The useful question for an organisation is often less “Do we have data?” and more “How much effort are we spending making it usable?”
A report may arrive on time and still leave people uncertain. Two teams may calculate the same measure differently. Someone may spend hours reconciling spreadsheets before a meeting. A dashboard may look polished while a problem further upstream keeps producing unreliable figures.
These are the kinds of issues I look for when thinking about data efficiency: the avoidable work between information being recorded and someone being able to use it with confidence.
Start with the decision
Before recommending a new tool or another automated report, I want to understand what the organisation needs to decide. Who uses the information? What do they need to know? Where does the current process leave them waiting, checking or guessing?
That conversation helps define a useful scope. Sometimes the priority is a clearer measure. Sometimes it is a broken reporting workflow, inconsistent definitions or missing checks at the point where data enters a system.
Follow the information back to its source
A practical review can begin with one important report. Trace its inputs, transformations and handovers. Look for repeated manual corrections, unclear ownership and differences between what a measure is called and how it is actually calculated.
It is also worth asking the people doing the work where they lose time. Their experience can reveal problems that are difficult to see from a dashboard alone.
Define what improvement would mean
Agree a starting point before making changes: time spent preparing a report, frequency of corrections, turnaround time or the checks needed before figures can be trusted. Choose measures that fit the problem, then review whether the change helped.
Efficiency is about making effort and resources work better while protecting quality. A faster report is valuable when it also supports a sound decision.
That is the approach I bring to Predictive Insight: understand the question, investigate the information and build a proportionate solution around the people who will use it.