What is Self-Service Analytics?

Modern brands are drowning in data. Rather than empower brands, this influx of riches can form crippling bottlenecks, strain IT teams, and slow time-to-insight to a crawl. To counter this, brands can leverage analytics and business intelligence (BI) tools to make sense of their data. As these tools evolved, a new subset of BI tools emerged: self-service analytics.

What is self-service analytics?

Self-service analytics enable your entire team to interact with data, perform queries, and glean business insights–all with minimal IT support and little technical skills required. For example, say your product team needs to know why customer drop-off rates have suddenly increased. Instead of waiting on IT to perform the ad-hoc query, a self-service analytics tool would allow the product team to answer this question themselves quickly. From there, they would be able to iterate their queries and further explore the data on their own, reducing time to insights and driving productivity. 

The benefits of self-service analytics 

• Faster time to insights: Waiting on your IT teams to complete queries can be time-consuming, especially if high query volume results in a bottleneck. Enabling your teams to answer queries themselves means discovering faster, more accurate insights.

• Minimized reliance on technical teams: Tedious query requests can be a huge strain on data science and analyst teams. Self-service analytics minimizes your reliance on technical teams and allows them to redirect focus on their own projects and productivity.

• Improve data literacy: In addition to reducing reliance on data scientists and analysts, self-service analytics also empowers non-technical teams to explore data and gain insights on their own. Self-service analytics eases the transition toward democratized data by visualizing data in non-technical UI, or, for example, a platform that operates on no-code querying. 

• Integrations with other tools and existing infrastructure: Most self-service analytics are designed to integrate seamlessly with other tools and platforms. This allows brands to leverage existing infrastructure and hit the ground running without lengthy onboarding and downtime. 

• Customized data governance: Just because your entire organization can access your data doesn’t mean they should. In addition to augmenting your brand’s existing security management, self-service analytics can minimize privacy compliance risks by limiting sensitive data to only your most essential teams. Additionally, most platforms already have policies and procedures in place to ensure and work in tandem with your data compliance and security. 

Challenges within a self-service analytics

• User adoption: No matter how intuitive self-service analytics platforms are, converting your teams to a new tool isn’t always easy. Inadequately trained teams may not leverage the platform to its full potential, resulting in missed insights and diminished ROI.

• Cost: The true cost of a self-service analytics platform may not be immediately apparent. Besides the cost of training your teams, concurrent workloads can expend serious computing power that, if mismanaged, could result in expensive overages.

• No one size fits all: There is no self-service analytics platform that will fit every brand and accommodate all their needs. Every self-service analytics tool is designed to serve different users with different needs. If your brand wants to maximize ROI, you’ll need to invest time in researching and trying out various tools before making a decision. This means, selecting the right self-service analytics platform requires careful consideration and thought.

Examples of self-service analytics tools

• Tableau: Tableau is known for its self-service visual reporting that allows users to interact with their data in real-time. The platform is capable of creating deeply complex graphs with a similar feel to Microsoft Excel’s pivot graphs–however, it can be challenging for non-technical users to adopt and use effectively.

• Looker: Similar to Tableau, Looker is known for its ease of use and ability to customize visuals to the user's needs. Unlike Tableau, however, Looker is completely browser-based, which allows multiple users to work off the same dashboard simultaneously.

• Scuba Analytics: Beneath its intuitive, user-friendly UI, Scuba Analytics’ continuous intelligence platform offers expansive and comprehensive analytics. Both Scuba’s data ingestion and tracking scale to the petabytes, give brands the flexibility to glean both macro and granular insights–all in real-time.

Want to learn more about how Scuba can help you take your analytics to the next level? Request a demo today or talk to a Scuba expert.