Imagine working with cloud data that actually works, without the complexity old-school business intelligence requires. That’s the dream. That’s the Sigma reality.
Sigma is a cloud analytics platform that uses a familiar spreadsheet interface to give business users instant access to explore and get insights from their cloud data warehouse. It requires no code or special training to explore billions of rows, augment with new data, or perform “what if” analysis on all data in real time.
Absolutely. Legacy vendors in analytics have been in the market for a long time—decades, in many cases. They have great features, but they’ve all struggled mightily with the rapid customer adoption of the cloud. Additionally, data volumes that used to be on-premise (and somewhat manageable) are now in the cloud—and have become massive. Think: billions of rows and columns.
Legacy vendors have large amounts of technical debt, and may also have to support on-premise implementations. Adding a cloud option to their portfolio means refactoring their architecture to address how the cloud works. This is unmanageable, and we’ve seen this play out in the market with vendor consolidations, acquisitions, and other struggles to support customer requirements.
From day one, Sigma was built for the cloud as well as massive data scale with a familiar interface. We take full advantage of modern cloud-design principles and data warehouses to deliver scalable performance—even in billion-row use cases.
Customers of all sizes have adopted Sigma for these and many more reasons.
Legacy analytics platforms worked fine when the data was small, and only limited insights from rolled-up data was needed. Analysis was usually done by people with deep analytical expertise. But then the cloud data warehouse entered the scene. The modern cloud data warehouse became popular in the mid to late 2010s. Cloud-based data warehousing solutions started to gain traction as companies increasingly realized the benefits of moving their data storage and processing to the cloud.
For sure!
One example is Input Tables, which are really unique and powerful for business users who want to do their own ad-hoc scenario modeling of existing data with values they provide, outside of existing data.
When data isn't in the warehouse, it usually requires a cumbersome technical and people process to ETL data into the warehouse. Now users who need to add data to the warehouse are able to do so directly.
Sigma customers already use Input Tables for:
Manual data entry of key values
Analytic Modeling
Scenarios
Forecasts
Territory Planning
Sales Planning
Supply Chain Modeling
For example, a Sales Director may want to see the effect of changing Connecticut from the New England territory to the Mid-Atlantic. Input Tables allows them to make that adjustment and see how it could change forecasted revenue by observing the BEFORE and AFTER bar charts.
It’s important to understand that this does not change the data in the warehouse. Input Table data is always persisted in the warehouse separately, and no data is ever held by Sigma.
In a word, Sigma support is fanatical. So much so, support is directly in the product, including live chat with a Sigma support associate. Sigma customers enjoy working with us rather than sitting frustrated and waiting for a response.
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