This question usually doesn't come first. First people ask what a tool costs, or which platform delivers reports fastest. The question about lead time only comes up when someone realizes that a report is only as good as the data underneath it, and that data has to come from somewhere, has to be counted, and has to be checked by someone who is responsible for it.
There is no fixed answer to this question, and anyone who gives one is selling something. What we can do is explain where the time goes, so that you can assess for yourself what is realistic for your organization.
The first factor is the number of sources a data point runs through. An emissions figure that comes directly from a single energy invoice is different from an emissions figure that is built up from procurement data, a spreadsheet with conversion factors, and an estimate from a supplier who doesn't precisely know where its own figure comes from either. The more links, the more time it takes to map the lineage from source to report.
The second factor is ownership. If it is clear for a data point who supplies it, who checks it, and who is responsible in case of deviations, setting up quality rules goes relatively fast. If that question has never been asked, and the answer differs per department, figuring out who is responsible for what often takes more time than setting up the rules themselves.
The third factor is how much of the process is currently manual. A data point that travels via copy-pasting between three spreadsheets has more points of concern than a data point that comes automatically from a system. Not because spreadsheets are the problem by definition — why spreadsheets are not the problem explains that the messy spreadsheet is often a symptom of a process that was never documented — but because every manual step is a place where something can go wrong without anyone noticing.
Most organizations going through this process start with CO2 emissions. Not because the topic is the most important, but because it is often the most clearly defined topic: there are clear calculation methodologies, and the main sources — energy, fuel, travel — are usually already recorded somewhere. Why CO2 is usually the first topic describes that choice in more detail. For CO2, setting up a data point register, lineage, and ownership can go faster than for a topic such as biodiversity or working conditions in the supply chain, where sources are more often missing or have never been systematically documented.
This is no guarantee that CO2 will be finished faster. It is, however, the reason why many organizations don't start with it by coincidence: it is the topic on which the most is already in place to build on.
The lead time of this process is not a fixed sprint with an end date. It is not something that is completed in a one-day workshop, and it is also not something that is finished after a one-time exercise and never needs attention again. Quality rules need to be maintained, ownership changes as people change roles, and new reporting obligations sometimes bring new data points that have to go through the same steps again.
What is realistic: an organization that gets a clear picture, for a limited number of data points, of where the data comes from, who is responsible for it, and what checks are in place, then has a template that can be applied more quickly to other topics. The first round is usually the slowest, not because the topic is more complex, but because the way of working still needs to be found.
Before the question about lead time can be answered meaningfully, it helps to know where your organization currently stands. What is data maturity and how do you measure it describes how you can assess that without making assumptions about where you should already be. An organization where nobody knows who supplies a figure starts from a different point than an organization where that is already established but the checks are missing. Both can go through this process, only the time estimate differs.
Part of the delay in this process is not in the thinking, but in the execution work around it: retyping figures, searching through sources, keeping track of statuses. That is the kind of work of which a part can be taken over by AI, without giving up control over ownership and quality rules. If you want to know which part of that work in your organization qualifies for this, the work scan from FTE TO AI provides an estimate of that per task.
Vraag maar waar een datapunt vandaan komt. Dat is meestal de hele vraag.
Answers come from this site’s knowledge base. Not tailored advice, and not a scan of your company.