In my most recent blog \u201cDesign Thinking Humanizes Data Science<\/a><\/span>\u201d, I discussed how Design Thinking and Data Science complement each other.\u00a0<\/span> They are not just two sides of the same coin, but the same side of the same coin in their objectives to \u201cdiverge before converging\u201d in driving business stakeholder collaboration with respect to identifying, brainstorming and envisioning the variables and metrics that might be better predictors of performance (see Figure 1).<\/p>\n Figure<\/strong> 1<\/span><\/strong>: \u00a0<\/span>Design Thinking Humanizes Data Science<\/strong><\/p>\n The complementary natures of Data Science and Design Thinking are bonded by several common characteristics including:<\/span><\/p>\n Maybe the most important cultural similarity between Data Science and Design Thinking is the mentality that if you don\u2019t have enough \u201cmight\u201d ideas, you\u2019ll never have any \u201cbreakthrough\u201d ideas.<\/span><\/p>\n In this blog, I want to combine the value creation focus of Economics with Data Science and Design Thinking.\u00a0<\/span> I want to use Economics as the Digital Business Model Transformation guide in leveraging Data Science and Design Thinking to drive cultural change and business model disruption.<\/p>\n A recent article from the University of Chicago Booth School of Business titled \u201cWhy Artificial Intelligence Isn\u2019t Boosting the Economy\u2014Yet<\/a><\/span>” highlights a common problem with new disruptive technologies \u2013 there are substantial upfront investments in these disruptive technologies, resulting in a negative short-term Return on Investment (ROI).\u00a0<\/span> To quote the article:<\/p>\n \u201cThe economy is early in the AI adoption wave, with start-up funding for AI having increased from $500 million in 2010 to $4 billion in 2016. According to the researchers, this implies intangible investments in AI may have accounted for 0.55 percent of \u201clost\u201d output\u2014or output that national productivity statistics didn\u2019t measure\u2014in 2017.\u201d<\/em><\/p>\n In my blog \u201cWhy Accept the Hype? Time to Transform How We Approach Emerging Technology<\/a><\/span>\u201d, I highlight that the biggest cost for emerging technologies is the lost economic potential caused by early mis-positioning.\u00a0<\/span> The emerging technologies never gain the level of organizational adoption necessary to drive material economic impact (see Figure 2).<\/p>\n Figure<\/strong> 2<\/span><\/strong>: Lost Economic Value Due to Over-hyped and Mis-positioned Technologies<\/strong><\/p>\n In further exploration of the dangers of the Hype Curve, I realized that I had the right idea but the wrong perspective. It\u2019s not a \u201ctechnology hype\u201d issue, it\u2019s an \u201ceconomics productivity\u201d issue!\u00a0<\/span> The real issue is the economic \u201cProductivity J-Curve\u201d, which is the time period in emerging technology investments where productivity growth is underestimated, followed by a period where its productivity growth is overestimated.<\/p>\n The research paper \u201cThe Productivity J-Curve: How Intangibles Complement General Purpose Technologies<\/a><\/span>\u201d written by Erik Brynjolfsson, Daniel Rock, and Chad Syverson introduced the Productivity J-Curve (see Figure 3).<\/p>\n Figure<\/strong> 3<\/span><\/strong>: From the article<\/strong> \u201c<\/strong>Why Artificial Intelligence isn\u2019t Boosting the Economy\u2014Yet<\/a><\/strong><\/span><\/p>\n The Productivity J-Curve postulates that companies must make significant upfront investments in order to exploit the economic potential of new, disruptive technologies.\u00a0<\/span> For example, organizations looking to exploit the economic potential of artificial intelligence would need to make upfront investments such as new hardware, new software, updated information technology architecture, establishment or expansion of a data lake, employee training, new hires with new technology skills, and management education.\u00a0<\/span> And, of course, the biggest investment will be in gathering, integrating, validating, cleaning, correcting, normalizing, engineering, transforming and enriching the data.<\/p>\n Note: it is interesting to see that the economic Productivity J-Curve is nearly the inverse of the Hype Curve, for whatever that\u2019s worth (see Figure 4).<\/p>\n Figure<\/strong> 4<\/strong>: Hype Curve versus Inverted Productivity J-Curve<\/strong><\/p>\n So how do we minimize the Productivity J-Curve \u201cUnderestimated Productivity\u201d ramifications?\u00a0<\/span> How do we get to a faster return on investment on these emerging technologies?\u00a0<\/span>Enter the economic Learning Curve.<\/p>\n The economic Learning Curve<\/strong>describes the relationship between experience and productivity; how experience accumulated around a specific task drives down the cost of the execution or performance of that task (see Figure 5).<\/p>\n Figure<\/strong> 5<\/span><\/strong>:\u00a0<\/span> The Learning Curve<\/strong><\/p>\n The Learning Curve concept was the subject of Malcolm Gladwell\u2019s book \u201cOutliers, The Story of Success<\/a><\/span>\u201d that postulated that practicing a specific task for 10,000 hours (20 hours a week for 10 years) is required to achieve world class performance (a rule that has been challenged, but that\u2019s not the focus of this blog).<\/p>\n So how do we accelerate the learning curve in order to get the area of higher productivity when we don\u2019t have 10,000 free hours to invest?\u00a0<\/span> Meet the world of economics.<\/p>\n The key to accelerating the economic learning curve isn\u2019t just accumulating experience, but also requires a few other items to successfully accelerate the learning curve.\u00a0<\/span> Going back to the \u201cWhy Accept the Hype? Time to Transform How We Approach Emerging Technology<\/a><\/span>\u201d blog again, we get the following recommendations:<\/p>\n 1) Begin with an End in Mind.<\/strong>\u00a0<\/span>Understand your organization\u2019s key Business Initiatives. Understand what\u2019s important to the organization from a business, financial and\/or customer perspective, and use that to frame and accelerate the monetization of your artificial intelligence efforts. \u00a0<\/span>While we may not understand the technology journey that we\u2019ll experience trying to reach that end, the end point should not be a mystery.<\/p>\n 2) Understand the Technology Capabilities\u2026But Within a Business Frame.<\/strong>\u00a0<\/span> It\u2019s important for IT to gain familiarization with how the artificial intelligence technologies work, what\u2019s required to support the AI technologies, and what sorts of business and\/or operational opportunities can potentially be addressed with artificial intelligence technologies.<\/p>\n 3) Build out the Solution Architecture.<\/strong>\u00a0<\/span> Organizations should embrace a holistic architecture that supports big data, IoT, and agile application development in order to leverage artificial intelligence technologies to deliver \u201cintelligent\u201d applications (applications that get smarter with every customer interaction) and \u201csmart\u201d entities (that leverage edge-to-core IOT analytics to create \u201ccontinuously learning\u201d entities).<\/p>\n 4) Use Design Thinking to Drive AI Organizational Alignment and Adoption.<\/strong>\u00a0<\/span> Embrace Design Thinking as a way to drive organizational alignment and adoption with respect to where and how artificial intelligence technologies can be best deployed to drive meaningful business and operational value.<\/p>\n These four items provide the recipe for prioritizing financial and human investments in those tasks designed to accelerate an organization\u2019s Artificial Intelligence learning curve. Ultimately, we can blend the value creation focus of Economics \u2013 with the customer, product and operational analytics insights discovery of Data Science and the ideation, alignment and adoption capabilities of Design Thinking \u2013 to help organizations of all sizes to exploit the economic value of Artificial Intelligence.\u00a0<\/span><\/p>\n \u00a0<\/p>\n<\/div>\n Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" Author: Bill Schmarzo In my most recent blog \u201cDesign Thinking Humanizes Data Science\u201d, I discussed how Design Thinking and Data Science complement each other.\u00a0 They […] Read More<\/a><\/span><\/p>\n","protected":false},"author":1,"featured_media":459,"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\/1597"}],"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=1597"}],"version-history":[{"count":0,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts\/1597\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/media\/458"}],"wp:attachment":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/media?parent=1597"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/categories?post=1597"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/tags?post=1597"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}<\/a><\/p>\n
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Economics, Say Hello to Design Thinking and Data Science<\/strong><\/h1>\n
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Understanding the Economic Learning Curve<\/strong><\/h1>\n
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Call to Action:\u00a0<\/span>Data Science meets Design Thinking, Ruled by Economics<\/strong><\/h1>\n