What Earns Another Quarter? A Funding Review for UK Data Products

Most UK data portfolios do not fail in a single dramatic moment. They drift. A dashboard is built for one budget cycle, a pipeline is added to support a regulatory return, an AI pilot survives because no one wants to be the person who switched it off. By the time the next planning round arrives, the portfolio resembles a filing cabinet rather than a set of decision tools.

In 2026, the pressure is sharper. Cloud bills are visible to finance, AI suppliers are competing for the same budget line, and data teams are being asked to demonstrate value without adding headcount. The question is not whether every data product is useful in isolation. It is whether the portfolio, taken together, deserves the money and attention it consumes.

A magnitude filter helps. It is not a formal accounting method. It is a way of ranking candidates by the size of the decision they affect, the consequences of error, the number of teams that can reuse the output and the cost of keeping it alive. The aim is to make trade-offs explicit, not to create a scoring industry.

Why Portfolios Drift

Data products accumulate because each one had a sponsor at a point in time. A finance director wanted a margin view. A compliance team needed an evidence trail. A commercial director wanted a churn signal. None of those requests was unreasonable. But once built, products tend to acquire a life of their own. They are renewed because they exist, not because they still change a decision.

The drift is rarely visible in a catalogue. A catalogue will show that a product has an owner, a description and a refresh schedule. It will not show that the owner left two years ago, that the refresh has failed silently for a month, or that the main consumer now exports the data to a spreadsheet and ignores the dashboard. Portfolio reviews need to test use, not just existence.

The Six Questions

Use a simple 1 to 5 scale for each question, where 1 is weak and 5 is strong. The weights can vary by organisation, but the discipline of answering all six is more important than the precision of the arithmetic.

1. Which decision changes if this product is right? Name the decision, the person who makes it and the cadence. A product that supports a weekly pricing meeting is different from one that produces a monthly report nobody recalls reading.
2. What happens if it is wrong? This is where regulatory exposure enters. A customer segmentation model and a statutory return carry different consequences. Score the cost of error, not the likelihood alone.
3. Who else can reuse the output? Reuse is a multiplier. A clean supplier reference dataset may serve procurement, finance and risk. A bespoke dashboard built for one team may serve only that team.
4. What does it cost to keep alive? Include licence, compute, storage, support and the people time needed to fix failures. The largest cost is often the analyst who spends two days a month reconciling a pipeline that nobody owns.
5. What would we miss if we switched it off for 30 days? This is a practical test of decision value. If the answer is nothing, the product is a candidate for retirement or replacement.
6. Is there a simpler way to get the same decision? A spreadsheet, a bought-in benchmark or a change to a business process may beat a custom data product. Simplicity is a legitimate outcome.

The filter is deliberately biased towards decisions and away from outputs. It does not ask whether a product is interesting. It asks whether it earns another quarter.

A Worked Example

Consider a UK parts distributor with £90 million turnover, 650 staff and a data team of nine. The team has three candidates for the next quarter: a daily stock exception report, a customer churn propensity model and an AI pilot that triages supplier emails.

The stock exception report scores well on decision value. It supports daily replenishment decisions and prevents both stockouts and excess holding. It scores moderately on reuse. It is expensive to maintain because it joins three systems and has no clear owner. The churn model scores lower on decision value because the sales team has not changed its retention process. The AI email pilot scores low on decision value and high on operational cost, with unresolved questions about record keeping and supplier communications.

A sensible outcome is not to fund all three equally. The stock report should be retained and simplified, with a named owner and a smaller set of exceptions. The churn model should be paused until the retention process has a defined intervention and a way to measure it. The AI pilot should be stopped or moved to a narrow proof of concept with a clear records policy. The portfolio becomes smaller, but the decisions it supports become clearer.

Running the Review Without Creating a Bureaucracy

Keep the review light. A half-day workshop with product owners, finance and a representative consumer is enough for a first pass. Ask each owner to bring the decision, the cost and the evidence of use. Do not accept a catalogue entry as evidence. Look for login data, query logs, meeting minutes or a named decision forum.

Record the scores in a simple table and review them quarterly. The table is not the point. The conversation is. The most useful moment is when an owner cannot name the decision their product supports, or when a consumer admits they have not opened the dashboard in months.

What to Do With the Results

There are four common outcomes: keep, simplify, pause and stop. Keep is for products with clear decision value, manageable cost and a credible owner. Simplify is for products that matter but carry unnecessary complexity. Pause is for products whose decision process is not ready. Stop is for products that no longer earn their place.

The magnitude filter will not make hard conversations easy. It will make them honest. In a year when UK data leaders are being asked to do more with the same, that is a useful place to start.