Business Intelligence (BI) has long been a reliable friend of decision-makers in organizations. It helps executives answer questions like: What happened? Where did it happen? Who was affected?
The insights from BI are essential for making sense of historical data to determine the right path forward.
But tracking performance, identifying anomalies, and reporting results is only part of the picture.
BI, despite the clarity it offers, fails to explain the why behind the data or to predict what might, or should, happen next. Consequently, teams have to play a guessing game to find the root cause of their business challenges and decide the best course of action moving forward.
Simply put, BI is backward-looking.
This puts companies on the back foot, as they can only make reactive choices. They often optimize today’s operations based on yesterday’s problems, instead of anticipating tomorrow’s opportunities and preparing to capitalize on them.
Let’s look at the root causes of BI’s limitations to discover a remedy for this persistent problem.
The Limitations of Business Intelligence
The State of BI 2025 Report released by Sigma Consulting points out three critical issues with BI in modern enterprises:
1. BI Can’t Handle Volume, Velocity, and Variety
It has become easier than ever to collect user data in various forms. Companies are gathering a huge amount of diverse data at a rapid rate.
Sigma’s report notes that 87% of brands reported an increase in data volumes. As a result, 71% of organizations faced scalability issues with their BI tools.
Even the basic functionalities of these analytics platforms performed poorly due to data overload. Data confirms that 76% of businesses experienced performance degradation on their BI tools’ dashboards.
2. AI Integration is Difficult
Conversational analytics democratizes data for enterprises. All team members, even non-technical professionals, can query databases in natural language and receive contextual answers.
However, while 89% of the surveyed organizations have adopted generative AI, 80% of them lack real-time data access.
This can happen because BI tools are typically built for static reporting, which makes them backward-looking, as mentioned earlier.
3. Customization Stops at Dashboards
Insights should lead to action. BI tools generally focus on providing insights in the format you want through customizable dashboards, but they can’t be connected to automation tools or agents to streamline workflows or initiate actions.
Large teams need to build custom data apps, not mere visuals, to break silos and ground their daily operations in tangible metrics. Unfortunately, BI platforms often lack these functionalities, locking historical data insights in dashboards.
Sigma Consulting’s CEO, Mike Palmer, sums it up: “The volume, variety, and velocity of today’s data have left businesses reeling. Data apps and AI-driven solutions are critical for helping businesses transform insights into action.”
So, what can you do?
Adopt a decision-intelligence platform that can ingest live streaming data to predict future performance.
How Decision Intelligence Complements BI
Decision Intelligence (DI) leverages historical and real-time data to predict what might happen.
Consider that you are running an e-commerce store. DI will look at past sales data, holidays, and market events to forecast demand for your products. This will allow you to stock up appropriately, reducing waste and boosting profits.
Enterprises can use DI to predict revenue, customer churn, and product performance, among other things. It complements BI by providing the other half of business insights: forecasts.
DI works by integrating three pillars of data analytics:
- Data Preparation: Involves cleaning raw data to convert it into machine-readable formats.Data Science-Focused Applications: Predictive models and algorithms that process datasets.Business Analytics: Real-time dashboards, visualizations, and self-service insights.
When data prep, data-science apps, and analytics sit in one workspace, analysts no longer export CSVs or juggle log-ins to multiple dashboards, so insights flow freely across teams.
Moreover, AI overlays can be added to unlock predictive analytics in a conversational interface at scale, keeping your operations lean.
Pyramid Analytics CEO and co-founder Omri Kohl, explained it best: “A holistic approach to decision intelligence consolidates all three ecosystems onto a single analytics platform. It automates key aspects of the decision-making process, leveraging contextual data to support successful decisions for business users, without reliance on technical personnel.”
Wrapping Up: Monitor and Iterate
DI ingests real-world data in huge quantities and varieties. This makes the overall DI-BI integration vulnerable to imperfections that can offer incorrect or invalid predictions.
Furthermore, your forecasting solutions may amplify existing biases in large, multi-source datasets from different aspects of your business. Data points from supply chains, customer feedback, or on-field operations can be skewed from the start.
Akash Takyar, CEO of LeewayHertz, recognizes this: “Decision Intelligence systems can unintentionally incorporate biases present in the data or the algorithms themselves.”
Therefore, it’s pivotal to monitor the reliability and efficacy of your DI system. Compare real-world performance with its predictions. Then, iteratively retrain the ML model to mitigate bias and improve accuracy.
Takyar strongly recommends to enterprises, “Organizations must regularly audit their DI systems for bias and ensure they represent diverse perspectives.”
Gaurav Belani is a Senior SEO and Content Marketing Analyst at Growfusely, where he specializes in crafting data-driven content strategies for technology-focused brands.
