Do you want to extract actionable insights from massive business datasets in one go using an all-in-one cloud analytics solution? Your best shot is Sigma Comuting!
Real-time cloud analytics apps like Sigma Computing are changing the data analytics ecosystem. You just need to connect your data to the tool online and choose a pre-built template to create awesome data modeling, data dashboards, data visualization, and big data analytics in a few minutes. Not to mention without writing a single line of code.
Sounds interesting to you? Keep 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, you’ll rock on Sigma Computing.
This no-code cloud analytics tool enables you to 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 helps you produce quick data insights using templates or custom workflows.
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Role of Sigma Computing as a Cloud-Native Analytics Platform
As a cloud-native data analytics and BI tool, Sigma Computing is here to make your data analysis projects a lot easier and more affordable. If you’re a small and medium business owner and don’t have a different data science team, Sigma Computing is the perfect tool for you to create the same outputs as large enterprises get with millions of dollars in investments in data science teams.
Also, legacy data analytics tools like Excel, Google Sheets, Looker, etc., require a complicated manual approach to make the tools fit for data analytics on the cloud without maintaining an on-premise or local database.
Sigma Computing brings you the plug-and-play and drag-and-drop approaches to data analytics, report creation, and sharing. There are lots of templates that will surely fit all of your business requirements for data analytics formats and styles.
Benefits of Sigma Computing
Find below the advantages you enjoy when you use Sigma for all things cloud data analytics and business intelligence over your competitors who use legacy data analytics tools:
- It’s the easiest cloud data analytics platform you can get.
- As a business owner or manager, you can analyze, visualize, and extract actionable insights from massive databases yourself.
- Sigma lets you dive deep into 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:
#1. Data Connectors
Sigma offers various data connectors so you can import databases from all modern CDWs and analyze your data right now. In contrast, the same task would days in legacy data analytics tools.
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
It requires a connection string to communicate with the database through a data connector. The string could include data like server address, user ID, password, database configurations, security policies, etc.
Sigma also automatically manages the Refreshing and Closing of the data source connections. Therefore, you don’t need to invest time in setting up a new connection when you close the current database querying task.
#2. Data Modeling
The Data Modeling feature of Sigma lets you create custom reports and dashboards for your unique business logic. The Dataset feature on the Sigma UI allows you to create custom data models like 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 created data model as a template for future use. Also, it’s easy to customize these data model templates by adding new metrics by referencing those in the formula bar or dragging and dropping from a column.
Its Materialization feature allows you to save the dataset changes to the data warehouse as tables.
#3. Embedded Workbook and Analytics
You can use workbook embedding to showcase your workbooks and data elements in different mobile apps, web apps, and websites. These could be your internal or external properties. Your embedded data will always stay up to date, syncing with changes in your data warehouse.
The embedding analytics works at the workbook, single dataset page, and single elements level.
If you’re an organization Admin, you can choose from three embed types based on your needs. Sigma embedding allows three embed types: public, private, and User-Backed embedding.
#4. Data Visualization
To add a visual context to your CDW databases on Sigma, you can use various visualization elements in just four clicks. It lets you create quick visual contexts using a Table, Pivot table, and Linked input table. For more visuals, you can access the Visualization menu as well.
It comes with 14 different data visualization objects like Bar charts, KPI charts, Scatter plots, Pie/donut charts, Gauge charts, Geography maps, and many more.
The Custom Configurations feature allows you to further personalize these objects using Properties and Formatting menus.
Element Properties menu controls things like axis categories, tooltips, colors, metrics, data aggregation, chart orientation, etc.
The Element format, on the other hand, facilitates the editing of axes,m background, data labels, data references, trend lines, legends, etc.
#5. Sigma AI
The Sigma AI is a Generative Pre-trained Transformer for natural language-based data analytics. Instead of creating data models and visualizations yourself, you can instruct the Sigma AI tool to create these for you by describing your needs in the natural English language.
With this data analytics AI, you can also classify, autofill, clean, and extract data tables in a few seconds. There’s also an AI chatbot to help you learn what more you can do with Sigma AI.
#6. Input Tables
For structured data entry into dynamic Sigma workbooks, you can use Input Tables. Thus, you can introduce new data points in your data analytics project. You can also augment existing Databricks and Snowflake data for quick what-if analysis, prototyping, advanced modeling, forecasting, etc.
Input tables can be the sources of data elements like pivot tables, visualization objects, and tables. Alternatively, you can use input tables with lookups and joins for data incorporation.
#7. Online Collaboration
With Sigma Computing, you no longer need to copy content from your data analytics workbooks and paste those into an email. You can simply share the workbook with authorized collaborators for workbook editing, data pattern exploring, and content-sharing purposes.
Its collaborative feature comes with 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
#8. Security and Governance
Sigma doesn’t cache, extract, or keep your data in transit. Your data never goes anywhere else from your warehouse. On top of that, every action you make on Sigma is encrypted by SSL protocols.
There are role-based access policies to give different views of the same workbook to different employees or contractors. For example, as a business owner, you can drill down to the smallest dataset hierarchy that creates a performance dashboard. Contrarily, a sales agent can only see a top-level dashboard with sales performance figures. They have no idea where the sales figures are coming from.
Sigma supports data compliance protocols like 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:
- 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.
- Sales
- Perform accurate and fast revenue planning
- Quickly handle customer churn threats
- Create insights on upsell opportunities
- Create a commission dashboard for sales agents
- 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
- 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
- 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 use cases of Sigma for any business is revenue planning. It can include a sales performance deep dive table to give you an idea about sales and revenue earning by quarter.
Here, you can plan revenue goals and create a revenue forecast. By analyzing the gap between these two metrics, you can strategize whether you must boost sales drives or not.
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
You can use Sigma to monitor your expenditure for maintaining databases on CDWs like Snowflake. You can create a workbook and import data from your Snowflake account. Then, link the workbook calculation outputs to a dashboard object to monitor the following:
- Credit usage
- Cost for contract and storage
- Total usage
- Monthly spend
- Usage Statement
Comparison of Sigma Computing With Competitors
#1. Looker
Looker is a search engine by Google that finds actionable insights from raw business data. It helps you to analyze data and create visualizations from raw data on the cloud.
However, using Sigma is more easy and affordable than Looker. You must appoint an expert LookML developer to produce actionable insights on Looker. However, with Sigma, you can do it all by yourself by using templates and Sigma AI.
Looker data models also come with high maintenance costs as compared to Sigma.
#2. Domo
Domo lets you create custom business apps for data insights via pro-code and low-code methods. It’s also a popular data integration, visualization, governance, and security app for large businesses.
Sigma and Domo are almost similar except for the additional app creation capability in Domo. However, the Sigma user interface is easier than Domo since Sigma uses the spreadsheet format.
Author’s Note
From the features and user-interface perspective, Sigma Computing is the recommended cloud-native data analytics tool for small, medium, and startup businesses.
You can quickly get started with Sigma because you already know how to navigate a spreadsheet app. Its data analytics functions, data modeling objects, and visualization elements are also very similar to spreadsheet apps.
On top of that, you can import data from various data warehouses in a few clicks and manipulate data for insights securely. Not to mention, Sigma is the ideal tool for collaboration on data analytics projects as it facilitates secured and role-based workbook-sharing features.
Next, check out the best data analytics software to create powerful insights.