With businesses eagerly pursuing big data analytics, it only stands to reason that they'd look for the methods and strategies that will best help them get the most out of it. There are many ways to perform analytics, and each will change depending on the type of business and what insights organizations want to gain. With this variety, big data has clearly grown in popularity, with a recent survey from Gartner showing that 75 percent of companies are either currently investing in big data initiatives or plan to do so within the next two years. Even so, many companies have found utilizing their big data to be a difficult and at times arduous process. The traditional analytical approaches have trouble managing the vast volumes of data businesses can now collect, and as a consequence, the results aren't always the most accurate. That's not to mention how long it takes to get those results in some instances. To combat these issues, many organizations are turning to machine learning techniques, with promising outcomes hinting at its potential.
From the outside, data science is often thought to consist wholly of advanced statistical and machine learning techniques. However, there is another key component to any data science endeavor that is often undervalued or forgotten: exploratory data analysis (EDA). At a high level, EDA is the practice of using visual and quantitative methods to understand and summarize a dataset without making any assumptions about its contents. It is a crucial step to take before diving into machine learning or statistical modeling because it provides the context needed to develop an appropriate model for the problem at hand and to correctly interpret its results.
Machine Learning (ML) algorithms are embedded in the fabric of much of the technology we use every day. ML innovations spanning computer vision, deep learning, natural language processing (NLP), and beyond are part of a larger revolution around practical artificial intelligence (AI). Not autonomous robots or sentient beings but an intelligence layer baked into our apps, software, and cloud services that combines AI algorithms and Big Data under the surface.
It's a brand new year, and a good time to look into the future to see what the next 12 months will bring. I listened to my friend Bridget Karlin make her predictions on the radio program Coffee Break with Game-Changers, which compiled what 80 thought leaders in technology, business and academics foresee for companies and industry in the coming year. Karlin, who is Intel's managing director of Internet of Things (IoT) Strategy and Technology, made the prediction that in 2017, artificial intelligence in all its various forms will go mainstream.
AI, machine learning, and predictive analytics rely on massive data sets. While holding the potential for great benefit to society, this explosion of data collection creates privacy and security risks for individuals. In this episode, one of the world's foremost privacy engineers explores the broad privacy implications of data and artificial intelligence.
In the movies, the emergence of Artificial Intelligence usually leads to robotic cyborgs going haywire, machines that threaten humanity, or a choice between a red or blue pill. The plotline almost always assumes the worst will happen. Thankfully, these aren't the droids you're looking for. Instead, AI has tremendous potential to help improve performance for nonprofits and social good organizations and drive meaningful change in the world.