Why every business leader should care about Data Remediation

Fixing mistakes isn't a tech problem. It's a business responsibility.

Let’s start with personal example.

A Mortgage. A Payment. A Penalty.

I have a mortgage with an overpayment allowance - essentially, I could pay extra every month, but only up to a fixed limit.

On the last day of the month, I used that allowance. Then, on the first day of the new month, I made another payment, assuming the new period had started and the limit had reset.

Instead, I was hit with penalty fees.

Why?

• The contract defined the allowance period using the payment date - when I made the payment. That’s what I (logically) assumed.

  
  

• But the system, in this particular company, assigned the payment based on the settlement date - when the money cleared. And that date still fell in the previous month’s settlement period.

  
  

Because of this subtle misalignment, the second payment was counted in the previous month - where I had already hit my limit. The result: an invalid penalty, compounding interest fees, and potential legal friction.

But here’s what really matters:

Fixing it from a customer perspective was simple. I made a call. Within two minutes, the customer service agent said the fees had been credited. Case closed.

Everyone's happy, right?

Well - not quite.

Behind the scenes, someone still had to clean it up.

The Hidden Work: What Happens After the Call

This is the part no one talks about. Because even after I was satisfied, I assume their data work had just begun.

To make that correction flow through the system properly, the data and analytics teams had to ensure:

• Interest was correctly reversed

• Balances reflected the correction

• Updated statements were issued

• Reports to regulators were fixed (some were likely already submitted)

• Customer-facing data platforms showed the right numbers

  
  

All of this - because one date was misunderstood.

This Happens in Every Company

It may not be mortgages for you. It might be invoices. Subscriptions. Claims. Loans. Refunds. Returns.

But the pattern is the same:

• A rule changes.

• A business system backdates something.

• A contract gets reinterpreted.

• Last month’s P&L is suddenly wrong - especially if it's already been reported.

And no one notices until it’s already caused damage.

That’s when the question hits:

“Can our data platform fix this?”

And in too many companies, the honest answer is:

Not without a lot of duck-tape work.

What Is Remediation?

Remediation is the ability to correct the past when something has gone wrong - and ensure every downstream system reflects that correction.

It’s not a simple "undo."It’s not just fixing one number in one place.

It’s a ripple effect:

• KPIs shift

• Forecasts change

• Customer balances update

• Regulatory reports require revision

• Trust in the data is on the line

  
  

And when the data team tries to fix it, they often run into a hard truth:

The data ecosystem wasn’t built for correction - mostly for collection.

The Illusion of Clean Data

Most executives assume data quality is about "getting it right the first time."

But even with the best systems and most careful people, the business will change its mind.

Because that’s what business does:

• Product definitions evolve

• Policies get clarified

• Systems are migrated

• Backlogs are cleared

• Contracts are reinterpreted

  
  

When that happens, the past changes - and the data must change with it.

If it can’t? You’re flying blind with numbers no one can trust.

What You Need to Ask Your Data Team

You don’t need to understand every detail of your data architecture.

But you do need to ask this:

If something upstream changes - a rule, a product, a policy - can we go back and fix our data without breaking everything else?

If the answer is no, it doesn’t matter how beautiful your dashboards are or that the data is right in your core systems. Your data is fragile.

And that fragility will cost you:

• Misreported results

• Frustrated teams

• Regulatory exposure

• Missed revenue

• Lost confidence from your board and customers

  
  

The Real Test of a Data Platform

You don’t test data maturity by how fast reports refresh.

You test it by how well it handles change.

Remediation is that test.

It asks:

• Can we go back in time and correct mistakes?

• Can we do it without weeks of manual effort?

• Can we trace what changed, when, and why?

  
  

If your platform - and your team - can do that, you’re in control.

If not? You’re at the mercy of complexity and technical debt.

Final Thought

Most data conversations focus on scale, speed, and automation.

But trust doesn’t come from speed. It comes from integrity.

And integrity in data means this:

When something breaks, we can fix it - fully, accurately, and without panic.

That’s the true power of a modern data team.

And if you want to lead with confidence, start here:

Can we fix the past?

Because sooner or later, you’ll have to.

//AK