March 27, 2023

How Companies Evaluate Between Sigma and Domo

Don't get caught using the dated Domo model, upgrade to modern analytics with Sigma

There’s no such thing as a perfect analytics or business data tool. Across the industry of data analytics and data visualization, there are dozens of different approaches to scratching the itch. But by and large, there are many common anti-patterns that have developed over time that simply don't meet the needs of modern data teams — and in turn, modern business teams. And while Domo was a decent tool for accomplishing analytics, it simply doesn’t meet the needs of teams trying to quickly, securely, and scalably work with data.

Your data doesn't stay in the warehouse, when it really should

In today’s data-driven world, businesses are constantly seeking more efficient and effective ways to analyze and derive insight from the ever-growing amounts of business data. In the realm of Business Intelligence (BI) and data analytics, the efficacy of a platform often hinges on its ability to handle the data processing and storage of data. In Sigma, our cloud-native architecture that’s 100% web-based lives on top of your data warehouse and uses machine-generated SQL to directly interface with data in your CDW in real time. In contrast, Domo’s data model requires you essentially ETL your warehoused data into a secondary storage layer on Domo’s own infrastructure. The results? Additional management overhead for your data teams, more issues in understanding the underlying data model for your analysts, and the inability for business users to actually work with real-time, live production data.

In short, Sigma's commitment to keeping business data within the data warehouse, combined with its cloud-native architecture, delivers a more secure, efficient, and scalable BI solution compared to Domo. Businesses seeking a sophisticated approach to data analytics can rely on Sigma to effectively manage and analyze their data without compromising the integrity of their data warehouse.

You need “analytics tools experience” to really use it

Domo was founded shortly after many of the most well known BI and analytics companies out there and is built on a similar “only for analysts” model. A self-reported solution to the issues teams faced early on with legacy analytics tools, Domo’s user interface and experience working with the tool is still highly complex and requires the expertise of trained analytics professionals to navigate it’s proprietary Magic ETLC interface and multiple point connections. With Sigma, the entire organization can immediately get value via our spreadsheet-style interface, letting both analytics pros and business teams explore, analyze, calculate and model their data directly in the warehouse the same way they would in Excel or Google Sheets. Sigma supports familiar formulas and functions, facilitating a much higher rate of adoption across the organization than traditional analytics tools like Domo.

The Domo data model is simply out-of-date

Sigma and Domo differ significantly in their approach to data modeling, with each platform utilizing distinct methodologies to structure and analyze data. Sigma's data model emphasizes flexibility and real-time interaction with the data warehouse, allowing users to explore and transform data directly within the warehouse without the need for pre-modeling. This approach not only reduces the complexity of the analytics process, but also ensures that users are working with the most up-to-date information. In contrast, Domo's data model relies on a proprietary Magic ETL interface, which necessitates a separate extract, transform, and load (ETL) process to prepare data for analysis. This method can introduce additional complexity and potential delays, as data must be appropriately transformed before business users can access it, even when similar transformations have already been performed with tools like dbt.

The key differences between Sigma's and Domo's data models:

Sigma:

Allows direct interaction with the data warehouse without pre-modeling.
Reduces complexity by eliminating the need for a separate ETL process.
Ensures users work with the most up-to-date data.

Domo:

Utilizes a proprietary Magic ETL interface for data transformation.
Requires a separate ETL process, introducing additional complexity and potential delays.
Necessitates data transformation prior to user access, even when similar transformations have already been performed.

The important take-aways;

Intuitive Interface: Sigma's user-friendly spreadsheet UI, with familiar formulas and functions, enables users of all skill levels to effectively work with data, promoting a higher rate of adoption across business teams.
Direct Data Warehouse Interaction: Sigma allows users to query and analyze data directly within the data warehouse, ensuring real-time access to the most up-to-date information without the need for data relocation.
No Pre-Modeling Required: With Sigma, users can freely explore and transform data without pre-modeling, simplifying the analytics process and empowering non-technical users to work with massive datasets.
Cloud-Native Architecture: Sigma's cloud-native design enables lower costs per user at scale and rapid deployment of innovative features, such as Drill Anywhere, Live Edit Collaboration, and "Bring Your Own Data" with Input Tables.
Enhanced Data Security: By keeping business data within the data warehouse and not moving it into its own infrastructure, Sigma improves data security and governance.
Flexibility in Data Curation: Sigma's flexible data curation allows users to endorse and curate datasets for collaborative analytics on trusted data, fostering a more collaborative and inclusive analytics environment.
Seamless Integration with Existing Tools: Sigma provides the option for analysts to leverage their existing expertise in spreadsheets or SQL, saving time and resources while ensuring a smooth transition for users.
Reduced Complexity: Sigma eliminates the need for a separate ETL process and multiple point connectors, streamlining the analytics process and making it more accessible for business users and analytics professionals alike.

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