Why Marie Kondo Would Rock at Data Migrations
Before your next data project, ask yourself: Does this data spark joy?
Everything I own currently lives in brown cardboard.
These boxes line my walls—they reach their blocky hands towards the ceiling.
“Home Depot: How doers get more done” echoes through my hall; any place your eyes could land shoves these words into your mind.
Do I live somewhere made of wood? Or does cardboard compose my shelter?
Moving sucks.
But the thought of paying to move things that I don’t even want fills me with such rage that I push on.
During the process of packing everything, we’ve been able to declutter a surprising amount of stuff.
Here’s what decluttering my home before a move taught me about [B2B] data migrations.
Why Marie Kondo Would Excel—ha!—at Data Migrations
When it comes to data migrations, marketers generally find themselves in a predicament like me—wanting to be done as quickly as possible, but also not to suffer later in the process.
On one hand, packing everything indiscriminately would reduce the front-end time. But it would add a TON of effort onto the moving and unpacking side.
On the other hand, adding a decluttering step early on will slow down the packing phase—but then unpacking is a breeze
That’s where Marie Kondo comes in.
Kondo is THE decluttering guru.
The principles she champions for decluttering and organizing a home can be equally effective for data migrations.
(At the risk of running into an overworked metaphor) Here’s why Marie Kondo would be awesome for managing your data infrastructure during a migration.
The Impetus for Decluttering
Moving is an Opportunity for Decluttering
Instead of looking at a migration as a chore, Kondo would see it as an opportunity to clean up your data.
During a migration, you have to take a critical look at their accumulated data and make informed decisions about what should be moved.
Managing data is not free, and keeping unneeded records is really expensive.
This process not only streamlines the migration but also improves system performance—for ANY tool (pretty nifty, right?).
Marie Kondo’s approach to decluttering begins: “Does it spark joy?”
In the context of business data, this translates to “does this data adds value?”1.
If not, it’s time to let it go.
I know this is hard to hear—and hard to convince superiors of—but data bloat is a millstone around your neck.
Crude oil is only useful once processed and refined; business data is the same.
[Using] unprocessed data will result in very computationally expensive garbage.
This selective approach ensures that only relevant and useful data makes it to the new system, reducing clutter and improving data quality.
Are we sure a move is necessary?
At this point, you have a good understanding of your data.
Are your working assumptions still correct?
If you learned that your current tool is plenty powerful with the reduced data bloat, don’t bother with a full-blown migration.
You can kick back, relax, and still know you won.
Kondo would be equally happy with your work.
If you still need to move
If you’ve done your declutter and STILL need to migrate, a fantastic checklist for doing this comes from Sara McNamara—an incisive MarOps leader.
Instead of transcribing the steps here, I insist you check out her newsletter or Linkedin for more details.
More Kondo Concepts
Minimalism vs. Decluttering
Marie Kondo often emphasizes that minimalism and decluttering are not the same.
Minimalism: Living with less
Decluttering: Organizing what you have and keeping only what is necessary.
For data migrations, the goal isn’t to reduce the volume of data.
If you have only useful data, don’t delete anything just because you feel like you should.
Instead, decluttering data means to only organize and retain data that serves a clear purpose.
During a data migration, it’s important to distinguish between data that is genuinely needed and data that is merely taking up space.
This is where a Kondo-inspired approach shines. By evaluating each dataset for its relevance and utility, you can make sure your data is lean2 and aligned with operational needs.
Ensuring Everything Has a Purpose
Marie Kondo’s philosophy revolves around ensuring that everything kept has a purpose.
Every piece of data that is moved to a new system should have a clear and defined purpose.
That purpose cannot be “We wanted historical records”3.
In practical terms, this means categorizing data into Critical, Important, and Non-essential categories.
Critical data is indispensable for daily operations and decision-making
Leaders use this data regularly to make decisions
Important data supports strategic initiatives
Managers might check this quarterly to track their team goals
Non-essential data can be archived or discarded
This could be marketing “qualified” leads that talked to a booth during a conference 5 years ago, but haven’t contacted the company since.
This triage process helps in creating a focused and purposeful data environment.
It’s much like an organized home where every item has its place and function.
Conclusion
Data migrations, like moving homes, can be a horror filled with the clutter, swearing, and confusion.
By adopting Marie Kondo’s principles of decluttering, you can transform your data into something that helps you grow instead of keeping you up at night.
The most important factor for a data migration is the pre-work before you even move the first byte.
In the world of data management—much like the world of moving, Marie Kondo’s wisdom offers a refreshing and practical approach.
Use Kondo’s advice to turn what could be a chaotic process into an opportunity for renewal and improvement.
Before your next data migration, ask yourself: Does this data spark joy?
If not, it’s time to thank it for its service and let it go.
Some examples of common filters that you can use would be:
Has this been accessed in the last 6 months?
Does anyone read this report we use this data for?
Has this customer interacted with the business in 6 months?
Has this customer spent more than some cutoff?
Generally the fields of recency, monetary impact, and frequency can be used to filter useful vs useless data.
Additionally, you can remember this by “reduce data ROT” and drop redundant, outdated, or trivial records.
As a side note, smaller datasets process faster—for some operations, MUCH faster. It’s an extra decluttering treat for your team.
If you’re ever in a pickle about deleting data you’re not sure is outdated, put it into an archive somewhere.
If you need to access it within the next year, summarize it for historical purposes and bring it into the common use data infrastructure. If it’s not used, delete it.
Obviously, compliance-related data retention has a value to the organization. DO NOT DELETE THAT
This post sparks joy.
The amount of data I've seen collected, analyzed, stored, mapped, and re-mapped forever just because it's there will certainly put your moving boxes to shame.