Faced with the choice of governed analytics or self-service data exploration? In this blog, Eric Heidbreder delves into a comparative analysis of two BI platforms — Looker and Sigma. This comparison will guide you through the complexities of each platform to determine which best aligns with your organization's analytics objectives.

Picture this: after weeks of intensive work, a dedicated team of Looker developers have crafted bespoke ‘Looks’ (a report in Looker) to meet users’ specific requirements. These meticulously designed reports are validated, approved, and are finally launched. Yet soon after, the dynamics change — users desire more flexibility, perhaps a different way to engage with the data they’ve been presented.

Such shifts in user needs and preferences are common challenges across BI platforms. While Looker offers structured and governed insights, Sigma brings to the table a design catering to on-the-go adjustments and exploratory analytics.

This divergence in approach brings forth an essential question: When faced with the choice between traditional, governed analytics and dynamic, self-service exploration, which path should you take?

Dive in as I unpack the strengths and nuances of Looker and Sigma.

A Closer Look at Looker and Sigma

Looker is a data platform that combines data governance, API-driven development, and embedded analytics to streamline reporting and dashboarding within the modern data stack, known for its unparalleled security standards.

Sigma is a cloud-based analytics platform that streamlines data analysis for business users. It offers easy data exploration, collaboration, and sharing of insights, along with integrated customer support. Moreover, Sigma directly integrates with widely used data sources and tools, eliminating the need for coding or technical know-how. The platform presents a familiar spreadsheet-like interface, allowing users to delve deeper into their data.

Looker and Sigma address several key use cases spanning across areas like self-service, data governance, embedded analytics, and security. While both tools provide strong individual features, each have respective strengths specific to the use cases below that enhance productivity, flexibility, and data reliability.

What are the Use Cases for Using Looker and Sigma?

Use Case #1: Self Service

Both tools provide self-service options, but Sigma particularly shines in this area when users are hungry to do their own analysis on existing data. Sigma enables users with a penchant for analysis to add new metrics and delve deeper into the data, all while maintaining a connection to the database, eliminating the need to extract data into Excel.

Comparison graphic detailing the differences between Looker and Sigma solutions for self-service. Features compared include self-service implementation, adding new metrics, and incorporating new data sources. Looker requires LookML developers for setup and has limitations in adding data directly, whereas Sigma offers a more user-friendly, spreadsheet-style interface with flexibility in data source additions.

Side-by-side comparison of Looker and Sigma solutions, highlighting their approaches to self-service implementation, metric additions, and data source integrations.

Use Case #2: Data Governance

Sigma provides a built-in data governance solution to tag the trusted version of reports and versions of worksheets. If you are running into scalability issues in Looker with multiple users editing on top of each other, you might consider maintaining certain reports in Sigma.

However, Looker has the edge when it comes to data governance because it integrates with Git and can take advantage of CI/CD tools to establish automated processes, tests, and review whenever a metric or table is changed.

Comparative chart between Looker and Sigma solutions in the context of data governance. Features examined include database connection, data transformation methodologies, data sources, version control mechanisms, and additional usability notes.

A comparison of Looker and Sigma’s approaches to data governance, emphasizing their differences in database connections, transformation processes, and version control capabilities.

Use Case #3: Embedded Analytics

Looker has more resources for developers but requires a more mature development team to create and maintain embedded content. And although Looker has all the features needed to embed, updates to an existing embedded dashboard can be difficult to test before they hit production.

Sigma has an edge here when it comes to the time-to-value and maintenance of embedded content. One can easily manage production versus development versions of embedded content within the UI without needing to set up advanced development pipelines.

Comparison chart highlighting differences between Looker and Sigma's offerings in embedded analytics. The focus is on developer tools, authentication methods, and the capability to extract data via API.

A direct comparison between Looker and Sigma in the realm of embedded analytics, underscoring their distinct developer tools and shared extraction capabilities.

Use Case #4: Security

Looker and Sigma both adhere to modern security best practices. I cannot give an edge to either tool for this category. However, if you find yourself with a category of user who is savvy with spreadsheets but does not have the development mindset of a data engineer, you may find that Sigma is a place to send that group of users and empower them to find new insights.

In Looker, we would typically give these users permission to develop on a Looker spoke, which can become a ‘wild-west’ of inefficient and broken code if users are not adhering to best practices.

Comparative chart showcasing the security features of Looker and Sigma.

Contrasting Looker and Sigma’s security protocols, emphasizing differences in access granularity and the nuances of content permissions.

Talk to an expert about your technology needs.

Make the Right Tool Selection Based on Your Unique Business Needs

Understanding the difference in technical use cases is critical when evaluating Looker and Sigma. However, the technology itself should not be the driving factor in your selection.

Look at what matters most to business users in the process of generating insights. Are they more inclined to adopt guided analytics in the form of traditional dashboards and reports to find what they’re looking for? Or do they have a gravitation toward data exploration, and finding the trail of breadcrumbs themselves?

Knowing this — along with what technical use cases truly matter to your organization — equips you to make the most informed decision possible on selecting the right BI platform for your organization’s future state data analytics strategy.

Eric Heidbreder Eric Heidbreder is a Senior Consultant with Analytics8 based out of Chicago. He primarily works within the Analytics space to help customers pick the right tool for the job and build maintainable and user-friendly BI environments with Looker and Sigma. In his spare time he plays bassoon, guitar, and dreams of pugs.
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