What Is an AI-powered analytics platform?

Deren Tavgac
August 28, 2022
5 min read

Organizations are currently wasting billions of hours a year manually performing complex analytical tasks that are too hard for them to automate with existing tools. These manual tasks include orchestrating data consolidation from a large number of sources, rebuilding analytical workflows from scratch every time new data becomes available, building unwieldy Excel spreadsheets to handle multi-step transformations, and hiring resources to write ad hoc code for repetitive analytics and data science projects.

Most organizations currently attempt to manage their complex analytics workflows through a mixed bag of manual, disconnected solutions at each step of the chain with humans exerting significant effort to connect the dots.  This represents a huge opportunity for consolidation and cost savings. 

An AI-powered analytics platform is architected with the deep capabilities and flexibility needed to automate any analytical process, end-to-end. This can be accomplished through no-code intelligent building blocks, which leverage advanced configurability and responsiveness to adapt to even the most complex workflows.

As an example, at Redbird, through work we've done with existing customers, we've been able to reduce analytics operating costs by up to 90% across a diverse set of use cases.

Let's break down what an AI-powered analytics platform does, and how it can help your organization.

Giving Nontechnical Users Data Superpowers

An AI-powered analytics platform is no-code and helps companies easily automate their most complex analytical workflows. This means a single nontechnical user can effortlessly do the work of an entire technical team.

In the case of Redbird, this is accomplished through an intelligent building block architecture. Using a point-and-click interface, users rapidly create building blocks that:

  • Have the advanced configurability needed to automate complex analytical tasks
  • Adapt automatically to changes in data without the need for manual reconfiguration

These building blocks are linked together to create workflows that orchestrate your analytics across four main functional areas:


Integrations with any data source through APIs, connectors, and other custom integrations, ingesting them into a centralized location with a standardized structure. 


Intelligent processing to transform and interpret data on a deep level, covering a wide range of capabilities -- wrangling, multilayered data calculations, custom logic, text analytics, supervised and unsupervised learning, predictive analytics, and more.


Automated analytical outputs. Senior executives and less technical users access results in visual formats (e.g. dynamic web dashboards or powerpoint presentations).  Technical users access results through interactive applications (e.g. data table exploration views, REST APIs and business workflow apps).


A marketplace that lets users exchange analytical assets created on the platform.  Analytical assets can be datasets, dashboards, data science models, workflows, or entire applications. Users can leverage these assets as blueprints to accelerate their automation efforts.

Full-Lifecycle Platforms Drive the Most Efficiencies

An AI-powered analytics system is most effective when it covers end-to-end workflows. In the absence of a unified analytics approach, automation breaks down and users resort to a high amount of manual effort to connect the dots across multiple siloed tools.

AI-powered Analytics Platform vs. Data Analytics Tools

The enterprise data analytics space can be reduced to two buckets for the purpose of analyzing how an AI-powered analytics platform compares to more generic data analytics tools:

Basic data analytics tools

These tools are not able to automate complex analytics workflows.  They are one-size-fits-all tools that deliver visualization that sits on top of datasets already transformed through other means.  Any analytics workflow capabilities that exist are primarily within the realm of basic ETL.  Organizations using these tools have failed to achieve deep automation and are often looking for competitive alternatives. 

AI-powered Analytics Platform

An AI-powered analytics platform tackles the automation of complex analytical workflows across data prep, wrangling, data science, reporting and custom functional apps. Some of these platforms are geared towards a generic audience and others are more technical. A platform like Redbird is built with the user friendliness and intuitive UI for business stakeholder needs, but with deep flexibility and capabilities to satisfy technical audiences.


An AI-powered analytics platform can be a powerful way of driving productivity across your organization. The key is choosing a platform that has broad coverage across all parts of the analytics lifecycle, yet is deep enough to tackle the complexity of your workflows.