Enterprise AI: Data Analytics, Data Science and Machine Learning Posted on : Feb 23 - 2019

Key building blocks for applying artificial intelligence in enterprise applications are data analytics, data science and machine learning, including its deep learning subset. Data engineering also plays an important role.

In the first article in this series, we discussed how humans have always desired to better understand the present and predict the future.1 The algorithms to help achieve this understanding have been around for decades, including even those of artificial intelligence (AI) approaches for enabling computers to reason about things that normally require human intelligence. However, only in recent years have we accumulated the massive digital data and developed the sufficiently powerful processors needed to put these AI algorithms to work on real human and business problems, with excellent performance and accuracy, on a broad scale.

In this article, we describe some of the fundamental technologies and processes that enable enterprises to put AI to work to transform their businesses. In particular, we explain the concepts of data analytics, data science and machine learning, including deep learning. We also describe data engineering, which is an essential enabler for all of the above. This discussion will provide the basis for understanding deeper dives into machine learning (ML) and deep learning (DL) to follow in subsequent articles. These deeper ML and DL dives will, in turn, provide the foundation for additional articles in this series to demonstrate how these techniques are being applied in real-world enterprise use cases.

Data analytics

In today’s world, all enterprises generate massive amounts of data from diverse sources. Whether it’s from enterprise systems themselves, from social media or other online sources, from smartphones and other client/edge computing devices, or from sensors and instruments comprising the Internet of Things, this data is extremely valuable to organizations that have the tools in place to capitalize on it. The overall toolbox for these tools is called data analytics. View More