Artificial intelligence is a data hog; effectively building and deploying AI and machine learning systems require large data sets. Companies have needed cadres of data scientists or high-level analysts to put AI and machine learning algorithms in place, AI itself may ultimately help automate such roles to a large degree. As Matt Przybyla, senior data scientist and author of Toward Data Science, points out, there often still needs to be trained human guidance to AI and machine learning initiatives, especially if the output is critical to the tasks at hand. "Sure, use an automated data science platform if you already have a data analyst on your team. Data scientists and high-level data analysts will continue to be in demand, and are critical to helping enterprises design and test algorithms and data needed to predict trends, automate processes, understand customers, and engage with customers. Artificial intelligence is a data hog; effectively building and deploying AI and machine learning systems require large data sets. Companies have needed cadres of data scientists or high-level analysts to put AI and machine learning algorithms in place, AI itself may ultimately help automate such roles to a large degree. As Matt Przybyla, senior data scientist and author of Toward Data Science, points out, there often still needs to be trained human guidance to AI and machine learning initiatives, especially if the output is critical to the tasks at hand. "Sure, use an automated data science platform if you already have a data analyst on your team.

Data is the fuel for AI. Artificial intelligence is a data hog; effectively building and deploying AI and machine learning systems require large data sets. "The development of a machine learning algorithm depends on large volumes of data, from which the learning process draws many entities, relationships, and clusters," says Philip Russom of TDWI. "To broaden and enrich the correlations made by the algorithm, machine learning needs data from diverse sources, in diverse formats, about diverse business processes." At the same time, AI itself can be instrumental in identifying and preparing the data needed to increase the value of AI-driven or analytics-driven systems.

Artificial intelligence is a data hog; effectively building and deploying AI and machine learning systems require large data sets.