Understanding CRM data entropy in complex marketing and revenue systems.
“These systems were working fine a few months ago. Why are
they going wrong?”
This is a classic data systems question, and one that’s
perfectly reasonable for a senior leader to ask of their organisation.
It’s one I’ve grappled with from the first time I worked with big data sets back in 2009. It’s reasonable to expect your systems to function effectively and efficiently, right?
A marketing automation platform, a
CRM/Sales Operations platform, and the analytics/reporting platform should all
function smoothly — like a car, right? Make sure you service it regularly, add
oil, pump the tyres, and Bob’s your uncle.
But no.
One big difference with CRM systems is all the gremlins; They
screw things up for you. I’m joking, of course. Gremlins were invented by
engineers in WWII to explain strange behaviour in Air Force planes.
Rather like planes, with data you are not just dealing with
systems; you are dealing with a lot of human beings. And humans do weird and
wonderful, and often irrational things with systems.
In addition, you are dealing with code, and coders. Each coder has their own way of coding. Not only that, but they often save the same chunks of code that they insert time and time again into various systems to fix issues.
You also have coders increasingly using AI to write code for them. How do you monitor and control for that, especially if you are using short-term contractors?
When you have
contract coders, they may be carrying out numerous “workarounds” that are only
temporary patches for longer-standing issues.
Which brings us to the next response to that question:
“But was the system really working fine a few months ago?”
But how do you know for sure it was 'working fine' before? Could it have been patched up by developers in a way that worked fine at that time, only? Could additional data entry, and additional systems (a new software integration, for example), have exposed deeper issues, cracks in the system?
In short, was it fully functioning, or was it functioning at that point with these workarounds?
A good example of this would be when you build a bridge. The bridge may appear perfectly sound under light traffic.
But as traffic increases, small structural weaknesses begin to show — bolts loosen, joints flex, stress fractures appear.
We have this exact same issue around the corner from my house, at Hammersmith Bridge - closed to all traffic except pedestrians and cyclists for the last seven years.
Secondly, the second law of thermodynamics states that all
systems gravitate toward entropy. Meaning that even if a system is truly
working 100% effectively (highly unusual with large integrated systems), it
will naturally gravitate toward disorder and chaos.
If you think of several complex data systems all working
together, they all rely on human inputs. It is partly a machine, yes, but it’s
partly an organism.
It could function more like a beehive or an ant colony than
a high-performance automobile.
When the Hive Works
A beehive works great when all the bees know what to do, and everything is in order.
When the hive is healthy:
- Nectar flows in
- Honeycomb is built efficiently
- The queen lays eggs
- The hive grows
- The hive produces honey.
Information flows cleanly through the colony.
In CRM terms:
- leads are captured correctly
- Fields are filled consistently.
- Deals are updated accurately.
- Dashboards reflect reality
- The organisation produces revenue insight.
- The company grows revenue, and profits.
When the Hive Starts to Break Down
Now imagine something begins to go wrong in the hive.
- Some bees start bringing back the wrong pollen.
- Others forget where the nectar fields are.
- Some bees begin storing nectar in the wrong cells.
- A few bees stop communicating their waggle dances correctly.
Individually, these errors seem trivial.
But collectively they begin to destabilise the colony.
The CRM Equivalent
In CRM systems, the same thing happens.
Small behaviours introduce tiny distortions:
- A salesperson skips required fields
- A campaign uses inconsistent tracking parameters
- A deal is created manually rather than through the lead flow
- A Marketing Manager forgets to save lead scoring on a form
- A Data engineer makes an error in the dashboard logic
- A contact is duplicated
- Attribution fields are overwritten
Each individual action feels harmless.
But the system is cumulative.
Just as with the hive, thousands of small inconsistencies
compound.
Data Entropy in CRM Systems
Going back to my entropy point, to ensure that your
“beehive” is functioning well and driving all the “honey” you possibly can, you
need to expend a lot of energy to keep everything in order.
1. Human Behaviour
Sales teams optimise for closing deals, not data quality.
Examples:
- Creating manual deals
- Editing deal values after closing
- Skipping mandatory fields
- Overwriting campaign sources
- Each action introduces micro-disorder.
2. System Complexity
Modern stacks involve many systems:
- Customer Relationship Management (CRM)
- Marketing automation
- Intent data platforms like Demandbase or 6sense
- BI tools
- Sales engagement tools, like Clay, or Zoominfo.
Every integration increases the chance of:
- Mismatched IDs
- Sync failures
- Schema drift
- Attribution conflicts
Complex systems amplify entropy.
3. Time
Even if nothing changes:
- Definitions evolve - How many times have the definitions of leads changed at your company (even subtle changes in lead scoring)?
- New fields get added.
- Legacy data persists
- Over time, the data model drifts from its original design.
- New, increasingly complex tech integrations are added
- CRM systems are not simply databases. They are living organisms.
They are socio-technical systems, a mixture of humans, incentives, processes, software, integrations, and, if you want to dig deeper and darker, perhaps even corporate fiefdoms, and unwritten rules going back generations.
People who constantly ask: “Why do these numbers not match?' are perfectly reasonable to do so. But behind the clean dashboard they see, any number of 'gremlins' can lurk; It could even be numerous gremlins working as a team!
The Reality for Leaders
So to sum up, it’s perfectly natural as a CEO, President of a division, a CMO, or a CRO to expect these systems to work efficiently, providing the functionality teams need, the actionable insights, and the ability to execute.
Senior leaders face so many demands that frankly blow my mind, and would likely overwhelm someone like me. Why shouldn’t they expect these systems to be the firm bedrock on which to build their business?
But the reality is that managing these systems requires
constant vigilance, thought, testing, imagination, planning, and long-term
strategy, just as the rest of the business does.
Because left unattended, complex systems drift toward disorder. That's science!
And CRM/Marketing Automation/Analytics systems are no exception.
Like any complex system built on human inputs, software integrations, and evolving processes, they naturally accumulate entropy over time.
Which means the question is never whether disorder will appear.
The real question is who is paying attention when it does.
