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Neil Raden

Managing Partner at Hired Brains Research LLC

Santa Fe, New Mexico, United States

18300 Followers

Industry Analyst, Consultant and Author
Neil Raden has for more than a quarter-century delivered a greater understanding of what's happening analytics, decision management, AI, Edge Computing and AI Ethics. He does not rank software products, but he has been a consistent force for pushing the industry. to do more. He is the founder of Hired Brains Research, co-author of the book Smart (Enough) Systems, 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 as one of the Top 100 Thought Leaders in Big Data and Analytics.

In 2019, Hired Brains created a 2-day on-site workshop in Ethical Framework for AI. We have been engaged by the Society of Actuaries to research and develop ethical practice guidelines for AI in Actuarial practice, resulting in the report "Ethical Uses of Artificial Intelligence for Actuaries.".

Began career as P&C actuary with AIG, focused on reinsurance and the startup Transatlantic Reinsurance Company. In 1985, started Archer Decision Sciences, a consulting company, and integrator for analytics projects for F500 companies. Archer was one of the first to develop large-scale data warehouses and BI environments, In 2003, expanded into a role as an industry analyst, publishing over 80 white papers, hundreds of articles, blogs, keynote addresses, and research reports.

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

Neil RadenPoints
Academic5
Author264
Influencer204
Speaker49
Entrepreneur0
Total522

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

AI 37.34
Analytics 50.71
Big Data 36.35
Business Strategy 33.79
Change Management 31.03
Customer Experience
Design Thinking 36.17
Digital Transformation
Emerging Technology 30.44
Fintech
Healthtech 30.24
InsurTech
IoT
Leadership
Marketing
Predictive Analytics 37.37
Privacy
Quantum Computing
Risk Management
RPA
Supply Chain
Sustainability
Agile 31.07
Diversity and Inclusion 30.95
Innovation 30.04

Industry Experience

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

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Publications

6 Analyst Reports
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

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Tags: 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

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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.

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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: AI, 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.

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Tags: Agile, Analytics, Big Data

15 Article/Blogs
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, Business Strategy, Diversity and Inclusion

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
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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.

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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

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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

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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

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Tags: Analytics, AI, 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: Analytics, AI, Big Data

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

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Tags: Analytics, AI

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

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, 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?

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Tags: Analytics, Big Data, Emerging Technology

1 Workshop
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

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Tags: Analytics, AI

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