Stop Asking Your Data Questions

You don’t discover things on Google.

You go to Google when you already know what you’re looking for: top places to go in Rome; <name’s> net worth; how to cook asparagus to impress my in-laws. You type a question, get an answer, and leave. It’s a tool for intent.

Discovery happens somewhere else. It’s Instagram showing you a restaurant you didn’t know existed in a neighborhood you walk through every day. It’s X telling you that the latest model has dropped and it’s scary good. It’s TikTok teaching you a cooking technique (en papillote!) at 11pm that you never would have searched for because you didn’t know there was a name for it.

Nobody asked for any of that. It was pushed. And it was relevant anyway.

Now look at how every company interacts with its data today: someone has a question, they query a dashboard, they get an answer. A VP pings a data analyst at 4:47pm on a Friday: “Can you pull the numbers on power user engagement for the board meeting next week?” Query, chart, send, squint, follow-up. Everyone is doing Google Search. Pull works when someone knows what to ask.

But what if the question that would actually change that VP’s behavior isn’t the one she asked? What if it’s the one she didn’t know to ask? Maybe mid-market healthcare deals are closing 40% faster this quarter and nobody’s reallocated pipeline. Maybe the Southeast rep who just hit 200% of quota is running a completely different playbook than everyone else.

Those insights don’t emerge from Google Search. They sit in the warehouse like letters nobody opens.

The two modes of knowing

The data industry has been building for one mode of interaction for twenty years. It’s worth stepping back and noticing that there’s a second mode we’ve almost entirely ignored.

“I have a question” is ad-hoc analysis. Why did conversion drop? What’s our CAC by channel? How well is our latest campaign performing? This is search. This is pull. It’s valuable, it’s not going away, and the industry is very good at it.

“Tell me what I should know” is something else entirely. It’s not answering a question, it’s telling you which questions you ought to be asking. It’s the system saying: here’s something worth your attention that you haven’t thought to look for.

Think about the difference: one is reactive: a human has a hypothesis and goes to validate it. The other is proactive: the system surfaces a pattern the human hasn’t formed a hypothesis about yet.

Twenty years of data tooling, billions of dollars of infrastructure, and nearly all of it serves the first mode. The second mode is where the highest-leverage insights live, because the things you don’t know you don’t know are, almost by definition, the things that matter most.

The things you don’t know you don’t know are, almost by definition, the things that matter most.

A conversion metric declining 3% per week for six weeks while everyone’s focused on the product launch. A customer segment churning in a pattern that only appears if you cut the data a way nobody thought to cut it. A region outperforming every other region and nobody’s discussed it once.

Those are the insights that change strategy. And they require push.

The three tiers of push

Push isn’t one thing. It’s a spectrum, and the mistake most people make is equating push with “more alerts.”

Alerts are just the loudest, most annoying flavor. There are actually three tiers, each with a different job and a different cadence.

Tier 1: Alerts

Interruptive. Buzzing phone. Something needs attention now. This is the fire alarm.

A good Tier 1 is your Slack lighting up at 2pm on a Tuesday with a note: “Step 3 of your onboarding funnel sharply dropped on iOS with the latest push indicating possible bug.”

It tells you what happened, why, and where to look next.

Most companies are stuck at Tier 1, and doing it badly. Their alerts are dumb threshold triggers with no context, no explanation, no signal about whether something is a blip or a trend. The result is alert fatigue, which is why “push” has a bad reputation. People hear “proactive insights” and picture Slack turning into an unreadable wall of bot messages.

The fix: If it’s not something someone should stop what they’re doing to address, it’s not Tier 1. Most teams have 50+ alerts. They should have 0-5 interruptive ones a day.

Tier 2: Scheduled briefings.

Predictable. Your morning newspaper for business data.

A good Tier 2 arrives at 8am with an intelligent summary that leads with what’s different: “Pipeline coverage for Q3 dropped below 3x for the first time this quarter. APAC added $1.2M in new opps overnight, offsetting a slowdown in EMEA. Three deals over $500K are scheduled to close this week, all in healthcare.” Coffee, briefing, first meeting. It replaces the ritual of opening four dashboards and trying to remember what the numbers looked like yesterday.

A dumb scheduled report shows you the same charts regardless of whether anything changed or not. A smart Tier 2 briefing knows what changed and leads with that.

The fix: Start with one briefing for one team. Define the five metrics that matter, set the delivery window, iterate on signal-to-noise weekly. If people aren’t reading it after two weeks, the content need improvement.

Tier 3: Ambient discovery.

This is the feed. Not urgent, not scheduled. There when you have a moment to browse, like standing in line for coffee and checking in on what’s interesting between meetings.

Imagine opening a feed that says: “Customers who activate Feature X within their first week retain at 2x the rate, but only 12% of new accounts are doing it.” Or: “Support tickets mentioning ‘billing confusion’ are up 60% since the pricing page redesign. Here are the top complaint clusters.” Or: “The APAC region quietly overtook EMEA in net-new ARR for the first time in three years.”

Some of these might be analyses other people have done, but hasn’t made it over to your world yet. Others are proactively surfaced by an agent.

Tier 3 is the most underbuilt tier and the most valuable. It’s where serendipity lives. It’s the Instagram of your business data: you open it with no specific intent and you leave with a better mental model of your business.

The fix: Hardest to build, easiest to test. Have your best analyst spend some time every week writing “three things I noticed in the data that nobody asked about.” Send it to a channel. If people start forwarding it, you’ve validated the concept. Now automate it.

Why now?

It’s worth asking why push is the future. The answer is because we’ve entered the agent era, and this i.

Look at what’s happening in consumer software. OpenClaw went from zero to hundreds of thousands of users in a few months. Why? Because people want agents that think and act on their behalf so they can wake up to a morning summary prepared by their agent, or get messaged when something matters, or scroll a feed of tasks their agent completed overnight. Three tiers, running autonomously, in the background of someone’s personal life.

That paradigm is coming to business data. Not as a chatbot sitting in the corner of your BI tool waiting to be asked a question but as a system that runs continuously in the background, understands what your metrics mean, and tells you what you need to know before you ask.

We’re not talking better BI, we’re talking about a different kind of software entirely: Software that operates on your behalf instead of waiting to be operated.

What this requires from you

If the future of data is automatic, the most salient question for data leaders is: “what do I need to decide so the system can run itself?” Ask yourself these questions:

1. What counts as an emergency? This defines Tier 1. Not the specific thresholds but the principle. What rises to the level of actually interrupting someone’s flow state? Is it churn above a certain rate? A deal backsliding late in the quarter? A production incident affecting revenue?

2. What does your team need to orient every morning? This defines Tier 2. If your CEO had three minutes with coffee, what would they need to know? What’s the comparison window that reveals meaningful change? What’s the right delivery time and channel?

3. What parts of the business should the system be watching? This defines Tier 3. Is it monitoring user behavior? Pipeline health? Feature adoption and retention? Support trends? Revenue mix? The more territory you hand over, the more your team will discover.

These are questions of strategy and business context. Your job should be to tell the system what matters, and the system intelligently mines the depths to tell you what’s happening, why, and what you may want to do.

Where this goes

Five years from now, the idea that a human had to open a dashboard and manually notice a problem will feel as quaint as printing a spreadsheet to review in a meeting. The daily rhythm will be: Tier 1 for emergencies, Tier 2 for morning context, Tier 3 for discovery, and ad-hoc pull for the occasional deep dive.

The question stops being “did you check the dashboard?” and becomes “did you see the latest?”

The future of data is push. Not more dashboards. Not faster queries. Not better charts. A fundamentally different relationship between people and the information they need to make decisions.

This is what we’re building at Sundial.ai. Pull isn’t going away, but it’s going to stop being the center of gravity.

Google Search had its decade; now it’s time for the feed.