In a recent blog I stated that "Crossing the AI Chasm" is primarily an organizational and cultural challenge, not a technology challenge. That "Crossing the AI Chasm" not only requires organizational buy-in, but more importantly, necessitates creating a culture of adoption and continuous learning fueled at the front-lines of customer and/or operational engagement (see Figure 1). A recent Harvard Business Review (HBR) article "Building the AI-Powered Organization" agrees that despite the promise of AI, many organizations' efforts with it are falling short because of a failure by senior management to rewire the organization from the bottom up. The above points – interdisciplinary collaboration, data-driven at the front-line, and experimental and adaptive – are the hallmarks of an organization where everyone has been trained to "Think Like a Data Scientist." So, how can your organization embrace the liberating and innovative process of getting everyone to "Think Like a Data Scientist"?
AI is a game changer. And being a data and analytics guy, I could not be more excited about it. The McKinsey research study "Notes from the AI frontier: Applications and value of deep learning" provided some valuable insights into where and how Artificial Intelligence (i.e., Deep Learning / Neural Networks (CNNs, RNNs, GANs), Reinforcement Learning and Deep Reinforcement Learning) will derive and drive new sources of customer, product and operational value, especially when compared to traditional analytic approaches. AI will add billions of dollars of financial and economic value to ALL industries. A no-brainer if ever one existed.
There are several technology and business forces in-play that are going to derive and drive new sources of customer, product and operational value. As a set up for this blog on the Economic Value of Data Science, let's review some of those driving forces. "Due to its ability to substantially improve productivity and boost economic output, Artificial Intelligence (AI) has the potential to increase economic growth rates by a weighted average of 1.7% and profitability rates by 38% across a variety of industries by 2035. Source: NorthBridge Consultants "The Artificial Intelligence Revolution: New Challenges & Opport..." Figure 1: Source: "The Artificial Intelligence Revolution: New Challenges & Opportunities" Data Science (Artificial Intelligence, Machine Learning, Deep Learning, Reinforcement Learning) holds the potential to exploit Big Data and IoT to create new sources of economic value (wealth). But what is the source of this economic value when the AI tools that are driving this economic growth (TensorFlow, Spark ML, Caffee2, Keras) are open source and equally available to all players?
I love watching the NBA's Golden State Warriors play basketball. Their offensive "improvisation" is a thing of beauty in their constant ball movement in order to find the "best" shot. The coordinated decision-making is truly a thing of beauty, but here's the challenge: how would you "scale" the Warriors? You can't just add another player to the mix – even a perennial all-star like Boogie Cousins – and have the same level of success. One of the biggest challenges in this age of Digital Transformation is how are organizations going to exploit new technologies such as IoT and AI to "scale innovation?"
Did you ever have a concept that you knew was right, but just couldn't find the right words to articulate that concept? I know that Data Science and Design Thinking share many common characteristics including the power of "might" (i.e., that "might" be a better predictor of performance), "learning through failing" (which is the only way to determine where the edges of the solution really reside), and the innovation liberation associated with "diverge to converge" thinking (where all ideas are worthy of consideration). A recent McKinsey article titled "Fusing data and design to supercharge innovation" confirmed the integrated potential of Data Science and Design Thinking: "While many organizations are investing in data and design capabilities, only those that tightly weave these disciplines together will unlock their full benefits." The combination of Design Thinking and Data Science is a powerful combination, but they must be fused on deriving and driving new sources of value and actionable outcomes. To make Data Science and Design Thinking more actionable, we must begin with an end in mind.