Modern business organizations generate and store large volumes of data as part of their daily operations. However, this data was underutilized earlier due to two key reasons:
- Scattered Data Sources: Data was stored across multiple archive systems that were not integrated.
- Lack of Tools: Organizations lacked awareness or access to software tools that could extract useful insights from the data.
To solve these problems, Machine Learning (ML) has emerged as a powerful technology that enables organizations to process large datasets and make better decisions.
Reasons for the Popularity of Machine Learning
Machine learning has gained widespread popularity due to the following three factors:
- Explosion of Data:
Companies like Facebook, Twitter, and YouTube generate massive amounts of data, and this data is estimated to double every year. - Reduced Storage Cost:
Hardware costs have dropped significantly, making it easier to capture, process, store, and transmit digital data. - Advanced Algorithms:
With the emergence of complex algorithms and deep learning techniques, more powerful and efficient ML models are now available for practical use.
The Knowledge Pyramid
Before diving deeper into ML, it is important to understand five core concepts that form the Knowledge Pyramid:
| Level | Explanation |
|---|---|
| Data | Raw facts such as numbers or text that can be stored and processed. E.g., sales records. |
| Information | Processed data showing patterns or associations. E.g., identifying the best-selling product. |
| Knowledge | Condensed information that shows trends. E.g., forecasting future product demand based on sales history. |
| Intelligence | Knowledge that is put into action. E.g., using trends to adjust marketing strategies. |
| Wisdom | Human-level decision-making based on intelligence. So far, this is achieved only by humans. |