TLDR; Cube now has a first-party agentic analytics frontend that deeply integrates with our semantic layer and is designed from scratch to support AI-augemented workflows.
Since open-sourcing in 2018, Cube has been a headless universal semantic layer. We even had "Headless BI" as a slogan on our website back in 2022. When we started Cube, the main use case was embedded analytics. Our customers would build the data model in Cube and use it to power charts or dashboards inside their apps via our APIs.
Later, we added integrations with different BI tools via our SQL API - a Postgres-based SQL interface extended to support querying multidimensional data. SQL API enabled our customers to run different BI and visualization tools on top of Cube.
Cube users were constantly asking us whether we'd consider adding our own frontend. We were slow to do that because, up until recently, we were unsure of how our business intelligence frontend would be unique and stand out in the market. That has changed with the arrival of modern AI. We believe that AI will change the landscape of data tools similar to how other major technological shifts have done it in the past. We also believe that Business Intelligence as a category of software won't disappear, but rather transform into Agentic Business Intelligence.
What is agentic business intelligence?
In the Business Intelligence workflow, we have three key functions: Modeling, Exploration and Presentation.
Modeling is a process of defining the semantic data model, that includes measures, dimensions, joins, etc. Historically that has been done in a drag-n-drop UI. Looker pionereed code-first data modeling with LookML and, since than, that approach has gained more traction. Many modern companies, including Cube, offer code-first semantic modeling.
In exploration we slice and dice data to find and curate insights. That is usually done in a Workbook-style interface and considered to be one of the core BI experiences. During exploration, user can produce multiple reports. Most of them would be thrown away, but some of them will made it to the next step to be shared across organization.
Presentation is about organizing insights into data artifacts that can be shared across organization, that usually takes the form of dashboards.
These three functions are quite diverse
Idea behind agentic business intelligence is to augement these three functions of BI workflow with AI. It is based on two key architecture principles:
- AI can do everything humans can do in BI either reactively or proactively
- Humans have fine-grain control over the result of every AI action
In Modeling, AI can bootstrap new data models or modify existing ones, with humans maintaining the authority to reject, partially accept, or correct these changes as needed. During Exploration, AI collaborates with humans in workbooks to construct and refine reports, accelerating the journey from data model to insights. In Presentation, AI assists in creating and designing customized visuals and dashboards that align with the organization's aesthetic requirements.
That workflow can be interactive, where AI is prompted by a human to work on data artifacts. The human sees the results and makes corrections either via UI or via continuous prompting. Alternatively, it can involve AI working in the background to produce useful changes to the data content and then sharing these with humans.
Ultimately, it leads to increased productivity of data professionals and lowers the entry barrier to using business intelligence for broader knowledge workers.
What are the benefits?
Similar to what is happening in software engineering, AI can both increase the productivity of professionals and lower the entry barrier to the technical field.
The former would enable data teams to move faster. At Cube, we have been in the business of semantic modeling since 2018, and I can tell you based on my experiences - everyone loves semantic layers and wants to have one. No one likes to build and maintain them. Not only is it an intellectually complicated task, but it also requires a lot of mundane work to get it up and running across systems. We have a unique opportunity now with AI to significantly streamline this process. AI can take on a lot of mundane work, but also acts as an omniscient helper ready to pull any reference knowledge, best practice example, or specific how-to guide on how to solve a particular analytics problem in the particular data modeling framework.
The latter, lowering the entry barrier to using business intelligence for broader knowledge workers, is all about self-service analytics. Almost no data leader is satisfied with level of BI and analytics adoption in their organization. While we are constantly searching for ways to increase it, it only improves on the global scale during the transformative technological changes. I believe AI is one of them. It can enable knowledge workers to slice and dice data to find insights and build complicated visualizations just by asking AI to do so. No need to go through Tableau certification process and search Youtube for a video on how to make that funnel chart in your BI tool.
But what if AI hallucinates and creates wrong insights? That is where we need a semantic layer more than ever. That is the exact reason why we are building a BI layer for Cube today.
Why Cube?
Semantic layer is not a new concept. It has been in and around BI world for decades. Some's BI tools semantic layers are better than others, but almost every one has it. It enables safe self-serve analytics , at least, through the foundational governance layer. Semantic layer exposes explorable objects (explores in Looker, views in Cube) that are safe to use in the BI tool UI. It means data consumers can safely slice and dice these objects to create insights without a risk to create wrong analysis. These safe zones are guarded by semantic layer and can provide foundation for exploration and presentation workflows to data consumers.
While having a semantic layer in BI comes with a lot of benefits, there are some practical implementation challenges:
- Semantic layer would constantly require updates and changes. Data reflects the business and business is not static. Data consumers are restricted to analyze only what they have in semantic layer, therefore they will constantly request more dimensions and measures in explorable objects and more new explorable objects.
- That might easily lead to explosion in amount of explorable objects to a point where it would be impossible to navigate them.
This creates a situation where it is difficult for data consumers to understand which object to use for exploration. Similarly, for data teams, it is challenging to understand what objects are being used and how.
We believe AI can help navigate this inevitable BI complexity from both sides. First of all, it can increase productivity of the data team in managing semantic layer, from rapidly adding new definitions to deprecating and sunsetting old ones. For data consumers, AI can search within the semantic layer to find the relevant models and build insights from them.
Next Steps
Today, we are just scratching the surface of what is possible. While many in the industry are focused on Text-to-SQL, I see the biggest unlock in applying AI to solve BI complexity and workflow problems.
If you like to learn more, join one of our upcoming Inside Cube product demo series or reach out to request demo of what we are building.