Data readiness is one of the most consistently underestimated constraints in supply chain transformation — and one of the hardest to assess honestly from inside the organisation.
The conversation about data readiness usually starts in the wrong place. Someone proposes a capability investment — a planning platform, an AI use case, a forecasting improvement — and data quality surfaces as a concern. The question then becomes whether the data is good enough to proceed. That framing almost always produces the wrong answer, because good enough depends entirely on what you are actually trying to do with it, and that question has usually not been answered precisely enough to test against.
The more useful question is not whether your data is good enough in the abstract. It is which specific decisions you are trying to improve, what data those decisions actually require, and what the gap is between what you have and what you need. That diagnostic is harder to do than it sounds, because the people best placed to assess data quality are often the ones most adapted to working around its limitations — which makes the gaps invisible until something external forces them into view.
The most consistent failure mode is attempting to boil the ocean — designing a single, global data model before there is clarity on which specific decisions it needs to support, who owns those decisions, and what level of data quality each decision actually requires. The result is a programme that delivers infrastructure long before it delivers decisions, and a gradual erosion of confidence in both the programme and the people running it.
A related pattern is the persistence of Excel. Despite extensive investment in ERP, analytics platforms, and partner portals, Excel remains the primary decision tool for many supply chain teams. This is not a cultural failure — it is a data model failure. Spreadsheets persist because they allow individuals to reconcile data from multiple systems in ways central platforms cannot yet support. When organisations attempt to eliminate Excel without first understanding what it is doing, the local logic it encoded simply reappears elsewhere.
The ownership question is also consistently underestimated. Most supply chain data involves inputs from commercial teams, finance, manufacturing, logistics, and IT — each with its own standards, incentives, and governance. When a cross-functional digital initiative is introduced, someone is made accountable for delivering the outcome without necessarily having authority over all the required inputs. That gap between accountability and control is where data programmes most often stall.
The assumption most organisations are operating on is that data readiness is a prerequisite... a problem to be solved before meaningful progress on capability investment can begin. The result is that data quality surfaces as a blocker and stays there, either deferring investment indefinitely or triggering a programme to fix everything at once before anything else can move.
Neither response tends to work.
The organisations that make progress treat data readiness as a design problem rather than a prerequisite. The question is not whether the data is good enough in general... it is which specific decisions need to improve, what data those decisions actually require, and what the gap is between what exists and what is needed for that specific use case. That diagnostic is harder to do from inside the organisation than it sounds, because the people best placed to assess data quality are the ones most adapted to working around its limitations.
The sequencing that tends to produce better outcomes is narrow scope first - one high-value decision flow, aligned ownership, demonstrated value - before expanding. That allows governance and definitions to stabilise through use rather than through design, and it produces a working capital or balance sheet argument that tends to land with finance in a way that efficiency arguments consistently do not.
BPC's outside-in view on this pattern comes from practitioners who have worked through data foundations decisions in comparable organisations. Tell us about your context and we can find the most relevant comparisons.
Examines the strategic context that should sit upstream of any capability investment, including operating model design, partner ecosystem constraints, and the shift toward AI-enabled best-of-breed components.
BestPractice.Club is not a consultancy and does not provide advisory services based on full organisational discovery.
What you see here reflects pattern recognition drawn from many years of conversations with supply chain and operations leaders facing real, high-stakes decisions. It is intended to help you orient yourself, clarify your decision position, and understand what often proves useful at similar points — not to provide definitive advice tailored to your specific circumstances.
Any suggestions are indicative, not exhaustive, and are made without full visibility of your organisation, constraints, or risk profile. Decisions remain yours, and should be tested against your own data, context, and governance processes.
If a pattern doesn’t quite fit, that’s normal. They are distilled from many examples from varying contexts. Decisions rarely move in straight lines with teams often revisiting earlier stages as new information emerges. If it would help to talk through your situation and sense-check where you are, you’re welcome to schedule a short conversation.