Stop Building Dashboards: The Proactive Notification Blueprint

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Your dashboard looks alive on launch day, clean charts, fresh numbers, executive smiles.

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But decay starts the moment you publish it because the whole model depends on someone opening it, scanning it, and spotting the problem before the moment passes.

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That's the break.

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Most BI still runs on pull.

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People check data when they remember or when they have time, or when they already suspect something is wrong.

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But high-value decisions usually don't fail because the chart didn't exist.

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They fail because nobody looked at it at the right time, so the shift isn't from one report style to another.

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It's from static reporting to notification architecture where power BI, power automate, dataverse, and AI work together to push action when context changes.

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Because if you keep building dashboards as the end product, you're not funding visibility.

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You're funding delay, the death of the data graveyard, a dashboard usually dies in a very boring way.

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It doesn't end with a technical failure or some dramatic outage.

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It dies because work moves on and conditions change, while the dashboard stays exactly where it was, sitting in a tab and waiting to be visited.

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What looked like visibility slowly turns into storage.

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That's why I call it a data graveyard.

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The floor isn't the chart design.

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It's the assumption behind it.

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You're assuming that people will stop what they're doing, go looking for insight, and then translate that insight into action fast enough for it to matter.

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In most organizations, that doesn't happen.

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People are already buried in tools, meetings, approvals, and chat threads.

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Analytics becomes just another place to check, and once it becomes another chore, it becomes optional.

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Research on dashboard fatigue points in that direction.

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Workplaces already run across huge ab counts.

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And that constant switching adds stress.

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One study in enterprise settings tie digital overload and burnout together with employees dealing with more than 100 apps on average.

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Another finding that matters here is that dashboard use often falls hard after launch.

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Some research on automated BI put utilization around 20% after six months,

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which shows the problem isn't getting a dashboard live, but getting it used when timing matters.

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And that timing part is everything.

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If a finance lead needs to notice a budget drift or an operations manager needs to catch an SLA risk,

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a passive screen creates lag by default.

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You navigate, you search, you compare, then maybe you act.

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That sequence sounds normal because we've accepted it for years, but it's a slow sequence.

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It turns awareness into a manual task.

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Executives don't need more views.

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They need fewer missed moments.

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That's a different requirement.

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A dashboard is good at exploration.

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It can help you inspect trends, compare segments, and ask follow-up questions.

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But it doesn't intervene.

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It doesn't interrupt the day when a decision has started to go off track.

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It just waits there, hoping someone checks in before the issue gets bigger.

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So what teams called stale dashboards are often stale operating models.

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You can redesign the page, you can clean up colors, you can reduce KPI clutter.

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That may help a little, but if the system still depends on memory and manual review,

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the basic weakness stays put.

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DataRot is not mainly a design problem.

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It's an operating problem.

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The information might be fresh in the model and still be useless in practice

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because nobody gets pulled into action at the right time.

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And this is where the conversation needs to change.

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The question is no longer how do we build a better dashboard page.

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The better question is, what business moment deserves intervention,

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who owns that moment, and how should the system respond when it starts to drift.

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Once you look at it that way, the old pull model stops looking mature.

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It starts looking incomplete.

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So if the old model is pulled, the next step is defining the push model clearly.

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The shift from dashboards to event thinking.

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Once you see the pull model for what it is,

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the next shift becomes obvious.

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You have to stop starting with pages and start starting with events.

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That sounds like a small tweak,

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but it changes the entire strategy.

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Page first thinking asks what charts we should show,

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how we should group them, and who gets access to the file.

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Event first thinking asks a much harder question,

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which is what specific change in the business actually deserves a response.

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Not a view, a response.

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This means you stop organizing your BI work around reports

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and start organizing it around moments.

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A budget threshold breaks,

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a delivery slips outside of tolerance,

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or an approval sits on a desk for too long.

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Maybe a churn signal rises, or a data loss pattern appears where an unusual variance shows up.

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Those are not just reporting artifacts, they are operating moments.

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And that is the shift.

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Most teams build information products as if the user's main job is interpretation.

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But in real work, people are usually not sitting around waiting to interpret data

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because they are busy in a call,

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approving spend, or trying to unblock a team.

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What helps them is not another page with another slicer.

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What helps is a system that notices the deviation

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and pushes the right context directly into the flow of work.

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To design that well, you need a simple structure for every event.

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First, you define the signal to show exactly what changed.

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Then you define the threshold to decide at what point it matters enough to interrupt someone.

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You need an owner who is actually responsible for acting,

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and a route so the message appears where it has the best chance of being used.

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Finally, you need a response.

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You have to know what should happen next, right away.

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If the event is real, if one of those pieces is missing,

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the whole system gets weak fast.

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This clicked for me when I stopped asking,

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whether a dashboard looked complete and started asking

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whether a business issue had an owner and a route.

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A lot of reporting work feels finished when the visuals are polished,

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even though the actual decision path is still undefined.

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The chart may be accurate,

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but the operating system around it is still vague.

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And vague systems are slow systems.

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There is also a massive difference between awareness and intervention.

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Awareness means someone could know something if they went looking for it.

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Intervention means the system enters the work at the exact moment the issue starts to matter.

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Those are not the same thing,

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and a lot of BI programs accidentally blur them together.

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If you remember nothing else, remember this.

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Dashboard support exploration,

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but event systems support decision velocity.

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That matters at the executive layer more than anywhere else.

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Senior leaders do not need a larger review ritual,

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but they do need less friction between a signal and an action.

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The value here is not just better visibility,

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it is a shorter time to pay out and faster exception handling.

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It creates a lower mental load,

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because nobody has to keep polling for trouble across 10 different places.

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Research on decision-making backs this up.

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Only a small share of organizations think they are actually good at making

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decisions quickly, even after spending huge amounts on BI.

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The issue isn't access to data by itself.

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The issue is whether intelligence shows up in time with enough meaning

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for someone to act without another round of searching.

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That is why event thinking is stronger.

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It removes the search step.

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Once you remove the search step,

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you start designing systems that fit how work happens now.

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Context appears inside teams, approvals, tasks, or escalations,

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instead of waiting inside a dashboard tab for a weekly review.

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But there is a trap here.

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A lot of teams see this shift,

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and then they build basic threshold alerts everywhere and call it progress.

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That usually creates a second problem,

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that is just as bad as the first one.

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Why most alert strategies fail?

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The first version of proactive reporting usually goes wrong in a very predictable way.

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A team realizes dashboards are too passive,

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so they start adding alerts on top of the existing data.

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Revenue drops below a certain number,

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Q-length rises above a limit,

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or spend crosses a fixed line.

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For a week or two,

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it feels better because messages are moving,

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and people think they have modernized the model.

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Then the noise starts.

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What looked proactive turns into another stream people learn to ignore

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because most alerts are built on raw numbers,

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without enough business meaning.

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A number moved,

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but the system doesn't explain if anyone should care right now.

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Is this normal for this hour, this day, or this customer segment?

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A threshold by itself cannot answer that.

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That is where basic alerting breaks.

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If you trigger on raw variants without context,

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you get false urgency.

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A backlog might rise every Monday morning,

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or an approval queue might spike right before the month ends.

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None of those should create the same level of interruption as a true exception,

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but weaker alert models treat them as equal

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because they do not understand patterns or consequences.

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And users learn fast.

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If six alerts out of ten do not need action,

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the seventh alert starts losing trust before it is even opened.

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The thing most people miss is that alert fatigue does not come only from volume.

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It comes from ambiguity.

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People can handle an interruption when the message is clear.

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What they cannot handle is a steady stream of messages

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that dump the interpretation back on them.

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If the recipient has to stop,

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open three systems,

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and guess the cause,

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the notification did not remove friction.

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It just relocated it.

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That means a red badge is not enough.

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A good notification has to do more work before it reaches a person.

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It should explain what changed why it matters now

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and what action path is expected.

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Without those details,

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you are not sending operational intelligence.

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You are just sending unfinished homework.

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This is also why anomaly detection matters more than a lot of teams think.

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Static thresholds are blunt tools that ignore trends and baseline behavior.

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Research around AI builder anomaly detection

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points to a better pattern here

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because dynamic baselines can reduce false positives.

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It is not perfect.

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But it is better in environments where normal moves around.

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There is another failure point that shows up later.

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Teams build alerts,

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but they never define the response path.

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The system can spot a problem and post to teams.

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But then nothing happens because nobody agreed on what happens next.

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There is no task, no approval,

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and no audit trail.

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It is just a message hanging in a chat.

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That is not architecture.

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That is leakage.

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If you want notifications to work,

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each one needs a specific job.

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It has to detect, explain,

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root, trigger and track.

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The message itself should be one part of a controlled flow rather than the whole solution.

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The design rule is simple

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and it is stricter than most teams expect.

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Every notification should answer four things before it ships.

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What changed?

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Why now?

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Who owns it?

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And what happens next?

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Once you build with that rule,

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a learning starts acting less like system noise

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and more like a managed intervention.

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That changes what the architecture has to carry

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because the hard part is no longer sending messages.

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The hard part is carrying context inside the message.

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The proactive notification blueprint.

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So what does the architecture actually look like

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when you stop thinking in terms of reports

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and start thinking in terms of intervention?

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You need six layers and each layer does a different job.

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Source systems, event detection, AI reasoning,

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orchestration, delivery, feedback,

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start with the source systems

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because this is where many teams get confused.

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The goal is not to replace your operational systems

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with one giant alert engine.

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The goal is to watch the systems that already hold the truth.

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ERP, CRM, service data, security signals,

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finance records, inventory movements,

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case updates, power BI can sit on top of those sources

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and help model the signals,

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but it should stop being treated as the place

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where the process ends.

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In this model, power BI becomes a sensor.

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That's an important change.

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You still use it to shape metrics,

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compare states and expose patterns.

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You still use it for analysis and exploration.

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But when a business condition crosses

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from observation into action,

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the output should move beyond the report surface.

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So instead of asking people to keep returning to a dashboard,

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you use that analytical layer

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to help identify a business event worth acting on.

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Then you need event detection.

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Some events are simple.

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A value crosses a known tolerance.

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An approval is overdue.

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A case sits untouched too long.

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Others need more judgment.

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The pattern looks unusual,

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but only relative to trend or expected behavior.

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This is where AI reasoning starts to matter

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because not every useful signal lives inside a fixed threshold.

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AI builder anomaly detection and power BI AI features

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can help spot unusual patterns.

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While co-pilot-based reasoning can help summarize,

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classify, or explain what just happened in plain language.

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That doesn't replace governance.

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It gives the flow a better first pass before a human sees it.

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After detection and reasoning,

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the center of gravity shifts to power automate.

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This is the orchestration layer.

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Not just the message sender, the conductor.

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It decides what flow runs,

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00:10:41,360 --> 00:10:42,480
what system gets updated,

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00:10:42,480 --> 00:10:43,600
whether approval is needed,

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00:10:43,600 --> 00:10:45,120
whether a task should be created,

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00:10:45,120 --> 00:10:46,960
whether an escalation should happen,

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00:10:46,960 --> 00:10:49,280
and what conditions suppress duplicate noise.

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If power BI notices,

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power automate acts.

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That's the split.

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Now that action needs memory.

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Without state,

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notification systems get messy fast.

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You don't know what was sent,

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00:10:57,440 --> 00:10:58,240
who acted,

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00:10:58,240 --> 00:10:59,520
whether the case is still open,

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00:10:59,520 --> 00:11:00,720
whether the issue was dismissed,

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00:11:00,720 --> 00:11:02,320
or whether the same alert keeps reappearing

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00:11:02,320 --> 00:11:03,680
with no learning built in.

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00:11:03,680 --> 00:11:05,120
That's where data verse earns its place.

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00:11:05,120 --> 00:11:07,200
It gives you a place to store notification history,

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00:11:07,200 --> 00:11:08,080
workflow status,

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00:11:08,080 --> 00:11:09,680
owner changes, escalation state,

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00:11:09,680 --> 00:11:11,680
retry outcomes, and audit detail.

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00:11:11,680 --> 00:11:13,840
So the flow is not just firing and forgetting.

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It is tracking.

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00:11:15,120 --> 00:11:16,320
And once you track state,

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routing gets smarter.

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00:11:17,520 --> 00:11:19,360
Not every event belongs in the same channel.

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00:11:19,360 --> 00:11:22,160
A CFO budget exception may need an approval flow.

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00:11:22,160 --> 00:11:25,200
A frontline service issue may need a team's post plus a task.

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00:11:25,200 --> 00:11:27,920
A risky compliance pattern may need an incident record

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00:11:27,920 --> 00:11:29,600
and a secure route for follow up.

294
00:11:29,600 --> 00:11:32,080
A senior leader may only need a one-liner alert

295
00:11:32,080 --> 00:11:34,480
with a confidence summary and a decision prompt.

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The route depends on role, urgency,

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00:11:36,240 --> 00:11:38,320
and what kind of action the event is asking for.

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00:11:38,320 --> 00:11:40,080
That's why delivery is its own layer.

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00:11:40,080 --> 00:11:42,240
Teams, email, approval workflows,

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00:11:42,240 --> 00:11:44,400
planner tasks, incidents, escalations,

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00:11:44,400 --> 00:11:45,680
even external systems,

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they are not the architecture.

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They are endpoints.

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00:11:48,160 --> 00:11:49,840
Pick them based on response behavior,

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00:11:49,840 --> 00:11:51,760
not based on what connector feels easiest.

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If the person who needs to act lives in teams all day,

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route there.

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If the action needs traceable approval,

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use the approval path.

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00:11:58,000 --> 00:12:00,080
If the issue belongs in a managed queue,

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create the case directly.

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00:12:01,440 --> 00:12:04,240
Delivery should fit the work pattern already in place.

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00:12:04,240 --> 00:12:07,040
One level deeper, the part most teams skip is feedback.

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Did the recipient act was the alert useful?

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00:12:09,040 --> 00:12:10,000
Was it noise?

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00:12:10,000 --> 00:12:13,280
Was the issue confirmed, dismissed, reassigned, or escalated?

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00:12:13,280 --> 00:12:15,920
If you don't capture that outcome, the system stays blind.

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00:12:15,920 --> 00:12:18,160
It keeps notifying with no memory of value.

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The better model is closed loop,

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detect the signal,

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00:12:20,480 --> 00:12:22,160
route the context, capture the result,

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00:12:22,160 --> 00:12:23,200
then tune the rules,

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00:12:23,200 --> 00:12:23,840
the summaries,

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00:12:23,840 --> 00:12:25,680
and the routes based on what led to action

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and what didn't.

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That's how notification architecture

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00:12:28,240 --> 00:12:31,360
starts acting like an operating layer instead of a collection of flows.

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00:12:31,360 --> 00:12:32,560
And once that model is clear,

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00:12:32,560 --> 00:12:34,720
the next question gets practical fast.

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00:12:34,720 --> 00:12:36,160
Where do you start without turning this

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00:12:36,160 --> 00:12:38,880
into an overbuilt automation project on day one?

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00:12:38,880 --> 00:12:41,120
High value use cases that justify the shift.

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00:12:41,120 --> 00:12:42,560
So where do you apply this first?

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Not everywhere.

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00:12:43,280 --> 00:12:44,320
That's usually the mistake.

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00:12:44,320 --> 00:12:47,680
Start where a late decision already costs money, trust, or control.

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Finance is one of the clearest places.

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00:12:49,280 --> 00:12:51,760
A dashboard can show budget drift after the fact,

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00:12:51,760 --> 00:12:53,600
but a notification flow can intervene

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00:12:53,600 --> 00:12:55,280
while there's still room to respond.

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00:12:55,280 --> 00:12:57,600
Say a cost center moves outside expected variance,

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00:12:57,600 --> 00:12:59,040
the system can detect the change,

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00:12:59,040 --> 00:13:00,640
attach the last approved baseline,

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00:13:00,640 --> 00:13:02,000
route it to the budget owner,

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00:13:02,000 --> 00:13:04,640
and trigger an approval or review path right away.

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00:13:04,640 --> 00:13:06,080
Same with cash flow movement.

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00:13:06,080 --> 00:13:07,440
Same with exception approvals.

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00:13:07,440 --> 00:13:09,120
The point is not to admire the variance.

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The point is to shorten the path from detection to financial action.

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00:13:12,240 --> 00:13:13,920
Operations works the same way,

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00:13:13,920 --> 00:13:15,600
just with different pressure points,

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00:13:15,600 --> 00:13:17,120
stock issues, backlog movement,

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00:13:17,120 --> 00:13:19,280
and SLA breach risk all decay fast

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00:13:19,280 --> 00:13:21,120
when nobody owns the moment early enough.

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00:13:21,120 --> 00:13:22,720
If an item drops below safe stock,

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a team doesn't need another report tab.

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00:13:24,480 --> 00:13:25,600
They meet the current level,

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00:13:25,600 --> 00:13:27,040
the affected product or region,

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00:13:27,040 --> 00:13:28,240
the likely consequence,

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00:13:28,240 --> 00:13:30,080
and the route to replenish or escalate.

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00:13:30,080 --> 00:13:31,520
If an SLA is drifting,

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00:13:31,520 --> 00:13:32,960
the flow should identify the queue,

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the owner, the age of the work,

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and what action closes the gap before the breach lands.

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00:13:37,200 --> 00:13:40,320
Security and compliance are even more dependent on timing.

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00:13:40,320 --> 00:13:41,760
In those areas, people already know

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00:13:41,760 --> 00:13:43,520
raw alert volume can get out of hand,

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00:13:43,520 --> 00:13:45,200
so value comes from better triage,

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00:13:45,200 --> 00:13:46,640
not just faster messaging.

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00:13:46,640 --> 00:13:48,160
A risky behavior pattern,

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00:13:48,160 --> 00:13:50,720
a DLP signal, or an insider risk case,

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00:13:50,720 --> 00:13:52,160
needs more than a warning.

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00:13:52,160 --> 00:13:53,920
It needs enough context for the right

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00:13:53,920 --> 00:13:55,760
team to decide whether this is noise,

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00:13:55,760 --> 00:13:57,840
policy tuning, or real investigation.

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00:13:57,840 --> 00:14:00,080
We've already seen that tuned proactive models

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00:14:00,080 --> 00:14:01,440
in Microsoft environments

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00:14:01,440 --> 00:14:03,840
can reduce high-civility DLP events

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00:14:03,840 --> 00:14:06,240
and cut response time for insider risk teams.

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00:14:06,240 --> 00:14:07,440
That only happens when the route

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00:14:07,440 --> 00:14:09,840
and the action path are built in from the start.

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00:14:09,840 --> 00:14:11,600
Service teams are another strong fit

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00:14:11,600 --> 00:14:13,200
because sentiment and handoff failures

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00:14:13,200 --> 00:14:16,080
often hide inside normal reporting until the issue spreads.

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00:14:16,080 --> 00:14:19,120
If negative feedback starts clustering around one process,

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00:14:19,120 --> 00:14:21,360
one account group, or one service step,

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00:14:21,360 --> 00:14:24,400
a summary alert can surface the shift before churn shows up

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00:14:24,400 --> 00:14:26,240
in the quarterly review.

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00:14:26,240 --> 00:14:27,840
If a handoff fails between teams,

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00:14:27,840 --> 00:14:29,840
the system can flag the stuck case,

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00:14:29,840 --> 00:14:31,280
attach the customer context,

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00:14:31,280 --> 00:14:33,680
assign ownership, and move it back into motion.

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00:14:33,680 --> 00:14:36,000
That's the difference between seeing a trend later

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00:14:36,000 --> 00:14:38,320
and correcting a customer experience while it still matters

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00:14:38,320 --> 00:14:39,520
and at the executive level,

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00:14:39,520 --> 00:14:41,840
the use case gets simpler, not bigger.

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00:14:41,840 --> 00:14:43,280
Leaders rarely need another ritual

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00:14:43,280 --> 00:14:45,440
where they open 10 views and hunt for meaning.

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00:14:45,440 --> 00:14:48,160
What they need is a small number of one-line decision alerts

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00:14:48,160 --> 00:14:49,440
that say what changed,

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00:14:49,440 --> 00:14:50,480
why it needs attention,

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00:14:50,480 --> 00:14:52,400
and what decision is sitting in front of them now.

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00:14:52,400 --> 00:14:56,240
Research on executive decision-making points the same way.

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00:14:56,240 --> 00:14:59,520
Very few organizations think they're good at making decisions quickly

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00:14:59,520 --> 00:15:01,520
and one reason is that information arrives

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00:15:01,520 --> 00:15:03,360
as volume instead of direction.

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00:15:03,360 --> 00:15:05,600
So when you choose your first use case,

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00:15:05,600 --> 00:15:07,680
force the design through four filters.

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00:15:07,680 --> 00:15:10,400
Trigger, context, owner, action path,

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00:15:10,400 --> 00:15:12,080
if any one of those is fuzzy,

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00:15:12,080 --> 00:15:13,120
don't automate it yet

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00:15:13,120 --> 00:15:14,400
because the right place to start

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00:15:14,400 --> 00:15:16,080
isn't where the data looks interesting.

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00:15:16,080 --> 00:15:17,520
It's where the delay already hurts.

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00:15:17,520 --> 00:15:20,320
Governance, limits, and cost control.

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00:15:20,640 --> 00:15:23,040
Once teams see a few notification flows working,

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00:15:23,040 --> 00:15:24,800
they usually want to scale fast.

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00:15:24,800 --> 00:15:26,080
That is the right instinct,

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00:15:26,080 --> 00:15:27,920
but this is also where sloppy architecture

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00:15:27,920 --> 00:15:29,200
starts getting expensive.

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00:15:29,200 --> 00:15:31,440
First, you have to plan for metered AI use.

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00:15:31,440 --> 00:15:33,920
In 2026, AI builder credit models

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00:15:33,920 --> 00:15:36,480
are shifting toward co-pilot credit models

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00:15:36,480 --> 00:15:38,320
and that changes how you think about cost.

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00:15:38,320 --> 00:15:41,200
You cannot treat AI summarization or reasoning steps

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00:15:41,200 --> 00:15:43,440
like free decoration inside every flow.

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00:15:43,440 --> 00:15:45,360
They need budgets, environment allocation,

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00:15:45,360 --> 00:15:46,640
and constant monitoring.

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00:15:46,640 --> 00:15:48,640
Power Platform gives you capacity reporting

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00:15:48,640 --> 00:15:50,720
in the admin center and you need to use it.

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00:15:50,720 --> 00:15:52,560
Monthly resets and delayed reporting

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00:15:52,560 --> 00:15:55,520
can hide overuse until a massive bill shows up at your door.

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00:15:55,520 --> 00:15:57,120
Then there are the delivery limits.

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00:15:57,120 --> 00:15:58,640
If teams is your main endpoint,

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00:15:58,640 --> 00:16:00,640
payload size and request volume matter

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00:16:00,640 --> 00:16:02,560
more than most people expect.

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00:16:02,560 --> 00:16:06,000
Adaptive cards in teams run into a 28 kilobyte payload limit

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00:16:06,000 --> 00:16:08,560
and standard posts have their own size constraints.

439
00:16:08,560 --> 00:16:10,720
Request limits in Power Platform and Teams throttling

440
00:16:10,720 --> 00:16:12,720
can also hit you when one design gets copied

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00:16:12,720 --> 00:16:14,240
across dozens of flows.

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00:16:14,240 --> 00:16:16,400
A notification pattern that works fine in a pilot

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00:16:16,400 --> 00:16:18,800
can start failing quietly once volume rises,

444
00:16:18,800 --> 00:16:20,800
especially when every branch posts messages

445
00:16:20,800 --> 00:16:22,400
and updates records in parallel.

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00:16:22,400 --> 00:16:24,640
That is why low code is enough until it isn't.

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00:16:24,640 --> 00:16:26,000
If your flow volume is moderate

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00:16:26,000 --> 00:16:29,120
and the response path stays inside Microsoft 365,

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00:16:29,120 --> 00:16:31,680
power automate plus data verse can carry a lot.

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00:16:31,680 --> 00:16:33,280
But if you need high volume scheduling

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00:16:33,280 --> 00:16:35,760
or burst handling across thousands of notifications,

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00:16:35,760 --> 00:16:39,040
this is where Azure Functions or Service Bus start to make sense.

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00:16:39,040 --> 00:16:40,800
It is not because low code failed

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00:16:40,800 --> 00:16:42,560
but because the workload changed.

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00:16:42,560 --> 00:16:44,880
The model should stay simple for as long as it can.

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00:16:44,880 --> 00:16:47,600
Then separate when throughput or reliability demands it.

457
00:16:47,600 --> 00:16:50,080
There is also a more basic form of waste to control.

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00:16:50,080 --> 00:16:51,760
You have to watch for duplicate alerts,

459
00:16:51,760 --> 00:16:53,600
dead alerts and onalous alerts.

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00:16:53,600 --> 00:16:56,160
If a condition keeps firing after someone already acted,

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00:16:56,160 --> 00:16:57,120
you need to suppress it.

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00:16:57,120 --> 00:16:59,520
If nobody has responded to an alert type in 60 days,

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00:16:59,520 --> 00:17:01,120
it is time to review it.

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00:17:01,120 --> 00:17:03,280
If routing keeps landing in the wrong place,

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00:17:03,280 --> 00:17:05,600
fix the route instead of adding another message.

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00:17:05,600 --> 00:17:07,680
Good governance is less about publishing standards

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00:17:07,680 --> 00:17:09,920
and more about keeping the system clean after launch.

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00:17:09,920 --> 00:17:11,680
You should measure response rates,

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00:17:11,680 --> 00:17:13,920
track dismissals and watch escalations

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00:17:13,920 --> 00:17:15,920
to see which notifications lead to action

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00:17:15,920 --> 00:17:17,680
and which ones just create traffic.

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00:17:17,680 --> 00:17:18,560
So the rule is simple.

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00:17:18,560 --> 00:17:20,960
Notifications are products, not side effects.

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00:17:20,960 --> 00:17:21,760
They need owners.

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00:17:21,760 --> 00:17:22,720
They need maintenance.

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00:17:22,720 --> 00:17:24,400
They need life cycle reviews.

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00:17:24,400 --> 00:17:25,600
And they need cost visibility

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00:17:25,600 --> 00:17:27,520
because a noisy system burns money twice.

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00:17:27,520 --> 00:17:29,440
It burns money once in platform consumption

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00:17:29,440 --> 00:17:31,280
and it burns it again in human attention.

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00:17:31,280 --> 00:17:34,080
So keep the dashboard but change its job.

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00:17:34,080 --> 00:17:35,680
Use dashboards for exploration,

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00:17:35,680 --> 00:17:37,280
trend review and deeper analysis.

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00:17:37,280 --> 00:17:39,280
Do not treat them as the final operating surface.

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00:17:39,280 --> 00:17:41,120
Move action into notification flows

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00:17:41,120 --> 00:17:42,720
where the system can detect change,

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00:17:42,720 --> 00:17:44,560
route context and trigger the next step

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00:17:44,560 --> 00:17:46,240
without waiting for someone to go look.

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00:17:46,240 --> 00:17:47,440
If you want to start tomorrow,

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00:17:47,440 --> 00:17:50,560
pick one decision that still depends on somebody checking a chart.

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00:17:50,560 --> 00:17:53,680
Map the event, the owner, the route and the response.

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00:17:53,680 --> 00:17:55,200
If this changed how you think,

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00:17:55,200 --> 00:17:57,840
subscribe to M365FM,

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00:17:57,840 --> 00:17:58,800
leave a review

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00:17:58,800 --> 00:18:01,520
and connect with Mirko Peters on LinkedIn.