The world is crazy about data. There has been much talk lately about possession of data, quality and quantity of data. Data is a prerequisite for sensible automation and analytics. Data is at the heart of every AI solution. Artificial intelligence, like machine learning, is the processing of data into specific results like predictions, recommendations, or summarization. But possessing data is not enough to receive valuable results.
What is data?
Data is everything. And everywhere. Data is emails, phone call logs, textual documents, budgets, invoices, contracts, Internet searches, pictures, graphs. Every piece of information you work with retains data. Some of it is structured, clearly understandable and easy to use (e.g., budgets, excel sheets, invoices, quantitative information under the form of tables), but most of it is not. 80% of the data you possess is unstructured and contained in your emails, customer claims, legal forms.
To be understood fully, structured and unstructured data require machine learning models based on various techniques and approaches, like natural language processing (see our article: What is Natural Language Processing (NLP)?). These techniques turn data into business gold.
Is data really gold?
If you are a scientist or just involved in R&D processes, you know how difficult it is to find that specific information hidden somewhere in your files. With only manual research, you will have to review all your documents referring to the topic or issue you are researching. Even minor omissions may lead to unexpected and negating effects.
The rapid development of AI-based tools gives many 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 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 global overview.
Learn how to use your data. Machine learning models (also called deep learning models) can find correlations, causes for effects and sometimes surprising dependencies between what appeared to be unrelated data. With such support, scientists and workers can spend more time on their research and companies can organize their work more effectively, draw parallels where needed and save time and money.