Research

This article describes the potential for AI to augment risk estimation for both individual investors and financial market assets. AI processes vast amounts of a variety of data to identify patterns underpinning processes and metrics. Evolving data resources including digital touch points provide AI with attributes that can enhance risk estimation to ultimately augment elements of modern portfolio theory.

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Artificial Intelligence (AI) today merely has specific intelligence as opposed to generalized intelligence. This means that an AI process can appear quite intelligent within very specific bounds yet fall apart if the context in which the process was built is changed. In this blog I will discuss why adding an awareness of context into an AI process – and dealing with that context – may prove to be the hardest part of succeeding with AI. In fact, handling context may be the Achille’s heel of AI!

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Studies show that only 26% of data-related jobs are held by women. I attended the Women in Technology International meeting featuring a panel discussion with Rehgan Avon, Katie Sasso and Kristen Stovell on the “Future of Data Analytics”. This blog covers insights from the event across a wide range of topics including machine learning, educational resources, responsibilities of data scientists, and managing big data

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Agile Analytics Manifesto

By David Alles, Oct 25, 2018

Many organizations struggle with implementing agile or don’t see the expected results because they treat agile as just another process. If you are considering the use of agile development for analytics, it is critical to remember that agile is much more than a process – it is a philosophical and cultural approach to delivering customer value.

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A First Step Towards Strong AI

By Larry Bookman, Oct 16, 2018

A common view in the press and in artificial intelligence research is that sentient and intelligent machines are just on the horizon. How much longer can it be before they surpass our intelligence and take our jobs? Before we decide if machines can surpass our intelligence, let us first define two terms that will help us get a better handle on this topic: Weak AI and Strong AI.

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Yesterday, almost 200 of IIA’s clients, analytics experts and advisory network members gathered together to discuss the top trends and challenges of the analytics industry. The presentations were insightful, drove conversations and had actionable takeaways. The three themes of transformation, artificial intelligence (AI), and data-driven cultures were threaded in all of the presentations. This blog covers the key highlights of each session.

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It goes without saying that the analytics and data science space is undergoing change at an unprecedented pace. At the same time, many organizations have a lot of people who are still working in the same way using the same skills they have had for years. Organizations certainly need to move beyond those legacy skillsets and the need for a skills update is real. However, I see many organizations assuming that those with legacy skillsets are no longer valuable and must be replaced. This is a mistake. There is a huge difference between a legacy skillset and a legacy mindset. The two are not the same!

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Cloudera and Hortonworks Cease Fire

By Daniel Graham, Oct 08, 2018

Like brothers on opposite sides of a conflict, Cloudera and Hortonworks have decided in favor of peace and reuniting the family. This blog explores the implications of Hortonworks and Cloudera’s merger on the analytics software industry.

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Five Ideas to Improve Your Analytics Return

By Phil Kelly, Oct 05, 2018

At the most basic level, improving analytics returns requires discipline – namely a way baseline and measure analytics return. Assuming your process for measuring analytics value is in place, here are five ways an analytics team can improve their analytics returns.

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Is Machine Learning Analytics or AI?

By Thomas H. Davenport, Oct 04, 2018

One of the definitional debates that bedevils the artificial intelligence (AI) field is whether machine learning is an AI-based method or technology. Or is it just an analytics-based activity? After all, it is statistical in nature, and attempts—as virtually analytical methods do—to fit a line or curve to a set of data points. So is it really AI? And what difference does it make

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