Big Data to Big AI: What This Transition Means to Modern Day Business Intelligence - A Series

For the next couple weeks, I will write a series on the current transition from Big Data to Big AI and its implications on how we understand and consume technology. In this first instalment, the focus is on how this transition is affecting business intelligence. So strap in!

In the past decade, the business landscape has been shaped by the advancement of Big Data. Companies have amassed vast amounts of data, striving to extract meaningful information to drive business growth. By collecting data from a range of sources, such as social media, transactional records, and sensor data, businesses are able to identify patterns and correlations that were previously impossible to discover. But as data grows in volume, diversity, and speed, so do the challenges of organising it and extracting useful insights from it.? As a result, data lakes have often turned into data swamps. Today, as artificial intelligence (AI) takes centre stage, we are witnessing a transition from the traditional Big Data BI approach to a new Big AI approach.

The Big Data Challenge: Volume, Variety, and Velocity

With Big Data businesses were introduced the 3Vs challenge: volume, variety, and velocity. Businesses collect massive amounts of data from various sources, but struggle with both storage and processing. This variety of data further complicates integration into a unified system for business intelligence since this data often comes in different forms i.e images, audio, text, videos to mention but a few. Additionally, the rapid speed of data generation often demands real-time analytics, which most businesses lack the infrastructure nor the expertise to handle, thus causing delays in deriving insights. As a result, businesses face the paradox of being “data rich but insight poor.”

The Transition from Big Data to Big AI

The emergence of Big AI marks a pivotal shift in how organisations approach business intelligence. AI technologies excel at handling complex, large datasets and uncovering patterns that traditional BI tools often miss. With this transition, we are looking at a move from a backward-looking, descriptive approach in business intelligence into a forward-looking, predictive, and even prescriptive discipline.

Big AI is the term for the extensive application of artificial intelligence in business, which uses sophisticated algorithms and massive data sets to solve challenging issues, automate processes, and aid in decision-making. It encompasses technologies that require a lot of data and processing power, such as generative AI, deep learning, and natural language processing. Big AI is crucial for contemporary industrial platforms and corporate solutions since it enables extensive applications including automation, tailored experiences, and real-time decision-making.

With Big AI-driven business intelligence, companies are no longer just reporting on what has happened; they can now predict what will happen and suggest actions accordingly. This transcends traditional BI, which is often limited to descriptive analysis. Traditional BI tools aggregate and visualise data to understand past performance. For example, a BI tool might report that sales dropped by 10% last quarter, but not why, or how. The burden is then on the business owner to investigate why this happened and how to address it. In contrast, a Big AI-driven BI system can do much more. It can analyse data across multiple sources, identify potential reasons for the decline, such as changes in customer behaviour or market trends, and even recommend actions to reverse the trend, such as optimising pricing strategies or adjusting marketing campaigns.

Expanding the Role of Big AI in Business Intelligence

One critical change this transition means is a move from traditional BI to intelligent BI. Traditional business intelligence systems are largely reactive, focusing on reporting historical data. However, with a Big AI business intelligence model, BI becomes proactive and adaptive. AI can recognise patterns and trends that are invisible to human analysts or traditional BI tools. For example, AI can detect subtle shifts in customer sentiment based on social media posts or anticipate supply chain disruptions by analysing weather patterns and geopolitical data.

Additionally, intelligent BI empowers organisations to discover "unknown unknowns"—those insights that aren't captured by traditional metrics. As a character from The Boondocks wisely put it, "There are known knowns, and there are known unknowns, but there are also unknown unknowns—things we don't know that we don't know." This is exactly where Big AI comes in, as it can reveal patterns and correlations beyond what traditional KPIs measure, offering fresh perspectives and untapped opportunities for innovation. It's a perfect example of the Goodhart's Law, which states that "When a measure becomes a target, it ceases to be a good measure." Traditional business intelligence often falls into this trap, with companies focusing so much on certain KPIs that they start optimising for the metric itself, rather than the true business goal it’s meant to represent. In contrast, Big AI-powered BI goes beyond these preset metrics, uncovering trends, connections, and anomalies that might otherwise be missed. This allows businesses to make more adaptive, holistic decisions, avoiding the risks of over-reliance on specific measures, and aligning better with their long-term goals.

Looking forward, the potential of Big AI in business intelligence is vast. As AI technologies continue to mature, we can expect even more sophisticated tools that offer deeper insights and more accurate predictions. Businesses that embrace Big AI stand to gain a significant competitive edge.