2019 was a year full of outstanding customer engagements and provocative teaching experiences across numerous universities.\u00a0<\/span> My eyes were opened to many new opportunities to integrate economics, design thinking, big data and data science (AI \/ ML \/ DL) to further my case for a Nobel Prize in Economics (which I\u2019d prefer not to be awarded posthumously).\u00a0<\/span> That includes helping organizations:<\/p>\n So, while we wait for that call from Stockholm, let\u2019s take a look at my 10 favorite 2019 blogs:<\/p>\n There are many valuable lessons that data scientists can learn from the movie \u201cMr. Incredible\u201d about the challenges of creating smart products like autonomous vehicles, trains and factory robots. And maybe the biggest challenge for the development of smart, autonomous products is knowing when \u201cgood enough\u201d is actually \u201cgood enough\u201d. When trying to optimize the operations of these smart, autonomous products, one must be prepared to realize that the current path to performance optimization may not actually be the optimal path, and the data science team must be prepared to jettison their existing work and try a different approach that might lead to a better performing analytic model.<\/p>\n This is an important lesson for the creation of our AI-induced \u201csmart\u201d products \u2013 that there must be constant testing, learning, and maybe even some unlearning and re-starting afresh in order to find the optimal models.<\/p>\n \u201cHow effective is your organization at leveraging data and analytics to power your business models?\u201d<\/em><\/p>\n Organizations that expect to survive the avalanche of new digital technologies must \u201cthink differently\u201d about how they structure their operational and business models. Just applying new technologies to optimize existing operational processes is just \u201cpaving the cow path\u201d; taking existing inefficient processes and making them marginally better. And marginal improvements won\u2019t win the day from a business model digital transformation perspective.<\/p>\n One has to take the time to think\u2026to think differently\u2026about how new digital technologies and the resulting customer, product and operational data can be used to create new sources of customer, product and operational value.<\/p>\n New digital technologies coupled with advanced analytics will enable organizations to create autonomous entities that not only reduce the need for human technicians and engineers, but radically transform the sources of customer, product and operational value. These organizations can embrace the economic value curve to digitally transform their business and operational models by converting human-observed static heuristics into AI algorithms that can drive automation and maybe even autonomous operations.<\/p>\n Time to think outside the box. Instead of focusing on replicating yesterday\u2019s operational best practices, grab the economic value curve by the throat and dramatically change how your organization identifies, captures and scales these new sources of operational value via autonomous capabilities.<\/p>\n While the actual mechanics of how a neural network works are much more complicated (lots of math and calculus), the basic concepts are really not that hard to understand:<\/p>\n In order to make a holistic AI utility determination, collaboration across a diverse set of internal and external stakeholders is required to identify those metrics against which AI model progress and success will be measured. The AI utility determination requires the careful weighing of the metrics associated with the financial\/economic, operational, customer, society, environmental and spiritual dimensions.<\/p>\n The key to accelerating the economic learning curve isn\u2019t just accumulating experience, but also includes:<\/p>\n 1) Begin with an end in mind by focusing on your organization\u2019s key business initiatives<\/p>\n 2) Understand the technology capabilities\u2026but within a business frame<\/p>\n 3) Build out the solution architecture to deliver \u201cintelligent\u201d applications and \u201csmart\u201d entities<\/p>\n 4) Use Design Thinking to drive AI organizational alignment and adoption<\/p>\n Blend the value creation focus of Economics with the customer, product and operational insights of Data Science and the ideation, alignment and adoption capabilities of Design Thinking to help organizations to exploit the economic value of Artificial Intelligence.<\/p>\n Data in of itself provides zero value as defined by General Acceptable Accounting Principles, or a \u201cvalue in exchange\u201d valuation methodology. However, if we use an economics approach \u2013 a \u201cvalue in use\u201d valuation methodology \u2013 then we have a framework for defining the value of data, which is determined by where and how the data is used to create new sources of quantifiable customer, product and operational value.\u00a0<\/span><\/p>\n Check out \u201cApplying Economic Concepts to Big Data<\/a>\u201d for more details on the University of San Francisco research project with Professor Mouwafac Sidaoui and myself on determining the economic value of data.<\/p>\n The Economic Digital Asset Valuation Theorem \u2013 which leverages the economic characteristics of assets never wear out, never deplete and can be re-used across an infinite number of use cases at a near zero marginal cost \u2013 highlights how the unique characteristics of digital assets manifest themselves at the macro-economic level:<\/p>\n \u201cIf you buy a Tesla today, I believe you’re buying an appreciating asset, not a depreciating asset.\u201d<\/em> \u2013 Elon Musk<\/p>\n Tesla cars appreciate in value as a result of the collective knowledge \/ wisdom \/ intelligence gleaned from the operational and driving data that is being captured across the usage of the growing fleet of Tesla autonomous cars; that what is experienced and learned by one Tesla car, is validated, codified and propagated back to every other Tesla car making the collective Tesla cars more intelligent, and therefore more valuable.<\/p>\n \u00a0 My biggest personal accomplishment for 2019? No, not United 1K (ugh). I wrote my 3rd<\/sup> book \u201cThe Art of Thinking Like a Data Scientist<\/a>\u201d, which is a workbook that I use in my Big Data MBA classes. This blog (and supporting infographic) details what you can expect from the workbook.<\/p>\n I hope you enjoy the workbook. It\u2019s one of my steps on the path in teaching business stakeholders to \u201cThink Like a Data Scientist,\u201d a culmination of lessons-learned working with great clients over many years.<\/p>\n In fact, maybe I can create an equation that pretty much summarizes my 2019:<\/p>\n 2019 Schmarzo = Economics + Design + AI – Airplanes + Customers\/Students + Value Engineering<\/em><\/p>\n There, I guess that\u2019s the official end of 2019. Looking forward to flying cars, virtual reality travel, blockchain-supported teleporting, real-time drone delivery and avoiding the Terminators lined up at Starbucks for their Venti Chai Tea fixes in 2020!<\/p>\n<\/div>\n Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" Author: Bill Schmarzo 2019 was a year full of outstanding customer engagements and provocative teaching experiences across numerous universities.\u00a0 My eyes were opened to many […] Read More<\/a><\/span><\/p>\n","protected":false},"author":1,"featured_media":472,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"footnotes":""},"categories":[26],"tags":[],"_links":{"self":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts\/2994"}],"collection":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/comments?post=2994"}],"version-history":[{"count":0,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts\/2994\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/media\/468"}],"wp:attachment":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/media?parent=2994"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/categories?post=2994"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/tags?post=2994"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}\n
10.<\/strong> What We Can Learn about AI and Creating Smart Products from \u201cThe Incredibles<\/strong><\/a>\u201d<\/strong><\/h1>\n
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9.<\/strong> Importance of Thinking Differently\u2026Hint: Don\u2019t Pave the Cow Path<\/strong><\/a><\/h1>\n
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8.<\/strong> Exploit Economics of Autonomous to Transform Your Organization<\/strong><\/a><\/h1>\n
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7.<\/strong> Using a Bathroom Faucet to Teach Neural Network Basic Concepts<\/strong><\/a><\/h1>\n
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6.<\/strong> Why Utility Determination Is Critical to Defining AI Success<\/strong><\/a><\/h1>\n
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5.<\/strong> Exploit Economics of Artificial Intelligence with Design Thinking and Data Science<\/strong><\/a><\/h1>\n
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4.<\/strong> Executive Mandate #1:\u00a0<\/span> Become Value Driven, Not Data Driven<\/strong><\/a><\/h1>\n
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3.<\/strong> Why Tomorrow\u2019s Leaders MUST Embrace Economics of Digital Transformation<\/strong><\/a><\/h1>\n
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2.<\/strong> How AI Is Manipulating Economics to Create Appreciating Assets<\/strong><\/a><\/h1>\n
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1.<\/strong> The Art of \u201cThinking Like a Data Scientist\u201d Workbook and Why It Matters<\/strong><\/a><\/h1>\n
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