History says the 16th century was the time during which the rise of Western civilization occurred. During this time, Spain and Portugal explored the Indian Ocean and opened worldwide oceanic trade routes, and Vasco da Gama was given permission by the Indian Sultans to settle in the wealthy Bengal Sultanate. Large parts of the New World became Spanish and Portuguese colonies, and as the Portuguese became the masters of Asia’s and Africa’s Indian Ocean trade, the Spanish opened trade across the Pacific Ocean, linking the Americas with India.
Another linking happened between minds and machines during this time. French philosopher, scientist & metaphysician, René Descartes (1596–1650), came up with a world in his mind where machines could make decisions. And then, in 1956, an American computer scientist and cognitive scientist John McCarthy coined the term Artificial Intelligence (AI), which defines “the science and engineering of making intelligent machines.” AI is the ability of a computer program or a machine to think and learn.
As time rolled over, at present, in 2020, we are now using AI widely across sectors. Be it supporting organizations to take well-thought-out decisions, or something as regular routine as sorting our emails, or to even helping credit risk manager or detecting financial fraud, this branch of technology, by teaming up with advanced data analytics has all the markings for creating a revolutionary effect.
The AI scenarios show the technology’s unbelievable computational power, but in practical, operative applications begin with data. Data is the fundamental of any advanced analytics algorithms, which are the backbone of AI/ML models. It must be supplied in the required form that the algorithm understands. The main function of AI/ML algorithms is to unlock the concealed information available in the data. The algorithm will be resulting in incorrect insights if the data available is of poor quality. This might end in revenue loss for the project or organization. A Forrester report on “AI Experiences A Reality Check” indicates that the data quality is one of the utmost challenges towards accomplishing a desired result from the AI/ML systems in enterprises. Most organizations lack a clear understanding of the right data needed for ML models (according to Forrester), and hence businesses often struggle with data preparation as per need.
Human beings learn from experience. I remember when I learned things in my life, when I was younger, like hitting my finger on a hot plate taught me how to deal with it in the future through perception. On the contrary, machines follow instructions. They need to be trained, programmed to do things, e.g., any car manufacturing company has machines that put different parts together — they are programmed, they are just following instructions. But for machine learning is a process where both things are tied together — learning from experience and following instructions. Here the only difference is “learning from data,” so we need good quality data to make it effective. And to control the quality of data, one needs rules in place. So how is good data defined?
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