttering our lives. Because clutter leads to confusion.
So what data “sparks joy” enough to retain?
Decluttering and organizing your house and belongings feels great. But put into practice, to go through the minimization process, you face the challenge of murking your way through the swamp of anxiety that comes along with letting things go.
This is the same feeling many people in IT and security have about our data.
“Shouldn’t I capture everything?"
“Maybe one day I’ll need this log?”
“Better to be safe than sorry.”
“What’s really valuable?”
So we hoard data in massive data stores, paying high rental fees to harbor items we’ll never use again. Because it makes us feel good to know we have it if we need it. But this allure of safety leads to a false sense of security.
We tend to treat data in the same way we do to an old pair of Chuck Taylors that have run their course. They stay in our closet, unused, collecting dust.
All data has the potential to hold value, especially as everyone moves to the cloud and machine learning gets more advanced. But it also has the potential to waste space. And beyond that, it has the potential to be abused or stolen. Organizations that blindly collect every bit of data for the sake of having it without organizing it and knowing what use cases it solves at best end up spending more money to store it than they need, and at worst expose themselves to increased cyber risk.
One of the things I really liked about Marie Kondo’s approach was the focus on reminding yourself about what really matters. It’s so simple yet so overlooked. Decluttering enables focus. So why not pay attention to that from the start with the use cases that matter to you?
So let’s take a hard look at our data. If it’s not useful for the use cases that matter today, then, figure out what we can purge, put the rest in cold storage (cheap places like AWS Glacial) for a rainy day, and focus on organizing and indexing what can help you produce the outcomes you want today.
I find it a bit funny that a show about organizing your house can apply so directly to data analytics. What is immediately relevant to your needs? What do you really need to protect? What data can you eliminate that has no value to you yet may expose you?
Fortunately today the technology is just starting to exist to Marie Kondo’s your data for you, so there’s really no excuse to have a messy house of data.
So clean up your house, then clean up your data store. You’ll be in a much better place after.
