Engineered

2019 HBR McKinsey Award Finalist: Cross-Silo Leadership

I've been reading the finalist entries in the 2019 Harvard Business Review McKinsey Awards Competition and one that caught my attention is Cross-Silo Leadership from the May-June 2019 issue. There is a lot of talk in academia these days about interdisciplinary work, especially at places like SMU, the Vanderbilt of Texas, so I read the article with an eye on the lessons to be drawn for universities.

The authors highlight four practices to make effective "interface work".

1. Develop and deploy cultural brokers. In industry, they can be "bridges" (allowing different units to "work more synergistically even without directly interacting") or "adhesives" (who "bring people together and help build mutual understanding and lasting relationships"). The authors explain: "Adhesives facilitate collaboration by vouching for people and helping them decipher one another's language. Unlike bridges, adhesives develop others' capacity to work across a boundary in the future without their assistance." This takes strong interpersonal skills and a growth mindset. 
2. Encourage people to ask the right questions. "Managers with high levels of curiosity were more likely to build networks that spanned disconnected parts of the company." Leaders can encourage inquiry by being a role model in asking questions (especially "Why?", "What if...?" and "How might we...?") and by teaching employees the art of inquiry (favoring open-ended questions, processing the answers by checking that what they hear, which is based by their expertise, is what the person meant to say). 
3. Get people to see the world through others' eyes. For instance, organize cross-silo dialogues such as focused event analysis at Children's Minnesota and hire for curiosity and empathy.
4. Broaden your employees' vision, bringing employees from diverse groups together on initiatives and urging employees to explore distant networks.  

Academia of course faces its own unique situation and challenges. In trying to foster interdisciplinary work - which could lead to a multi-year project, in contrast with industry's shorter-term engagements - it matters to introduce the right people to each other and also to keep them talking for long enough so that they can define the right project to work on. But industry projects have always involved teams of people, while academia promotes its faculty based on their contributions in a specific field. Where to publish interdisciplinary work? How to assign contributions properly? How to evaluate such work? Perhaps such studies should be reviewed through synchronous panels rather than solely written reviews, giving the reviewers the opportunity to educate each other on the work's strengths in their own discipline, or published in special issues of prestigious journals or as book chapters at a top university press (rather than everyday submissions to academic journals).

Joint appointments would be useful too but the faculty member should be explicitly evaluated based on how well he educates each department about the other - that could be his service work, in addition to research and teaching. It is also good to make a distinction between interdisciplinary appointments and joint appointments. According to Columbia University, "An appointment is joint when a faculty member is qualified in the discipline of two (or more) schools/departments and fulfills significant functions and responsibilities in each school/department. An appointment is interdisciplinary when the faculty member is qualified in one discipline, fulfills significant functions and responsibilities in more than one school/department of instruction OR in a school/department and in an institute or Center." William & Mary has a helpful document about procedures.

The American Psychological Association devotes an entire guide on the topic. Here is a great quote from UC San Diego mentioned in the report: "Research at the interface between traditional academic disciplines is quickly becoming the norm for scholarly advances and innovation. The research university of the 21st century is a horizontally integrated academy where the generation of human and intellectual capital thrives on collaboration and interaction across traditional academic disciplines. To achieve this multidisciplinary culture without academic impediments requires that the campus re-examine how departments, divisions, schools, and campus units approach their traditionally vertical education and research missions. Multidisciplinary education programs exist and are increasing in number, and multidisciplinary research is supported by research centers and institutes (organized research units, multi-campus research units) that are outcome- based and not discipline-based. However, faculty appointments are most often governed by a vertical departmental structure that may not be conducive to recruiting or rewarding excellent faculty whose research focus is at the interface between disciplines and/or in new research areas that do not fit into the traditional departmental structure."

The guide advocates for a Memorandum of Understanding between the two academic units when the joint appointment is created and provides a detailed list of issues that need to be addressed in it. The best recruitment method is through cluster hiring with search committee members representing components of the interdisciplinary research. The document provides extensive guidance on the review processes. It is also good to evaluate the interaction faculty members with courtesy appointments have in their host department. 

Interdisciplinary work can happen on the teaching side too. Team teaching is often touted like the next big thing because the students will have to think "interdisciplinarity" in the workforce, but I've never seen team teaching in action and none of my immediate colleagues practices it either. It is tricky to pull off because the time commitment if you teach with a colleague is much more than half the contact hours. One idea I had to celebrate the start of Provost Loboa's tenure is to use the end of January term in a manner similar to the Independent Activities Period at MIT and use that time to have mini-lectures of team teachers, pairs of professors teaching audiences an interdisciplinary subject for an hour or an hour and a half. This would lower the barrier to get faculty to talk to each other about interdisciplinary work, as team-teaching for an entire semester can be daunting. And perhaps it would spark ideas in the audience. I don't know if anything will come out of this idea but it would be a good start.  

McKinsey on The future of work in America

 McKinsey has been in the headlines for the wrong reasons recently (and more recently) so I'm not sure how much credibility I put in their research reports anymore but there is no doubt that, given the renewed interest in artificial intelligence and its implication for future job prospects in the U.S., many people are wondering what data science and automation will mean for them, both in terms of their own jobs and the economic prosperity of the area where they live - the latter point being a novel focus of the present report, understudied in the rest of the literature. So here is an overview of McKinsey's recent report The future of work in America. 

Of its analysis of over 3,000 US counties and 315 cities, it concludes that job growth will be concentrated in 25 megacities and high-growth hubs (Dallas-Fort Worth being one of them), while 54 cities and 2000 rural counties, home to 1/4 of the US population, have "older and shrinking workforces, higher unemployment and lower educational attainment." The report's authors write: "Automation technologies may widen these disparities at a time when workforce mobility is at historic lows." It will become even more important to hold a Bachelor's degree or higher, which has implications in terms of the job prospects of underrepresented minorities too, since they often have more limited access to education. 

Here are some sobering statistics: "Jobs held by nearly 15 million workers age 18-34 may be automated, so young people will need new career paths to gain an initial foothold in the working world. Roughly 11.5 million workers over age 50 could also be displaced and face the challenge of making late-career moves. The hollowing out of middle-wage work could continue."

According to the authors, the largest occupational categories in the US economy have the highest potential displacement rates, while strong job growth is expected in "healthcare, STEM occupations, creatives and arts management, and business services." The McKinsey consultants recommend mapping alternative career pathways for displaced workers by identifying "growing occupations that utilize compatible skills and require similar education credentials."

My favorite graph in the report is naturally Exhibit E8, which shows Collins County (Dallas/Plano, TX) high in the top right quadrant of high employment growth and high index of educational attainment of the population. McKinsey estimates that educational attainment index for the Dallas area, by the way, much higher as that of Austin. I also found Exhibit E10 very interesting, but most valuable was Exhibit E11, which details the different challenges various types of communities will face, from the urban core to the mixed middle and low-growth and rural areas. I won't post them here so that you get to read the whole report on the McKinsey website, but I'll tell you that I wholeheartedly agree with the consultants' assessment that a challenge of megacities and high-growth hubs will be to increase affordable housing near employment centers. As for the need to "involve employers in creating high school and community college programs to develop key skills needed in growing fields," that is already been done in Dallas thanks to a groundbreaking partnership between SMU, Toyota and the Dallas ISD. (Also read this great article about it in Dallas Morning News, Bridging the economic divide in Dallas is a moral imperative.) The report's authors also raise the issue of retraining workers and providing lifelong learning, which may have to take the form of "short, flexible courses that follow the boot camp model". While successful training programs abound throughout the country, it remains to be seen how they can be scaled. 

Beyond the impact on workers, it is worth also discussing the impact on companies, as "organizations will face cross-cutting challenges that require multidisciplinary solutions." "While some will simply pursue labor cost reductions, those that deploy automation and AI technologies in pursuit of innovation and growth can accelerate profitability growth and even add jobs." The consultants discuss employers' opportunities and challenges based on company footprint and workforce characteristics. 

At the end of the report, I concluded that, although McKinsey has received a lot of negative press, that report does show the type of superb work it is capable to produce in consulting engagements. The top decision makers may have committed errors in judgement in their dealings in Saudi Arabia and South Africa, but McKinsey's foot soldiers - its army of analysts and associates - remain the best in the business. A must-read.

 

Stanford Social Innovation Review on Aspirational Communication

Much to my surprise, Aspirational Communication by Doug Hattaway has turned out to be one of the best articles I've read so far this year, far superior in insights than much of what I've seen in Harvard Business Review and MIT Sloan Review. It appeared in the Winter 2020 of Stanford Social Innovation Review with the tagline: "The US marriage equality and youth antismoking campaigns transformed public attitudes by connecting their causes to the personal aspirations of their audiences. Other social change movements can follow their successful model by applying a six-step framework detailed here." Or, more succinctly: "Organizations must connect their causes to the personal aspirations of their audiences to transform public attitudes." This framework allowed support for gay marriage to shift from 27 percent in 1996 to 67 percent in 2018. It also led to a decrease in teen smoking fro 23 percent in 2000 to 5 percent in 2019. So how did that happen? In a nutshell: "We're highly motivated to take actions that help us live up to our image of the kind of people we truly want to be -- our aspirational identity."

Step 1: Focus on people who are ambivalent.

Step 2: Understand their anxieties. In the case of same-sex marriage, some people felt "alarm over the future of society", a feeling heightened by President George W. Bush's announcement supporting a constitutional ban on gay marriage in 2004. For teen smoking, it was important to understand teen anxiety about fitting in and being cool, so that the campaign against teen smoking sought to make it cool not to smoke.  

Step 3: Connect your cause to their authentic aspirations. For gay marriage, this was the ideal of staying together "for better or for worse", leading ambivalent people to express respect for same-sex couples eager to get married, saying that they must "truly love each other." The anti-teen-smoking campaign "worked directly with young people to design a brand focused on adolescent aspirations: to be independent from adults, express their individuality, and take more control over their lives."

Step 4: Frame it with winning words. The campaign for gay marriage connected it with civil rights and equal protection under the law and reframed it as "honoring love and commitment." For the anti-teen-smoking campaign, the winning word was Truth.

Step 5: Share strategic stories. The gay marriage campaign shared stories of couples in loving, committed relationships, such as two women in Massachusetts that stayed together after one was diagnosed with cancer. "Love and commitment" became the heart of the movement. The first national ad in the teen-smoking campaign "featured young people dumping body bags outside the Philip Morris headquarters to dramatize the 1,200 deaths that tobacco use causes every day."

Step 6: Help people think it through - and be their best selves. "The final step in achieving durable attitude change was to help the audience of ambivalent voters think through the issue on their own terms - and decide to live up to their own aspirations for the kind of people they wanted to be." For instance, the gay-marriage campaign ran ads featuring friends and family members of same-sex couples who had changed their mind on the topic. The anti-teen smoking campaign shared "information about the addictiveness of smoking, deaths and diseases attributed to tobacco use, marketing practices of the tobacco industry, and other areas of concern."

A must-read!

 

 

GrAIt Expectations: AI in Business

 

I recently came across this special report on AI in Business, entitled "GrAIt Expectations", in an old issue of The Economist. Overall I found the special report thoughtful and well documented, with lots of interesting examples. Some are better known than others, for instance it is no surprise that big companies such as Johnson & Johnson and Accenture are now using Artificial Intelligence (AI) to sort through job applications and pick the best candidates, but AI can also be used in more cutting-edge and perhaps disturbing ways: the Chinese insurance company Ping An thinks it can spot dishonesty by having prospective borrowers answer questions by video.    

I am not sure if I agree with the definition of AI: "AI and machine learning (terms that are often used interchangeably) involve computers crunching vast quantities of data to find patterns and make predictions without being explicitly programmed to do so." I tend to think that artificial intelligence is the ability of computers to make decisions, rather than predictions, without being explicitly programmed to do so. Or we can adopt the definition in the Oxford Dictionary, which defines Artificial Intelligence as "the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages." According to a report by MIT's Sloan Management Review and the Boston Consulting Group, "around 85% of companies think AI will offer a competitive advantage, but only one in 20 is "extensively" employing it today."

This got me interested in reading the MIT/BCG report itself. In fact there are three reports, since this is a yearly effort that started in 2017. The authors give the example of Airbus, which uses AI to match about 70% of the production disruptions to solutions used previously, shortening the amount of time it takes to deal with disruptions by more than a third. Because the report is already about 2 1/2 years old, we are nearing the time where we can check the validity of predictions such as "within the technology, media and telecommunications industry, 72% of respondents expect large effect from AI in five years." They split their survey's respondents into 4 groups: pioneers (19%), investigators (32%) [referring to organizations that are deploying AI at the pilot stage only], experimenters (13%) [organizations that are piloting or adopting AI without deep understanding, which frankly sounds like a bad thing] and passives (36%) [organizations with no adoption or much understanding of AI]. The 2017 report particularly emphasized the need for good data in order to create good AI models. Apparently a lot of companies didn't have the historical data required. Some applications such as drug development need negative as well as positive data (what didn't work as well as what did work), while only the positive data tends to get published. The report's authors make an interesting point about the make-or-buy problem applied to AI capabilities. They say that the leaders in using AI for business develop their own in-house AI capabilities, while the followers tend to work with independent AI vendors. But it is going to be important for organizations to have their own AI in-house capabilities. 

The MIT-BCG report lists several managerial challenges: (1) developing an intuitive understanding of AI, (2) organizing for AI (especially having the organizational flexibility that enables collaboration between humans and machines on project teams, taking into account whether AI resources should be managed centrally or locally or in a hybrid model) and (3) re-thinking the competitive landscape. Among the authors' recommendations (ensure customer trust, perform an AI health check, brace for uncertainty, adopt scenario-based planning, add a workforce focus), I found the scenario-planning one the most intriguing. "Companies need to think even more expansively about their businesses, build cohesive future scenarios, and test the resilience of their directional plans against such scenarios."

In a 2018 update, the MIT-BCG authors identify four major patterns in their survey and interview data:

• Pioneers are deepening their commitments to AI ("fully 88% of Pioneers invested more in AI than in the previous year")
• Pioneers are eager to scale AI throughout their enterprise ("85% [of Pioneers] agree that they have an urgent need for an AI strategy and 90% say that have a strategy in place already.")
• Pioneers prioritize revenue-generating applications over cost-saving ones because "a much higher penalty is incurred by missing an opportunity in the external market" rather than internally. Examples include "complex pricing applications, churn analysis in the customer portfolio, new delivery forms that significantly speed up [the company's] ability to meet customer demand" as well as "wholly different business model[s]."
• AI is creating both hope and fear among workers due to the possibility of workforce reduction.

Many respondents also expect change to their business model. Also, it is important not only to get more data science resources but also train more businesspeople in the potential of AI to think about potential use cases.

The 2019 update started on a less upbeat note: "Many AI initiatives fail. Seven out of 10 companies surveyed report minimal or no impact from AI so far." The authors argues that "companies that capture value from their AI activities exhibit a distinct set of organizational behaviors. They integrate their AI strategies with their overall business strategy, take on large, often risky AI efforts that prioritize revenue growth over cost reduction, align the production of AI with the consumption of AI, through thoughtful alignment of business owners, process owners and AI expertise to ensure that they adopt AI solutions effectively and pervasively, unify their AI initiatives with their larger business transformation efforts, invest in AI talent, data and process change in addition to (and often more so than) AI technology. They recognize that AI is not all about technology." As an example, Aetna already uses AI to "design provider networks, prevent fraud and discover overpayments - traditional applications of analytics within the insurance industry" but "is now pursuing strategic initiatives to create more customer value with AI. In one Medicare-related offering, product designers used an AI-based method to customize benefit design." Interestingly enough, the percentage of Pioneers + Investigators remains at 50% of respondents, with 20% of Pioneers. Risk from AI is particularly strong in highly regulated industries like banking, which are being disrupted by new entrants such as Apple Card and Amazon Cash. "Out of three major possible applications of AI - cost reduction, revenue generation and new product development - most of the Pioneers are applying AI in at least two ways." The report's authors also emphasize the need to focus on the consumption of AI, not just its production. This means "developing a fertile environment in which producers can develop, champion, and implement AI solutions in strong collaboration with the business" and "developing sufficient expertise among business users so they can properly use the probabilistic nature of many AI solutions." Basically, it is about cultivating nontechnical leaders and business users.

AI is "not just a corporate race but an international one," especially between the United States and China. The MIT-BCG authors even started to conduct a separate survey with Chinese executives in 2018. "Chinese AI Pioneers are investing more aggressively and report a greater focus on business model transformation."

The MIT-BCG authors argue, based on their written questionnaire and interviews, that the key benefit of AI is not cost cutting but revenue generating, in particular in areas such as new product design. This actually seems to contradict The Economist, which claims that "in private, many bosses are more interested in the potential cost and labour savings than in the broader opportunities AI might bring." A consultant insists: "If you just cut costs and don't increase value for customers, you're going to be out of the game." Both The Economist and the MIT-BCG teams agree that AI pioneers may develop an unsurmountable competitive advantage, leading to corporate concentration and monopoly power.

The best known application of AI is for operations, specifically early maintenance. "Companies can combine data on past performance with those generated by smart sensors on machinery (part of the much-hyped "internet of things") to predict when a jet engine or a wind turbine is likely to fail," says The Economist. Other improvements will occur in demand forecasting and inventory management, leading to decreases in overstocking cost (estimated at $470bn worldwide) and understocking cost ($630bn). Packages are routed more efficiently - and INFORMS long-time member Jack Levis is even cited in the article, saying that "for every mile that [UPS] drivers in America are able to reduce their daily route, the firm saves around $50m a year." There are also enormous opportunities in customer service, where text mining (for emails) and voice analysis (for phone calls) can triage the most urgent service requests from the others. 

In human resources, a novel application will be to identify potential candidates for new positions from the pool of past applicants, and re-direct candidates who are not a good fit for the position they applied to toward positions that better match their skill set. But companies need to monitor their algorithms regularly to make sure they are free of bias, in particular for groups that are under-represented in the training set. The workplace of the future, though, may be much "creepier" than what we are used now due to employee monitoring. The example of startup Humanyze was particularly telling. Employees wear an "ID badge the size of a credit card and the depth of a book of matches. It contains a microphone that picks up whether they are talking to one another; Bluetooth and infrared sensors to monitor where they are; and an accelerometer to record when they move." This is supposed to provide a "full picture of how they spend their time at work." 

The potential corporate market for AI software, hardware and services is estimated at around $58bn by next year. Cloud providers such as Amazon and Microsoft have a clear advantage, while consultancies have been strengthening their capabilities. For instance McKinsey bought QuantumBlack in 2015. It remains to be seen whether AI wizards go to work for conventional consultants. The Economist argues that even IBM "finds it hard to get hold of the best talent. None of the top doctoral candidates in AI goes to work for IBM."

Personalized recommendations and faster deliveries sound like good things, and creating jobs that only exist because of AI or developing new drugs thanks to the power of machine learning is very exciting. But according to the McKinsey Global Institute, 14% of the global workforce could have their jobs automated away. This will require workers' retraining, which companies are reluctant to pay for. The second issue is of course data privacy. A third concern is a winner-take-all situation where the companies that win the AI race put their competitors out of business, increasing concentration in the tech sector and beyond.

Clayton Christensen (1952-2020)

 I found out a few days ago that Harvard Business School professor Clayton Christensen died in late January at age 67. Christensen had become uber-famous thanks to his theory of disruptive innovation -- basically, great firms falter because they focus on their most profitable segments while upstarts take away market share in less profitable segments until they are ready to move upmarket with groundbreaking offerings that spell doom for the incumbent.This insight led to the best-selling Innovator's Dilemma, quickly followed by a cottage industry of books that repeated the same theory and often the same anecdotes but applied those concepts to different industries. I personally far preferred the lesser-known Innovator's Solution, my review of which you can read here.

Among management gurus he was undoubtedly the best known one, having overshadowed his HBS colleague and strategy expert Michael Porter. Only Thomas Davenport of Babson College came anywhere close to him in fame, and even then always a distant second, although Davenport has, like Christensen, been able to transform a thoughtful article for C-suite executives - in his case about the rise of analytics, although he is not a data scientist - into a long string of books and articles co-authored with technical experts. But strategy still beats analytics, and so Christensen had stood at the top of the business-academic mountain for about 25 years. I agree with the writer of the Economist article that articulating this theory has led to such profound changes in incumbents' behavior that the theory no longer holds, because incumbents now "buy up the competition before it hurts them" or "take stakes in potential disrupters to keep an eye on them" but it certainly is a great accomplishment, and Christensen's undeniable legacy, that he was able to transform the thinking of industry executives so thoroughly. His theory, however, was not always viewed charitably by the people on the ground in the industries he studied, as I mention in my review of Starving the Beast, a documentary about the challenges facing public universities in the United States. In attempting to evaluate Christensen's work, Jill Lepore's article in The New Yorker is a must read. You can read my review of it here. (Hint: "Lepore doesn't think highly of Harvard Business School's Clayton Christensen.")

One thing I particularly disliked about Christensen was his holier-than-thou attitude in his Harvard Business Review article "How will you measure your life?" which, predictably, he turned into a (thin) book. In it he takes pride that, as a starting basketball player in the undefeated Oxford University team that "had worked [its] tail off" all year, surrounded by "guys on the team [who] were the best friends [he'd] ever had in his life", he refused to play the championship game of the equivalent to the NCAA tournament because it was on a Sunday. But he "had made a personal commitment to God at age 16 that I would never play ball on Sunday." And he didn't and the team lost. I hope to measure my life by not letting my teammates down, thank you very much. Real people were impacted by his decisions, whom he chose to ignore. This being said, it is obviously tragic that he died and some people who have directly interacted with him say he changed their life. He received the Herbert Simon Award in 2014, an award "given annually to an outstanding scholar in the field of business and management, whose works have contributed to the understanding or solving of practical business problems and had a substantial influence, over a long period of time, on the studies and intellectual activity of the students of the" Rajk László College for Advanced Studies in Budapest, Hungary, as well as the Edison Achievement Award in 2015, which "recognizes one or more individuals who, through their careers, their leadership and their achievements, have distinguished themselves by making a significant and lasting contribution to the world of innovation."

Here is Christensen's obituary in the New York Times. It includes a piece of advice he gave Harvard Business School graduates: "Don’t worry about the level of individual prominence you have achieved,” he continued; “worry about the individuals you have helped become better people."

The Future of Texas

The George W. Bush Presidential Center held an event not too long ago - in partnership with Texas 2036 - about The Future of Texas and put the video online. So here I am, writing about it. In 2036 Texas will celebrate its 200th anniversary and 10m more people are expected to call the Lone Star State home (compared to 28m now). Texas 2036, an organization led by past Bush Presidential Center President and past U.S. Secretary of Education Margaret Spellings, who also served as the past president of the University of North Carolina system, seeks to ensure that Texas remains "the best place to live and do business", even after the state gains in population the equivalent of another Dallas + Houston + San Antonio in less than 20 years. 

For Texas to maintain its prosperity, Margaret Spellings explained that Texas must add 8m jobs by 2036, the same amount of jobs in Dallas and Houston today (combined). However, only 28% of high school graduates currently earn a postsecondary degree or certification within six years of graduating high school, while in 2036, 77% of jobs will require a 2- or 4-year post-secondary degree or certificate. In a nutshell, "Texas students are not ready for the jobs of the future." Academic performance in key subjects has slipped: from 36th state to 44th in 8th grade reading over the past decade, and from 19th to 28th in 8th grade math.  

Another area of concern is road congestion. Further, 2m Texans don't have access to broadband, especially in rural areas. Population growth also puts a strain on natural resources. Air quality is strained and we are not prepared for droughts. Juliet Garcia, past longtime president of UT Brownsville now called UT Rio Grande Valley after its merger with UT Pan-American (and first Mexican-American woman to head a U.S. college or university when she became President of Texas Southmost College in 2986, gave fascinating insights into the population of the Rio Grande Valley and the unique challenges this brings. The population is younger than the Texan average and growing quickly, 91% Hispanic, and the poverty rate is twice that of Texas as a whole. Juliet Garcia spoke movingly about their enthusiasm for learning and, unexpectedly, their great love of chess. Interestingly, 87% of the Rio Grande Valley students attend "A" or "B" schools vs 65% statewide, 61% in Dallas County and 52% in Bexar County (where San Antonio is located). SpaceX was looking for a new place for its headquarters and UT Rio Grande Valley already happened to have some very strong physicists already and the State of Texas and the UT system added some incentives. The Rio Grande Valley clearly met all the requirements of SpaceX except for human capital and the community came up together to prove to SpaceX they should locate here. Young physics students made a presentation to SpaceX and convinced them the valley had enough human capital. Isn't that a wonderful story?   

Someone from Anderson Medical Center also took part in the panel discussion, making the distinction between a sick care system and a health care system. Every Texan needs to have access to a primary care provider, who may not be a physician. Instead, a lot of such Texans use the emergency room to get care. 30% to 50% of cancers today could be prevented if we could deploy tools that work such as HPV vaccines. The highest vaccination rates occur in areas where we have the lowest health literacy, such as the Rio Grande Valley. The lowest vaccination rates occur in areas where we have the highest health literacy, specifically, the Highland Park area of Dallas, a super-wealthy area that has its own, super-successful public school system so that those kids don't have to attend the Dallas public school system. Texas has both a growing elderly population (mostly white), which can rely on Medicare but Medicare was designed at a time when life expectancies were much lower and is strained, and a growing young population (mostly non-white). 

Then the conversation returned to Juliet Garcia, whom I love although I hadn't heard of her until I watched this video, and who said when she first became President, she asked other presidents about the most important parts of their job and the President of Miami Dade Community College answered: "Preserving the democracy of the United States" (a timely topic these days). That was because he felt that if he taught his (mostly minority) students well, they would become productive members of the United States. Then they will defend and sustain democracy and this is critical for our nation to succeed.  

Back to the Anderson Center CEO Peter Pisters who talked about the 3 critical areas of strategic immigration policies (because the medical field currently relies a lot on foreign workers), massive investment in STEM fields (against, significant dependence on foreigners), use our graduate education program [GME] strategically for state needs so that students who are trained at Anderson are more likely to stay in Texas after they complete their training. 

Another speaker, the CEO of Lyda Hill Philanthropies Nicole Small, talked about the necessity of building a more robust system of innovation in biotech and while Houston and Austin have done a good job on this, North Dallas has room for improvement in terms of the business side of medicine. Texas needs to have jobs to draw back people who have moved to the coasts. Texas by some is even viewed as a "Third Coast" for biotech, which is the first time I heard that name, but it is a thing, really, it is, in order to leverage MD Anderson in Houston and UT Southwestern in Dallas, as evidenced by an article in TribTalk: Perspectives on Texas and a report on Texas biotechnology and life sciences among other publications that mention it. Cancer is an example of where Lyda Hill Philanthropies use both philanthropy and investment dollars, with philanthropy going for high-risk early-stage pilot projects. The speakers provided examples of first-generation college students who rose to middle-class prosperity and the involvement of many stakeholders to make it happen.

The second panel talked about how we grow Texas's economy and invest in infrastructure. The speakers were Hunter Hunt of Hunt Energy, Brett Perlman the CEO of the Center for Houston's Future and Kelvin Walker, the CEO of Dallas Citizens Council. They talked about oil but also wind power in Texas. In fact last year wind power surpassed coal for the first time in the Lone Star State. Texas is a bit behind in solar energy. Some challenges are that Texas is the No 1 polluting state in the country and the risk of drought, which was mentioned by another speaker earlier as well. Can Texas become the low-carbon energy capital of the world? The moderator asked questions about resiliency in infrastructure and water, given that there will be floods and droughts. Floods also led to shocks and stresses to the housing stock, for instance in Houston. We have to invest more in infrastructure. According to a speaker, McKinsey estimates that we have a $50bn gap over the 25 years where Texas didn't invest enough in infrastructure so there is a huge amount of money that has to be invested in it now to catch up. This includes freight, rail, broadband and not just cars. Such amounts of money are going to involve private investments like infrastructure banks, said one speaker.

5G is going to transform the world in ways we haven't even envisioned yet because of low latency allowing for groundbreaking innovations such as autonomous vehicles. 42% of Dallas residents don't have fixed Internet to their home, meaning they do not have a subscription to home broadband, which is in part due to infrastructure. These days if you don't have access to Internet you are in serious trouble for instance for tasks like applying for a job, which is often done online now. We may miss an opportunity for the next inventor to create something in the digital economy. Overall growth and prosperity isn't shared equally in Texas. A speaker pointed out that Texas has added about 4.5m new residents over the past 10 years, which is equivalent in population to the state of Alabama. Dallas-Fort Worth gained 1m people in less than a decade. (I'm not sure how the conversation transitioned to that, frankly.) Then someone else talked about opportunities for everyone such as a free Associate's degree and another speaker advocated for coalitions. This reminded me that UT Austin recently announced it would offer free college tuition to in-state undergrads whose families make $65,000 or less. UT Rio Grande will offer free tuition to students with household income of $75,000 or less. University of Houston has a similar program for families earning $50,000 or less.

HBR on When Data Creates Competitive Advantage

The January-February 2020 issue of Harvard Business Review has for theme "When data creates competitive advantage... and when it doesn't." I found two articles particularly interesting: "When data creates competitive advantage" by Andrey Hagiu and Julian Wright and "Competing in the age of AI" by Marco Iansiti and Karim Lakhani. 

In "When data creates competitive advantage", Hagiu and Wright argue that network effects are more important than data effects. (Network effects occur for instance when more and more people use a certain social media platform, making it more valuable.) The authors explain: "Gathering customer information and using it to make better products and services is an age-old strategy, but the process used to be slow, limited in scope, and difficult to scale up... That changed dramatically with the advent of the cloud and new technologies that allow firms to quickly process and make sense of vast amounts of data... but this does not guarantee defensible barriers [to entry by competitors]."

To determine if data provides a competitive advantage, the authors argue companies should ask themselves those seven questions (they provide a lot of details about each question in the article and you are highly encouraged to read it):

1. How much value is added by customer data relative to the stand-alone value of the offering?
2. How quickly does the marginal value of data-enabled learning drop off? (how soon does the company reach a point where additional customer data no longer enhances the value of an offering?)
3. How fast does the relevance of the user data depreciate? (does the data become obsolete quickly?)
4. Is the data proprietary—meaning it can’t be purchased from other sources, easily copied, or reverse-engineered?
5. How hard is it to imitate product improvements that are based on customer data?
6. Does the data from one user help improve the product for the same user or for others? (if it improves the product for the same user, that user will tend to stay with the company; if it improves the product for other users, it will be easier for the company to attract new customers but maybe not to keep them)
7. How fast can the insights from user data be incorporated into products? (Rapid learning cycles make it hard for competitors to catch up)

"Competing in the age of AI" describes the case of Ant Financial Services, a spinoff from Alibaba that relies on artificial intelligence (AI) to make its decisions. In 2019, five years after it was launched, it passed the one billion user mark. The company "run[s] an extraordinary variety of businesses, including consumer lending, money market funds, wealth management, health insurance, credit-rating services, and even an online game that encourages people to reduce their carbon footprint" and is currently valued at "$150 billion —almost half that of JPMorgan Chase, the world’s most valuable financial-services company." 

The authors explain: "AI runs the show. There is no manager approving loans, no employee providing financial advice, no representative authorizing consumer medical expenses. And without the operating constraints that limit traditional firms, Ant Financial can compete in unprecedented ways and achieve unbridled growth and impact across a variety of industries." The authors describe the decision-making process at such firms as "AI factory". "In each case the AI factory treats decision-making as a science. Analytics systematically convert internal and external data into predictions, insights, and choices, which in turn guide and automate operational workflows."

They list four components essential to this AI factory: data, algorithms, experimentation platform and infrastructure. "Scale, scope [production variety], and learning [improvement and innovation] have come to be considered the essential drivers of a firm’s operating performance." The authors explain that digital companies don't necessarily suffer from the issue of diminishing returns for scale that plague traditional, silo-driven, brick-and-mortar companies, but they are also careful to point out that digital companies can "aggregate harm along with value," as "risks can be greatly magnified", especially with relatively unproven institutions.

The issue has many other interesting articles, including on how to value a company by analyzing its customers, the analytics of culture and managing the most expensive health-care patients.   

 

California's data-privacy law

The Economist had an article a couple of weeks ago about California's data-privacy law, which is known as the California Consumer-Privacy Act (CCPA) and went into effect January 1. The CCPA adopts a broader definition of personal information, extending to internet cookies and requires companies to "enable Californians to opt out of the sale of persona data with a clear "do not sell" link on their home page", which is easier than the European General Data Protection Regulation (GDPR) process. The California law "will apply to from with revenues of $25m or more that do business in the state or process its residents' data", so startups don't have to worry too much at first about complying, but "law-breakers face fines of up to $7,500 for every violation". I found it fascinating to read that "some 250,000 companies have been lodged under the EU rules." Companies like Microsoft and Apple "say they are not only ready for CCPA, but also plan to implement it across America." There is also the possibility that federal rules would be implemented, in which case they would supersede California's, "unless [a national data law] explicitly made federal rules the floor which states could raise if they wished." Business-friendly federal rules would nullify the CCPA. But for now the CCPA is here to stay.  

Every semester when I teach Analytics for Decision Support I assign an essay about ethics in data science and it fascinates me to see that so few students are aware of the challenges surrounding data science, although many say afterward they appreciated learning about it. Not all feel strongly that companies shouldn't make money off their data, some don't care and some say we have to pay for the convenience of free services somehow. The issue of bias in the models tends to resonate with some minorities, as does the possibility of back-engineer the identity of certain people behind data instances. On occasion, my students have found enlightening articles and videos that have nicely complemented the readings I had assigned. (This semester for a change I didn't assign any reading to use for the essay but told them to find their own sources. We will see if it was a good idea or not.) 

I like to think I am careful with my data and I often erase cookies and reset identifiers to make sure companies track me as little as possible, but I was surprised the other day when I was looking into the advanced setting on my iPhone and noticed cookies from companies like Facebook. I was quite surprised because I don't use Facebook, I have a Facebook account I rarely if ever log into and I loathe Facebook as a company due to the non-existing leadership of Mark Zuckerberg, who condones lies in ads and Russian interference in US elections for the sake of his own profit, lacks any sort of gravitas or credibility as a top leader and perpetually looks like the eternal adolescent playing the role of a businessman in his father's clothes, preferring to exploit his fellow Americans instead of attempting to look like a seasoned executive with a non-zero awareness of the moral implications of his actions. (I like Sheryl Sandberg, but I disagree so fundamentally with the way the company is run that it is of little help. History will show that Facebook was the epitome of data greed and user manipulation.) So why did I have Facebook cookies on my phone? I think the reason was that, in a moment of weakness, I had actually bookmarked a Facebook page on my laptop, also a Mac like my phone. Tech companies are so eager to track your every move that they connect laptop and smartphone, and so my bookmarks were also showing up on my phone (a dumb thing, as I look for different things when I use my laptop than when I use my phone). And so Facebook was enjoying tracking me on my phone. In the end I deleted all my Facebook bookmarks and blocked cookies on my phone. At some point I'll reallow cookies (if I remember where to go to unblock them), but for now I'm enjoying not being as easy to track on my phone.   

The 2020 INFORMS Franz Edelman Finalists

The INFORMS Franz Edelman Prize, which crowns achievement in advanced analytics, operations research and management science, recently announced its finalists: Amazon.com for its robotics system, Carnival Corporation for the Yield Optimization and Demand Analytics system (yes it spells YODA), Deutsche Bahn (for its train rotation optimization), IBM for its new tool to identify systems at risk for an outage before that outage occurs, Intel for its novel product feature design and supply chain planning, and Walmart for its new markdown timing system. What a strong set of finalists! It is so exciting to see high-caliber operations research being implemented in such a wide range of companies, in the US and abroad. And surely there were many outstanding semi-finalists too. It is going to be really tough for the judges to decide. 

I only wish the press release included the names of the people included in the entries. For instance I know operations researchers at Amazon, Carnival and IBM, but I'm not sure if their unit submitted the entry or someone else. It is more fun to follow the contest when you know who is on the teams. 

Last year the Louisville Metropolitan Sewer District with TetraTech beat powerhouse teams from IBM, Boston Public Schools/MIT, Microsoft, the Spanish Aviation Safety & Security Agency/Royal Academy of Sciences and Vattenfall/University of Bologna/Technical University of Denmark. 

All the finalists' talks are videotaped. You can watch them here.