Is machine learning for big data analytics just a new buzzword, or is this approach really finding its own way? If we want to answer this question we should probably start from recognizing the fact that big data is definitely too much information for a human analyst; and if we think about all of the possible correlations and relationships that occur between entities and sources, big data tends to look even bigger than what the name suggests.
Now, let’s suppose that you are a company, and you are collecting this huge mass of information, which is quite a challenge on its own. Then, you start to dig into it to find the possible clues that would help your business, or simply ive you more confidence in making better decisions faster. As we said, you realize that you are dealing with something immense, and that your analysts could use a little help in searching out useful insights.
That’s where machine learning for big data comes into play. As a matter of fact, in a pure machine learning process, the more data you provide to the system, the more it can learn from it, returning all of the clues you were looking for, and that’s why it works so well with big data. Without it, the machine learning cannot run at its optimal level and this is due to the fact that with less data, the machine has fewer examples to learn from, and as a consequence, the result of its efforts might be affected.
“That’s not a big deal” you may start thinking. In fact, the solution might seem obvious: “Let’s use machine learning for big data analytics, and that’s all!” Lots of data equals lots of examples for the system, equals good results. But is that really so?
How many times have we heard and probably agreed with the concept “quality above quantity”? Then why should we rely on a system that is too much exposed to the quality of the data? What If the “good examples” for its training are too few compared to the mass of data with which we fed the system? And what if some of the data is misunderstood because of the natural complexity that lies within the human language used to create it?
We would certainly be disappointed thatour results weren’t as good as we expected after such an investment! But don’t worry, at Expert System we also believe that machine learning is a good solution for big data analytics, but we prefer to add our semantic experience and expertise to it.
Think about the results you could achieve with a system that combines the machine learning approach to Cogito’s natural language processing. You won’t have to worry about finding enough samples to train the system, or about trying to understand the resons behind sub-par results because Cogito will fully understand all of your data and documents like a human would do. With Cogito, you get high quality results from the beginning thanks to its embedded knowledge graph and with its ability to progressively improve the results using a combination of linguistic rules and machine learning. This approach will provide you only the relevant information you need to speed up your business decisions and get the most out of your big data.