Analytics for people who have work to do.
Joined February 2024
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It's funny how often "data-driven" companies still make decisions based on whoever talks first in a meeting.
The data usually exists.
The answer usually exists.
Neither arrives fast enough.
The biggest competitor to most BI tools isn't another BI tool.
It's asking a coworker.
Think about it.
When people need an answer quickly, they rarely open a dashboard first.
They message someone.
The winning analytics products will be the ones that are easier than asking a colleague : )
Insights live in silos.
The data is in one place.
The dashboards are in another.
The conversations happen somewhere else.
Nobody has the full picture.
It was designed around data, not decisions.
This is the biggest one.
Most BI tools were built for exploring datasets.
Modern teams care about making decisions faster.
The future of analytics isn't more dashboards, more filters, or more charts.
It's reducing the distance between a question and an answer.
It takes longer to find an answer than to ask a colleague.
If people trust Slack messages more than your BI tool, you've already lost.
It expects everyone to know SQL.
SQL isn't going away, but business users shouldn't need to learn database syntax to understand their business.
The evolution of business intelligence over the years:
2010: Companies built dozens of dashboards and waited weeks just to get basic answers.
2015: Everyone pushed self-service BI but teams were still stuck waiting on analysts for reports.
2020: We had too many tools yet almost no one could actually make fast decisions with data.
2023: ChatGPT arrived and suddenly people wondered why they couldn't just ask questions to their own business data.
2026: You type one plain English question and get instant insights, charts, and answers.That's exactly why we built Supaboard. Natural language analytics that actually works for non-technical teams. No SQL.
No tickets. No long waits.Just real answers in seconds.
What year does your company's data setup still feel stuck in? Drop it below.
Something I've noticed over the last year:
Companies are collecting more data than ever, but most employees still struggle to answer basic business questions.
How many customers did we gain last month?
Which channel is performing best?
Why did revenue dip this week?
The issue isn't a lack of data. The issue is that accessing it is still harder than it should be.
We've spent years solving storage, infrastructure, and visualization. The next challenge is making information actually accessible to the people who need it.
Most teams still wait days for a dashboard. We built Supaboard so anyone can ask “Why did revenue drop last week?” in plain English → get charts + why + what to do next.
No SQL. No data team. Just answers.
Try it today supaboard.ai
The most interesting AI story right now isn't a new model.
It's electricity.
Every week there's news about bigger data centers, more chips, more infrastructure, and larger investments. The industry is pouring hundreds of billions into AI infrastructure.
A few years ago the question was:
"Can AI actually do useful things?"
Today the question is:
"Can we build enough infrastructure to keep up with demand?"
That's a huge shift.
The bottleneck is no longer ideas.
It's compute. Power. Data.
The companies that win the next phase of AI may not be the ones with the smartest models.
They might be the ones that can actually run them at scale.
The biggest lie in analytics:
"We need more data."
Most of the time you don't.
You need:
• Better definitions
• Better visibility
• Better questions
Collecting more data is often just delaying a decision.
One prediction:
In a few years, "building dashboards" will sound a lot like "writing HTML" does today.
Still useful.
Still important.
But not something most people do directly anymore.
The interface will change, the need for insights won't.
What are your thoughts on this?
Creators spend hours learning complicated tools.
Imagine if the tool was the easy part.
That's the future we're building!
Everyone is talking about AI replacing analysts.
I think the bigger change is something else.
For the first time, non-technical people are starting to ask data questions directly.
- Sales wants to know why conversions dropped.
- Marketing wants to know which campaign is actually working.
- Founders want revenue breakdowns.
Historically, every one of those questions had to go through someone.
A data analyst.
A BI team.
A SQL expert.
The bottleneck wasn't the data.
It was access.
Now we're moving toward a world where asking a business question becomes as simple as sending a message.
That doesn't make analysts less important.
It makes them more important.
Because when everyone can ask questions, someone still needs to make sure the answers are correct.
The future of analytics isn't fewer analysts.
It's fewer barriers between people and data.
And that's a much bigger shift than most people realize : )
