Simply put, AI Analytics is the discipline of automating data analytics using artificial intelligence and machine learning.
This approach to analytics takes tasks that are manual and repetitive for human beings, and structures them in a way that machines can understand and solve for computationally, ideally with no human input during the process.
AI Analytics helps significantly reduce the time and effort spent to arrive at a solution for an analytical problem, with just as good (if not better) accuracy.
How does Machine Learning fit into AI Analytics?
Machine learning is a subset of Artificial Intelligence, in which a machine is presented with a large amount, and recognises the patterns pertaining to the question that is being asked.
Whilst machine learning algorithms employed in AI analytics are powerful, they do not represent artificial general intelligence (AGI) – they have some common prerequisites:
- Different algorithms are employed to answer different questions – for example, identifying customer sentiment from comments likely requires Natural Language Processing, which is a different discipline to identifying cancer markers in MRI scans using computer vision, and again yet to answering business questions like “Why are sales down today?”
- These algorithms depend on the data being appropriate structured – even with the proliferation of data warehouses and data lakes, poorly structured data is of little use.
- There are a number of different classes of machine learning – supervised, unsupervised and reinforcement – each depends on the scenario and how much training data and system response is available.