With the penetration of computers into all aspects of business, all enterprises today generate huge amounts of data from a wide range of sources. This can come from a wide range of sources including the internal systems the enterprise uses to record and manage its customer interactions as well as social media and other online sources, mobile devices and other edge computing devices and sensors and instruments that are used in the course of producing products or providing services. This data is extremely valuable to an organization but the challenge is how to manage it and capitalize on the insights that are hidden within it.
Advanced data analysis tools provide the means to capitalize on this sea of information through the use of a range of techniques that find patterns in the data from which insights (and foresights) can be drawn. They can be used not only to determine what has happened in the past and draw conclusions about present activities but also to predict what may happen in the future based on past events. To do this advanced methods are required; there are no easy formulas for deciding what information is valuable to a particular organization and how it must be analyzed.
Artificial intelligence, as it stands today, refers to computer software that can be programmed to analyze data and make decisions about classification according to certain rules and parameters that would ordinarily be performed by a human being; the huge amount of data that can now be processed, however, makes human intervention impractical. Examples of advances in AI that are in use today include image recognition, natural language processing and speech recognition. In business similar insights can be drawn by analyzing aspects of customer behavior from reams of data harvested from their interaction with the organization and finding patterns that can provide unexpected, and sometimes counter-intuitive, results and insights.
Machine learning (sometimes called deep learning) is the aspect of AI that is most capable of performing this type of advanced data analysis. For example, a computer system that analyzes massive numbers of credit card transactions can determine when a transaction has attributes that indicate it may be fraudulent. This can be done by analyzing a customer’s usual spending habits and determining when a transaction is anomalous based on that data set.
In fact the potential uses for this type of AI are almost unlimited; it can provide insights in data gleaned from financial services, healthcare, manufacturing and any other type of directed business activity. If an organization has massive amounts of data, these systems can be used find and understand patterns in it that can often lead to surprising and valuable insights.
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