Traditional business intelligence platforms tout the ability for nontechnical stakeholders to “self-serve” without requiring help from their technical counterparts. In reality, most nontechnical stakeholders lack the technical knowledge and skills (e.g. SQL, Python, etc.) to manage their data analytics and reporting initiatives without involving their data or IT teams.
With modern advancements in AI, enterprises no longer need to struggle with data access and utilization. Imagine a world where anyone (CEO, CFO, CMO) can ask any data-driven business question (as they would to their data or analytics team), and get desired insights and reporting outputs in real-time. Platforms like Redbird enable just that - nontechnical users now have access to truly self-serve analytics.
What is AI-Powered Analytics?
Traditional business intelligence platforms require extensive setup and configuration to enable data pipelines, analytical workflows and reporting outputs for stakeholders. However, there is a new breed of business intelligence platform, the AI-powered analytics platform. Redbird has built this next gen BI platform from the ground up, with AI infused throughout. An AI-native business intelligence platform is significantly more efficient than alternatives. AI functionality drives connectivity, orchestration, deep automation and self-service through natural language across the data analytics and reporting lifecycle. This means more time for business users to run the business vs collecting data from clunky UIs, doing excel jockeying or building repetitive PowerPoint presentations.
The Evolution of Self-Serve Analytics
Visualization tools such as Tableau, Looker and Power BI have positioned their offering as a way to easily democratize analytics for the less technical business user. In reality, these tools operate within a specific component (dashboarding) of the overall analytics value chain. They also often assume that for stakeholders to self-serve the data must already be “clean” (i.e. within a clean SQL-accessible table). Additionally the UIs within these platforms involve a learning curve, which results in friction in the data democratization process.
But what would a truly self-serve analytics platform of the future look like? Enter Redbird, an AI-infused conversational BI platform that operates end-to-end across the full data analytics and reporting value chain. Users can utilize natural language to ask any business question, and Redbird automates the data collection, wrangling, analytics, data science and reporting steps without a single manual SQL query, workflow configuration or dashboard build.
By enabling less technical audiences with a ChatGPT-like interface for asking data analytics and reporting questions, Redbird customers are able to see >100x increases in data-driven decisionmaking across the organization.
Advancements in Generative AI / LLMs
How is this magical conversational BI experience possible? In recent years, there have been significant advancements in generative AI and LLMs, which enable real-time AI-driven chat communication through human-inputted prompts to answer any question. This empowers users to drive efficiencies in workflows through automated document summarization, natural language generation and other applications.
The Custom Enterprise LLM for Data Analytics
ChatGPT, Llama and other LLMs come pre-trained on years of historical data from across the web, but don’t come ready to answer business questions on top of an enterprise’s data. Customizing an LLM to the enterprise involves securely connecting into all of an organization’s data and using that data to fine-tune a custom instance of the LLM. In the absence of this connectivity, LLMs will not be contextually aware of the enterprise’s data and will be unable to answer questions that are posed. Importantly the data must be clean, which is a rarity in most organizations. Garbage in, garbage out, as data practitioners often say. To feed clean data into an LLM and orchestrate components, data engineering organizations must invest significant time and resources. This is where many tools like ChatGPT and Copilot fall short when enterprises begin experimenting with business applications for LLMs.
Another challenge with LLMs is that they’re notoriously bad at doing basic math, data analytics and reporting work. LLMs are great at predicting the best next word from text, but are stumped by basic data, analytics and reporting tasks and tend to hallucinate. In order to empower LLMs with analytical chops, you’d need to build an analytics translation layer that understands your enterprise’s data ontologies, business logic and analytical rules – this undertaking can be daunting for most organizations who lack the resources to invest in R&D to build such a solution (which can cost millions of dollars to develop).
Redbird’s AI-Powered Conversational Analytics Platform
Let’s dig into how Redbird solves these challenges as your AI-native, end-to-end conversational analytics platform.
Competitive differentiation
How does this differ from existing tools in the market? Here’s a quick breakdown:
ChatGPT - ChatGPT is trained on 3 years of historical data from across the web, and is not contextually aware of the enterprise’s data. Hooking ChatGPT up to an enterprise’s data requires an army of data engineers who are scrambling to ensure data pipelines are up to date. ChatGPT also out of the box cannot do data, analytics and reporting tasks, so will be unable to answer most analytics and reporting questions
Copilot - Copilot is a Microsoft product that works within the Microsoft stack but cannot be easily leveraged for non-Microsoft data sources or output formats. A marketer looking to leverage Copilot to analyze campaign performance data from various marketing SaaS platforms for example would not be able to. Copilot also can only produce basic recommendations on how to construct business workflows or more simple text- / image- based Microsoft suite generative outputs but struggles with more complex data-driven tasks.
Legacy BI dashboarding tools - Tools like Power BI, Tableau or Looker are beginning to release very simple generative AI capabilities, which cannot perform advanced analytics and reporting tasks, frequently hallucinate, and often fall short of customers’ expectations.
Conclusion
In the fast-evolving landscape of analytics and business intelligence, traditional solutions have fallen short in their ability to deliver truly self-service analytics. Most organizations struggle with a hub and spoke divide between technical builders and nontechnical consumers. Most business stakeholders want more access to data, and don’t want to have to bother their technical team counterparts to answer every data-driven business question that emerges; however, they often lack the technical expertise to build data pipelines or construct dashboards. As a result, technical team members have to build a dashboard for most requests that arise (which comes at a cost), and dashboards often sit unused as they don’t exactly meet the specific business stakeholder need or require labor-intensive changes in response to follow on questions that emerge.
Advancements in AI have enabled Redbird to release the AI-powered conversational analytics platform, which solves for these pain points. Redbird helps teams produce insights and reporting outputs 100x faster via an intuitive conversational interface that anyone can learn.