CGT at Estee Lauder

How Estée Lauder and Best Buy Use Analytics to Solve for Tomorrow’s Disruption

Anne-Marie Kennedy

Pradipta Saha, executive director of digital supply chain at Estée Lauder, talks supply chains of the future.

What are today’s consumer goods and retail executives most excited about regarding supply chain changes they’ve seen across both industries in the last few years?

Elena Alikhachkina, digital and data analytics executive at CDO, and moderator, posed the question at this year’s Analytics Unite event. 

“Supply chain became the ‘cool kid’ in the last three years, but there’s a lot to live up to because nowadays supply chain is both a dinner table and a boardroom conversation,” according to Ashok Viswanathan, director of supply chain analytics at Best Buy. “But I’m excited about supply chains using analytics to transform themselves for the future.”

Viswanathan believes that using scale, speed, and flexibility as criteria, supply chains have evolved from being cost centers to sources of competitive advantage for organizations. Thanks to COVID, he said, the supply chain is a key ingredient in global commerce. 

Pradipta Saha, executive director of digital supply chain at Estée Lauder, is of the same mindset. 

Saha and Viswanathan both agree that a major supply chain change comes from organizations looking to enhance and evolve their strategy and investing in a more holistic manner so that long-term capital investments create the supply chains of the future. 

Also read: Supply Chain Investments to Continue, Plus 8 Key Areas of Growth From Gartner

But that’s only a recent iteration and not how a lot of supply chains got built in the last five years, according to Viswanathan. “The supply chain now has a more important role to play in measuring and providing solutions for all these organizational decisions.” 

Saha emphasizes that it’s not that supply chain teams have changed, so much as priorities are now more geared toward resilience, supply visibility, and, at the core, getting away from functional silos to see everything from the lens of the consumer. It’s about asking, “What is the end-to-end impact of everything you do?” 

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His suggestion? Organizations should be looking at 18-month to three-year horizons in order to recognize signals early enough in the decisioning processes to make the right choices — not just for the supply chain but cross-functional relationships throughout the business. 

“You want to use all the resources you have at-hand to anticipate the future and ask ‘What are the things that could happen, or that we suspect might happen, and how do we best prepare for it?’” Viswanathan poses. 

Ashok Viswanathan, director of supply chain analytics at Best Buy

He likens the process to financial planning. “You don’t know what’s going to happen, but you  have your portfolio, you have your outlook and your risk-tolerance, and then you design your investment and continue to fine tune it or intervene as you go forward.” 

The continued evolution of omnichannel remains a recurring theme. While e-commerce growth has remained relatively stagnant since the pandemic, the omnichannel experience has undergone a significant change. 

“We’re thinking a lot about speed at Best Buy when it comes to omnichannel, not in terms of transit times or delivery days, but defining speed in other aspects that is good for Best Buy and good for the customer,” according to Viswanathan. “Do we have to be the fastest? Is that what the customer really wants? We don’t have the answers yet, but we have a lot of resources.”  

Long term, enterprises are looking to expand their influence, shifting from a narrow focus on growing e-commerce to providing that seamless, unified omnichannel brand experience, regardless of which channel customers use. 

“It should not matter,” Saha stresses, “however the customer wants to experience your product and have it delivered to them, it’s about providing them with choice. The ability to go digital is a top element of our mission.”   ... '

Pushing the Frontiers of Mathematical Research

My earliest work did this kind of research

Pushing the Frontiers of Mathematical Research,   By Allyn Jackson

Commissioned by CACM Staff, September 8, 2022

For decades, mathematicians have turned to computers for help with tasks like big numerical calculations and visualizing complex geometric objects. Like a blackboard, the computer has been a handy tool that picks up where human capacity to juggle numbers, symbols, and pictures drops off.

Today, however, computers are playing an entirely new role: they are learning modern mathematics.

A loose-knit international group is using computer proof assistants, originally developed to check formal software correctness, to create online libraries of mathematical theorems and proofs. The theorems housed in these libraries can then be called upon as building blocks for proofs of new mathematical results. The hope is that the libraries one day will encompass the entirety of mathematical knowledge.

"It's a completely new way to do mathematics that is very satisfying," said mathematician Kevin Buzzard of the U.K.'s Imperial College London.

Buzzard discussed this work in one of the most-watched lectures at the 2022 International Congress of Mathematicians in July this year. (The Congress, originally scheduled to be held in Saint Petersburg, Russia, was transformed into an entirely online event after that country's invasion of Ukraine.) Around the same time, a paper appeared on the arXiv containing what might be called a "proof assistant manifesto," laying out progress achieved and describing challenges ahead. Buzzard is one of the paper's 10 authors, along with ACM A.M. Turing Award recipient Leslie Lamport.

During the course of his career in number theory and algebraic geometry, Buzzard has worked on pure mathematics with no obvious applications. He describes his latest work with computer proof assistants as also "100% blue-sky research," explaining, "It's interesting, important, and beautiful. But I can't see the future. I don't know what killer app might come out of this work."

Verifying Proofs Down to the Axioms

Computer proof assistants, also called interactive theorem provers, began to have a significant impact in mathematics starting with the work of Thomas Hales of the University of Pittsburgh.  In the late 1990s, Hales announced a solution to a venerable sphere-packing problem called the Kepler conjecture. While his approach was generally accepted as correct, Hales' use of a computer program to sort through a huge number of possible packings left many skeptical.

Hales then embarked on a multi-year project to validate his solution by recasting it as a "formal proof," a thoroughgoing version of the proof in which every logical inference is checked, down to the fundamental axioms of mathematics. Formal proofs, far too verbose and tedious for humans to read, are tailor-made for computer proof assistants. In 2014, Hales and 20 collaborators completed a computer verification of the Kepler proof. 

In parallel with this work, during the early 2000s, a few mathematicians began to use proof assistants to formalize proofs of several classic mathematical results. The work was slow and painstaking, and the technology rather off-putting. As a result, formal proofs remained something of a niche area, far from the frontier of mathematics.

Buzzard got into the act after listening to a 2017 lecture in which Hales described his experiences using proof assistants. "That lecture changed my life," said Buzzard, as he realized that proof assistants could handle the theorems that mathematicians are working on at the frontier of research today. "Computer scientists were figuring out how to write the software, to make it useful," Buzzard said. "They've done that now. Now it's our turn."

The True Value of Proof Assistants

As the name suggests, a computer proof assistant is very good at verifying proofs, but its true value in mathematics lies elsewhere, in its ability to store mathematical results and the fundamental logic supporting them. Every theorem, lemma, and proposition that was used or proved in the course of verifying, for example, Hales's solution of the Kepler conjecture, now sits in the memory of a proof assistant, ready to be called upon as raw material to build proofs of new theorems.  ... ' 

Advanced Analytics for Agricultural Supply Chain Shocks

 Good Piece by McKinsey, My domain, call me if you want to discuss.

How advanced analytics can address agricultural supply chain shocks

April 14, 2022 | Article  (Excerpt) 

Building automated supply chain planning systems to address global shocks could lead to significant cost savings for agricultural organizations

Between July and October 2021, torrential rains drenched northern parts of China, flooding major agricultural provinces such as Hebei, Henan, and Shandong (Exhibit 1). The floods affected millions of acres of cropland,1 causing billions of dollars in food losses and greatly increasing prices for consumers.

Agricultural disruption caused by extreme weather is not unique to China: research has found that several regions critical to global agricultural production are susceptible to the effects of climate extremes. For example, more than 40 percent of the variation in crop yields in recent decades was caused by extremes in precipitation and temperature, affecting production of Asian corn and rice, European wheat, and North American corn, wheat, and soy.2

In September 2021, parts of China experienced a more than sixfold increase in rainfall compared with the past 20 years–serving as just one example of extreme weather around the world.

We strive to provide individuals with disabilities equal access to our website. If you would like information about this content we will be happy to work with you. Please email us at: McKinsey_Website_Accessibility@mckinsey.com

The COVID-19 pandemic has also intensified supply chain shocks. Canadian farmers, for instance, lost an estimated $2.9 billion in earnings due to labor shortages, and 47 percent of agricultural employers were unable to hire all the workers they needed.3 European farmers and agribusinesses suffered economic losses in the first wave of the pandemic due to the closure of trade channels (such as food services), labor shortages, delays in food and raw-material deliveries, and slowdowns in food production caused by virus outbreaks at processing plants.4

Food processing plants in the United States have also been hard-hit. One research group counted 100,000 cases and 466 deaths from COVID-19 among farm and food processing workers in every state between May 2020 and September 2021. The researchers identified almost 2,000 outbreaks at meat processing plants, fruit and vegetable farms, snack food facilities, and seafood trawlers.5

In addition, extreme weather and the pandemic have caused fertilizer prices to increase, adding to global inflationary pressures. Average 2021 nominal prices of diammonium phosphate and urea, for instance, were about 96 percent higher than their 2019 averages.6 In turn, increasing fertilizer prices have contributed to global food insecurity, as global food prices in 2021 jumped to their highest level in a decade.7 The ongoing conflict in Ukraine is further compounding the situation, with the Food and Agriculture Organization of the United Nations stating that the global supply gap from potential shortfalls in agricultural exports from Russia and Ukraine could raise international food and feed prices by more than 20 percent  ... '

Automating Data Science?

 Good to have consistent,  easily understood efforts in a library of solutions. 

Automating Data Science

By Tijl De Bie, Luc De Raedt, José Hernández-Orallo, Holger H. Hoos, Padhraic Smyth, Christopher K. I. Williams

Communications of the ACM, March 2022, Vol. 65 No. 3, Pages 76-87   10.1145/3495256

Data science covers the full spectrum of deriving insight from data, from initial data gathering and interpretation, via processing and engineering of data, and exploration and modeling, to eventually producing novel insights and decision support systems.

 Data science can be viewed as overlapping or broader in scope than other data-analytic methodological disciplines, such as statistics, machine learning, databases, or visualization

To illustrate the breadth of data science, consider, for example, the problem of recommending items (movies, books, or other products) to customers.

 While the core of these applications can consist of algorithmic techniques such as matrix factorization, a deployed system will involve a much wider range of technological and human considerations.

 These range from scalable back-end transaction systems that retrieve customer and product data in real time, experimental design for evaluating system changes, causal analysis for understanding the effect of interventions, to the human factors and psychology that underlie how customers react to visual information displays and make decisions.

As another example, in areas such as astronomy, particle physics, and climate science, there is a rich tradition of building computational pipelines to support data-driven discovery and hypothesis testing. For instance, geoscientists use monthly global landcover maps based on satellite imagery at sub-kilometer resolutions to better understand how the Earth's surface is changing over time.50 These maps are interactive and browsable, and they are the result of a complex data-processing pipeline, in which terabytes to petabytes of raw sensor and image data are transformed into databases of a6utomatically detected and annotated objects and information. This type of pipeline involves many steps, in which human decisions and insight are critical, such as instrument calibration, removal of outliers, and classification of pixels.

The breadth and complexity of these and many other data science scenarios means the modern data scientist requires broad knowledge and experience across a multitude of topics. Together with an increasing demand for data analysis skills, this has led to a shortage of trained data scientists with appropriate background and experience, and significant market competition for limited expertise. Considering this bottleneck, it is not surprising there is increasing interest in automating parts, if not all, of the data science process. This desire and potential for automation is the focus of this article.

Webinar: How Metadata Management Must Evolve to Support Data Fabric

Looks to be useful, plan to attend:

TopQuadrant: How Metadata Management Must Evolve to Support Data Fabric   by Irene Polikoff | Mar 8, 2022 | Metadata Management, Webinars

About This Webinar

On Thu, Mar 17, 2022 11:30 AM EDT

If you have not heard the term “data fabric” yet, you will. It is rapidly growing in popularity. Gartner identified data fabric as the top trend for data and analytics in 2021.

You can think of data fabric as a web connecting multiple locations, types, and sources of data – both on-premises and in the public cloud. It is an architectural approach designed to help organizations better deal with the growing number of available data sources and ever-changing application requirements. The backbone of data fabric design is a Knowledge Graph capturing information about data sources in RDF. This is a new type of a data catalog with semantically augmented and enriched metadata.

Join us for this webinar to:

Learn What Is Data Fabric

Understand Why Data Fabric Requires a Knowledge Graph

Get Advice on Moving from Traditional Metadata Management to Metadata Management With Knowledge Graphs

Envision How Tools Participating in Data Fabrics Will Interact With Knowledge Graphs

See these Concepts in Action  ... 

 Register:   https://register.gotowebinar.com/register/7807142108219203083 

Another Good Intro to Digital Twins

Well done, overview that is only minimally technical.

Virtual Duplicates, By Neil Savage,   Communications of the ACM, February 2022, Vol. 65 No. 2, Pages 14-16  10.1145/3503798

Back in 1970 during the seventh crewed mission of the Apollo space program (the third intended to land on the Moon), the three astronauts aboard Apollo 13 were calmly going about their duties when an explosion in an oxygen tank rocked the spacecraft, spilling precious air into space and damaging the main engine. Personnel in Mission Control suddenly had to devise a plan to get the crew home, and to do that they had to understand what condition the damaged ship was in and the materials available for repairs, and then test what the astronauts might be able to accomplish.

To figure it out, they turned to the flight simulators used to plan and rehearse the mission. They updated the simulators with current information about the physical state of Apollo 13 and tried various scenarios, eventually coming up with the plan that safely returned the astronauts to Earth. This was, some argue, the first use of a digital twin, a model that simulated the state of a physical system with real-time data and made predictions about its performance under varied conditions.

Digital twins are growing in popularity, especially as the Internet of Things provides data from sensors in all sorts of places. The concept is being applied in a range of areas, from buildings to bridges, from wind turbines to aircraft, from weather systems to the human heart.

A digital twin is more than just a simulation of some arbitrary object or system. "It's not a generic model of an airplane or a car or wind turbine or a generic person," says Karen Willcox, director of the Oden Institute for Computational Engineering and Sciences at the University of Texas at Austin (UT Austin). "It's a personalized model of one particular aircraft or one particular person."

To qualify as a digital twin, Willcox says, the model needs to take into account current information about the state of the system and evolve over time as it is updated with new data about the system. Another distinguishing feature is that the model and the data help people make decisions about the system, which in turn can change the data and require the model to be updated again.

Perhaps the most obvious use of digital twins is to monitor the long-term health of expensive or complex equipment, such as engines, manufacturing equipment, or industrial heating, ventilation, and air conditioning (HVAC) systems. That sort of use is increasingly being touted as part of Industry 4.0, which incorporates digital technology, machine learning, and big data to improve industrial processes.

IBM, for instance, is combining sensors with its Watson artificial intelligence technology to help large companies make decisions about what maintenance to perform and when, to extend the lifetime of equipment and cut costs. In one example, the company created a digital twin of an engine blade in a 777 aircraft to monitor when it begins to degrade and requires upgrade or replacement. Similarly, GE created digital twins of its wind turbines to predict when the equipment will need maintenance and builds that into a schedule, so wind farm operators can address issues before a turbine breaks, avoiding costly downtime. Researchers at Siemens are applying a similar approach to the human body, developing digital twins of individual human hearts they hope could predict the effectiveness of a specific therapy for a particular patient, instead of merely relying on statistics about hearts in general. .... '

Embedded Analytics and AI

Good piece, below the intro.   Embedded yes, but the best way how? 

Embedded Future of Analytics and AI

Alex Woodie

Data makes the world go round; you won’t get much argument from us on that. But if there’s one thing better than having the right piece of information, it’s having it at the right time. And that, in a nutshell, is why embedded analytics and AI will be so critical to big data’s future.

Despite the progress we’ve made since the big data revolution began, access to data and data tools is still largely restricted to a select few. In many companies, it’s the BI dashboard-wielding business analysts who ask tough questions and the data scientists who really dig in to spot patterns and anomalies that can be monetized, often through machine learning and AI-powered automation.

But that leaves the bulk of the regular works out of the data loop. While the customer service representatives, bus drivers, and teachers may not have the SQL skills of a Tableau or PowerBI power user, they do have ample opportunities to impact the business through data. But currently they’re not given the opportunity because they don’t have the right tools.

One of the folks who is dedicated to closing this information access gap is Amir Orad, the CEO of Sisense, which is one of the top five providers of embedded analytics solutions, according to Nucleus Research.

“Gartner shows 80% of all employees don’t leverage BI or insights because it takes skills and learning. You have to go to another system and log in. It’s too much,” Orad tells Datanami. “I’m a big believer that insight will be embedded in everyday tools people use, and stop being as much as possible a side tool to come to and ask ridiculous questions, and that will truly open the other 80%.”

Nucleus Research Senior Analyst Alexander Wurm, author of the recently released Embedded Analytics Technology Value Matrix 2022 report, would likely agree with Orad’s assessment.

“The embedded analytics market has taken off in parallel as organizations look to empower internal departments with analytics embedded in the applications they already use, and many consumer-facing businesses are embedding analytic capabilities to differentiate their products and elevate their customer experience,” Wurm writes in the report, which you can access here.

(Source: Nucleus Research)

“To support this broadening adoption, embedded analytics providers are investing in complete cloud services to enable data discovery, modeling, reporting, and visualization creation within external applications and compete to deliver highly usable services to empower analysts, creators, and end-consumers,” he continues.

Several different types of companies are turning to embedded analytics. Tech firms and ISVs are looking to embedded analytics to “elevate end-user experience and unlock additional revenue,” Wurm writes, while less technical teams have also embraced embedded analytics as a way to enable data democratization.

“This is crucial for business-level users who may not be familiar with statistical methods but can be taught to turn data into relevant insights with the correct tools,” he writes.

The broader BI and visualization tool market is turning to machine learning and AI, which is a trend that IT analyst groups like Gartner have been documenting for years. Nucleus says the embedded analytics market is also adopting ML and AI, as well as natural language query (NLQ) and natural language generation (NLG) tools. Industry leaders like Sisense, Domo, Tableau, Oracle, and Infor Birst are leading the way with NLG and NLQ capabilites, and the rest of the market is about 18 months behind, Wurm writes.  ... ' 

Phases of Analytic Evolution

 Been a while since I looked at Kellogg Insight, useful thoughts

4 Phases of Analytics Evolution: From Spreadsheets t o AI Workbenches

What lies ahead for business leaders looking to incorporate data analytics?

BY   Joel K. Shapiro

Many years ago, I went looking for some “desktop AI”—artificial intelligence that would allow me to tease out trends in data. I knew about IBM’s Watson, so I went to the website in hopes of uploading data and getting access to some machine learning. “That’s not how it works,” a friend who worked at IBM told me at the time. “You can’t just press a button and get AI.”

That’s not the case anymore. Today, AI tools and workbenches allow you to click your way to some pretty advanced modeling techniques, without requiring any knowledge of coding or programming language. That’s certainly not to imply that everyone can be a data scientist. But business professionals today can have AI at their fingertips for making data-based projections. Sometimes, it’s as simple as clicking a button that says “predictor.”

To put this in perspective, let’s examine the four phases of data-analytics technology for the businessperson—three that brought us to this point, and one on what lies ahead.

Phase One—Simple Data Management

For years, businesses have been organizing information on spreadsheets—most notably Excel—to run analyses, such as summary statistics by group. Of course, to get value out of the tool, the user needed to understand the output; otherwise, the exercise was meaningless—and potentially dangerous when making decisions based on something not fully known or understood.

For example, back in the spreadsheet days, a data analyst might have compared customer purchasing behaviors across different groups or segments. Let’s say the analysis showed higher average purchasing among Segment 1 than Segment 2, which led to the conclusion that Segment 1 customers were more important to the business. A slightly deeper analysis of variability might reveal that Segment 2 customers were actually more consistent purchasers. That is, a better understanding of what the tool could produce yielded a more effective use of the tool.

While the spreadsheet was a great start for the everyday business professional’s analytic needs, it could be a blunt tool without the necessary nuances for deeper insights.

Phase Two—Powerful, but Inaccessible, Tools

The next phase brought advanced AI technology for analyzing data into the business-world mainstream. But until a few years ago, the most powerful AI analysis was limited to only the programmers and developers who built models with code. If someone wanted to compare 10 different models to see which one was most effective at predicting an outcome, it would be necessary to write code for 10 different models.

Phase 3—Powerful, Accessible AI Workbenches

Today’s new consumer-facing tech tools combine powerful algorithms with great user interfaces. Unlike the Phase 2 tools, AI workbenches no longer require knowledge of coding to build many AI models. Additionally, the best AI workbenches easily run many models simultaneously and identify the best model for the data.

What remains unchanged from the earliest days of simple data management, however, is the importance of understanding the outcome. When an algorithm produces a result, there are right and wrong, simple and nuanced, ways of using that result.

Business professionals today can have AI at their fingertips for making data-based projections.

— Joel Shapiro   .... ' 

IOS Shortcuts For Analytics Management?

Examining the possible use of IOS Shortcuts for the management of data, analytical and ML solutions to be tested on an Iphone.  My specialty. See:  https://support.apple.com/guide/shortcuts/welcome/ios   My first involvement in using shortcuts.  Have any ideas, want to participate?   Let me know.

Procter & Gamble and Microsoft: Data and Digital Transformation

Microsoft talks about how they work with key customers.   Notably with my former employer, where I worked on many related problems, and can agree that using data effectively was well understood to be key.  Fairly good detail here about using Microsoft Azure.

For Procter & Gamble, data is at the heart of digital transformation

August 17, 2021   Microsoft

For more than 180 years, Procter & Gamble (P&G) has built a thriving business by challenging norms and delighting consumers. Its range of household products, from toothpaste and soap to paper towels and laundry detergent, can be found in millions of homes—in nearly every country on Earth.

True to its innovative nature, P&G sees data and algorithms as a way to constructively disrupt how it operates, leading to growth. P&G developed a multicloud-driven data strategy—and data culture—that informs every decision the business makes to fuel its future success.

In order for P&G to understand and best serve consumers, we need to drive a data-enabled culture and operationalize algorithms into every major business decision. 

Guy Peri: Chief Data & Analytics Officer, P&G

“The way we approach excellence as a company is to get the right product, the right package, the right communication, and the right in-store execution,” says Guy Peri, Chief Data & Analytics Officer for P&G. “Pretty much everything about how we do that with our customers and the consumers who buy our products is changing—and data and algorithms are central to staying ahead of that change.”

Lots of data, but out of reach

Given its broad scope and long history, P&G had numerous data points about its business, including what drives sales and product preferences. But the data resided in geographically dispersed silos, and P&G’s legacy systems couldn’t link the hundreds of disparate data types to gain collective insights.

P&G sought a platform that could unlock those silos and provide a single view for business users. At the same time, company leaders realized that they needed to foster a data-driven culture—a growth mindset that put data and analytics at the forefront. AI, machine learning, and advanced analytics were all seen as crucial to becoming a true digital leader.

P&G found what it needed with the powerful suite of Microsoft Azure data and AI solutions, such as Azure Synapse Analytics, Azure Data Lake Storage, and Power BI. “Azure is helping optimize the impact of digitization across Procter & Gamble, from democratizing AI with no-code features to embedded capabilities in Power BI,” says Peri. “We appreciated the scale, flexibility, and analytical muscle of Azure. The stack can go up and down depending on our business needs. It also gives us leading data and algorithm capabilities with both the Azure Synapse back-end and front-end Power BI tools.”

For us, only two variables are important: performance and scale. Can we scale at a cost-effective rate? With Azure and the co-innovation we did together, we’re able to get positive outcomes on both fronts. 

Guy Peri: Chief Data & Analytics Officer, P&G

With these scalable, dynamic data solutions, P&G is bringing the power of data to bear on three key challenges: creating a resilient supply chain, improving retail execution, and delivering superior products and packaging.

A more resilient supply chain

P&G data scientists and business analysts combine information from existing and new data sources inside the data lake to create predictions that help ensure consumers can get the products they need, when they need them.

Better retail execution

P&G is using granular analytics capabilities to ensure that retail partners are putting the right products and right in-store displays in front of consumers. 

Superior products and packaging

P&G is using data and analytics in its products and packages to shift from a point-in-time assessment to a real-time, sense-and-respond approach. A key component of this shift is a global architecture with automated collection of data to fuel analysis that uncovers insights, speeds the innovation process, and determines manufacturability and cost optimization.

“We need to act with agility to be competitive with the fast startups in the world,” says Peri. “But we also need to act with precision to leverage our scale for competitive advantage. Data algorithms help us do both. So, the faster we can bring data insights to our decision makers, the more competitive we’ll be.”

Neighborhood analytics and smart selling

In addition to gaining important insights at a global level, P&G has been able to do the same at a local level. One initiative, called “Neighborhood Analytics,” helps P&G understand at a local level how consumers shop and what they need. By combining external and internal data sources, the company can more effectively partner with retail customers to expand specific categories for consumers.

A second initiative, called “Smart Selling,” gives P&G insight into what its distributors are doing in developing markets. By using big data and AI, the company can help distributors be precise when ordering products. “COVID-19 is a great example of where we’ve been able to use these tools to be much more precise,” says Peri. “We overlaid our Azure analytics capability with COVID-19 data and then understood exactly where we had the biggest risk of out-of-stock opportunities to address or where we anticipated the need for inventory.”

Creating a data-driven culture

Beyond specific use cases, P&G is also using Azure to create a strong data culture that makes employees aware of the power of data and how to use it every day.

The ease of use with Azure fosters P&G’s data culture from the technology side, making it easy for employees to understand data, manipulate it, and use it to inform major decisions. These decisions, in turn, positively affect P&G’s ability to better serve consumers and constructively disrupt how the company operates.

“Our data culture is a combination of data technology, work processes, and reward systems,” says Peri. “If we have only one or two of those things, we’re not going to deliver the outcomes we want. We have partnered closely with our line business leadership and our function leadership to really transform in a way that constructively disrupts how we operate.”

A foundation for the future

Looking ahead, P&G will connect more data types and find new ways to delight its customers—and the consumers who fill their homes with P&G products. And it will continue to strengthen its data culture, operationalizing algorithms into key decision processes across the company. Procter & Gamble may be 183 years old, but its future has never been brighter. ...

Very few vendors can serve our needs at scale, given how much data we’re processing. Azure Synapse Analytics can, and Microsoft’s vision on how to scale is among the best in the industry. 

Guy Peri: Chief Data & Analytics Officer ....

Stocks to Watch for Analytics

If analytics are the future, how to invest in them?    Here a reasonable set of well known choices.    We worked with all of them, and many more.  Note that the term 'AI' is not often mentioned, which is good, analytics is a good place to start. 

Using predicted stock price as a measure, here a look at potential results.  Much more at the link.

Entrepreneur writes:

3 Stocks to Watch Amid Rising Usage of Business Analytics

Watch out for the likes of Adobe (ADBE), International Business Machines (IBM) and SAP (SAP) as the reliance on business analytics for insights ...

September 6, 2021   This story originally appeared on Zacks

As the shift to digitization has accelerated over the years, the importance of business analytics has also increased manifold. With the help of business analytics, organizations are able to gather data, gain valuable insights and make informed decisions, thereby boosting profitability.

Businesses can gather data from various departments as well as smart devices that are connected via the Internet of Things. Upon collecting such data, they are separated for identifying patterns. The advent of machine learning (“ML”) has been instrumental in data mining as ML algorithms can accelerate the process by comprehending trends and patterns. Once the patterns have been established, organizations can study the data to better understand their business operations and utilize analytics to predict possible outcomes by building models based on existing data and trends.

This helps organizations to increase their productivity and boost efficiency as they can take note of the aspects that are working best for them and improve upon other areas as well. Moreover, organizations can better serve their customers with the help of marketing analytics, which is part of business analytics, as they can understand their behaviors, likes, and dislikes, and offer them products and services based on those parameters. What’s more, the analytics can also be presented in a visualized manner for better comprehension by technology experts as well as businesspersons.

In fact, the spread of the coronavirus boosted the adoption of data analytics, through which firms can make informed business decisions. During the height of the pandemic last year, a TechRepublic article published on May 28, 2020, citing Sisense’s State of BI & Analytics Report 2020: Special COVID-19 Edition mentioned that 49% of companies were utilizing data analytics “more or much more” than before the pandemic. The article further stated that 55% of companies were utilizing data to become more efficient as well as predicting outcomes while 47% were using it to aid customer interactions.

The advent of cloud computing has also provided a leg up to business analytics as organizations are opting for cloud analytics services due to factors such as reduced costs, flexibility, availability of several analytics tools, and so on.  ... '

Northwestern Retail Analytics

Just received the latest Northwestern Retail Analytics Council mailing.   Good review of what they are up to.

The quarterly Retail Tech Bulletin, published by the Retail Analytics Council, Northwestern University, includes articles on current retail trends, case studies, artificial intelligence, and related retail technologies. The Bulletin also includes updates on Retail Analytics Council activities, the Retail Robotics Initiative, and the Retail AI Lab at Northwestern University. If you would like to contribute to the Retail Tech Bulletin, please contact us.  ... 

Responsible and Explainable AI

I would call this more broadly, 'responsible decision making' to embrace what is called 'AI' and analytical decision making.  Useful and considered look at what this means. 

Responsible AI: Bridging From Ethics to Practice   By Ben Shneiderman  via CACM

Communications of the ACM, August 2021, Vol. 64 No. 8, Pages 32-35  10.1145/3445973

The high expectations of AI have triggered worldwide interest and concern, generating 400+ policy documents on responsible AI. Intense discussions over the ethical issues lay a helpful foundation, preparing researchers, managers, policy makers, and educators for constructive discussions that will lead to clear recommendations for building the reliable, safe, and trustworthy systems6 that will be commercial success. This Viewpoint focuses on four themes that lead to 15 recommendations for moving forward. The four themes combine AI thinking with human-centered User Experience Design (UXD).

Ethics and Design. Ethical discussions are a vital foundation, but raising the edifice of responsible AI requires design decisions to guide software engineering teams, business managers, industry leaders, and government policymakers. Ethical concerns are catalogued in the Berkman Klein Center report3 that offers ethical principles in eight categories: privacy, accountability, safety and security, transparency and explainability, fairness and non-discrimination, human control of technology, professional responsibility, and promotion of human values. These important ethical foundations can be strengthened with actionable design guidelines.

Autonomous Algorithms and Human Control. The recent CRA report2 on "Assured Autonomy" and the IEEE's influential report4 on "Ethically Aligned Design" are strongly devoted to "Autonomous and Intelligent Systems." The reports emphasize machine autonomy, which becomes safer when human control can be exercised to prevent damage. I share the desire for autonomy by way of elegant and efficient algorithms, while adding well-designed control panels for users and supervisors to ensure safer outcomes. Autonomous aerial drones become more effective as remotely piloted aircraft and NASA's Mars Rovers can make autonomous movements, but there is a whole control room of operators managing the larger picture of what is happening.

Humans in the Group; Computers in the Loop. While people are instinctively social, they benefit from well-designed computers. Some designers favor developing computers as collaborators, teammates, and partners, when adding control panels and status displays would make them comprehensible appliances. Machine and deep learning strategies will be more widely used if they are integrated in visual user interfaces, as they are in counterterrorism centers, financial trading rooms, and transportation or utility control centers.

Explainable AI (XAI) and Comprehensible AI (CAI). Many researchers from AI and HCI have turned to the problem of providing explanations of AI decisions, as required by the European General Data Protection Regulation (GDPR) stipulating a "right to explanation."13 Explanations of why mortgage applications or parole requests are rejected can include local or global descriptions, but a useful complementary approach is to prevent confusion and surprise by making comprehensible user interfaces that enable rapid interactive exploration of decision spaces.   ... '

Note in particular a look at 'trustworthy certification' proposed outlines for a number of large industries, to show how this might be applied by the nature of their operation ... '

Facebook Research Allocates Ad Funding (without AI)

Struck me because we did something similar and also quite different for advertising fund allocation very early on.    We the results of integer optimization models with montecarlo simulation, constrained to advertising agreements.   I like the fact that AI is never mentioned!  But in much later spins on this,  what could be called AI was used to check for accuracy and regulatory compliance to contractual agreements.  Nice.

Auto-placement of ad campaigns using multi-armed bandits  in Facebook Research

By: Vashist Avadhanula, Riccardo Colini Baldeschi, Stefano Leonardi, Karthik Abinav Sankararaman, Okke Schrijvers

What the research is:

We look at the problem of allocating the budget of an advertiser across multiple surfaces optimally when both the demand and the value are unknown. Consider an advertiser who uses the Facebook platform to advertise a product. They have a daily budget that they would like to spend on our platform. Advertisers want to reach users where they spend time, so they spread their budget over multiple platforms, like Facebook, Instagram, and others. They want an algorithm to help bid on their behalf on the different platforms and are increasingly relying on automation products to help them achieve it.

In this research, we model the problem of placement optimization as a stochastic bandit problem. In this problem, the algorithm is participating in k different auctions, one for each platform, and needs to decide the correct bid for each of the auctions. The algorithm is given a total budget B (e.g., the daily budget) and a time horizon T over which this budget should be spent. At each time-step, the algorithm should decide the bid it will associate with each of the k platform, which will be input into the auctions for the next set of requests on each of the platforms. At the end of a round (i.e., a sequence of requests), the algorithm sees the total reward it obtained (e.g., number of clicks) and the total budget that was consumed in the process, on each of the different platforms. Based on just this history, the algorithm should decide the next set of bid multipliers it needs to place. The goal of the algorithm is to maximize the total advertiser value with the given budget across the k platforms.   ... "    

Full paper:  https://arxiv.org/abs/2103.10246 

Google and Ford Sign Deal to Put Android into Car, onto Plant Floor

Perhaps unexpected partnership.  Recall we recently mentioned that Ford was the leader in Self Driving Car patents.  We worked with Ford and they were impressive in analytics/data analytics utilization.  The mention of in car Android method should also spread the use of voice and AI more widely un cars.  

Google Snags Ford Cloud Partnership in Coup Over Microsoft

Deal includes Google license fees for every Ford and Lincoln car sold starting in 2023

By Nico Grant, February 1, 2021, 11:00 AM   in Bloomberg

Google signed a six-year deal with Ford Motor Co. that will bring Android technology to the automaker’s cars and cloud services to its factory floor, in a triumph for the internet giant over rival Microsoft Corp.

Google Cloud Platform will be Ford’s “preferred cloud provider” and the partners will form an innovation team called Team Upshift to jointly work on future projects, the companies said Monday in a statement. Ford dashboard infotainment screens will be powered by an Android operating system starting in 2023 and the automaker will adopt Google’s artificial intelligence and data-analytics technology.

The companies did not disclose the value of their deal, but Google will receive revenue from cloud services as well as a Google apps and services licensing fee for every Ford and Lincoln vehicle sold starting in 2023.

Thomas Kurian, chief executive officer of Google Cloud, is trying to sell the Alphabet Inc. company’s computing and storage services through broad strategic partnerships. Fresh off a multiyear alliance with Deutsche Bank AG announced in December, Google has snagged Ford, a longtime Microsoft client. Google will need to continue accumulating these types of customer wins to catch cloud market leaders Amazon.com Inc. and Microsoft.  .... " 

Descriptive Analytics

 Always been a fan of the idea, of descriptive analytics,  an excellent predecessor to more complex work as needed. .   Here an overview of its value.

The Value of Descriptive Analytics: Evidence from Online Retailers  by Ron Berman and Ayelet Israeli   HBS Working Knowledge

Analytics are descriptive when they describe what happened. Descriptive-analytics solutions are popular among marketers and retailers. This paper provides a benchmark for the benefits of using a descriptive dashboard and illustrates how to potentially extract these benefits.

Author Abstract

Does the adoption of descriptive analytics impact online retailer performance, and if so, how? We use the synthetic control method to analyze the staggered adoption of a retail analytics dashboard by more than 1,000 e-commerce websites, and find an increase of 13–20% in average weekly revenues post-adoption. We demonstrate that only retailers that adopt and use the dashboard reap these benefits. The increase in revenue is not explained by price changes or advertising optimization. Instead, it is consistent with the addition of prospecting and personalization technologies to retailer websites. The adoption and usage of descriptive analytics also increases the diversity of products sold, the number of transactions, the numbers of website visitors and unique customers, and the revenue from repeated customers. In contrast, there is no change in basket size. Put together, these findings are consistent with an indirect effect of descriptive analytics, and they suggest that the adoption of analytics serves as a monitoring device that allows retailers to assess the value of new technologies. Without using the descriptive dashboard, retailers are unable to reap the benefits associated with these technologies.

Full publication. ... 

Machine Learning Algorithms You Should Know

A nice piece which covers the basic concepts of the most used data science algorithms.  Not enough to know how to use them, but good for knowing when and why.  I often reviewed this level of understanding with executives who were involved in the results.  I would also not say 'all',  it depends on your domain.

All Machine Learning Algorithms You Should Know in 2021  in KDNuggets

Tags: Algorithms, Decision Tree, Explained, Gradient Boosting, K-nearest neighbors, Machine Learning, Naive Bayes, Regression, SVM

Many machine learning algorithms exits that range from simple to complex in their approach, and together provide a powerful library of tools for analyzing and predicting patterns from data. If you are learning for the first time or reviewing techniques, then these intuitive explanations of the most popular machine learning models will help you kick off the new year with confidence. 

 

By Terence Shin, Data Scientist 

Tags: Algorithms, Decision Tree, Explained, Gradient Boosting, K-nearest neighbors, Machine Learning, Naive Bayes, Regression, SVM    ... " 

Lopsided Digital Transformations

Can confirm this in the large enterprise.  Broadly done in supply chain world, uncommon in marketing.  Depends on things like data access, expectations around automation.  

Digital Transformation Is Lopsided, Even Within the Same Organization

By ZDNet, December 29, 2020

An unbalanced structure.

A study of organizations in Harvard Business Review found that some corporate departments can be more digitally advanced than their counterparts at another location, or even down the hall. The IT department itself often finds itself on the wrong side of progress.

A Harvard Business Review study of organizations found "a growing divide between teams with ready access to automation and [artificial intelligence (AI)] tools and teams without," with the latter trailing "in terms of productivity and AI skill development."

Accenture's Ramnath Venkataraman and colleagues determined that two-thirds of investigated companies relied on a less-than-optimal blend of cloud-based and on-premises enterprise solutions.

Exacerbating the problem is information technology (IT) departments being mired in maintenance and upkeep, while other departments have access to cutting-edge systems.  The Accenture team also learned that some coders might devote 60% of their time executing automatable tasks.

The researchers suggested greater inter-enterprise collaboration to address these challenges, by de-siloing walled-off departments and encouraging open access to shadow IT systems.

Deep Learning is Overused

 Well put, always consider the simpler analysis that leads to better understanding first. 

Deep Learning Is Becoming Overused

Understanding the data is the first port of call

By Michael Grogan  in TowardsDataScience

There is always a danger when any model is used in a black-box fashion to analyse data, and models of the deep learning family are no exception.

Don’t get me wrong — there are certainly occasions where a model such as a neural network can outperform more simplistic models — but there are plenty of examples where this is not the case.

To use an analogy — suppose you need to buy a vehicle of some sort for transportation purposes. Buying a truck is a worthwhile investment if you regularly need to transport large items across long distances. However, it is a blatant waste of money if you simply need to go to the local supermarket to pick up some milk. A car (or even a bicycle if you are climate-conscious) is sufficient to carry out the task in question.

Deep learning is starting to be used in the same way. We are starting to simply feed these models with the relevant data, assuming that performance will surpass that of simpler models. Moreover, this is often done without properly understanding the data in question; i.e. recognising that deep learning would not be necessary if one had an intuitive grasp of the data. ... ' 

Last Year in AI, Analytics, Machine Learning and Data Science ....

Good end of the year piece from KDNuggets that was instructive.

AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2020 and Key Trends for 2021

Tags: 2021 Predictions, AI, Ajit Jaokar, Analytics, Brandon Rohrer, Daniel Tunkelang, Data Science, Deep Learning, Machine Learning, Pedro Domingos, Predictions, Research, Rosaria Silipo

2020 is finally coming to a close. While likely not to register as anyone's favorite year, 2020 did have some noteworthy advancements in our field, and 2021 promises some important key trends to look forward to. As has become a year-end tradition, our collection of experts have once again contributed their thoughts. Read on to find out more.

By Matthew Mayo, KDnuggets.

To the chagrin of absolutely no one, 2020 is finally drawing to a close. It has been a rollercoaster of a year, one defined almost exclusively by the COVID-19 pandemic. But other things have happened, including in the fields of AI, data science, and machine learning as well. To that end, it's time for KDnuggets annual year end expert analysis and predictions. This year we posed the question:

What were the main developments in AI, Data Science, Machine Learning Research in 2020 and what key trends do you see for 2021?

Last year's noted main developments and predictions included continued advancements in many research areas, NLP in particular. While there can be debate as to whether 2020's big NLP advancement was as formidable as some may have originally thought (or continue to think), there is no doubt that there was a continued and intense focus on NLP research in 2020. It should not be difficult to surmise that this continues into 2021 as well.  ... "