Driving Digital Strategy – How Does it Work? – Part 4

AI and Machine Learning in Business Data Analysis: How Does it Work?

All three forms of advanced software rely on automation and model-building to collect, analyze, and interpret data. For data analysis software to be useful in companies, it must be able to perform three key tasks, which are anomaly detection, clustering, and classification. These three tasks are the base of data analysis, and, at the same time, very well integrated into the concept of the machine.

Self-Improvement

With added AI features, you can further create algorithms that equip a technology to self-improve. This means that it can use statistical information to detect pattern changes, and then make decisions without needing external programming. For example, you can use both supervised and unsupervised (AI) software to estimate the price of real estate. The first will browse similar properties online, review their prices, and compare relevant parameters, while the unsupervised software will also look for hidden patterns.

Applied Algorithms

Applied algorithms determine how the software will manage information. This means that different algorithms are used for different tasks and operations. Operations such as these combine the use of four types of four main algorithms that are crucial for data analysis:

• Clustering. Clustering searches and identifies input patterns. This algorithm is a form of ML algorithm that searches for all specified items within a certain category. It can help you distinguish how each category of your products performs sales-wise, and you can use this information to cross-compare. Or you can use clustering to quickly sort through your products or their data.

• Learning. AI algorithms apply the so-called “decision tree” learning, whose visual representation resembles a tree, but upside-down. The bottom of the tree contains the biggest number of options, which then narrows down towards the choice that is most likely to produce a targeted outcome. This form of learning can be used, for example, to create product lines depending on the most sought-after product traits by a certain customer segment or based on hard data.

A second type of learning is known as ensemble learning. This form of learning combines a multitude of decision trees, each consisting of relevant data sets, and then adds a random element to make a most probable prediction.

• Support-vector. This algorithm identifies typical patterns to categorize data according to parameters. When trained to recognize certain object traits, it can identify a type of product or even a human face.

• Regression. This operation works with cause and effect, and uses two different algorithms for detecting causal relationships:
  • Linear. This form of regression compares two data sets and discovers variables and correlations between them. It is often used for business forecasting.
  • Logistic. While linear correlations are more straightforward, and they speak of how one factor affects the other, logistic regression is better suited to linear classifications.

However, no matter how much you invest in cutting-edge improvements, any final decisions and evaluations must be made by human experts. No matter how refined AI algorithms are, they are also prone to making mistakes and not accounting for sudden, yet relevant, factors when making decisions.

All three forms of advanced software rely on automation and model-building to collect, analyze, and interpret data. For data analysis software to be useful in companies, it must be able to perform three key tasks, which are anomaly detection, clustering, and classification. These three tasks are the base of data analysis, and, at the same time, very well integrated into the concept of the machine.