We know that Artificial Intelligence (AI) has something to do with machine learning and so does business intelligence (BI) with statistical modeling and data science. However, the details about the key differences can be difficult to understand. If you can comprehend how each of these technologies works, then it can be a lot easier for us to recognize these differences or know how they are interrelated.
When a machine learns by itself using advanced algorithms that analyze data automatically, it is termed as machine learning. Basically, it can be termed as the subcategory of AI.
Machine Learning uses data science extensively to arrive at inferences and hypothesis. On the other hand, BI relies on historical records stored in the relational databases. The data is mostly derived from an organization’s business system and is collected in data warehouses before data scientists analyze and organize it to answer important business questions efficiently. The structure of the database and the warehouse often decides the type of questions that could be answered.
Difference between BI & Predictive Analysis
The main objective of BI is to make better and more accurate strategic decisions. It is highly analytical and serves as a diagnostic tool which answers ‘what happened’ for which we got the result.
Furthermore, the analysis of data can predict the probabilities which otherwise would need intuition. As the outcome of the decisions getting predictable, it led to the development of predictive analytics which can answer ‘what would happen if we take a certain action. It uses more scientific methods to investigate the unknown, conducting different experiments and forming hypothesizes.
Difference between BI & Data Science
BI’s objective is to improve the strategic decisions of a business, while data science develops laws, hypothesizes and algorithms that can drive more business success. To apply and benefit from data science businesses need highly automated processes where the advanced algorithms and hypothesizes can be used to get better business results.
Difference between BI & Machine Learning
Machine learning goes even one step ahead helps the machines to learn and handle unprecedented situations. A system can be termed as a learning system when its behavior is not coded, but through its decisions, actions, and recommendations it improves its functioning on its own as it is exposed to a larger amount of data and unprecedented situations.
Self-driven cars are a classic example of machine learning. The reason is the self-driven cars have to navigate new roads and terrains under varying weather conditions moving through the traffic, roads, and pedestrians that they have never witnessed before. Even then they can do it successfully.
But how does the learning happen? Is there any way in which the machines learn and are they different in varying situations? Yes, there are different categories of machine learning algorithms which vary according to the level of human interventions necessary. Broadly, machine learning can be categorized into four types. They are: a) Supervised learning, b) Unsupervised learning, c) Reinforced learning and d) Deep learning.
- Supervised Learning: The algorithm is fed with a set of data which contains labels on some portion of the observation. For instance, the data set being fed may contain labels on some of the data points identifying the ones that are valid, fraudulent and invalid or not fraudulent. From this data set, it will learn and formulate a general rule of classification of data which it can use to the other data sets. This is termed as supervised learning as data sets are being fed with labels which assist in building the cognitive system and learning.
- Unsupervised Learning: In the case of unsupervised learning there are no data labels fed, but instead, the algorithm itself goes ahead and detects patterns by identifying characteristics and clusters. For instance, an unsupervised machine learning algorithm can look for financial securities which are illiquid and are hard to price. Pricing of other securities can be used to price the illiquid security.
- Reinforced learning: It falls in between supervised and unsupervised machine learning where the algorithm is fed with an unlabeled set of data. It chooses an action plan for each cluster of the data point with an intervention of human feedback which helps the algorithm to learn. This kind of learning is seen in self-driven cars, game theory, and robotics.
- Deep Learning: Just like the human brain, deep learning uses layers of an algorithm whose structures are termed as artificial neural networks. Such layered algorithm can be used for all the three types of learning like supervised, unsupervised and reinforced machine learning. Deep learning employs a kind of a mathematical model which is rather disparate blocks of endowed cognitive ability which can be adjusted to refine the accuracy of the final outcome.
Thus, business intelligence and machine learning deploy different approaches to solve different problems. To put it simply – business intelligence is directed to understand, infer, and improve business situations by improvising better decision making, while machine learning automates this entire process of decision making.