Decision intelligence is an emerging discipline that uses advanced technologies to help companies make better decisions. In simple terms, it combines tools like artificial intelligence (AI), machine learning (ML), and business intelligence (BI) to supplement and even automate how decisions are made in an organization​. 

Instead of relying on gut feeling or scattered reports, decision intelligence provides a structured, data-driven approach to decision-making. In fact, Gartner recently named decision intelligence a top strategic technology trend, reflecting how quickly it has become important for businesses of all sizes​.

What is Business Intelligence?

Business intelligence (BI) refers to the technologies, applications, and practices for collecting, integrating, analyzing, and presenting business information to support better decision-making.

Business Intelligence is the set of techniques and tools for the transformation of raw data into meaningful and useful information for business analysis purposes to enable more effective strategic, tactical, and operational insights and decision-making.

Core concepts include:

• Data warehousing
• ETL (Extract, Transform, Load) processes
• Data visualization
• Reporting tools
• OLAP (Online Analytical Processing)
• KPIs (Key Performance Indicators)
• Dashboards

Effective BI implementation empowers organizations to gain competitive advantages through data-driven strategies that identify market trends, operational inefficiencies, and growth opportunities.

Why Is Business Intelligence So Important Today?

In a data-rich business environment, making the right decision can be informed with more data than ever before, with studies showing a strong link between decision quality and business performance. 

For example, one study found a 95% correlation between decision effectiveness and financial performance​. 

On the flip side, poor decisions can be extremely costly, with the average S&P 500 company losing an estimated $250 million per year due to subpar decision-making​. 

The truth is that business leaders face more choices, more frequently, and with more data to consider than in the past. As a matter of fact, 74% of employees and executives feel the number of daily decisions they make has increased tenfold in recent years, leaving many feeling overwhelmed​. 

Often, managers must make choices without enough time or information to fully understand the situation​, which is exactly the problem that decision intelligence aims to solve​.

By leveraging AI and analytics to digest large amounts of data, decision intelligence can deliver insights or recommendations quickly, ensuring that decision-makers have the right information at the right time. 

Decision intelligence helps businesses navigate complexity with confidence, leading to smarter strategies and better outcomes.

How Businesses Collect Data and Use It for Decisions?

Modern businesses are gathering more data than ever before. Every interaction, transaction, and process can generate data. Companies collect information from a wide range of sources, such as customer relationship management (CRM) systems, enterprise resource planning (ERP) systems, websites and mobile apps (tracking user behavior), social media, customer surveys, supply chain and operations logs, and many other internal databases. 

This Data can be structured (organized in tables, like sales figures or inventory levels) or unstructured (like emails, documents, or social media posts). Organizations typically utilize multiple data sources simultaneously, about five internal and several external sources, to inform their decision-making processes. The goal is to capture a 360-degree view of the business environment.

However, in an ideal scenario, the various streams of data a business collects would all come together to paint a coherent picture that decision-makers can act upon. In reality, pulling together all these disparate data points and extracting meaningful insights is a major challenge. This leads us to an important question: How much of this abundant data is really being utilized?

The Data Usage Gap: Used vs. Unused Data

Despite massive data collection efforts, businesses face a significant utilization gap. Forrester estimates that 60-73% of enterprise data goes unused for analytics. Similarly, the Business Application Research Center (BARC) reported that, on average, companies use only about 50% of the information available to them when making decisions

In addition, only 24% of organizations describe themselves as truly data-driven, with just 30% of employees actively using analytics tools.

This underutilization stems from scattered data systems, data silos, accessibility challenges, quality issues, and inadequate analysis tools. Information often isn't readily available when decisions must be made. This gap between data collection and utilization represents a fundamental challenge that better systems and practices must address to unlock the full value of existing data assets.

Systems and Tools for Data-Driven Decision Making

Businesses employ various technologies to transform raw data into actionable insights for informed decision-making:

• Data Warehouses - Centralized repositories storing structured data from multiple sources in a consistent format, providing a single source of truth for historical data analysis.
• Data Lakes - Flexible storage solutions that accommodate all data types (structured, unstructured, semi-structured) in their native formats, enabling advanced analytics and machine learning on diverse datasets.
• Business Intelligence (BI) Platforms - Technology-driven tools that analyze data from multiple sources and present it through intuitive dashboards, charts, and reports, helping companies evaluate and understand their data for better decisions.
• Analytics and Data Science Tools - Specialized solutions ranging from statistical software to advanced machine learning platforms that enable deeper analysis, such as forecasting, customer segmentation, and process optimization.
• Data Integration and ETL Pipelines - Processes that extract data from source systems, transform it into consistent formats, and load it into warehouses or other systems, ensuring unified data access across platforms.

Despite significant investments in this technological ecosystem, many organizations still struggle with data underutilization. Traditional BI has limitations with unstructured data and siloed information, prompting the development of new approaches to augment existing toolkits.

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Key Components of Business Intelligence

When people talk about business intelligence, they often think of the user-facing components that turn data into insights. These are the tools and features that business teams interact with regularly to understand performance and make decisions. Some of the most important components include:

• Dashboards and Reports - Interactive visual displays that organize information in easily digestible formats, allowing decision-makers to monitor key metrics at a glance. These customizable tools often update in real time, enabling prompt identification and response to important changes in business performance.
• Key Performance Indicators (KPIs) - Specific metrics tracked closely as indicators of success, including financial metrics, customer metrics, and process metrics. 
• Data Visualization - Presentation of data through charts, graphs, maps, and other visual formats that make information easier to understand. Visual representations reveal insights that might be obscured in raw numerical data, with modern BI tools offering rich visualization options accessible to non-technical users.
• Real-Time Analytics - Up-to-the-minute data and predictive insights based on streaming information, enabling faster decision-making without waiting for scheduled reports. Real-time capabilities are crucial for fraud monitoring, equipment maintenance, and tracking user behavior.
• Reporting and Query Tools - Functionality to generate scheduled reports and perform ad-hoc queries, allowing users to explore data and investigate specific anomalies beyond pre-set dashboards.

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All these components work together to support decision-making. They transform raw data into meaningful information. Dashboards and visualizations help summarize the current state, KPIs focus attention on important goals, and real-time capabilities ensure timeliness. Together, they form the backbone of traditional business intelligence.

However, even the best dashboards are only as good as the data they can access. If some information is locked away in a separate system or not updated, it won’t show up on the BI reports. This is why companies are looking at ways to bring more of their data together and make it easily searchable and analyzable. 

Enterprise Search and AI: Unifying Data for Better Decisions

The enterprise search platform addresses the problem of incomplete business intelligence by organizing content from all these sources (files, intranet, emails, support tickets, transaction records, and so on) and making it retrievable in one unified interface. 

These systems typically use connectors to regularly pull data from each system and build an up-to-date search index.​ When all is set and done, enterprise search with AI enables organizations to have:

• Unified Access Across Data Types - Enterprise search handles both structured data (databases, spreadsheets) and unstructured data (documents, emails, images), organizing them within a single searchable system. This is usually achieved with vector graphs that create numerical representations of content that capture semantic relationships, enabling similarity searches beyond exact keyword matching.
• Entity Mapping - These systems identify and map entities (people, products, departments, and projects) and their relationships across the organization, creating a navigable knowledge network that mirrors the organization's actual business structure.
• AI-Powered Insights - The combination of enterprise search and AI, particularly integration with large language models, transforms information retrieval into knowledge generation. Users can ask complex questions in natural language and receive synthesized answers drawn from multiple sources.
• Comprehensive Intelligence - By unifying disparate data sources, the system creates a comprehensive intelligence layer where any business question can be answered, regardless of where the underlying data resides.

In practical terms, this means enterprise search with AI can act like an intelligent assistant. When users ask a complex question, an AI-powered search can give a synthesized summary of the most important information needed for the decision

For example, if a manager asks, "Summarize our Q1 customer feedback on product X," the system could pull in all customer comments from various channels and produce a concise summary of common issues and praises

This powerful combination of enterprise search and AI enables organizations to extract maximum value from their data assets, democratizing information access while providing deeper insights than traditional BI dashboards alone could deliver, effectively turning the search engine into a “digital knowledge analyst” that understands your business context.

How Akooda Ties All Your Data Together for Superior Decision Intelligence

Akooda is built to solve the core challenges businesses face with data today. It brings together all of your company’s information by using more than 50 prebuilt connectors that pull from emails, documents, CRMs, support platforms, cloud storage, and more. Whether your data is structured in databases or scattered across unstructured files, Akooda makes it searchable, usable, and accessible in one place.

What makes Akooda stand out is its integration with large language models, allowing it to understand natural language questions and deliver clear, relevant answers. Instead of relying on dashboards or waiting for a custom report, anyone in the company can ask a question and immediately get the insight they need. These answers are drawn from the full scope of your business data, not just one system or source.

This turns your company’s data into a living knowledge base that supports faster, better, and more informed decisions. There are no barriers between departments, and no need for technical skills to get to the right information. With Akooda, decision intelligence becomes a reality. It gives you everything you need to use your data fully and confidently, turning information into action when it matters most.

See how Akooda can transform your scattered data into powerful insights. Book a demo today and experience the future of decision intelligence firsthand.