AI Articles and Analysis about Business intelligence and analytics

Expertise: Data science and economics

Brief Recognition: Elena Grewal leads a team of data scientists responsible for the user’s online and offline travel experience at Airbnb. Her team partners with the product team to understand and optimize all parts of the product, using experimentation and machine learning in a wide variety of contexts. Prior to Airbnb, Elena was a doctoral candidate in the Economics of Education program at the Stanford University School of Education. She received a B.A. in Ethics, Politics, and Economics, with distinction, from Yale University, and a Masters degree in Economics at Stanford University. She was also the recipient of the Stanford Interdisciplinary Graduate Fellowship.

Predictive analytics for marketing would have been adopted years ago - if only the compute power were more ubiquitous, the data were more accessible, and the software were easier to use. Now "predictive analytics" itself is almost a buzzword, after nearly 30 years of backward-looking marketing tracking.
Today, well over 30 years after the founding of Lotus Software, even medium-sized businesses are often still operating their marketing "scoreboards" in Google Sheets or One Drive... "throw it in a spreadsheet" still works.
But businesses with an eye on the future want to know more than just what happened in the past. "Scoreboards" (most analytics tools and tracking) don't tell you what the score will be. Some of our recent "AI for marketing" articles have gained readership because more and more executives are searching for ways to look forward with their numbers, not just back. SAS defines the term well:

Episode Summary: How do you know if you’ve made the right decision for a hire? Often, employers go off gut instinct and make a decision retrospectively, but it turns out AI might be able to help out in human resource management through shedding light on best hiring decisions. In this episode, Pasha Roberts, chief scientist at Talent Analytics, tells us about how his company is working on helping companies make better decisions before they hire by applying machine learning and artificial intelligence to various data points on a given applicant, including information from aptitude tests that may help predict not only performance but retention.

Episode Summary: Predictive analytics and machine learning are all the rage in Silicon Valley, but how do companies actually derive value by leveraging these technologies? We asked this question to Dr. Ronen Meiri, CTO and Founder of DMWay, a predictive analytics and machine learning platform company based in Israel. In this episode, Ronen speaks about what his company does and how smart executives are starting to make decisions on how to choose and decide on a smart, user-friendly platform that fits their business' needs.

Security is a broad term, and in industry and government there are a myriad of "security" contexts on a variety of levels - from the individual to nation-wide. Artificial intelligence and machine learning technologies are being applied and developed across this spectrum.

Episode Summary: CTO and Co-founder of Sumo Logic Christian Beedgen gives his take on how to glean return on investment from applying machine learning to companies. There are no easy answers, but Beedgen boils down simple concepts for thinking about humans thinking through causation, machines working out correlations, and how the combination of the two can glean us better ideas and get to answers faster than humans can do alone.

Episode Summary: At Facebook headquarters, I learned there are 1 billion active users every month. In a more recent interview at eBay headquarters in San Jose, l learned that the well-known digital store has over 1 billion products for sale. eBay is, without a doubt, the world’s largest marketplace, and there’s enough incoming data to keep a large team of data scientists busy for years. I speak with Zoher Karu, eBay’s chief data officer, about how eBay leverages data and machine learning to create a better experience for its customers and also their sellers, shedding light on important lessons for anyone looking to sell a product online.

Machine learning has had fruitful applications in finance well before the advent of mobile banking apps, proficient chatbots, or search engines. Given the high volume, accurate historical records, and quantitative nature of the finance world, few industries are better suited for artificial intelligence. There are more uses cases of machine learning in finance than ever before, a trend perpetuated by more accessible computing power and more accessible machine learning tools (such as Google's Tensorflow).