ROI You Can Measure: 6 Analytics Features That Pay Off

Feature 1: Analytics as Code (AaC)

Analytics as code allows teams to manage data processes through code, rather than manual tasks. By automating workflows, resources can be saved that would otherwise be spent on repetitive updates and deployments. Reusable blueprints and templates allow teams to quickly replicate analytics setups across projects, speeding up delivery. AaC also enables bulk changes — meaning updates can be applied across the platform with a few lines of code, rather than individually.

Feature 2: Multi-tenant Architecture and Scalability

Multitenancy allows multiple users or teams to share the same analytics platform while keeping their data, settings, and insights securely separated. This setup ensures scalability, as it is easy to accommodate increases in volume, assets, or users without the need for additional tools.

Multitenancy saves time by allowing streamlined management and updates across all users at once. Costs are lower compared to a single-tenant analytics platform (as there is no need to pay for separate versions for each user). Plus, your solution will be future-proof: the platform is designed to support growth and there’s no need to shell out for new software as your analytics needs evolve.

Feature 3: Ability to distribute the analytics experience across all user touchpoints

An optimal analytics platform will ensure seamless data distribution to any downstream experience. There should be the option to seamlessly embed the analytics into your app, data product, or web portal so everything matches your brand. That way, you can access metrics and AI-powered insights within the data product, app, or portal you’re already familiar with, saving time and boosting efficiency.

A “headless BI” approach is also helpful with distribution: separating the backend analytics engine from the user interface will allow your business to fully customize the experience. Ultimately, this accelerates time to market as instead of investing significant resources to build an analytics solution from scratch, you can embed and tailor the analytics features to fit your needs.

Feature 4: An In-Memory Service Layer

An in-memory data service layer enables your business to efficiently transform data from various sources. It uses smart caching to manage data warehouse costs, reducing the need for extensive external storage. This means your business can access and analyze data quickly, without the delays associated with traditional data processing. Additionally, the ability to federate data from multiple departments allows users to create a comprehensive view of their organization’s performance without the hassle of data duplication, thanks to a "zero copy" approach. This saves time and data warehouse costs.

Feature 5: Innovative Tools for Effective Data Monetization

The best solutions provide monetization capabilities, allowing you to turn analytics into new revenue streams. You can deliver data-driven offerings through native platform features or by embedding metrics, insights, dashboards, or self-service analytics into your application. Implementing tiered or subscription-based access, for example, can instantly increase revenue and enhance customer retention by improving the user experience.

Feature 6: Advanced AI capabilities

An advanced analytics platform should offer AI-assisted tools, allowing users to easily ask questions, detect anomalies, and forecast trends. This self-service model saves time for BI teams and data analysts — who can rely on AI to handle inquiries — and also allows any user to navigate insights independently, reducing the need for a large team of analysts or developers to manage queries. This can lead to significant time savings, increased efficiency, and tangible cost reductions.

Use Your Analytics Platform to Improve Your Bottom Line – Starting Today!

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*Figures in this article are best estimates, based on case studies and general industry insights. Percentages may vary depending on the use case and usage patterns.