Connecting Data and Decisions with the Deltalyze Platform

01Decision latency after visualization

Data may be collected automatically while interpretation, follow-up, and action return to email and meetings.

The missing link is the rule that connects a signal to a business event, an accountable owner, and a next action.

The delay is measurable. In a survey of Japanese e-commerce operators (Mercart, published May 2026), 38.0% said that producing a figure spanning multiple data sources for a management meeting took two to three days or more, and a further 10.5% said it took over a week or could not be produced at all. 58.0% saw data fragmentation as a factor widening the gap against competitors. That survey covers e-commerce operators only and does not transfer directly to other sectors. It is cited here as one case where the latency that remains after visualisation has actually been counted.

The delay usually sits in one of four places. Take one of your own dashboards and check where things stall; that tells you where to start.

• Interpretation gap: nobody has written down what a change in the figure means on the ground
• Ownership gap: there is no agreed first responder or decision owner
• Criteria gap: the threshold for acting versus waiting differs from person to person
• Feedback gap: the outcome of a response is not recorded, so it never informs the next decision

02Write one decision rule per indicator

A decision rule closes all four gaps at once. It is a one-page agreement, for a single indicator, covering what a change means, who decides, the criteria, the options, where the decision goes, and where the result comes back. Write it per indicator, not per dashboard screen.

Copy the template below and fill in each field. An indicator whose fields you cannot complete is not yet ready to drive decisions. The empty fields become the agenda for agreeing with the people involved.

Use roles rather than names for the first responder and decision owner, so the rule survives transfers and leave. Wherever possible, express criteria as numbers or as yes-or-no conditions.

Do not leave the deadline blank. Without one, nobody notices when a decision is late. With a set time from signal to decision, counting the items that missed it shows exactly which flow the delay sits in.

In the scope-for-AI field, state whether AI drafts only or also proposes options. Settling this early keeps later discussions about adding generative AI from stalling over how much to automate.

03Worked example: late customer payments in accounts receivable

The following is a fictional example. It fills in a decision rule for an accounting team that uses late receipts as its indicator. The day counts and amounts are illustrative, not recommendations.

The point to notice is that the same "unpaid" signal leads to a different decision owner and a different response depending on whether it is a matching error or a late customer. Without a rule, that distinction gets settled by email every time, and the days slip by while it does.

The example criteria treat a first late payment differently from a repeat. Building past actions into the criteria only works if outcomes are recorded where the rule says they should be. The fields depend on each other, so leaving one blank weakens the rest.

04A shared model for process, data, and roles

Once you have written a few decision rules, you will notice several indicators hanging off the same process step. Attach inputs, outputs, owners, approvers, criteria, and AI capabilities to each process step, so the impact of a change can be traced from the purpose of the work rather than from individual screens.

For instance, changing who owns invoice issuance also changes the first responder in the late-payment rule. When process steps and decision rules are linked, that knock-on effect shows up before the change is made. For how to list process steps in the first place, see our guide to business process inventories.

Keep AI proposals separate from official operating state until review and approval are complete.

05Carry decision evidence into execution

Present related facts, comparable events, response options, and confidence alongside an alert. The decision owner reviews the evidence and chooses to adopt, modify, or hold.

The chosen response becomes tracked work, and its outcome returns to the original decision for learning.

Drafting the response proposal is a task generative AI handles well. The prompt below passes in the decision rule and the reference data and asks for a draft the decision owner can read. Requiring the output to open with "DRAFT (not approved)" keeps AI output from blending into confirmed decisions.

Use the four items below as a guide for what to attach to an alert. If any is missing, the decision owner will end up chasing it by email.

• Related facts: the data behind the signal and when it was retrieved
• Comparable events: earlier decisions under the same rule and how they turned out
• Response options: the options in the rule that apply this time
• Confidence: what is fact, what is inference, and what still needs checking

You are an assistant supporting receivables management in an accounting team.
For the alert below, prepare a draft response proposal for the decision owner to read.

# Alert
{indicator name and the value that changed}

# Decision rule
{paste the decision rule table above}

# Reference data
{related invoices, bank receipts, past action history}

# Output format
1. What may be happening (separate facts from inferences)
2. Which decision criteria apply, and which do not
3. For each response option, the reasoning and what to confirm before deciding
4. Confidence (high, medium, low) and why

# Constraints
- Do not recommend options that are not in the decision rule
- Do not invent figures that are not in the data. Write "unknown" where unclear
- Start the output with "DRAFT (not approved)"

06Distinguish drafts from official state

Use explicit draft, review, approved, and executed states with permissions for each transition.

Every material change should be attributable and reversible.

The table below shows which states a single response passes through under a decision rule, and who can do what in each. Adjust the "who can change it" column to match your segregation of duties. If AI can only change items in the draft state, every approval stays on record as a human decision.

07Move from operating design to implementation

Start with one flow where decision latency materially affects the business. Agree on the current state, the target state, the data required, and the division of responsibility before building anything.

A good first flow is one where signals occur often enough to learn from and where delay hits cash flow or customers directly. The more of the following questions you can answer yes to, the better the flow suits a first attempt.

First, does the signal occur several times a month or more? Second, would a delay of a few days affect a deadline such as a receipt, a payment, or a reply to a customer? Third, can the decision owners be narrowed to one or two people? Flows with many decision owners take longer to agree a rule for, so leave them for later. The five steps below take the first flow from rule to routine.

If you have not yet mapped the wider process, visualizing the work before adding AI makes it easier to choose that first flow.

The Deltalyze Platform aims to treat process, data, roles, AI, and decision criteria as one operating model. Deltalyze treats process design, platform implementation, and adoption as one continuous improvement cycle.