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Neil Raden

Managing Partner at Hired Brains Research LLC

Santa Fe, New Mexico, United States


In Santa Fe, NM, Neil Raden is a mathematician (algebraic topology), founder of a management consulting firm, consultant to large and complex international projects, an industry analyst, and a widely published author and speaker. His early background was in Property and Casualty actuarial R&D. He founded Hired Brains Research to provide thought leadership, context and advisory consulting and implementation services in Data Architecture, Predictive Analytics, AI, Data Science and organizational change for clients worldwide across many industries. His current portfolio includes Operationalizing AI in the Last Mile, Data Management, and Analytics. Neil is a recognized authority on AI Ethics, the author of more than fifty articles on the subject at Diginomica, and the author of the foundational report for the Society of Actuaries, “Ethical Use of Artificial Intelligence for Actuaries.” He, with James Taylor, is the co-author of the first book on Decision Management, “Smart (Enough) Systems.”

For more than a quarter-century, he delivered a greater understanding of what's happening in analytics, decision management, AI, Edge Computing and AI Ethics. He does not rank software products, but he has been a consistent force in pushing the industry. To do more. He is a contributing analyst at Diginomica, chairman of advisory boards at Sandia Labs, a lecturer at TDWI, as well as a member of the Boulder BI Brain Trust (BBBT) and a contributor to Forbes.com. AnalyticsWeek has named him one of the Top 100 Thought Leaders in Big Data and Analytics.

In 2019, Hired Brains created a 2-day on-site workshop for AI Ethics, which was curtailed by the COVID pandemic, but will be updated and re-launched in 2022.

Hired Brains was one of the first to develop large-scale data warehouses and has re-launched the practice as Data Warehouse Modernization and Migration.
Clients welcome his practical and valuable advice and counsel. He welcomes your comments at nraden@hiredbrains.com.

Available For: Advising, Authoring, Consulting, Influencing, Speaking
Travels From: Santa Fe, NM
Speaking Topics: AI Last Mile (Data, Ethics, Operations testing), Data Warehouse Modernization, AI Ethics Certification, Analytics, Customer Experience, Data Science,

Neil Raden Points
Academic 5
Author 284
Influencer 165
Speaker 52
Entrepreneur 0
Total 506

Points based upon Thinkers360 patent-pending algorithm.

Thought Leader Profile

Portfolio Mix

Company Information

Company Type: Company
Business Unit: Consulting/Mentoring/Assessment
Media Experience: 30 years
Last Media Interview: 03/08/2021

Areas of Expertise

Agile 30.37
AI 33.16
Analytics 48.26
Big Data 33.24
Business Strategy 33.27
Change Management 30.76
Cloud
Customer Experience
Design Thinking 35.67
DevOps
Digital Transformation
Diversity and Inclusion 30.30
Emerging Technology 30.35
FinTech
HealthTech 30.08
Innovation 30.04
InsurTech 30.79
IoT
Leadership
Marketing
Predictive Analytics 33.84
Privacy
Quantum Computing
Risk Management
RPA
Security 30.18
Supply Chain
Sustainability
National Security 30.59

Industry Experience

Aerospace & Defense
Automotive
Consumer Products
Federal & Public Sector
Healthcare
Higher Education & Research
Insurance
Manufacturing
Oil & Gas
Pharmaceuticals
Professional Services
Retail
Telecommunications
Travel & Transportation
Utilities

Publications

7 Analyst Reports
How AI ethics falls short - preserving jobs is not enough
Diginomica.com
December 07, 2021
AI ethics has been a hot topic for about five years, but are we getting to the fundamental issues? Here's why well-intentioned ideas for preventing AI job loss come up short.

See publication

Tags: Analytics, AI, Predictive Analytics

Technology for Operational Decision Making
Hired Brains Research
August 29, 2021
Success in business relies on making the right decisions
at every level. Organizations and executives focus on
high-impact, strategic decisions. Operational decision
making is often neglected because the individual
front-line decisions seem to lack impact. This is
a mistake because these little decisions add up. A
company’s brand identity is defined by thousands
of these little decisions

See publication

Tags: Analytics, Predictive Analytics, Change Management, Business Strategy

Market Report: Technology for Operational Decision Making
Smartenoughsystems.com
March 18, 2021
Analyst Market report on technologies and methodologies for implementing decision management

See publication

Tags: Analytics, AI, Predictive Analytics

Ethical Use of Artificial Intelligence for Actuaries
Society of Actuaries
February 25, 2021
In-depth report of the state of AI Ethics and recommendations for the Society of Actuaries to up date the Code of Practice to include AI.

See publication

Tags: AI, Predictive Analytics, Change Management

Natural Language Processing Augmented Analytics
Vertica
February 03, 2021
What stops analytics from becoming part of everyone’s daily routine? It isn’t a slacking data engineering team, or an imperfect data architecture, it’s the interface. If I need to know something for my job, instead of learning complex SQL queries, or interpreting a bunch of graphs, why can’t I just ask?

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Tags: Analytics, AI, Predictive Analytics

Data in Mind, Data in Hand: Frictionless Provisioning for Data Science and ML/AI with DataOps
Hired Brains Research
October 07, 2019
Data in mind, data in hand is a concept that shrinks the effort and latency from conceiving of a model and having the data to run it.

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Tags: Agile, Analytics, Big Data

Practical Examples of the Impact of AI in Data Management
Hired Brains Research
August 07, 2019
Data catalogs emerged as the must-have technology for dealing with vast collections of data files. But
a data catalog alone does not solve the problems facing organizations of providing a simple discovery
tool. A static catalog lacking an in-depth understanding of the variety of data formats only addresses
a fraction of the problem. The application of AI to provide recommendations of mappings of data
sources, and exposed through Natural Language Query, search and exploration in your own words with
continuous update, makes this possible.

See publication

Tags: Analytics, Big Data, Predictive Analytics

25 Article/Blogs
The cloud data migration challenge continues - why data governance is job one
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May 03, 2022
Cloud data migrations can bring significant operational benefits - not to mention opening up AI/ML use cases. That doesn't mean these migrations are easy. Getting migrations right starts with data governance.

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Tags: Analytics, AI, Big Data

Solving the data wrangling conundrum - can machine learning transform data management?
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April 26, 2022
Do data scientists really spend 80% of their time wrangling data? Last time around, we examined this notion. But when it comes to data management, how can machine learning change data platforms for the better?

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Tags: Analytics, AI, Big Data

Data science myths and realities - do data scientists really spend 80% of their time wrangling data?
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April 20, 2022
Do data scientists really squander the bulk of their time cleaning data sets? Not necessarily - but for robust machine learning models, we do need better data management platforms.

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Tags: Analytics, AI

From autonomous cars to autonomous weapons, the AI ethics issues can't be ignored
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April 05, 2022
Autonomous technology is racing towards us. But whether its autonomous cars or autonomous weapons, there are serious questions. This is where AI ethics comes to a head.

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Tags: Analytics, AI, Big Data

Can AI help to address the looming global food crisis?
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March 29, 2022
The global food supply is under duress, with Russia's invasion of the Ukraine applying even more pressure. With world populations mounting, can AI make a difference in farming and agricultural yields?

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Tags: Analytics, AI, Big Data

Quantum computing scenarios - the case for hybrid computing models
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March 22, 2022
Supposedly, quantum computing ushers in an age of revolutionary possibilities - thanks to quantum's unprecedented computing power. But the most compelling scenarios are actually a "hybrid" mix of quantum and classical computing.

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Tags: Analytics, AI, Big Data

Here's looking at you, kid - the problematic adoption of facial recognition systems
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March 15, 2022
Facial recognition systems are one of the most controversial areas of real world AI. But despite the ethical qualms, facial recognition is now in use across multiple industries. Can regulation catch up?

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Tags: Analytics, AI, Big Data


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March 08, 2022
DARPA has an aura of mystery around it, and is most often linked to the US Department of Defense. But DARPA's innovations frequently find their way into the enterprise - including important AI developments.

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Tags: Analytics, AI, Big Data

Towards a semantics of data in a digital world - why is effective data collaboration so elusive?
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March 01, 2022
Teams that collaborate around accurate data is an appealing concept - or is it an enterprise holy grail? Cloud data warehouses didn't solve the problem. Too many disparate data silos; too much digital exhaust. So where do we go from here?

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Tags: Analytics, AI, Big Data

Reigniting the analytics-to-decisions debate
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February 23, 2022
Analytics tools get shinier and fancier, but the classic problem remains: are we making better decisions? This is not a new debate. But in today's supposedly data-driven organizations, with hefty analytics investments in play, the issue takes on a new urgency.

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Tags: Analytics, AI, Big Data

Statistical bias in context - AI didn't invent quantitative methods of bias
diginomica.com
August 29, 2021
An effective approach to AI Ethics must reckon with bias, algorithmic discrimination, and privacy. These terms have a historical context that should be understood - if we want to deploy AI ethically. This time around, we delve into bias.

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Tags: Analytics, AI, Diversity and Inclusion

Trustworthy AI versus ethical AI - what's the difference, and why does it matter?
diginomica.com
August 29, 2021
We all want "trustworthy AI" - or do we? A closer look at the semantics of trust indicate the dangers of assuming trust is ethical. Fuzzy terminology will not help our pursuit of ethical AI.

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Tags: AI, Business Strategy, Diversity and Inclusion

The US National Security Commission issues its "Final Report on AI in Defense and Intelligence" - here are the takeaways
diginomica.com
August 29, 2021
The US National Security Commission has issued a massive, 700 page "final report" on the impact of AI on defense and intelligence. The report's conclusions are concerning, and not without controversy. US tech leaders were directly involved in this report - the proposed plan of action is worth a close look.

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Tags: AI, Security, National Security

How supercomputers found their industry mojo - the evolution of high performance computing
diginomica.com
August 29, 2021
Supercomputers used to be the domain of scientists and the military. Now the enterprise use cases are picking up steam. But in a way, all computers are supercomputers. Here's a look at how the field has evolved - and what's next.

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Tags: AI, Emerging Technology, Innovation

For John Snow Labs, doing good with NLP is in their DNA (and yours)
diginomica
March 02, 2021
Why was Dr. John Snow designated the "Father of Epidemiology?" His painstaking investigations of the outbreaks of deadly cholera in London in the 1850s led him to conclude that the disease was caused by contaminated water. His meticulous data gathering pinpointed the source at a single water pump.

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Tags: Analytics, AI, HealthTech

Metadata Shmedadata - today's approaches to metadata are inadequate, and that's a problem.
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February 23, 2021
Struggling with metadata is nothing new, but there's a misconception that advanced data tech and cloud storage solved this problem. That's not the case - so what is the way forward?

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Tags: Analytics, AI, Big Data

Data lakes, data lakehouses and cloud data warehouses - which is real?
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February 09, 2021
Cloud data warehouses aren't trendy enough - now we evidently need data lakehouses as well. But how should enterprises sort these terms? And has the data lake outlived its usefulness?

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Tags: Analytics, AI, Big Data

Moral licensing, AI teams, and you - a problem yet to be reckoned with
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February 05, 2021
Most of us have heard of cognitive bias - and the problem it can pose. Less well known is the practice of moral licensing. But it's an issue AI teams need to consider.

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Tags: Analytics, AI, Big Data

Robot empowerment - a viable alternative to Asimov's three laws of robotics?
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January 29, 2021
Human-robot interaction is upon us - we're in dire need of a framework that makes sense. Asimov's three laws of robotics are one model, but is it applicable to today's robots? An alternative based on robot "empowerment" is worth a close look.

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Tags: Analytics, AI, Big Data

How supercomputers found their industry mojo - the evolution of high performance computing
Import from wordpress feed
January 20, 2021
Supercomputers used to be the domain of scientists and the military. Now the enterprise use cases are picking up steam. But in a way, all computers are supercomputers. Here's a look at how the field has evolved - and what's next.

See publication

Tags: Analytics, AI, Big Data

Friday rant - Facebook's disinformation spreading, ad-server-economy must go
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January 15, 2021
Big Tech has been on the defensive lately, and for good reason. What was once perceived as a way to foster democracy has given way to algorithmic dystopia. But Facebook's algorithmic dangers are tied to an ad-server-based model we must dismantle. Rant time.

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Tags: Analytics, AI, Big Data

The problem of algorithmic opacity, or "What the heck is the algorithm doing?"
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January 13, 2021
Opacity in AI used to be an academic problem - now it's everyone's problem. In this piece, I define the issues at stake, and how they tie into the ongoing discussion on AI ethics.

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Tags: Analytics, AI, Big Data

Can fairness be automated with AI? A deeper look at an essential debate
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January 06, 2021
I've addressed whether fairness can be measured - but can it be automated? These are central questions as we contend with the real world consequences of algorithmic bias.

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Tags: Analytics, AI, Big Data

Can we measure fairness? A fresh look at a critical AI debate
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December 21, 2020
By now, most AI practitioners acknowledge the universal prevalence of bias, and the problem of bias in AI modeling. But what about fairness? Can fairness be measured via quantifiable metrics? Some say no - but this is where the debate gets interesting.

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Tags: Analytics, AI, Big Data

Musings on China's 'Global Initiative on Data Security' and the problem of security "back doors"
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December 15, 2020
A review of 'Global Initiative on Data Security' led me to an exchange with a company doing business in China. With new 5G security issues on the horizon, it's a good time to reflect on the implications of "back doors," ethical AI, and where the responsibility lies.

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Tags: Analytics, AI, Big Data

3 Books
Smart (Enough) Systems: How to Deliver Competitive Advantage by Automating Hidden Decisions
Prentice Hall
June 29, 2007
Predictive Analytics, Rules Engines, Decision Automation

See publication

Tags: Analytics, AI, Predictive Analytics

Smart (Enough) Systems
Pearson/Prentice Hall
February 24, 2007
The first book on Decision Management utilizing predictive analytics and rules engines. Publis

See publication

Tags: Analytics, AI, Predictive Analytics

Smart (Enough) Systems
Pearson/Prentice Hall
February 24, 2007
Created a new movement with this work in Enterprise Operational Decision Management. The central theme is that organizations are known by the decisions they make, and not just the major strategic decisions, but the myriad small decisions that their thousands of employees make on a day-to-day basis. Up until now, we had to make do with Decision Support, Knowledge Management, Business Intelligence,

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Tags: Design Thinking, Predictive Analytics, Business Strategy

1 Journal Publication
Ethical Issues in any Automated DecisionMaking Model
Society of Actuaries
March 18, 2021
The pace of new technology creates difficult ethical questions for insurance companies. The accelerating use of
unattended decision-making applications opens the door
to risk of reputation and liability. AI and Machine Learning
(ML) models can contain bias, provoke discrimination, intrude
on privacy, and unwittingly violate regulations

See publication

Tags: Analytics, AI, Predictive Analytics, Business Strategy

4 Keynotes
Big Data Analytics: The Art of the Data ScientistEuler
Slideshare
August 29, 2021
Refining the definitions of Big Data, Data Science and Analytics.

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Tags: AI, Big Data, Predictive Analytics

Smart (Enough) Systems
Pearson/Prentice Hall
March 18, 2021
Description of how predictive analytics fuel rules engines and how to create a decision management program.

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Tags: Analytics, AI, Predictive Analytics

•Keynote address to Caterpillar’s annual Analytics Day
Hired Brains Research
March 18, 2021
Survey of emerging technologies in Data Science, AI and ethical considerations

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Tags: Analytics, AI, Predictive Analytics

Ethical Use if AI for Actuaries
Actuarial Society of the Philippines: Annual Conference: Ethical Use of AI
March 18, 2021
Considering the ethical issues actuaries face with new technologies and advice how to avoid problems

See publication

Tags: Analytics, AI, Predictive Analytics

3 Miscellaneouss
How AI ethics falls short - preserving jobs is not enough
diginomica
December 07, 2021
AI ethics has been a hot topic for about five years, but are we getting to the fundamental issues? Here's why well-intentioned ideas for preventing AI job loss come up short.

See publication

Tags: AI, Digital Transformation, Business Strategy

What is the role of AI in pandemic response? The National Security Commission on AI provides a framework
diginomica
December 01, 2021
In 2020, the National Security Commission on Artificial Intelligence put out a landmark 750 page report on AI. This year, they followed up with a paper on putting AI to work in pandemic circumstances. But is it actionable? Here's my review.

See publication

Tags: AI, Business Strategy, National Security

The last mile in AI deployment - where the biggest risks (and payoffs) happen
diginomica
November 23, 2021
When it comes to getting business results from AI, the last mile is where it happens. But AI development projects invoke risk as well. Here's the pitfalls to address in the last mile of AI deployments.

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Tags: AI, Business Strategy, DevOps

1 Webinar
Natural Language Processing Augmented Analytics
Vertica
March 18, 2021
making analytics accessible to more people. What could be more accessible than asking your data a question in your own language?

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Tags: Analytics, AI, Predictive Analytics

2 Webinars
Unified Data Analytics
www.vertica.com
March 18, 2021
Discussion of AugmentedANalytics and the role of NLP

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Tags: Analytics, AI, Emerging Technology, Predictive Analytics

Adding Edge Data to Your AI and Analytics Strategy
Pivotal
March 18, 2021
where geographically should machine-learning models be trained: near the edge, in the data center, or perhaps at an intermediate point in between?

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Tags: Analytics, AI, Predictive Analytics, Business Strategy

2 Whitepapers
An Enterprise Data Hub, the Next Gen Operational Data Store
Cloudera
March 18, 2021
Defining the new role of the ODS in an architecture like an enterprise data hub. A data hub approach allows for increased opportunity to not only capture new incoming data but also combine that with historical data housed in the EDH.

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Tags: Analytics, Big Data, Business Strategy

Governing From Below: Eight Ways to Enhance Your DIY Analytics
https://alation.com/wp-content/uploads/2016/01/Whitepaper-Neil-Radon-Governing-from-Below-160126FINAL.pdf
March 18, 2021
Instead of creating a governance process to prevent, or even
penalize knowledge workers who violate the rules of use of
data, wouldn’t it be better to provide them with the tools and
opportunities to pursue their interests without violating those
rules?

See publication

Tags: Analytics, Big Data, Emerging Technology, Predictive Analytics

2 Workshops
Workshop on AI ethics
Actuarial Society of the Philippines: Annual Conference: Ethical Use of AI
March 18, 2021
Considering the ethical issues actuaries face with new technologies and advice how to avoid problems

See publication

Tags: AI, Analytics

Ethical AI for Health Insurance
Blue Cross Blue Shield of North Carolina
December 31, 1969

See publication

Tags: AI, Analytics, InsurTech

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