Are you finding a solution to extract actionable insights from massive business datasets in just one go with an all-in-one cloud analytics solution? It is Sigma Computing that will help you.
Sigma Computing, a real-time cloud analytics app, is changing the data analytics space. Simply connect your data to the tool online and select a pre-built template to create awesome data modeling, data dashboards, data visualization, and big data analytics in a few minutes. Yes you heard it write you don’t need to write a single line of code.
Sounds interesting to you? Continue reading this ultimate review of Sigma Computing to learn exactly what it is, its best features, the industries it serves, use cases, and competitor comparison so you can make data-driven decisions when subscribing to a cloud analytics service.
What Is Sigma Computing?
Sigma Computing is a cutting-edge cloud analytics platform for data analytics, visualization, data collaboration, and business intelligence (BI) online. It has a user-friendly spreadsheet interface, so if you know how to use Excel or Google Sheets, Sigma Computing won’t be hard for you.
With this no-code cloud analytics tool, you can effortlessly delve into your data in cloud data warehouses (CDW) like Snowflake, Databricks, Google Big Query, PostgreSQL, any CSV database, and all other CDW. It produces quick data insights using templates or custom workflows.
Role of Sigma Computing as a Cloud-Native Analytics Platform
As a cloud-native data analytics and BI tool, Sigma Computing makes your data analysis projects a lot easier and more affordable. If you're a small and medium business owner without a separate data science team, Sigma Computing is your go to solution. You can create the outputs similar to large enterprises without spending millions of dollars on hiring data science teams.
Also, legacy data analytics tools like Excel, Google Sheets, Looker, etc., are still complicated for data analytics on the cloud without maintaining an on-premise or local database.
Sigma Computing offers the plug-and-play and drag-and-drop feature for data analytics, report creation, and sharing. With plenty of templates, it will surely fit all of your business requirements for data analytics formats and styles.
Benefits of Sigma Computing
Check below the advantages you enjoy when you use Sigma for all tasks related to cloud data analytics and business intelligence over your competitors who use legacy data analytics tools:
- It's the easiest cloud data analytics platform as compared to the legacy tools.
- Being a business owner or manager, you can analyze, visualize, and extract actionable insights from massive databases yourself.
- Sigma lets you get the smallest and most granular details of your business datasets.
- It also allows you to create high-level data visualizations that the general audience, investors, and shareholders can understand.
- You can work on a familiar interface, which is Excel-like spreadsheets. So, you don't need to invest time and resources in learning a new tool. It applies equally to your employees. You don't need to invest in training a group of employees in a new tool. They're all familiar with the UI of Sigma since they've worked on Excel and Google Sheets.
- If you don't have the time to wait for a data analytics project cycle to wait for insights, Sigma is your go-to app for BI.
- You can share the Sigma workbooks with external and internal collaborators for collaborative data analytics.
- It comes with stringent data security and encryption protocols to safeguard sensitive customer and financial data.
- Moreover, you can create data governance policies for individual employees and contractors using Sigma.
Now, let's explore the best features of sigma computing.
Best Features of Sigma Computing
Here are the features of Sigma that are making a lot of buzz in the cloud data analytics market:
Data Connectors
Sigma offers variety of data connectors, allowing you to import datasets from all modern CDWs and analyze your data in real time. In contrast, traditional data analytics tools would take days to do the same operation.
Sigma supports the following CDWs at the time of writing:
- Snowflake
- Amazon Redshift
- Google Big Query
- PostgreSQL
- Databricks
- AlloyDB
You can also host your databases on any of the following cloud platforms and import them to Sigma:
- Google Cloud
- Amazon Web Services (AWS)
- Microsoft Azure
To communicate with the database using a data connector, you must first provide a connection string. The string could contain information such as the server address, user ID, password, database setups, security policies, etc.
Sigma also manages the Refreshing and Closing of data source connections. As a result, after you finish the current database querying operation, there is no need to spend time establishing up a new connection.
Data Modeling
Sigma's Data Modeling functionality allows you to generate customized reports and dashboards based on your unique business logic. The Sigma UI's Dataset functionality allows you to create custom data models, such as the following:
- Create calculations
- Joining more tables
- Extracting JSON from datasets
- Filtering your dataset
- - Relative Date Filters
- - Text Filters
- Link tables
- Add badges like Endorsed, Deprecated, Warning, etc.
You can save a newly built data model as a template for later usage. These data model templates can also be easily customized by adding new metrics by referencing them in the formula bar or dragging and dropping them from a column.
Its Materialization feature allows you to save dataset modifications in the data warehouse as tables.
Embedded Workbook and Analytics
Workbook embedding allows you to present your workbooks and data pieces in a variety of mobile apps, web apps, and webpages. These could refer to your internal or external qualities. Your embedded data will always be up to date, synchronized with changes in your data warehouse.
The embedding analytics are applicable at the workbook, single dataset page, and individual element levels.
If you are an organization administrator, you can select among three embed types based on your requirements. Sigma embedding supports three types: public, private, and user-backed embedding.
Data Visualization
You can add visual context to your CDW datasets on Sigma with just four clicks using various visualization elements. It allows you to quickly construct visual settings with a table, pivot table, and linked input table. You can also access more visualizations through the Visualization menu.
It includes 14 various data visualization items, such as bar charts, KPI charts, scatter plots, pie/donut charts, gauge charts, geography maps, and many more.
The Custom Configurations feature allows you to further customize these objects through the Properties and Formatting menus.
The Element Properties menu allows you to change axis categories, tooltips, colors, metrics, data aggregation, chart orientation, and other settings.
In contrast, the Element format makes it easier to alter text, backdrop, data labels, data references, trend lines, legends, and so on.
Sigma AI
The Sigma AI is a Generative Pre-trained Transformer designed for natural language data analysis. Rather than generating data models and visualizations manually, you can instruct the Sigma AI tool to do so for you by explaining your requirements in normal English.
In addition, our data analytics AI can quickly identify, autofill, clean, and extract data tables. There's also an AI chatbot to help you learn more about Sigma AI's capabilities.
Input Tables
The Sigma AI is a Generative Pre-trained Transformer designed for natural language data analysis. Rather than generating data models and visualizations manually, you can instruct the Sigma AI tool to do so for you by explaining your requirements in normal English.
In addition, our data analytics AI can quickly identify, autofill, clean, and extract data tables. There's also an AI chatbot to help you learn more about Sigma AI's capabilities.
Online Collaboration
Sigma Computing eliminates the need to copy and paste text from your data analytics workbooks into emails. You may simply share the workbook with authorized colleagues to change it, explore data patterns, and share information.
Its collaboration feature includes the following functionalities:
- Capture a screenshot of an element and annotate
- Save image annotations as element comments
- Live edit of workbook with collaborators
- Share a folder
- Commenting on workbooks
Security and Governance
Sigma does not cache, extract, or store your data during transit. Your data never leaves your warehouse. Furthermore, all actions on Sigma are encrypted using SSL protocols.
Role-based access policies allow different employees or contractors to view the same workbook. For example, as a business owner, you can drill down to the smallest dataset hierarchy to get a performance dashboard. In contrast, a sales agent can only see a top-level dashboard with sales performance data. They don't know where the sales data come from.
Sigma supports data compliance requirements including SAS70, GDPR, HIPAA, AWS Private Link, CCPA, Privacy Shield, CSA, SOC 1 Type II, SOC 2 Type II, and SOC 3.
Sigma Computing For Various Industries
This awesome cloud-native data analytics tool is suitable for any business and industry. However, the following are the popular sectors that utilize Sigma Computing:
1. Marketing Analytics
- Analyze customer touchpoint performance using metrics like bounce rate, customer acquisition cost, and average time on page
- Optimize marketing campaign targeting and costs by analyzing data by ROI
- Track brand engagement by traffic, search volumes, etc.
2. Sales
- Perform accurate and fast revenue planning
- Quickly handle customer churn threats
- Create insights on upsell opportunities
- Create a commission dashboard for sales agents
3. Retail and CPG
- Analyze inventory status and forecast inventory for special sales events and seasons in real-time
- Create customer buying journeys by connecting Sigma with data warehouses that store data from various customer touchpoints
4. Financial Services
- Model portfolio risk per exposure
- Create governed access to the company financial performance data for the valuations team on Snowflake
- Create easy-to-understand dashboards for clients
- Risk analysis, investment analysis, and trader analysis
5. Healthcare
- Healthcare providers can minimize leakages in health insurance expenses
- Monitor and process claims accurately and prevent fraud
- Effective and effortless Clinical Data Management (CDM) for research institutions
Now, we will explore the use cases of sigma computing.
Use Cases of Sigma Computing
Revenue Planning
One of the most common applications of Sigma in business is revenue planning. It may feature a sales performance deep dive table to provide insight into quarterly sales and revenue earnings.
You can use this tool to set revenue objectives and forecast revenue. By examining the difference between these two indicators, you may determine whether you need to increase sales efforts.
Marketing Campaign Performance Tracking
This Sigma use case focuses on three important marketing campaign components. These are:
- First-touch data analysis to monitor conversion rates and lead generation
- Analyzing marketing campaigns by exploring important metrics with preset filters
- Monitor customers, sales, leads, conversions, contacts, and their trends regularly on a dashboard
Snowflake Cost Monitoring
Sigma can help you track the costs for managing databases on CDWs like Snowflake. You can use a worksheet to import data from your Snowflake account. Then, connect the workbook calculation results to a dashboard object to track the following:
- Credit usage
- Cost for contract and storage
- Total usage
- Monthly spend
- Usage Statement
Comparison of Sigma Computing With Competitors
Looker
Looker is Google's search engine that extracts actionable insights from raw corporate data. It enables you to analyze data and generate visuals from raw data on the cloud.
Sigma, on the other hand, is simpler and less expensive to use than Looker. To generate actionable insights using Looker, you must hire a skilled LookML developer. Sigma, on the other hand, allows you to complete the process entirely on your own utilizing templates and Sigma AI.
Looker data models have higher maintenance costs than Sigma.
Domo
Domo allows you to construct custom business apps for data insights using both pro-code and low-code ways. It is also a popular data integration, visualization, governance, and security app for large enterprises.
Sigma and Domo are quite similar, with the exception of Domo's extra app building feature. Sigma, on the other hand, has an easier user interface than Domo because it employs a spreadsheet format.
Conclusion
Sigma Computing is the cloud-native data analytics tool of choice for small, medium, and startup enterprises based on its functionality and user experience.
You can easily get started with Sigma because you already know how to use a spreadsheet tool. Its data analytics features, data modeling items, and visualization aspects are all quite comparable to spreadsheet programs.
Furthermore, you may easily import data from numerous data warehouses and safely alter data for insights. Not to mention, Sigma is an excellent solution for data analytics project collaboration because it supports safe and role-based workbook sharing.
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