AI, big data, and Machine Learning are all trending buzzwords and you as a business leader or manager may get driven by these, but how can you know which business problems Machine Learning is capable of solving for you?
The best way is to think of the available data and ask questions about feasibility and expectations.
Typically businesses think of Machine Learning when it comes to automation. However, a key question to ask is – does the problem need learning?
For example, validating data coming from various sources could be handled by writing business rules as there is little learning involved. But if the problem needs an understanding of the text entered by the user, it is a valid problem to solve using Machine Learning. Because it needs learning from the past data.
If we look at the core of Machine Learning, it’s actually a set of statistical methods that derive patterns or trends in the data set and predict the possibility of future events based on it. Important to note here is that these methods cannot have knowledge outside of the data you fed to it.
The following two criteria can help assess what business problems are a good fit for machine learning –
- The problem needs prediction – by relating data to each other and not the causal relationship.
- The data available is enough – for the machine to learn with no outside influences.
A good example of a problem to solve using machine learning could be to predict if a user will click on the ad, while a bad example is predicting sales of a newly launched product. Especially when COVID circumstances changed the market dynamics completely.
Machine learning may seem magic but it is not. It is statistics. So, it is important to understand how statistical methods work and if a method applied roughly makes sense to you. Asking lots of questions to the data team is a good way to be comfortable with what and how a machine is going to do through its learning.
Also, you need to appreciate the fact that on average the algorithms work well but there can be instances when they go wrong. This comes with the expectation of the extent of errors you can work with – like 20% or 5% or 1%? and what types of errors are not acceptable. Once this is clear, Machine Learning is a smooth and comfortable ride for you and your business.
Reference: HBR guide to data analytics basics for managers