SAS Blog Directory

I discovered late , the SAS Blog directory, which contains lots of useful posts of interest including optimization, AI, Automation, Machine Learning, Data science and much more. We worked with SAS from the early days.  Will try to pass through from time to time. 

SAS on Data Driven to AI Driven

 Talk:    https://blogs.sas.com/content/sascom/2023/03/27/from-data-driven-to-ai-driven-scaling-human-productivity-and-decision-making/

https://blogs.sas.com/content/sascom/2023/03/27/from-data-driven-to-ai-driven-scaling-human-productivity-and-decision-making/

From data-driven to AI-driven: Scaling human productivity and decision making 

By Bryan Harris on SAS Voices March 27, 2023  topics | Analytics Artificial Intelligence

Given the headlines each week, it is clear that global disruption and economic volatility are not slowing down. At the same time, information overload is far exceeding human capacity.

Despite these pressures, business goals remain the same: improve revenue, increase margins, operate more efficiently and meet customer expectations. So, how do we keep up and, most importantly, get ahead in today’s world? Businesses must scale human productivity and decision making with AI to ultimately discover the future faster.

In the video below, I discuss the challenges organizations face and how they can become more resilient with AI-driven strategies.

https://blogs.sas.com/content/sascom/2023/03/27/from-data-driven-to-ai-driven-scaling-human-productivity-and-decision-making/

To learn more strategies for business resilience, check out our new Resiliency Rules Report.

Tagsdata culture and literacydata strategydigital disruptioninnovation

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ABOUT AUTHOR   Bryan Harris

Executive Vice President & Chief Technology OfficerWebsiteLinkedIn

As Executive Vice President and Chief Technology Officer, Bryan Harris is responsible for setting the technology direction for SAS and working with the executive leadership team to translate the organization’s strategic objectives and priorities into products and solutions. Harris has more than 20 years of experience researching and developing analytic techniques, enterprise search technologies, distributed computing and cloud architectures, and user experiences for both the federal and commercial industries. For nearly 10 years, he has been a critical senior leader of SAS R&D.  .... 

How is ChatGPT Bringing AI into Popular Culture?

Finally AI becoming 'Live'?   Has this changed everything?   For what set of tech customers? Jobs?      From SAS

In recent weeks, the ChatGPT hype has blown up my tech-heavy social feed. I follow many coders and content creators on TikTok and Twitter, and most of them are losing their minds over the disruption that OpenAI's ChatGPT represents to their disciplines. Their common refrain: "This changes everything." After trying it myself (more on that later) and reading more about it, I think that I share their view.

As someone who follows technology and AI topics, I should not have been surprised by this preview launch of the ChatGPT project. For many months my news feed has been littered with topics about AI tools like GPT-3, GitHub Copilot, DALL-E, Stable Diffusion and more. But up until now, these technologies required a certain amount of skill and hardware to configure, and that confined them to the realm of the technologist or savvy enthusiast. ChatGPT puts this technology in the hands of anyone who wants to create a free user account on the service. This unprecedented ease of access catapults it into everyone's imagination, including mine, and I'll admit that I didn't see it coming this soon.

Explaining the unexplainable AI

In a way, the popularity of ChatGPT makes it easier for me to tell my friends and family what we do at SAS. As an AI company, we trade in the application of machine algorithms to business challenges and world-changing initiatives. As I tell this to my wife's Aunt Susan, she nods her head and says, "Ohhh," even as her eyes glaze over. But when I say, "For example, we use natural language processing and reinforcement learning to build expert systems that can improve outcomes – you know, like in ChatGPT," suddenly we have a shared understanding (to a point).  .... ' 

SAS Does Analytics Gaming

Interesting approach, gamifying analytics, which we also tried.

Hooked on data science: gamification drives engagement among students and trainees

by ALEX COOP on MARCH 21, 2022

While studying business intelligence as an undergraduate student at business school HEC Montreal, Camille Duchesne encountered Cortex, an analytics simulation that pits participants against each other to develop the most accurate models for a particular task.

In this case, the simulation supports a fictional charity by predicting which subjects from hundreds of thousands of records would be the most likely to be converted by a phone call. It was this simulation game that got Duchesne hooked on data science.

“I think the way it was set up with SAS Enterprise Miner was great,” Duchesne says. Though unfamiliar with the models, “You could test things out, but you really didn’t know what was going on at first. But because you had to implement it, you really got to learn the stages and how things work at a higher level.”   ... ' 

SAS Customer Intelligence Blog

 I have been reviewing reviewing SAS's blogs.  They nicely have a 'customer intelligence blog:

Customer Intelligence Blog

Evolving relationships for business growth

Welcome to Customer Intelligence, a blog for anyone who is looking for ways to improve the business of marketing and communicating with customers.

We strive to prompt new thinking in the way you tackle customer-related business issues. And we hope to inspire the use of analytics for everything from multi-level marketing to social media campaigns.  ... ' 

Podcasting and all That

Never related SAS to Podcasts. Then I saw the below.  Have been interested this kind of promotion/broadcasting that you can ingest as and where you want, and repeat as you need.  I even did an internal podcast for P&G when I worked there that discussed the uses of technology. I still get questions about it. I now frequently follow podcasts. And mention them here.   Easy to do on a smartphone. Now SAS has written up a piece on branded podcasts, see below:    With pointers to some I will hear:  -Franz

Why I listen to branded podcasts (and 5 great ones to add to your list), by ALEX COOP on SEPTEMBER 30, 2021    in the SAS Blog. 

Since 2015, the number of Americans who to listen to at least one podcast per month has increased by approximately 40 million people. The number of podcast listeners has also ballooned globally in tandem with a rise in smart speaker adoption. People are consuming them at home, on the go and at work.

In many cases, podcasting has become a legitimate substitute for traditional advertising. Corporations understand the value of creating something that people want to consume instead of ads that listeners ignore.

It's been a pleasant surprise watching a personal podcast palette consisting of news, sports, video game and cybersecurity commentary invaded by branded podcasts covering a host of other topics. And while none of these branded podcasts crack the Top 50 list for most weekly listeners, some of them are legitimately worth your time. .... '

On the SAS and Microsoft Partnership

They write:

One year in: Six reasons you should pay attention to the SAS and Microsoft partnership  by NICK JOHNSON

Just over a year ago, SAS and Microsoft announced their strategic partnership. Since then we have been working together to provide the best experience and value to our customers as they migrate to the cloud. 

Here are six milestones you should know about; each highlights the early success of the two industry giants.  

The start of something big  

In February of 2021, SAS Viya – SAS’s cloud-native end-to-end analytics platform – launched on Microsoft Azure. This latest release of SAS is built to run seamlessly on SAS Cloud and Azure, making it easier to design, run, and manage analytics and data processing workloads in the cloud. 

A shared vision for a data-led future  

Satya Nadella CEO of Microsoft and Jim Goodnight CEO of SAS discussed the partnership in a webcast for SAS Global Forum 2021. The two discussed the potential for the SAS and Microsoft Partnership to shape the future of AI and analytics in the cloud.    ... ' 

SAS Gives some Quick AI Examples

 Some good examples, from SAS:

3 ways industries are using AI-powered decisions

By OLIVIA OJEDA on JULY 6, 2021

Having the ability to know what the best decision is in any given scenario sounds like a superpower. What may surprise you, is that this “superpower” already exists. SAS® calls it intelligent decisioning. Decisioning is a powerful tool in the business world. It is useful to both the company and the consumer because it allows insights on what customers want. Therefore, the customer not only gest their preference, but the consumer sells more as well. Companies all around the world have partnered with SAS and seen major growth by using the power of decisioning, and as businesses continue to flourish, it becomes more vital to be able to predict a wider range of customers’ needs quickly.   ... ' 

Data and Analytics for Better Decisions

From MIT Sloan, SAS, some thoughts on decisions for analytics.  I link to items I have read and liked.  All accessible from top link.

Data and Analytics for Better Decisions

Stepping up to business challenges and opportunities means knowing how to find relevant data — and put it to work. Free access to these four MIT Sloan Management Review articles is provided courtesy of SAS   

1. To Succeed With Data Science, First Build the ‘Bridge’  

2. Demystifying Data Monetization 

3. The Recession’s Impact on Analytics and Data Science 

4. Data Science, Quarantined 

...'  

SAS: How AI Changes the Rules

Looks to be a good paper from SAS, below an overview, link through for more.  Always liked how SAS looked at the broader aspect of 'analytics'.  Reading.

How AI Changes the Rules

New Imperatives for the Intelligent Organization

About this paper

Many leaders are excited about AI’s potential to profoundly transform organizations by making them more innovative and productive. But implementing AI will also lead to significant changes in how organizations are managed, according to our recent survey of more than 2,200 business leaders, managers and key contributors. Those survey respondents, representing organizations across the globe, expect that reaping the benefits of AI will require changes in workplace structures, technology strategies and technology governance.to manage the significant changes to software development and deployment processes that most respondents expect from AI.

AI will drive organizational change and ask more of top leaders. The majority of survey respondents expect that implementing AI will require more significant organizational change than other emerging technologies including cloud. AI demands more collaboration among people skilled in data management, data analytics, IT infrastructure, and systems development, as well as business and operational experts. This means that organizational leaders need to ensure that traditional silos don’t hinder advanced analytics efforts, and they must support the training required to build skills across their workforces.

AI will place new demands on the CIO and CTO. AI implementation will influence the choices CIOs and CTOs make in setting their broad technology agendas. They will need to prioritize developing foundational technology capabilities, from infrastructure and cybersecurity to data management and development processes — areas in which those with more advanced AI implementations are taking the lead compared with other respondents. CIOs will also need to manage the significant changes to software development and deployment processes that most respondents expect from AI. The survey also indicated that many CIOs will be charged with overseeing or supporting formal data governance efforts: CIOs and CTOs are more likely than other executives to be tasked with this.

AI will require an increased focus on risk management and ethics. The Global survey shows a broad awareness of the risks inherent in using AI, but few practitioners have taken action to create policies and processes to manage risks, including ethical, legal, reputational, and financial risks. Managing ethical risk is a particular area of opportunity. Those with more advanced AI practices are establishing processes and policies for data governance and risk management, including providing ways to explain how their algorithms deliver results. They point out that understanding how AI systems reach their conclusions is both an emerging best practice and a necessity, in order to ensure that the human intelligence that feeds and nurtures AI systems keeps pace with the machines’ advancements.

The report that follows explores these findings in depth. Read on to learn more about the changes that leaders must prepare for to successfully implement trusted AI.  .. "

SAS: Four Principles of Analytics

As usual, SAS does a good job of precisely outlining analytics. Here including AI and Big Data with statistics. 

It's no secret that technology's changing. A change accelerating so fast, it's hard to keep up.

Big data and AI are exploding. Industries are reinventing themselves. And breakthroughs seem to redefine our world every day. Yet thankfully, some things do stay the same.

The four principles of analytics are steadfast truths that inform your approach to data and analytics. They're truths because, well, they work – helping you make the best decisions no matter what else changes. Read this blog to learn why:

Analytics follows the data.

Analytics is more than algorithms.

Data and analytics should be available to everyone.

Analytics is a differentiator.

 .... After all, change isn't the only thing that's constant. So are good ideas.

Read the Blog

Imperatives for Intelligent Organizations

Reading, not sure I completely agree with the premise.  What rules really?  OK,  Our better understanding of the patterns in business we can leverage.  Still a good piece.   And from SAS/SMR , relatively quite credible.  Our use of SAS thinking made us strong from the beginning.  More intelligent organization, certainly.

How AI Changes the Rules
New Imperatives for the Intelligent Organization
Many leaders are excited about AI’s potential to profoundly transform their organization.

According to our recent survey of more than 2,200 business leaders, managers and key contributors, "63% of respondents overall said they expect AI to drive dramatic or significant organizational change."

Are you ready for what’s coming? Read the full MIT SMR Report, How AI Changes the Rules: New Imperatives for the Intelligent Organization, to learn what’s needed to prepare for – and benefit from – AI.

Paper is here.

SAS: How AI Changes the Rules

As usual, SAS does a good job when talking the impact of analytics of many kinds.  Good non technical depth.  Requires free sign up:

How AI Changes the Rules
New Imperatives for the Intelligent Organization

About this paper
Many leaders are excited about AI’s potential to profoundly transform organizations by making them more innovative and productive. But implementing AI will also lead to significant changes in how organizations are managed, according to our recent survey of more than 2,200 business leaders, managers and key contributors. Those survey respondents, representing organizations across the globe, expect that reaping the benefits of AI will require changes in workplace structures, technology strategies and technology governance.to manage the significant changes to software development and deployment processes that most respondents expect from AI.

AI will drive organizational change and ask more of top leaders. The majority of survey respondents expect that implementing AI will require more significant organizational change than other emerging technologies including cloud. AI demands more collaboration among people skilled in data management, data analytics, IT infrastructure, and systems development, as well as business and operational experts. This means that organizational leaders need to ensure that traditional silos don’t hinder advanced analytics efforts, and they must support the training required to build skills across their workforces.

AI will place new demands on the CIO and CTO. AI implementation will influence the choices CIOs and CTOs make in setting their broad technology agendas. They will need to prioritize developing foundational technology capabilities, from infrastructure and cybersecurity to data management and development processes — areas in which those with more advanced AI implementations are taking the lead compared with other respondents. CIOs will also need to manage the significant changes to software development and deployment processes that most respondents expect from AI. The survey also indicated that many CIOs will be charged with overseeing or supporting formal data governance efforts: CIOs and CTOs are more likely than other executives to be tasked with this.

AI will require an increased focus on risk management and ethics. The Global survey shows a broad awareness of the risks inherent in using AI, but few practitioners have taken action to create policies and processes to manage risks, including ethical, legal, reputational, and financial risks. Managing ethical risk is a particular area of opportunity. Those with more advanced AI practices are establishing processes and policies for data governance and risk management, including providing ways to explain how their algorithms deliver results. They point out that understanding how AI systems reach their conclusions is both an emerging best practice and a necessity, in order to ensure that the human intelligence that feeds and nurtures AI systems keeps pace with the machines’ advancements.

The report that follows explores these findings in depth. Read on to learn more about the changes that leaders must prepare for to successfully implement trusted AI.  ... " 

SAS And Microsoft Azure

Intriguing to see the linking of Azure and SAS.  Some particularly novel sample problems.

3 world-changing examples of SAS® on Azure   by  Oliver SchabenBerger

Last week we announced a new strategic partnership with Microsoft to further shape the future of AI and analytics in the cloud. This commitment will make it easy for SAS customers to move their analytics workloads to the cloud. And it will introduce SAS technologies to millions of Azure customers through APIs and deeper integrations that can enhance existing applications with analytics.

To help illustrate how you can use SAS on Azure, I am sharing three inspiring examples from a recent SAS hackathon. Participants in this event were challenged to solve problems related to the United Nations Global Goals for Sustainable Development using SAS® Viya®. Submissions ranged from optimizing resources at refugee camps to encouraging more sustainable consumer habits.

A technology team developed each application in partnership with a nonprofit to solve existing and immediate problems. The three revolutionary projects I am featuring here all use SAS Viya running on Microsoft Azure.

Analyzing honeybee dance moves leads more bees to food ....'

Podcast: Do we Need Data Scientists in a World of Automation?

New Podcast below, by SAS and DSC.    Good topic.  The question should be:  How many and how should the be involved in an enterprise?  Just like Computer Scientists, Economists, Statisticians ... and other technical fields.  Machine Learning can't do it all.

Do We Need Data Scientists in Today’s World of Automation?

Machine learning is said to be an important driver of the future of intelligent systems, automatically analyzing data and distilling new knowledge, actionable insights and compelling decisions. But why show market trends that investments in data scientists – those golden people that train machine learning models – have never been higher if machine learning can be fully automated? Why do we need data scientists if machine learning is designed to do it all?

In today’s Data Science Central podcast, Véronique Van Vlasselaer, Data & Decision Scientist at SAS, will discuss what machine learning automation entails, and how valuable human input in the machine learning process is.

Speaker: Véronique Van Vlasselaer, Data & Decision Scientist - SAS
Hosted by: Rafael Knuth, Contributing Editor - Data Science Central

SAS on How AI Changes the Rules

Always liked SAS as a analytical problem solving company, here a new paper from them of interest, register for it at the link.

How AI Changes the Rules
New Imperatives for the Intelligent Organization

About this paper
Many leaders are excited about AI’s potential to profoundly transform organizations by making them more innovative and productive. But implementing AI will also lead to significant changes in how organizations are managed, according to our recent survey of more than 2,200 business leaders, managers and key contributors. Those survey respondents, representing organizations across the globe, expect that reaping the benefits of AI will require changes in workplace structures, technology strategies and technology governance.to manage the significant changes to software development and deployment processes that most respondents expect from AI.

AI will drive organizational change and ask more of top leaders. The majority of survey respondents expect that implementing AI will require more significant organizational change than other emerging technologies including cloud. AI demands more collaboration among people skilled in data management, data analytics, IT infrastructure, and systems development, as well as business and operational experts. This means that organizational leaders need to ensure that traditional silos don’t hinder advanced analytics efforts, and they must support the training required to build skills across their workforces.

AI will place new demands on the CIO and CTO. AI implementation will influence the choices CIOs and CTOs make in setting their broad technology agendas. They will need to prioritize developing foundational technology capabilities, from infrastructure and cybersecurity to data management and development processes — areas in which those with more advanced AI implementations are taking the lead compared with other respondents. CIOs will also need to manage the significant changes to software development and deployment processes that most respondents expect from AI. The survey also indicated that many CIOs will be charged with overseeing or supporting formal data governance efforts: CIOs and CTOs are more likely than other executives to be tasked with this.

AI will require an increased focus on risk management and ethics. The Global survey shows a broad awareness of the risks inherent in using AI, but few practitioners have taken action to create policies and processes to manage risks, including ethical, legal, reputational, and financial risks. Managing ethical risk is a particular area of opportunity. Those with more advanced AI practices are establishing processes and policies for data governance and risk management, including providing ways to explain how their algorithms deliver results. They point out that understanding how AI systems reach their conclusions is both an emerging best practice and a necessity, in order to ensure that the human intelligence that feeds and nurtures AI systems keeps pace with the machines’ advancements.

The report that follows explores these findings in depth. Read on to learn more about the changes that leaders must prepare for to successfully implement trusted AI.   ... "

SAS CEO on Augmenting People and Processes

Jim Goodnight of SAS helped us through many of the early years of doing analytics in the enterprise.  Good to see him talking about natural language processes and AI, which we never hear of much of in those days, when classic statistics reigned.

AI technologies that matter now: Augmenting People, Processes, and Potential   Natural language processing, machine learning, and computer vision promise to extend human capabilities, and ultimately, improve the world around us.

by Jim Goodnight in Technology Review

Sponsored Content   Provided by SAS
Jim Goodnight is co-founder and CEO at SAS.

From mass surveillance to mind-reading machines, each new day seems to bring another alarming prediction about the potential of artificial intelligence to change the world.

But we don’t give enough attention to the practical AI applications that are in use every day. These real-world applications aren’t creepy or futuristic. You might even call some of them mundane. But they provide practical value to businesses and consumers, and they aren’t leading us to impending doom.

AI does have the potential to change our world. But it’s not going to do that through sentient robots or computers that take control away from humans. The AI applications that we see will more often augment human activity than replace it.

As we continue to witness increased computing power and a more connected world, practical AI technologies like natural language processing, computer vision, and especially machine learning will proliferate and become even more useful. These are the practical applications of AI that will improve our lives..... " 

(Updated) 6 Degree Intelligence

.... Have taken a further look at this, impressive.  A way to integrate analytical methods with human expertise using graph analytics. ....

Brought to my attention, note in particular the use of graph theory and strong data visualization. 

6 Degree Intelligence

It’s getting harder to find hidden assets and identities

6 Degree Intelligence develops solutions and tools to combat financial crime through actionable insights that quickly identify unknown risks beyond first-degree known relationships.   

Powered by SAS technology, 6 Degree Intelligence applies sophisticated Network Graph Theory and Link Analysis to enable investigators to search far deeper and faster for connections to potential risk and opportunity to commit financial crime.   .... " 

Short Video Intro:    https://youtu.be/W86mRcTJpyo 

Consider the 'Last Mile' of Analytics

The term is usually used just for supply chain  applications,  but I like the broadening view here.  To describe key operations needed to complete a process for value.  Links to a number of articles that cover several areas. .

Resources to help you conquer the 'last mile' of analytics 
By Sarah Gates on SAS Voices 

Getting value from analytics is becoming top of mind for businesses. Organizations have invested millions of dollars in data, people and technology and are looking for a return on their investment. That requires operationalizing analytics so that it can be used for strategic decision making -- often referred to as the 'last mile' of analytics.

The key? Developing a ModelOps practice which aligns the culture, processes and technology to accelerate the analytics life cycle. If you're ready to conquer the last mile of analytics and cross the finish line strong, take a look at the resources I've gathered below: .... ' 

Hierarchical Clustering Example

Here a good technical, but readily understandable,  example of hierarchical clustering.  With Code.  We used similar methods in the enterprise, but not for crime data clustering.  We used routines in SAS.

US Arrests: Hierarchical Clustering using DIANA and AGNES
Posted by Neeraj  in DSC:

Data Science and Machine Learning are furtive, they go un-noticed but are present in all ways possible and everywhere. They contribute significantly in all the fields they are applied and leave us with evidence which we can rely on and take data-driven directions. Today, a very interesting area we are going to see an example of Data Science and Machine Learning is ‘Crimes’. We are going to focus on types of crimes taken place across 50 states in the USA and cluster them. We cluster them for the following reasons:

To understand state-wise crime demographics
To make laws applicable in states depending upon the type of crime taking place most often
Getting police and forces ready by the type of crime done in respective states
Predicting the crimes that may happen and thus taking measures in advance
The above are the few applications which can be considered but they are not exhaustive, depending upon the data reports produced by the algorithms there can be more such applications which can be deployed. Our concern today is to understand how we can deploy Hierarchical clustering in two ways i.e. DIANA and AGNES for the USA Arrests data.  .... "