Obviously, more data will become a hallmark of artificial intelligence assisted solutions. The thirst for data may come from more challenging issues, better utilization of advanced artificial intelligence/analytics, or the growth of the end-to-end value chain.

Those engaged in machine learning (ML) projects know that machine learning requires a large amount of data to train algorithms. Some people may say that there is never too much data. There is usually a positive correlation between the amount of data and the complexity of the generated machine learning model. As artificial intelligence develops into new fields, the functions used in artificial intelligence become increasingly complex, and this thirst for data will only become stronger. In addition to the complexity of artificial intelligence, other trends are also exacerbating this issue, leading organizations to ask the question: "Do they have the appropriate data to successfully drive AI projects?" If they do not have sufficient resources, should they do more to prepare for the AI feast?

The development of artificial intelligence has changed data games

Although machine learning requires a large amount of data to correct its own behavior, as the complexity of artificial intelligence functions increases, the demand for data in artificial intelligence will also rapidly increase. The transition from machine learning to deep learning (DL) is a significant step forward, and deep learning requires much more data than machine learning. The reason is that deep learning usually only recognizes conceptual differences between layers of neural networks. When exposed to millions of data points, deep learning can determine the boundaries of concepts. Deep learning enables machines to represent concepts through neural networks, just like human brains, and thus solve more complex problems. Artificial intelligence can also solve more ambiguous problems, and the answers to these problems are often more uncertain or ambiguous. This is usually a matter of judgment or recognition, which can be extended to creative or other right brain activities. This in turn leads to more demand for data, which in some cases may be urgent or real-time in nature.

The transition from data-driven to result driven

Artificial intelligence continues to develop in assisting or solving complex problems, and with this trend, it will become data-driven and goal/result driven. This means that artificial intelligence may request data in real-time while solving specific problems or making specific inferences, making data management more complex. It may involve the interaction between the inductive data-driven part of the solution and the data deduction requirements assumed to achieve the goal. Result oriented problems require this type of dynamic interaction. This is very different from simply retrieving data to find events or patterns of interest. The decision driven approach falls precisely between these two distinct patterns. By matching data and results, it is possible to focus on the operational status of some decisions and make improvements. Both induction and deduction will lead to more strategic decisions. This is only one of the driving forces behind the demand for data usage.

The constantly changing scope of issues affects data requirements

The scope of artificial intelligence solutions typically starts from narrow areas and progresses over time