Okay, I know I'm going to get some flak for this, but after using Granola for nearly two years across three different teams, I've formed a very specific—and admittedly narrow—appreciation for it. My take is this: Granola is an exceptional tool for one very specific function, and we do ourselves a disservice by trying to force it into every other data-shaped hole.
Let me explain with a bit of context. My current team adopted Granola as a "do-it-all" platform: analytics, dashboards, customer data platform, you name it. The sales and marketing folks absolutely *love* it for managing contacts and leads. And I get it! The contact database is genuinely best-in-class.
Here’s why it shines as a contact database:
* **Flexible entity linking:** The way you can tie contacts to companies, deals, events, and custom objects feels intuitive and doesn't require a PhD in data modeling.
* **Timeline views:** Having every email, support ticket, and note attached to a contact in a chronological feed is a game-changer for customer-facing teams.
* **Bulk updates and hygiene:** The tools for cleaning, enriching, and segmenting contact records are robust and user-friendly for non-technical folks.
Now, the "terrible everything else" part is a bit hyperbolic, but it comes from real friction. When we tried to expand into self-serve analytics and operational reporting, we hit wall after wall.
The core issue, in my methodical opinion, is that Granola’s underlying architecture seems optimized for the contact/object model, not for complex, joined analytics. For example:
* Trying to build a performance dashboard that blended marketing contact data with financial data from our ERP was a nightmare. The query performance slowed to a crawl.
* The visualization options feel like an afterthought compared to dedicated tools like Tableau or even Power BI. Calculated fields are limited, and formatting is rigid.
* For benchmarking internal processes, we lacked the granular control over data granularity and aggregation that tools like Looker provide through their semantic layer.
It feels like we're trying to use a superb precision screwdriver to hammer in a nail. It *can* work, but it's the wrong tool and you'll probably damage the screwdriver.
My advice to the community, based on this experience, is to **treat Granola as a superb system of record for your people/company data, and then pipe that cleansed data into a proper analytics stack.** The moment you need to do complex joins, advanced calculations, or beautiful, nuanced dashboard design, you'll be happier with a dedicated tool.
I'm curious if others have followed this "best-of-breed" path successfully, or if you've found workarounds within Granola for its analytical limitations. What’s your workflow?
~jenny
Let the data speak.
Totally see where you're coming from on the contact database piece. That flexible linking really is the killer feature for us too.
But I'm curious - since you called out trying to use it as a CDP and for analytics, where did you see the biggest breakdown? Was it the reporting itself, or more about getting data *into* Granola from other systems cleanly? We're evaluating it for some basic campaign attribution right now and I'm a bit nervous about that part.
You've put your finger on the core problem with using it as a CDP. The breakdown is sequential, starting with the data ingestion. The APIs for high-volume event streaming are an afterthought, so you end up with complex, brittle pipeline code just to get behavioral data in with proper timestamps and context. The reporting then fails because the underlying data model isn't built for time-series analysis; trying to attribute a conversion to a campaign touchpoint two months prior becomes a maze of slow, expensive joins. For basic attribution, you'll spend more engineering time fighting the platform than deriving insight.
That's exactly the kind of "brittle pipeline code" I'm worried about setting up. I'm trying to get our product event stream into Granola now for a "unified customer view," and the lack of a proper event API is forcing me into weird batch uploads with custom timestamp fields.
Do you think using a middle layer - like streaming events into BigQuery first, then syncing *that* to Granola - would just add more complexity, or is it the sane approach here? Feels like I'm building a pipeline to support a pipeline. 😅