Both machine learning and artificial intelligence are important applications of data science. Each of these technologies is built upon our ability to analyze data, but now both are being used to analyze data. For the field of business intelligence, this has profound implications.
What Is Business Intelligence?
Business intelligence refers to the many ways that businesses can utilize data in order to learn more about their market, themselves, and their audience. Business intelligence has always been around in some form, but it is only in recent years since data went digital and access to large volumes of it became viable for the average person that business intelligence has really taken off as its own discipline.
Business intelligence is now an essential component of many businesses’ strategizing efforts and planning. By pulling data from a variety of different sources, businesses can gain access to insights that previously would have remained elusive to them. Business intelligence can be deployed in a variety of different contexts, and this is reflected in the range of businesses that implement business intelligence to some degree.
For example, perhaps the most familiar implementation of business intelligence is in the recommendation systems that websites like Netflix and Amazon use. These systems rely on an input of a large volume of data about customers’ buying preferences and what they do on the platform. By feeding this data into machine learning algorithms, businesses can identify patterns that would be invisible to a human, but which can be used to provide highly accurate recommendations.
Another very important, although not as obvious implementation of business intelligence, is as a fraud detection tool for banks and insurance companies. This implementation again relies upon the use of machine learning and artificial intelligence in order to spot patterns that would otherwise be invisible.
In more general terms, business intelligence provides businesses with valuable insights into the behaviors of their customers and their competitors. With business intelligence, data that is being generated anyway can be deployed to provide a solid foundation for actionable strategies. The insights provided by artificial intelligence and machine learning-driven business intelligence have endless applications and can provide businesses with invaluable insights into all areas of their operations.
If you are interested in either AI or machine learning, especially in the context of their potential business applications, then a business intelligence MBA could be the perfect degree choice for you. A number of leading universities, including Suffolk University, now offer an MBA with a focus on business intelligence. You can find out more information about the degree and the potential careers it unlocks at the Suffolk University website.
A New Frontier
The data is all around us in the modern world. Wherever we go and whatever we do, if a digital device is involved, then there will be some kind of data generated. In general, unless we take specific measures to prevent businesses from collecting our data, they will automatically gather as much of it as they can. Once you understand the power that this data potentially offers to businesses, it isn't difficult to understand why they're so eager to get their hands on so much of our data.
However, the data in and of itself is not very useful; it only becomes useful when it is properly analyzed and processed. Data is a raw resource, and like any raw resource, it must be mined before it can be deployed for anything useful. Business intelligence is just one application of data analytics, which itself is a field of huge importance to businesses today.
Artificial intelligence and machine learning are both different technologies that have made a huge difference in our data processing capabilities. By feeding machine learning algorithms more and more data, we can train them to be more and more sophisticated. Within the context of business intelligence, this enables us to train algorithms to intelligently analyze data and automatically gather insights from it.
Incorporating the conclusions that are drawn from business intelligence analytics into general strategies can lead to businesses turbocharging their workflow and achieving significantly improved efficiency throughout.
Both machine learning and artificial intelligence are needed if businesses are going to reliably automate their data analytics to a high degree. Advanced algorithms can certainly provide some valuable guidance to humans, but it is always best to have some level of human oversight. No AI system is yet advanced enough to run an entire business on its own, and some decisions require dynamic human thought regardless of what the data says.
The Importance of Good Data
Of course, as you can probably guess for yourself, data analytics is virtually impossible if you don't have good data to work with. In fact, even a relatively small amount of bad data can completely undermine any machine learning algorithm. In some cases, bad data can be inserted deliberately, in a process known as ‘poisoning the well’, with the intention of skewing the output of the machine learning algorithm.
The need for good data is at the heart of how machine learning and artificial intelligence works, so the first thing that you need to do with any machine learning algorithm that you want to train is to give it a large number of what you know to be correct solutions to the problem that you wanted to solve.
If you aren't able to provide the algorithm with an example of what is correct, it has no way of working that out for itself, and you have no way of writing code to tell it how to work it out. Take the example of a machine learning algorithm that is designed to work out if something is a banana or is not a banana.
As a person, it is (hopefully) easy for you to distinguish whether something is a banana or isn't a banana. But how would you explain this to a computer? You could describe the banana as being a yellow fruit, but there are lots of things that are yellow and there are lots of things that are fruit - there are even a few things that are yellow and fruit and aren't a banana.
There is no way of coding this innate understanding that human beings have into a machine. However, by showing a machine learning algorithm a huge database containing hundreds of thousands of pictures of things that are bananas and another database of things that aren't bananas, the algorithm can then compare the two and work out what it is that distinguishes one from the other.
If you want your business intelligence algorithms to be able to work for you, then you need to be able to train them appropriately. Even if you understand how to do this in principle, actually doing it can be significantly more difficult.
Where Does Data Come From?
Data is all around us in the modern world, and whether we are aware of it or not, we are all generating it constantly as we go about our day. This doesn't stop when we are at work; in fact, many of us will be generating the majority of data over the course of our professional lives. For businesses, there are always data being generated in some form, whether it's data about sales or data about staff or data about customers.
Much of this data ultimately goes to waste and is never gathered or recorded. However, when businesses allow data to simply vanish in this way, they are missing a tremendous opportunity. Given how valuable data can be when properly analyzed, businesses should be more aware of what data they are generating and how they could potentially collect and utilize it.
Data scraping from online sources is another common way for businesses to obtain the data that they need as part of their strategizing. With regard to analyzing their marketplace and their competitors, many businesses are finding that data scraping from online sources provides them with the most valuable data in the most cost-effective way. As soon as any business finds itself gathering large volumes of data, it is going to need to develop some kind of tool to at least partially animate the analysis process.
Human data analytics is a valuable skill, but it is very different from data analytics as carried out by an algorithm. The purpose of the algorithmic approach is to identify patterns that would elude a human - they would simply be unable to find those connections. This means that there is no telling how deep the insights that businesses can gain from AI-driven business intelligence run. Business intelligence algorithms might produce insight into relationships that would otherwise never have been discovered.
Machine learning has rapidly transformed the ways that businesses, consumers, and scientists think about data. We now understand that data is a powerful and valuable resource, provided it is processed properly from its raw form. Business intelligence is just one of the many implementations of data analytics in the modern world, albeit one of the fastest-growing. Both AI and machine learning are completely rewriting the rules when it comes to data analytics.