Many organisations are working through major ERP, data platform, CRM or reporting transformations. These programmes can be necessary and valuable, but they create a practical tension: the business must continue to make decisions while the future-state capability is still being designed and implemented.
A common response is to postpone analytical work until the new system is ready. Existing data is described as fragmented, manual, incomplete or difficult to access. Leadership accepts reduced visibility in the expectation that the future platform will resolve the problem.
The risk is that market conditions, customer behaviour and operational pressures do not wait for the transformation. Decisions continue, but with less support than the organisation may already be capable of producing.
Legacy systems usually contain years of operational history, transactions, customer activity, product movement, financial outcomes and process behaviour. The data may require cleaning, reconciliation or interpretation, but those limitations do not automatically remove its value.
The right question is not whether the current data is perfect. It is whether it is sufficiently reliable for the specific decision being considered, and whether its limitations can be made explicit.
A focused analysis may require only a small number of fields from several sources. It may be possible to connect them through a controlled extract, a temporary model or a repeatable manual process without waiting for full enterprise integration.
Delaying insight has a commercial cost. Opportunities may be missed, weak performance may continue, capital may be committed without clear evidence and important assumptions may remain untested.
Waiting can also reduce the quality of the future transformation. When requirements are defined without practical experience of how decision-makers will use the information, the new system may reproduce existing reporting rather than create a better decision process.
Using current data to solve a real problem provides evidence about which measures matter, how they should connect, what level of detail is useful and where data quality genuinely affects the outcome.
Begin with a specific decision rather than a broad objective to “improve reporting”. Clarify the choice leadership must make, the time horizon, the alternatives and the financial or operational consequences.
Then define the minimum viable insight required. Which drivers need to be understood? Which assumptions need to be tested? What level of accuracy is necessary? What would materially change the decision?
This discipline prevents the work from becoming a substitute systems programme. The objective is to create enough clarity to act.
Create a practical inventory of the relevant data, not a comprehensive catalogue of every field in the organisation.
The required evidence may sit across finance, sales, customer, product, operational or external sources. Identify who owns it, how frequently it changes, what known limitations exist and whether access can be provided safely through read-only or controlled extracts.
Where the same measure appears in multiple systems, agree which source will be used and document the reconciliation logic. Transparency is more valuable than pretending the data is cleaner than it is.
Data should be integrated according to the decision process, not simply because the fields can be joined.
For a customer profitability question, this may mean connecting revenue, discounting, product mix, acquisition cost and cost-to-serve. For a product portfolio question, it may involve demand, margin, inventory, customer behaviour and operational complexity. For a network decision, it may require demand potential, catchments, competition, location cost and capacity.
The business logic determines which relationships matter and which level of detail is sufficient.
Once the relevant drivers are connected, translate them into commercial consequences. Show the current position, the likely future path and the effect of alternative actions.
Scenario testing is especially useful when data is imperfect. Rather than presenting one precise answer, test a range of assumptions and identify which variables have the greatest influence. Leadership can then see whether the preferred decision remains robust under different conditions.
The purpose is to make the uncertainty manageable, not to conceal it.
A focused piece of work can often be converted into a lightweight, repeatable process. This may involve scheduled extracts, defined validation steps, a documented model and a clear review cadence.
The temporary capability does not need to become a permanent parallel system. Its value is to support decisions during transition, establish a working definition of what good looks like and create reusable logic that can inform the future platform.
Where appropriate, the process can later be migrated, automated or integrated once the target architecture is ready.
Using existing data should be pragmatic, but it should not be uncontrolled.
Access should be appropriate and secure. Source systems should be protected, preferably through readonly access or governed extracts. Assumptions, transformations and reconciliations should be documented. Material outputs should be validated with the people who understand the underlying process. Limitations should be visible to decision-makers.
The standard is not perfection. It is a proportionate level of rigour for the importance and reversibility of the decision.
The most valuable outcome may extend beyond the immediate decision. Practical analysis creates better requirements for the future state.
It identifies the data that genuinely matters, the definitions that need alignment, the level of granularity leaders use, the relationships that must be preserved and the scenarios the organisation needs to test. It can also reveal where process change, rather than technology, is the real constraint.
This allows the systems programme to be shaped by proven decision needs rather than abstract feature lists.
Organisations do not need to choose between acting now and building for the future. Existing data can support near-term clarity while the lessons from that work improve the long-term capability.
Enlite’s Strategic Decision Pilot is designed for this situation: a focused, low-risk way to use current data and systems, augmented where necessary, to clarify a defined decision and inform the next step.
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