The Second Biggest Productivity Drag . . .

Productivity, as I have discussed before, is a much-misunderstood and misrepresented topic.

Individual task productivity rarely scales to P&L impact (economic productivity).

I will never forget the CFO who taught me this very directly.

We had a business case that calculated that 2,000 employees each saving 15 minutes a day, would deliver a $2m p.a. saving.

I know, I was young 😉.

The CFO looked at me and explained that there wasn’t any saving to be had, because the 15 minutes per person per day would never see the light of a P&L.

“Everyone will just have another cup of coffee, and nothing will change” was the observation.

Maybe a little harsh on the younger me, but it does make sense.

The single biggest drag on enterprise productivity is effort that is wasted or poorly directed in functional, team and organisational silos. Effort that we often assume is important.

We can reduce this primary productivity drag by exploring and reframing our end-to-end processes, and eliminating those things that do not need to happen, simplifying what does need to happen, and changing behaviours early in the process, to avoid ineffective effort later on. Then applying the most appropriate technology to automate relevant tasks and workflows and support the new process.

My favourite personal experience of this was a DSO reduction challenge in a global manufacturing business. There had been substantial investments in outside consultants and new technology with marginal impact.

A mid level finance manager suggested we were looking in the wrong places and that there was a major “blind spot”.

It turned out that the incentive and reward structure of the global sales teams were unwittingly undermining all this investment and effort. A single change in performance and reward structures had a dramatic effect with zero external cost and relatively little pain and effort! 

So, what, I hear you ask, is the Second Biggest Productivity Drag?

I often ask GBS, Shared Services and Finance Operations leaders what percentage of their organisations’ efforts they estimate are spent in fixing errors, re-work, responding to queries, and all the other classic elements of “Failure Demand”.

The answers range between 25% and 50% of total effort.

This is a massive opportunity.

We tend to focus our time on speeding up the workflows and processes that support “Failure Demand” rather than getting to, and fixing, the root causes.

A major root cause is the quality and integrity of Master Data. This is the Second Biggest Productivity Drag . . . .

We spend a lot more effort in fixing individual failed transactions in collections, payables and closing than we do in identifying and fixing the root causes.

Master Data is at the root of many, if not most, of these issues.

Mauro Portela wrote a very good piece on the foundational element of master data recently in “Master Data Management as a Foundation for AI Success”, as did Malcolm Hawker in his report “The State of MDM 2026”.

Master Data integrity affects process efficiency, effectiveness in delivering the outcome, error-rates and customer/colleague experience/satisfaction.    

Some examples put this into stark relief;

• 157 different vendor master records for the largest single supplier to one organisation. Some duplicated for understandable reasons, and others simply erroneous, but the impact being an inability to see, and thus make informed decisions on, aggregated spend with that supplier.
• 3,500 duplicate master data records for currently active vendors causing errors at PO, Invoice, Receiving and Payment stages.
• Up to 75% duplicated, inconsistent master data with multiple versions of contradictory, incomplete “truth”, numerous naming conventions, embedded “codes” in company names (“XXX” etc).
• Different functional “silos” maintaining their own “shadow” single source of truth on the same information.
• Agreed payment terms not captured in vendor or customer master data in 20% of cases, resulting in varying payment practices, denuding working capital and creating unnecessary customer and vendor queries based on misaligned expectations.
• 44% of active suppliers with bank detail + Tax/VAT issues, causing the obvious payment, tax and reporting issues.
• 85% of long-term inactive customers and vendors having master data that is still very much available for use, causing mistakes, errors and re-work.  
• And the beat goes on . . .

The Harvard Business Review (HBR) estimates that “bad data” costs US businesses over $3 trillion per annum.

Gartner research asserts that 57% of organisations admit their data isn’t AI‑ready.

It is easy to see the impact of data quality and integrity having a big impact on the basic usefulness and effectiveness of a GenAI conversational service.

But it is a much deeper issue than this.

Before we even worry about “AI-ready”, we need to address the fact that much of our data isn’t even “transaction ready” . . .

Mauro states “you can’t AI your way out of a data problem, but you CAN data your way into AI success”. I would add that you CAN also data your way to transactional success!

Malcolm comments in his report that master data “must support deterministic (classic, transactional) and probabilistic (AI) worlds simultaneously.”

Master data is both the context and the “glue” that drives operational process efficiency AND effectiveness as well as providing the basis for effective analysis, assessment and decision-making.

It always seems that fixing master data is regarded as a technical challenge and an almost impossible mountain to climb.

But applying the Pareto Principle (80/20 rule) is a powerful way to drive “quick wins” in business impact and continuous improvement with master data.

It seems to me this is a very practical example of the proverb that “you can give someone a fish and you feed them for a day; teach them to fish and you feed them for a lifetime”.

That’s a productivity lesson in itself!

You can read “Master Data Management as a Foundation for AI Success” by Mauro Portela here . . . 

You can read Malcom Hawker’s “The State of MDM 2026” report from Profisee here . . . 

The Second Biggest Productivity Drag . . .

Thanks for reading . . .