Neil Raden

Bio
Website

Neil Raden, based in Santa Fe, NM, is an industry analyst, consultant and widely published author and speaker with a background in algebraic topology and P&C actuarial, he founded Hired Brains Research in 1985 to provide thought leadership, context and advisory consulting and implementation services in Data Architecture, AI, Analytics/Data Science and organizational change for analytics for clients worldwide across many industries. Neil is a recognized authority on AI Ethics, the co-author of the first book on Decision Man agent, “Smart (Enough) Systems,” and the foundational report for the Society of Actuaries, “Ethical Use of Artificial Intelligence for Actuaries.” He welcomes your comments at [email protected]

Neil Raden

Cloud computing concept © BillionPhotos.com - Fotolia.com

Read later

Quantum computing today - commercially viable, or held up by technical obstacles?

Assessing the commercial viability of quantum computing is no small feat. There are several quantum machines to consider - and what about the issue of hybrid quantum, and the need for orchestration? Either way, the atomic power of the qubit should not be overlooked.

Read later

How did a proprietary AI get into hundreds of hospitals - without extensive peer reviews? The concerning story of Epic's Deterioration Index

How is it possible for proprietary AI models to enter patient care, without extensive peer reviews for algorithmic transparency? That's a question we should be asking about Epic's Deterioration Index, which has been utilized for several use cases, including COVID-19 patient risk models.

Read later

Want better decision-making? You need role-based analytics - but providers are coming up short

In part one of our decision-making series, I made the case for distributed BI across the organization. But that's not enough. Getting better information does not necessarily yield better decisions.

Read later

Better decision-making requires better BI tools - and less fear-mongering about Shadow IT

With all the hype about data science and AI, we've lost track of something important: the focus on data-informed decisions. Shrill warnings about Shadow IT don't help. Here's part one of my series on better decision-making, and modern BI tools.

Read later

Want to democratize your analytics? Then stay away from Zero Trust security

Zero Trust security is often hyped as the solution to enterprise data breaches. But if your corporate goal is the democratization of analytics, Zero Trust security is a flawed approach. Here's my rebuttal to Zero Trust advocates.

Read later

Can synthetic data bridge the AI training data gap?

The problem of ineffective or biased AI training data persists. Can synthetically-generated data alleviate this, or complement real data? It's not a simple question but it's one we need to assess.

Read later

Why is AI harder than we think?

A new paper explains why AI is harder than we think - and predicts another AI Winter on the horizon. How well does the argument hold up? Let's delve in.

Read later

Why is your digital transformation falling short? Start with leadership - and data

We've gone from interfaces to web services, from Hadoop to the cloud. But that's not digital transformation. Few organizations have the leadership and drive to see transformations through.

AI robot finger on laptop keyboard with bokeh background © Zapp2Photo - shutterstock

Read later

AI ethics have consequences - learning from the problem of autonomous weapons systems

AI ethics can seem like an academic exercise - that's not the case for autonomous weapons. A defining AI ethics issue for our time is heating up.

Goldfish moving to larger bowl change management improvement concept © Romolo Tavani - Shutterstock

Read later

Out with ETL, in with ELT - why data preparation needs to change

ETL is giving way to ELT. Why does it matter? Because data preparation remains one of the toughest obstacles that a data-aware organization must overcome. Whether it's a data lake, a cloud data warehouse, or AI-enabled data prep, there are critical factors to consider.

Privacy threat at work - laptop with eyes in screen © Juergen Faelchle - shutterstock

Read later

Neil Raden

https://twitter.com/NeilRaden

Neil Raden

Quantum computing today - commercially viable, or held up by technical obstacles?

How did a proprietary AI get into hundreds of hospitals - without extensive peer reviews? The concerning story of Epic's Deterioration Index

Want better decision-making? You need role-based analytics - but providers are coming up short

Better decision-making requires better BI tools - and less fear-mongering about Shadow IT

Want to democratize your analytics? Then stay away from Zero Trust security

Can synthetic data bridge the AI training data gap?

Why is AI harder than we think?

Why is your digital transformation falling short? Start with leadership - and data

AI ethics have consequences - learning from the problem of autonomous weapons systems

Out with ETL, in with ELT - why data preparation needs to change

AI didn't invent privacy abuse - how the history of data privacy informs our future

Statistical bias in context - AI didn't invent quantitative methods of bias

Trustworthy AI versus ethical AI - what's the difference, and why does it matter?