Understanding the differences between BI, AI and analytics
The distinctions between BI (business intelligence), AI (artificial intelligence), and analytics are frequently sought about. There appears to be so much overlap in many businesses that it’s difficult to tell where one technology ends and the other begins — or even whether these technologies can be employed at the same time.
What is the definition of business intelligence?
Business intelligence is a broad term that encompasses data management, analysis, and reporting for both structured and unstructured data. Organizations can use BI to learn more about their markets, as well as the “fit” of their products and services in those markets and the efficiency of their internal processes.
The scope of the business intelligence toolbox is extensive. It may consist of the following:
- Standard reporting
- Analytics reporting
- Data mining
- Performance management
- Implementations of artificial intelligence
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.
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.
Is it possible to combine BI, AI, and analytics?
Analytics and AI can, but do not have to, be linked into a larger BI system.
The benefit of incorporating analytics and AI into a BI tech stack is that you can provide your company with an end-to-end data management, decision-making, and operational infrastructure.
If you decide to go this route, the first step is to create a BI platform that can handle both analytics and AI.
The next step is to fill in the blanks in this structure. For example, where will you apply analytics in your organization, where will you automate with AI, and how will you encourage data exchange across the board?