Data holds the key to business success and is increasingly the differentiator between winners and also-rans in today's most competitive markets. Artificial intelligence (AI) and business intelligence (BI) are two methodologies for working with data, and both are concerned with extracting insights in order to drive improved business performance.
Although the two concepts are often discussed together, they are not the same thing. BI generally focuses on the use of metrics, KPIs, and dashboards to analyze the past performance of a business in order to understand what happened, and why.
AI, on the other hand – in the business sense, at least – describes software algorithms, generally involving machine learning (ML), that can operate without being told specifically what to do. In other words, they are capable of analyzing data to become better and better at their task, which is usually making predictions about the future operation of a business.
(It's probably worth pointing out that outside of the context of present-day business use, AI refers to the whole spectrum of engineering, science, philosophy, and ethics involved with building machines that can simulate human thought. That's a bit beyond the scope of what we're talking about today, though. For the purposes of this article, AI refers to "smart" ML algorithms that predict business outcomes!)
Using BI and AI together effectively, in line with strategic goals, is vital for business success. It’s also the subject of an upcoming webinar hosted by AtScale, where one of the speakers will be Ram Kumar, Chief Data and Analytics Officer for Cigna. Kumar and other experts will discuss challenges around using data to create business value. He is also clear that although the processes can be challenging, the potential for generating positive business growth is huge, and it’s an opportunity that no organization can afford not to capitalize on.
How are AI and BI used?
BI has been widely used across many industries for several decades. However, although the terms AI and ML have been around for a while, it’s only comparatively recently that the technology has been within reach of most businesses. Over the last decade, however, a growing understanding of the technology and methodology, combined with falling hardware prices (AI is very compute-intensive) and an increase in the number of “AI-as-a-service” solutions, mean it is becoming increasingly accessible.
At the risk of slightly confusing matters – I should mention that although they are distinct methodologies, sometimes BI can involve AI! For example, some popular BI tools - such as Microsoft PowerBI – use AI to enhance certain functions, such as creating visualizations or cleansing and preparing data. But, as a basic rule of thumb … just remember that BI is about looking backward and reporting, whereas AI (specifically, ML) is about looking forwards and predicting!
Continue reading: https://www.linkedin.com/pulse/how-align-ai-bi-business-outcomes-bernard-marr/
Although the two concepts are often discussed together, they are not the same thing. BI generally focuses on the use of metrics, KPIs, and dashboards to analyze the past performance of a business in order to understand what happened, and why.
AI, on the other hand – in the business sense, at least – describes software algorithms, generally involving machine learning (ML), that can operate without being told specifically what to do. In other words, they are capable of analyzing data to become better and better at their task, which is usually making predictions about the future operation of a business.
(It's probably worth pointing out that outside of the context of present-day business use, AI refers to the whole spectrum of engineering, science, philosophy, and ethics involved with building machines that can simulate human thought. That's a bit beyond the scope of what we're talking about today, though. For the purposes of this article, AI refers to "smart" ML algorithms that predict business outcomes!)
Using BI and AI together effectively, in line with strategic goals, is vital for business success. It’s also the subject of an upcoming webinar hosted by AtScale, where one of the speakers will be Ram Kumar, Chief Data and Analytics Officer for Cigna. Kumar and other experts will discuss challenges around using data to create business value. He is also clear that although the processes can be challenging, the potential for generating positive business growth is huge, and it’s an opportunity that no organization can afford not to capitalize on.
How are AI and BI used?
BI has been widely used across many industries for several decades. However, although the terms AI and ML have been around for a while, it’s only comparatively recently that the technology has been within reach of most businesses. Over the last decade, however, a growing understanding of the technology and methodology, combined with falling hardware prices (AI is very compute-intensive) and an increase in the number of “AI-as-a-service” solutions, mean it is becoming increasingly accessible.
At the risk of slightly confusing matters – I should mention that although they are distinct methodologies, sometimes BI can involve AI! For example, some popular BI tools - such as Microsoft PowerBI – use AI to enhance certain functions, such as creating visualizations or cleansing and preparing data. But, as a basic rule of thumb … just remember that BI is about looking backward and reporting, whereas AI (specifically, ML) is about looking forwards and predicting!
Continue reading: https://www.linkedin.com/pulse/how-align-ai-bi-business-outcomes-bernard-marr/
