Sometimes it just takes a simple, provocative statement to kick-off the innovation process – to remove an everyday given like driving a car or possessing a landline phone or centralizing all of your data in the cloud – to fuel the innovation process. Henrik Christensen, director of University San Diego's Contractual Robotics Institute, issued such a provocative statement: "My own prediction is that kids born today will never get to drive a car." I have recently been promoted to Chief Innovation Officer at Hitachi Vantara. I am very excited about the opportunity to build upon my work to interweave data science, design thinking, value engineering and economics to create a "Pathway to Analytics-driven Innovation" map that helps organizations derive and drive new sources of customer, product and operational value. Think of the "Pathway to Analytics-driven Innovation" as a maturity model that measures how effective organizations are at leveraging analytics to deliver innovative products and services to the market.

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'd prefer not to be awarded posthumously). So, while we wait for that call from Stockholm, let's take a look at my 10 favorite 2019 blogs: There are many valuable lessons that data scientists can learn from the movie "Mr. And maybe the biggest challenge for the development of smart, autonomous products is knowing when "good enough" is actually "good enough". 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. This is an important lesson for the creation of our AI-induced "smart" products – that there must be constant testing, learning, and maybe even some unlearning and re-starting afresh in order to find the optimal models.

I hate it when I hear senior executives state that they want to become data-driven, as if somehow having data is value in of itself. Now, one can hardly blame the unenlightened executive whose only perspectives on data are associated with statements like "Data is really the new oil" (Wall Street Journal) or "The world's most valuable resource is no longer oil, but data" (The Economist). The infatuation with "data-driven" versus "value-driven" can be confirmed from Google Trends (Figure 1). However, this is where the value determination of data and oil diverge. Oil has value as determined by General Acceptable Accounting Principles (GAAP).

I grew up in a small manufacturing town in Northeast Iowa. The factory in my hometown made tractors (no surprise given that it was Iowa), but eventually the economics of cheap foreign labor and an interconnected global economy caught up with that factory – as it did with many US-based manufacturers – and soon the factory closed, and many people were laid off. But the technology world continues to evolve – especially with respect to IoT, Data Science and AI/ML – and so comes an opportunity for manufacturing to make a big return to the US. However, tomorrow's manufacturing battles won't be fought with cheap labor. In fact, measuring a country's manufacturing strength by the number of manufacturing jobs is fighting yesteryear's battle.

In "Importance of Thinking Differently…Hint: Don't Pave the Cow Path", I introduced the concept of the "3 Horizons of Digital Transformation." I wanted to provide a framework that helped organizations differentiate between "Digitalization" versus "Digital Transformation". Unfortunately, in succeeding client engagements, I realized I did a crappy job of explaining these 3 horizons. So, like how bad movies create "Return of" sequels in order to explain everything they screwed up in the original movie, consider this my justification for "Return of the 3 Horizons of Digital Transformation" thriller! This "Return of" blog will provide more details on the 3 stages – or horizons – through which your organization must navigate in order to achieve Digital Transformation.

Wait, the AI advantage is already here and gone? That's what Deloitte warns in their report "Future in the balance? How countries are pursuing an AI advantage".A noteworthy quote: "There are indications that the window for competitive differentiation with AI is rapidly closing. As AI technologies become easier to consume and get embedded in an increasing number of products and services, the early-mover advantage will rapidly diminish" (see Figure 1). How Countries are Pursuing an AI Advantage". But of course, it's not too late to benefit from the digital transformation potential of AI! Because having AI capabilities is not the same thing as exploiting AI capabilities. "AI success depends on getting the execution right.