As a former data analyst and current solutions engineer at Sigma, I’ve worked with, or competed against, every BI tool out there. When I’m helping customers transition from Mode to Sigma, one key reason comes up over and over again. Eventually the teams asking for data outscale the analytics team, and expanding self-service becomes pivotal for success.
Self Service Comes to a Head
When I worked as a data analyst for the Washington Department of Education, I would receive a deluge of requests to slightly modify reports or simply send over a csv with the data my peers needed. As these requests piled up, I realized I spent more of my day querying a database than searching for insights within that data.
I had the bright idea that I’d simply train my team on some simple commands so that they could get the exact data they needed on their timeframe. As you expect, all I managed to do was waste an hour of everyone’s time. No one was interested in learning a new way to analyze data. They understood spreadsheets and getting a csv or xlsx file was exactly what they wanted.
Now when I talk to customers using Mode, I hear this same request often: “We need a way to enable our users to self-service”. Mode works well for folks who know SQL, Python, or R, but falls short the moment a business user wants to ask a new question. So, the analyst team has two choices; find a new tool or expand.
This is where Sigma’s spreadsheet interface comes in handy. Any user who knows Excel or Google Sheets is immediately familiar with our UI. Sigma even takes things a step further. We’re connected directly to the underlying cloud data warehouse. That means we can act on data at scale and ask incredibly complex questions—just like we would in SQL—whether or not a user knows SQL.
Complex Questions Answered in Seconds
Let's say I want to find my top 10 click events for every week in the past year. If I did this in Mode, I’d need to write everything in SQL. I know there’s no way my users would be able to accomplish it on their own, the analysis is just too complex for someone who doesn’t understand SQL.
https://sigmacomputing.wistia.com/episodes/c8a43ye4ie/details
In Sigma, my users get the best of both worlds. They can use our rank function over any time period, visualize the results, and move on. If they want to know more, all of the row-level data is still available for them, so drilling into or filtering data is a natural process from their initial question.
The implications here are twofold: First, for your internal users, there’s faster time to value. Users don’t need to wait in a ticket queue to get the insights they need at the moment. Second, if you present data to your customers, it means customer success, account managers, or any analytics platform you create can be supported by the teams that interact with your customers most.
Customer-Facing Analytics with Sigma
Every single company has a reason to show data to their customers. We call this embedded analytics and the use cases can vary massively, whether you’re a logistics company, marketing agency, or SaaS tool, your customers want insights on what your platform enables them to accomplish.
Some organizations choose to build custom tooling, but this means you’re now a business intelligence company alongside your primary value proposition. Instead, more and more of Sigma’s customers are embedding Sigma visualizations and dashboards directly into their own platform.
Sigma offers a robust approach to embedding. As often as possible, you can offer the same features you use in our tool directly to your customers. They can create their own workbooks, slice and dice, drill anywhere, and even set up their own reports all governed by the row-based security you’d expect.
While Mode offers embedding, the features they offer are far more limited. In most cases, you’ll be limited to embedding a dashboard and needing to build out additional features yourself to augment your analytics offering. Things like scheduling exports and allowing your customers to create their own dashboards are unique to Sigma and give you new ways to monetize your data.
Additionally, they store your data in their proprietary Helix engine. While this may offer some advantages in terms of performance, it means you’ll need to go through significantly more work to secure your data and monitor costs. Sigma never stores your data ensuring your investment into a cloud data warehouse is well leveraged and the data itself stays secure on your servers.
Conclusion
Sigma makes it easy for users to hop in and start to perform analysis without waiting for a data analyst to step in and start writing SQL for them. This ultimately means your analytics teams have more time to do their jobs rather than wrangle queries for the broader organization. And, when you’re ready to start embedding data into your application, Sigma is there to make the process painless and sticky for your users.
