Data Mining

There's a significant issue with big data today: fully using it requires data scientists who are expensive and often a bottleneck on data utility. Big data is complex, and it takes specialist knowledge and skills to get a grip on the potential uses. For big data to fully reach its potential, businesses need to get beyond the data scientist. This relief is coming in the form of artificial intelligence. While AI won't replace the data scientist any time soon, it can assume some of the tasks currently handled by data scientists.

Northside Hospital in Atlanta is adopting machine learning technology to enable the organization to predict when insurance companies will end payments. The new technology it's using is from The SSI Group, which is providing technology that aggregates all remittance data coming through its clearinghouse to make the predictions. The goal is to enable providers that manually build their own spreadsheets to predict payments to use the SSI technology to determine when they can expect to get paid, down to the day and time, according to the vendor. "Without predictive analytics, hospitals and other providers are left guessing when they will receive payments," says Eric Nilsson, chief technology officer at SSI. Using analytics, SSI can give greater visibility on the payment of institutional, professional, in-patient and out-patient claims.

Across industries, Big Data and Artificial Intelligence (AI) have proven to be powerful tools when it comes to informing companies about their target customers. Gartner predicts that by 2019, more than 50% of organizations will redirect their investments to customer experience innovations. As a result, many organizations have built teams to collect and analyze data on every step of the customer journey – taking into account where, why and how customers interact with their channels. By analyzing this data in real time, companies are able to keep up with evolving customer demands. Dissecting every interaction to understand what drives customer behavior may seem like a gargantuan task for many.

We expect the landscape to be an integrated edge-to-core-to-cloud solution enabling what today is called IoT, Big Data, Fast Data and AI. Each time a promising new technology emerges, we seem to go through a period where it is proposed to be the solution to everything--until we reconcile how that technology fits into the bigger picture. Such is the case with artificial intelligence (AI). Clearly the advancements in deep learning will create new classes of solutions but rather than being a standalone solution, we are just now beginning to see how it fits into our IT landscape. AI emerges at a time when several other shifts in analytics technology are occurring.

May Masoud is a Solution Specialist at SAS Canada, as part of the Data Sciences team. Leveraging her analytics background, she helps businesses visualize the potential of their data, and surface insights using modern data mining and machine learning techniques. With a Master of Business Analytics following a Bachelor in Statistics & Economics, May aims to create value at every step of the analytics lifecycle: data discovery, model build, model deployment, and business strategy. She has touched the analytics landscape in a variety of industries, whether it is oil production models for the energy sector or solving churn problems in the telecom industry. May's aim is to ubiquitize self-serve analytics and enable citizen data scientists.

This Web page is aimed at shedding some light on the perennial R-vs.-Python debates in the Data Science community. As a professional computer scientist and statistician, I hope to shed some useful light on the topic. I have potential bias: I've written four R-related books, I've given a keynote talk at useR!; I currently serve as Editor-in-Chief of the R Journal; etc. But I hope this analysis will be considered fair and helpful. This is subjective, of course, but having written (and taught) in many different programming languages, I really appreciate Python's greatly reduced use of parentheses and braces: This is of particular interest to me, as an educator.

Artificial intelligence (AI) and its many related applications (ie, big data, deep analytics, machine learning) have entered medicine's "magic bullet" phase. Desperate for a solution for the never-ending challenges of cost, quality, equity, and access, a steady stream of books, articles, and corporate pronouncements makes it seem like health care is on the cusp of an "AI revolution," one that will finally result in high-value care. While AI has been responsible for some stunning advances, particularly in the area of visual pattern recognition,1-3 a major challenge will be in converting AI-derived predictions or recommendations into effective action.

Ben Lorica is the Chief Data Scientist at O'Reilly Media, Inc. and is the Program Director of both the Strata Data Conference and the Artificial Intelligence Conference. He has applied Business Intelligence, Data Mining, Machine Learning and Statistical Analysis in a variety of settings including Direct Marketing, Consumer and Market Research, Targeted Advertising, Text Mining, and Financial Engineering. His background includes stints with an investment management company, internet startups, and financial services.

Three years ago, if you told me that one day I would use python to analyze AI policy and make Guido van Rossum chuckle, I would think you are crazy. Three years later at PyCon 2019 in Cleveland, that's exactly what happened. I was by no means a tech person. I was trained as an economist (read: stats nerd), but somehow for the past three years I've been writing analysis on deep-tech fields including AI and 5G. What I hope to achieve with this post is not #humblebrag (ok, maybe a little happy dance) but to share with you all the struggles I had and am still experiencing on a daily basis and to reassure a fellow researcher somewhere feeling that he/she is faking it all the time, you are not alone.

Unstructured data will not only improve accuracy but achieve fundamentally new ways of thinking, communicating and using information. The process of making artificial intelligence (AI) systems interact more like humans makes some people uncomfortable, but AI is not about replacing humans. In reality, it is much more about removing the robot from humans. A big part of AI's value lies in automating manual processes and analyzing vast amounts of data quickly so that humans are free to accomplish higher-order tasks that require reason and judgment. To get to this point, however, AI systems must be able to communicate with users and analyze natural forms of data (aka unstructured data) -- all of the free-flowing stuff that is unable to be packaged in a neat way, things like voice, images and text.