TL;DR
Every second deployment of Honeydew today is to help an AI agent to understand a business. This is changing us and the whole industry around us – our plans, our priorities, and our pricing.
Honeydew’s Job in 2024
Organizations use Honeydew to create a source of truth for diverse metrics and data sources: from traditional financial and product metrics like gross revenue or user conversion to highly business domain specific metrics, like drug trial success rates or image processing efficiency.
Even internally at Honeydew, we use Honeydew – to analyze our website (using Google Analytics data in our Snowflake), understand our customers (with data flowing to Snowflake from HubSpot), and improve our product (using our product analytics data).
By having a semantic layer – a source of truth for business logic – every data tool and every user can get ad-hoc access to trusted data. When they need it, in the way they need it.
Traditionally, a semantic layer powers BI: reports and dashboards. Users equipped with tools like Microsoft Power BI or Tableau use Honeydew to slice and dice metrics directly from Snowflake.
Now, AI is here and it’s updating the game.
The Value of a Semantic Layer in the world of AI
With AI, relevant data can always be ready, with a simple Slack or Teams question.
Instead of searching in a catalog – ask AI.
Instead of browsing through a BI dashboard – skip the dashboard. Ask AI.
Instead of drilling down in data – ask AI to drill down.
Instead of begging an analyst to export data to Excel – order your AI to do it. Or ask nicely.
And it isn’t just about efficiency or productivity. When the data is somewhere in some dashboard, many users will never access it. It’s far, or complex, or intimidating. Simpler access means more people use data, make informed decisions, feel better prepared and more confident.
Which is only possible if the AI your run to get data understands your business well enough.
Creating a source of context for AI becomes one more job of a semantic layer. Our job.
AI is BI is AI is BI
Have you ever wondered what would happen if a user asks a question and gets a different number than the one she knows from the dashboard?
Data teams we talk to often wonder about that. And their conclusion is whenever that happens to a user, she’ll think both numbers are wrong. And then blame them. The data people.
When people talk about consistency with AI, they talk about consistency between subsequent questions. But a consistency that is even more important is consistency with other tools. That’s the type of consistency that builds trust.
People expect to see the same numbers in BI and AI. Sometimes to verify, sometimes when they are second guessing themselves. And sometimes because for some tasks and for some people – dashboards and reports are the best tool to dive into data, not AI.
That means that our job of being a source of truth for BI is as strong in 2025 as it was in 2024. We’ll make your Power BI fly with your metrics on a billion events in Snowflake.
But we’ll also make them match when you get them from AI.
A Pricing Model to Fit AI
With AI, users in an organization are more empowered every day. Some users can just ask an AI chatbot a question once a month. Some refresh a dashboard every 5 minutes.
The value of a semantic layer is helping everyone get access to data. That one question a month might give the salesperson the data point he needed to push for a $100,000 renewal. Or a custom support rep to detect an obscure issue in an hour instead of waiting a week for an engineer.
Semantic layers are about helping people. The more people we help, the more value an organization gets. The value of a semantic layer is by having data available whenever you need it. Whether its an ad-hoc natural language question, an ad-hoc Excel Pivot Table report or an ad-hoc Pandas dataframe.
So our pricing unit is now users and not queries, as it was in 2024. Anyone can use AI, use BI, use API – any time, any way, without limits.
Our Roadmap for 2025
As we go into 2025, we see a lot of work ahead of us in enabling a semantic layer as a foundation for an AI that understand the business, and a foundation for BI teams at large.
We will,
- Add more BI tool integrations (we have over 20 today).
- Add even more modeling capabilities (like time intelligence).
- Add more ways to use natural language with data – with Slack, Teams, Email or custom tools.
- Add more advanced management features for access control, audit and governance.
There are core workflows where we want spend our product efforts on –
- Create a workflow for data teams to build business context for AI. We’ll help data modelers identify user questions that underperform to pro-actively improve the AI.
- Improve validation and evaluation framework to keep AI and BI at bay as semantics change. When a modeler modifies what a metric represents, they need to see how that reflects on the user base.
- Integrate more across the full flow of data – from sources to target tools, to help data teams understand better how their semantics and data are being used.
Last but not least, we are also exploring ways for teams to build semantic layers faster – by leveraging AI to to extract semantics from existing sources, whether they are SQL queries, wiki pages or BI reports.
An exciting year is ahead of us!
Happy New Year,
Team Honeydew
