Human or Machine? Two Paths for Deploying Analytics

As data science and analytics teams continue to feel pressure to deliver more value from analytics, many organizations still struggle with the processes and technology required to deploy models into production and more rapidly make data-driven decisions. When evaluating how to best undertake these activities, organizations should consider an important distinction to determine the best path forward.

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Three Steps for Creating the Analytics Dream Team

Building your team is hard work. Finding the right skills, the right personalities, the right types of motivation to build a team that works well together like a well-oiled machine… feels almost impossible at times. So what do you do? We at International Institute for Analytics (IIA) have been answering that question for our clients for as long as we can remember. In this blog, we are going to share three key strategies we have learned working with analytics teams of all sizes and maturity.

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A Common Trap That Undermines Analytics Credibility

Over the years, I’ve seen analytics professionals of all stripes blow their credibility and lessen their impact by falling into a common trap. I have to admit that I fell victim to the same trap early in my career. While our intentions are pure, our analytical minds and approaches can get the best of us and we explain too much. We’ll be better off if we learn to provide less detail and stop talking sooner than we are naturally inclined to.

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Article Review: The New Job Description for Data Scientists

The New Job Description for Data Scientists, an InformationWeek article by Rich Wagner, outlines important trends about the future of data scientists and their role in analytics functions. This blog covers key takeaways from Wagner’s article and three recommendations for analytics leaders based on the trends discussed.

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The Benefits of Ignoring When Executives Misunderstand Artificial Intelligence

With the hype surrounding Artificial Intelligence (AI) today, almost everyone in the analytics and data science space has been asked about AI by their business partners. Unfortunately, during these conversations it often becomes apparent that the business person really doesn’t have a clue what AI really is or what AI is best able to solve.

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AI is a Linear — Not Exponential — Technology

Organizations like Singularity University are focused on what they call “exponential technologies,” for which “the power and/or speed doubles each year, and/or the cost drops by half.” They classify AI as exponential, but alas it is not. Ray Kurzweil, a co-founder of Singularity University, claims that the “singularity” for AI—the time when machines can do every intellectual task better than humans—will come in 2029.

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Tom Davenport Comments
Analytics Predictions and Priorities for 2019

IIA leaders Bill Franks, Tom Davenport and Bob Morison revealed their list of 2019 analytics predictions and priorities for data-driven enterprises. Pressing topics include ethics, unique data, artificial intelligence, security, model deployment, and organizing talent.

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The State of Analytics Degrees in Universities

If you want to hire students from universities with strong analytical skills, you need to know the landscape of available programs and skills. For companies hiring graduates of analytics master’s degrees in business schools, it’s important to be aware of the differences among programs. This blog discusses the skills you should consider when hiring analytics graduates.

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AI and Digital Resources in FinTech

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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Stephan Kudyba
The Achilles Heel of Artificial Intelligence

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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Women in Technology: The Future of Data Analytics

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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Kathy Koontz
Agile Analytics Manifesto

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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David Alles
A First Step Towards Strong AI

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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Larry Bookman
Highlights from the 2018 Analytics Symposium – Atlanta

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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Vania AhmadComment
Don't Mistake a Legacy Skillset for a Legacy Mindset

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

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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Dan Graham
Five Ideas to Improve Your Analytics Return

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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Phil Kelly