Your Enterprise Data Efficiency score
Enterprise Data Efficiency is the trusted business value an organisation gets per unit of data effort. This turns it into one figure you can state.
The score is the share of your data effort that produces an answer you did not already have. Everything else — holding the same information in three places, reconciling two reports that disagree, rebuilding a dataset somebody already built — is effort that produced nothing new.
Every input below is yours. Nothing is modelled on your behalf, the arithmetic is shown, and the number belongs to your organisation whether or not you ever adopt CryspIQ®.
Enterprise Data Efficiency
64%
64% of what you spend on data produces an answer you did not already have. The remaining A$846,000 a year is spent holding the same information more than once, reconciling it, or producing it again.
And a new question takes 6 weeks to answer. This sits beside the score rather than inside it: pricing a deferred decision would mean inventing a number, and that number would end up driving the headline.
| Platform spend | A$900,000 |
| — of which duplicated | A$270,000 |
| People (8 × A$180,000) | A$1,440,000 |
| — of which rework | A$576,000 |
| Total annual data effort | A$2,340,000 |
| Producing no new answer | A$846,000 |
How it is calculated
score = 1 − (effort producing nothing new ÷ total data effort)
Effort is expressed in one unit — money per year — so the two parts can legitimately be added:
| Part | Total | The wasted share |
|---|---|---|
| Platform | Annual cloud, warehouse and BI spend | What is spent holding the same information more than once |
| People | Headcount × fully loaded cost | Time spent reconciling, rebuilding and re-answering |
There are no weights and no hidden multipliers. A weighted index across three dimensions would have been easier to build and worth less: the weights would have been invented, and an invented number quoted to the nearest percent is worse than no number, because it looks like a measurement.
Why time sits beside the score rather than inside it
Delay is the third part of data effort and the one executives feel most, but folding weeks into a money ratio means putting a price on a deferred decision — and that price, being invented, would end up driving the headline figure. So the score reports two things: an efficiency percentage and a latency in weeks.
The latency figure to watch is not your average. It is the floor: how long a question nobody anticipated takes to answer. An estate that serves its existing reports instantly and a new question in six weeks is well optimised for the past.
What this measure does not tell you
It measures the efficiency of effort, not the value of what that effort produces. An organisation that answers useless questions cheaply will score well here. That is a real limitation and worth stating plainly rather than burying: efficiency is the denominator of the ratio, and it is the half that can actually be counted. The numerator — whether the answers changed a decision — is a judgement your executive team has to make, and no calculator will make it for you.
Used honestly, a low score is not an argument for buying anything. It is an argument for finding out where the effort goes.