I often get asked to define the differences between BI (business intelligence), AI (artificial intelligence) and analytics. For many organizations, there seems to be so much overlap that it’s difficult to know where one technology ends and the other begins — or even whether these technologies can be used concurrently.
What is business intelligence?
Business intelligence is a broad category of information management, analysis and reporting that operates on both structured and unstructured data. BI can yield insights for organizations about their markets, the “fit” of their products and services in these markets, and the effectiveness of their internal operations, too.
The business intelligence toolkit is far-reaching. It can include:
- Standard reporting
- Analytics reporting
- Data mining
- Performance management
- Implementations of artificial intelligence
Collectively, it is the orchestration and implementation of all of these technologies that comprises the operations of business intelligence for an organization.
SEE: Best business intelligence tools (TechRepublic)
What is analytics?
Analytics operates on both structured and unstructured data to support corporate decision-making. It uses standard report-style queries, as well as more complex AI algorithms that find unique patterns in data and deduce insights from them.
Several types of analytics are widely used across organizations — from marketing, to operations, finance, customer service, IT and HR. Analytics can be:
- Diagnostic: As in, what underlying events contributed to an increase in sales last quarter?
- Descriptive: Did we meet our company’s KPIs (key performance indicators)?
- Predictive: Which components in our assembly production lines are most likely to fail this year?
- Prescriptive: What is this online buyer likely to purchase next, based upon past purchase and browsing history?
SEE: Top Data Analytics Tools (Datamation)
What is artificial intelligence?
Artificial intelligence is “intelligence demonstrated by machines, as opposed to the natural intelligence displayed by animals including humans.”
In the context of BI, AI might incorporate human-provided input (from subject matter experts, research, etc.) with machine learning (ML) to identify patterns in data. The AI then begins to draw inferences based upon this pattern recognition.
AI relies heavily on complex statistical algorithms developed by data scientists to interrogate an array of both structured and unstructured data. In this way, AI can produce insights for decision support. It can also be used to autonomously operate processes without human intervention.
For example, one use case for AI is in the credit card industry, where a system is trained to look at consumer card usage patterns and identify possibly fraudulent behavior.
SEE: Top AI Software & Tools (eWeek)
What are the differences between BI, AI and analytics?
BI, AI and analytics all deliver insights that enable organizations to perform better, to predict the future and to meet the needs of their markets. However, there are some fundamental differences between these concepts in scope and function.
Business intelligence is an overarching framework for analytics and AI. In contrast, analytics can be used in more of a standalone fashion if desired. For instance, a sales team may purchase analytics software so it can assess markets.
SEE: Hiring Kit: Artificial Intelligence Architect (TechRepublic Premium)
AI automates reasoning processes to either eliminate or reduce human work. For example, an industrial robot with onboard AI may perform an operation on a manufacturing assembly line that a human formerly carried out.
Can you use BI, AI and analytics together?
Analytics and AI can be integrated into a larger BI framework, but they don’t have to be.
The advantage of integrating analytics tools and AI into a BI tech stack is that you have an end-to-end data management, decision-making and operational infrastructure for your enterprise.
If you choose to do this, the first step is to develop the BI framework that will accommodate both the analytics and the AI.
The next step is to populate this framework. For example, where in your organization are you going to use analytics, where will you automate with AI, and how will you facilitate data sharing throughout your entire company?