BI is an overarching framework used by businesses to prepare data for analytics reporting and AI use cases, which in turn uphold business operations and decision-making.
Defining the differences between business intelligence, artificial intelligence and analytics often poses a challenge to many people. For many business processes, 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 also yield insights for organizations about their markets, the “fit” of their products and services in these markets and the effectiveness of their internal operations.
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The business intelligence toolkit is far-reaching. It can include:
- Standard reporting is the generation of regular, routine reports, such as financial statements, sales performance and other key metrics, that furnish ongoing insights into business operations.
- Analytics reporting goes beyond standard reporting by analyzing data to uncover deeper insights, trends and patterns.
- Data mining involves exploring large datasets to ascertain meaningful patterns, correlations and insights, often utilizing statistical methods and machine learning.
- Dashboards are user-friendly, visual representations of key metrics and data points that furnish a quick and easy way to monitor business performance at a glance.
- Performance management involves tracking and managing the performance of the organization against its objectives.
- Implementations of artificial intelligence in BI involve using machine learning algorithms and other AI technologies to automate data analysis.
Collectively, it is the orchestration and implementation of all of these technologies that comprise the operations of business intelligence for an organization.
What is artificial intelligence?
Artificial intelligence is a technology that uses pattern-recognition to perform tasks that demand human intelligence at a scale that would be difficult or impossible for humans. In business intelligence, AI often combines insights from human experts, including subject matter experts and research, with machine learning algorithms to recognize patterns in data. The AI then begins to draw inferences based on this.
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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 uphold. It can even be used to autonomously function 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 recognize possibly fraudulent behavior.
What is analytics?
Analytics operates on both structured and unstructured data to uphold 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 human resources. Analytics can be:
- Diagnostic: This type of analytics investigates the causes of past events or outcomes, which helps users comprehend the factors or actions that gave rise to a particular result. For example, a rise in sales in the last quarter.
- Descriptive: In descriptive analytics, historical data is summarized and interpreted to comprehend an event or outcome. For instance, did the company confront its KPIs?
- Predictive: This type of analytics uses data statistical methods and machine learning algorithms to anticipate future outcomes based on historical data. For example, manufacturers can use predictive algorithms to monitor for infrastructure failure.
- Prescriptive: Prescriptive analytics goes beyond predicting future events to suggesting actions that can be taken to influence desired outcomes. For example, analyzing online past buyer behavior and influences.
What are the differences between BI, AI and analytics?
BI, AI and analytics all deliver insights that enable organizations to perform better, anticipate the future and confront 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 stand-alone fashion if desired. For instance, a sales team may purchase analytics software, so it can evaluate markets.
AI automates reasoning processes to either eradicate 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 select to do this, the first step is to evolve 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 streamline data sharing throughout your entire company?
