Self-Service Analytics: The Future of Data & BI Powered by AI

Self-Service Analytics

Self-service analytics—or self-service business intelligence—is often considered the holy grail for many data-driven enterprises. Why? Businesses have so much data to analyze, but decision-makers want to be able to analyze that data independently—without spending extra time, money, and resources.

Enter self-service analytics. As defined by Gartner, self-service analytics is “a form of business intelligence in which line-of-business professionals are enabled and encouraged to perform queries and generate reports on their own, with nominal IT support.”

Today, self-service analytics is entering a new era where AI and machine learning can enable capabilities beyond reporting and visualization so businesses gain access to data-driven insights more easily.

Learn how and why more organizations are modernizing their self-service offerings to get answers to their most granular data questions.

The history of self-service analytics

The implementation of data analytics for business use goes back much further than you might expect. Initial cases of organizations analyzing data to make informed decisions date all the way back to the early 19th century, when business owners began to analyze various aspects of their organization to improve performance and increase profit. Although these attempts look nothing like they do today, the basic concept remains the same.

As advancements were made in technology, data analysis and statistics became more digitized, launching the ever-expanding industry of business intelligence tools. 

Microsoft launched the trusty Excel spreadsheet back in 1985, and over 30 million people were using the desktop program just 11 years after its launch: In a 1996 press release, Microsoft specifically touted the “robust functionality and intuitive design” to help its users understand data.

Even today, spreadsheets are considered the standard approach to self-service analysis: Extracting data from source systems and then manually analyzing data through a point-and-click approach. 

Self-service BI solutions took another leap in the 2000s with the emergence of visualization tools. Qlik came to be in the ‘90s, and Tableau was founded in the early 2000s, enabling users to explore their business data through more interactive dashboards. Tableau was acquired by Salesforce in a multibillion-dollar deal just a few years ago, underscoring the massive market for self-service data visualization tools. The launches of these innovative BI products over the years demonstrate a longtime need for users to have improved access to data. 

That’s not the end of the story, though. We’re currently in an era of making data analysis even more accessible so that users can be even more productive. Thanks to the emergence of AI, ML, and automation, people can perform data analysis at a much larger scale than ever before. Self-service BI is about applying advanced processes while still making it accessible to more people (as well as better enabling the data analysts, too). 

Businesses today thrive when everyone—not just the data experts—has access to the tools and information they need to make quick, insightful decisions driven by data. 

And, of course, let’s talk about generative AI, which is continuing to revolutionize self-service analytics workflows.

2023 was a landmark year for GenAI, setting the stage for an even more transformative future in 2024 and beyond, where organizations can integrate these technologies with their data—creating new user experiences and boosting employee productivity.

Why do organizations need self-service analytics?

Self-service offerings, designed to empower the business user, are everywhere you look. 

We live in a self-service world that enables consumers to get things done on their own: from pumping gas (with the exception of a couple states), to shopping online, to ordering food delivery. The goal is to carry out tasks entirely independently (with the help of some computers), whenever and wherever you’d like. 

This concept translates to the business world of data analytics, which now goes beyond the traditional dashboards, reports, and data visualization that BI has offered for decades. As organizations need to solve more granular questions, self-service tools provide a path to inform those answers and subsequent decisions. 

Instead of relying on IT teams or data experts to get this information, users need to be able to have answers right at their fingertips. Businesses want to accelerate decision-making that’s informed by data, and executive and frontline workers want these answers when they need it—not just when an analyst is available to support them.

The automated self-discovery of insights reduces business users’ reliance on analysts—making it easier to independently answer in-depth questions with the help of machine learning algorithms running behind the scenes.

The core benefits of self-service analytics

The information and analytical needs of a business are a constantly moving target. With more and more questions asked every day, which calls for deeper analysis, it can be difficult for organizations to keep up with the demand. 

Self-service analytics abolishes complex data silos and streamlines the process of generating reports in real time. In turn, this reduces what is often an overwhelming BI backlog for organizations.

Using features like intelligent search functions, users across an organization (with varying levels of tech know-how) have immediate access to information, especially if they can use natural language queries rather than complex scripts, simplifying the process of getting answers.

A self-service BI tool enables even the most non-technical of users to pose real-world business questions and quickly harness data and analytics to aid their role. 

In addition to eliminating a BI backlog, other benefits of self-service analytics include the ability for organizations to:

  • Improve access to data across the business;
  • Generate real-time reports;
  • Eliminate data silos;
  • Reduce overhead costs; 
  • Decrease the workload of the existing IT team; and
  • Make informed, data-driven decisions quickly.

Who uses self-service analytics?

Traditionally, self-service analytics is designed for business users who want to get answers when they need them and without relying on IT. 

Because you don’t need specialized knowledge  or complex training to operate a self-service analytics platform, employees across your organization  can reap the benefits—e.g., human resources, marketing, customer success, R&D, finance, or sales— of having access to the same data as those in advanced IT sectors.

In addition, self-service analytics is increasingly finding a home in the world of data analysts. Instead of having to wait for data scientists to build models or execute the automation of analysis, data analysts are making use of self-service BI themselves. By using the right self-service tools, they can perform their own advanced analysis using AI and machine learning, which is often siloed for data scientists. 

For business users and data analysts alike, self-service BI brings a new level of autonomy to data analysis. 

self service bi tool

Modern self-service analytics: powered by AI and machine learning

Today, self-service analytics goes beyond just enabling users to generate a report or visualize data on their own—it’s also about making advanced insights and analysis available to more people.

With AI-enabled self-service tools, people can ask more questions of their data in plain English (i.e., natural language searches). 

Instead of using more limited parameters traditionally presented in BI tools, or manually slicing and dicing data in a spreadsheet, you can interact with your data in an intuitive way, enabling you to carry out these types of complex functions and more:

  • Automatically spotting anomalies or outliers in your data;
  • Discovering the most important contributors of change; or
  • Building and operationalizing machine learning models.

With self-service tools enabling deeper insights into data, you no longer have to come up with your own hypotheses on why things have changed. Instead, you have these key contributors of change automatically presented to you. In turn, you can lend your own business expertise to the data analysis at your organization without relying on IT or data scientists (and also reducing the headaches of spreadsheets).

Business intelligence platforms nowadays should deliver one thing: insight into the data that matters to the success of your organization. With modern self-service analytics tools, companies can more easily use data to drive profit for consistent growth.

GenAI: How it's revolutionizing self-service analytics today

Here’s a quick GenAI refresher: ChatGPT, first introduced back in November 2022, become a wildly popular household name in just a few months. ChatGPT, an AI chatbot developed by OpenAI, is built on OpenAI’s GPT-3 family of large language models, or LLMs (i.e., foundational models that can read, summarize, predict, and generate text). Generative AI creates text, images, or other media by using an LLM, which is trained to learn patterns from massive amounts of data.

The application of LLM technology on a self-service analytics platform built for multi-persona analytics (i.e., a platform for data scientists, analysts, and business users) is continuing to open new doors for collaboration among groups. An intuitive platform providing automated actionable insights also helps an entire organization become more data-driven.

GenAI tools are becoming increasingly more accessible as part of organizations’ self-service analytics workflows, but it’s not a simple plug-and-play solution. Data and domain experts must still exercise caution when adopting the technology, which requires setting up, mapping, and fine-tuning to specific needs (e.g., use cases, data size, individual expertise).

When you’re evaluating self-service solutions, look at GenAI as an enrichment, rather than the basis, for the solution. A platform shouldn’t rely on LLM technology as the backbone of its computational engine—instead, look at GenAI as a means of enriching search with an LLM-based contextual understanding for a given industry or use case. This helps increase the accuracy of search results for a given prompt, which essentially means there’s no chance of hallucinations.

Self-service analytics use cases

Self-service analytics is being used across myriad industries and departments within organizations. 

Consumer goods and retailers are enabling their category management and shopper insights teams to uncover crucial insights about customer behavior and market share. For example, a multibillion-dollar global consumer brand used Tellius for self-service analytics to gain deeper, actionable insights into consumer data while also freeing up time for the data scientist team members.

Like a lot of organizations, they were struggling with a growing BI backlog, with terabytes of data across sales, marketing, shipments, and third parties, but only a small data analytics team to support a whopping 100+ brands—20 of which have more than $1 billion in revenue each. 

Once the teams gained access to AI-powered, self-service intelligence, they were able to automate previously manual processes of analyzing data across brands. This enabled dramatically faster data modeling, leading to more timely insights shared across the organization. 

Enabled with more autonomy and better data, the teams were able to create higher-quality shopper profiles 10 times more quickly than before, optimize marketing campaign effectiveness, and realize a multimillion-dollar impact on sales. What’s more, with less time spent on data modeling, the data scientists were able to spend their time supporting more brands. 

Watch a preview of our webinar, Accelerating CPG Organizations’ Decisions with a 360 View of Data and AI-Powered Analytics:

Sign up to watch the entire webinar on demand here.


Learn more about self-service analytics

Tellius’ AI-powered self-service analytics platform enables anyone at your organization—regardless of their analytical skills—insights into what’s happening and why metrics change, as well as specific recommendations on how to impact business outcomes. Get the answers you need, whenever you need them.

Request a demo or check out a free trial to see what AI-powered self-service analytics can do for your business.


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