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Having data brings no results if you are not able to use it.

How ML and NLP can help transform data into valuable outcome


We are talking a lot about the possession of data (of good quality and quantity) that is a prerequisite for sensible automation or analytics. This is a fact. Data is the heart of every artificial intelligence (machine and deep learning) as every solution of this type is processing data into specific results, like predictions, recommendations, or summarization. You have probably heard – garbage in, garbage out. No doubts that it is true. But just having data is not enough to receive helpful results.


[Note: according to TechJury ‘the big data analytics market is set to reach $103 billion by 2023]


Data (structured and unstructured) require models based on various techniques and approaches, including machine learning and natural language processing, that are transforming data into ‘gold’. Models and tools are trained on data and the accuracy and effectiveness of the underlying model is the second (or equivalent) most important factor for every application of AI.


[Note: according to BARC ‘[t]he organizations that are reaping the benefits of Big Data reported an average 8% increase in revenues while there is a 10% reduction in costs’]


If you are a scientist or person involved in R&D processes, you know how difficult is to find that ‘one specific information that is hidden somewhere in files (if you are lucky since a lot of data is still not in digital format). With manual research, you have to review all (non)important document (be it books and articles) that refers to the topic or issue you are working on. Even minor omissions may lead to unexpected and adverse effects. Do you remember one of the episodes of the Big Bang Theory when Sheldon’s friends have discovered an old article that somehow disapproved of his theory? This might be the case. Losing even one ‘number’ or ‘word’ may be sometimes detrimental.


The rapid development of AI-based tools gives a lot of opportunities for data mining, extraction, and search-based tasks. With well-trained models, you will be able to scrape all resources you have or would like to have (the Web is quite large) and find relevant information in an accessible manner. Such tools can find phrases, keywords, or parts of the text, link them with other resources, and give you a truly wide overview and background. If you wish the machine or deep learning models will find correlations and sometimes surprising dependences. With such support, you will be able to spend more time on real research and organize your work more effectively. With little – but not zero – effort. Why? Let’s talk!



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