Evaluating business intelligence tools in 2023 can be quite the undertaking. Visualization-type checkboxes are just the start! For what can sometimes be referred to as a “commodities market”, BI tooling varies wildly from deployment models, expertise requirements, all the way to different support packages.
Once you peel back what’s behind a bar chart, each software vendor comes with its own backstory that adds color to their unique approach.
All-in-One, One Managing All
Sisense (Founded in 2004) is a great, all-in-one, BI, ETL, & DBMS tool with a strong assortment of connectors to data sources. They have built a strong book of midsize businesses with full-fledged embed capability.
If you don’t have a data warehouse, or an ETL/ELT tool — Sisense can fit the bill. A BI developer with expertise in Sisense’s Elasticubes can run a successful deployment given the right support from the business with clear expectations of dashboard requirements. All-in-one tools can be great if you have the particular expertise to manage all their components. The problem is they only go so far.
Enter Modern Data Stack Requirements
Common data trends today can complicate a single-tool approach:
- Companies have more data than ever.
- Cloud data warehouses are ubiquitous.
- Business users want the ability to explore and analyze data, without having to wait for a data engineer to prepare it for them.
The paradigm of having to move data from your systems of record to a data warehouse and then onceagain to a BI platform has become an increasingly irksome hop, skip, and jump.(Especially for an exploratory, high-velocity use case). The time between the data landing in the cloud data warehouse and your business users’ accessing it can make or break the relevance of an insight.
Lift and Shift Baggage Claim
Sisense has two versions of on-prem products (Windows & Linux) that they reliably support and maintain. The core product includes not just the front-end BI, but also their Elasticube technology. Sisense cloud deployments are a “lift & shift” of the same architecture. A cloud deployment assumes at least one dedicated server per client. Each additional server per deployment is a cost that Sisense has to take into account with their licensing to maintain profit margins, meaning they have to eat the cost or push it down to the customer.
Cloud-native technologies do not have to worry about the same kind of overhead. Sigma, for example, leverages cloud data warehouse compute cost instead. As a lightweight SaaS application that deploys without physical servers or virtual machines, Sigma has incredible flexibility to scale to thousands of users overnight on the same instance. This magnitude of difference between the scalability of on-prem software and cloud-born tools may be why some folks are moving on before even waiting for their bag to drop.
Key Evaluation Questions
What is my cloud data warehouse strategy?
Do I have the right support & staff for an all-in-one solution?