[Note on Brand Evolution] This post discusses concepts and methodologies initially developed under the scientific rigor of Shaolin Data Science. All services and executive engagements are now delivered exclusively by Shaolin Data Services, ensuring strategic clarity and commercial application.
Every year, a new “it” technology emerges. A new buzzword permeates the industry. And just like clockwork, countless organizations leap at the chance to adopt it, convinced that this single, innovative act will save them from inevitable demise. They spend millions on AI, automation, and “big data initiatives,” believing that innovation is a finish line to be crossed.
This is what I call “innovation euphoria”—the fleeting, climactic sensation that blinds a company to the long-term, unsustainable nature of its actions. You see it everywhere: companies rushing to implement AI with no data strategy, or replacing personnel with automation only to find themselves lost in the aftermath. Their pride prevents them from ever admitting it, but they often discover that a person they could yell at was a lot easier to deal with than a system they can’t control.
The market has a broken view of innovation. It’s often seen as a one-time event, an objective to be achieved. But as the “Shaolin Data Science” approach observes, this is a fundamental flaw in thinking. Innovation is a characteristic; not an objective. It is the byproduct of a sustainable, strategic organizational capability.
To put a finer point on it, consider a 2019 study published in Sustainability by Shengbin Hao, Haili Zhang, and Michael Song that examined the relationship between big data, big data analytics capability (BDAC), and sustainable innovation performance in firms in the United States and China (Hao et al., 2019). The findings should be a wake-up call for anyone caught in the throes of innovation euphoria.
The study found that for U.S. firms, the relationship between big data and sustainable innovation performance is an inverted U-shape. What does that mean? It means that up to a certain point, the more big data you have, the better your innovation performance. But after you hit that peak, the more big data you accumulate, the less innovative and sustainable you become. The sheer volume and velocity of the data, without the underlying capability to manage and utilize it effectively, becomes a drag on the organization. It’s a classic case of biting off more than you can chew, and the data is literally chewing up your bottom line.
This directly contradicts the common, broken mantra that “more data is always better.” It proves that the number one priority isn’t acquiring data; it’s cultivating the internal systems and strategic mindset to use it sustainably.
In stark contrast, the study found no such statistically significant relationship for firms in China. This tells us two critical things: first, that a one-size-fits-all approach to big data strategy is fundamentally flawed and ignorant of cultural and economic context. Second, and more importantly, it shows that the problems plaguing U.S. firms—the ones you see struggling to adapt to every new fad—are the result of a uniquely unsustainable culture.
When you look at companies that have achieved lasting dominance, you see that they are built on a foundation of sustainable innovation. Take Apple. They don’t rush to be first to market with every new technology. In fact, many features in Apple products have been thoroughly tested and validated by Android users for years before Apple adopts them. The “innovation” for an Apple user isn’t the technology itself; it’s the sustainable, reliable, and integrated experience that technology provides within their ecosystem. Apple launches a product that is, by its targeted definition, innovative to its audience and sustainable by its organization.
Consider Netflix. They are a leader not because their recommendation algorithms are wildly unique—they are, in fact, similar to those used by Google, Amazon, and others. Their leadership stems from the fact that they have a deep, unwavering commitment to applying their analytics capabilities to a market they understand better than anyone else. They don’t chase every new trend; they double down on what they do well. They are beholden to the cardinal rule that innovation is a characteristic, not an objective.
So, for any business leader or data professional reading this, I caution you to observe your organization clearly. If your company is chasing innovation as an objective, it is walking into a trap—a data-driven, quantifiable trap. Instead, look at the big picture and try to see the long-term impact relative to your goals. The one responsible for your success is you.
On a completely unrelated, but still similar note, for any project you handle, try to make your side effects intentional. This approach allows you some degrees of freedom while minimizing unintentional side effects. It’s analogous to managing the scope of your limitations, and it’s a mindset that prioritizes long-term sustainability over short-term chaos.
References
Hao, S., Zhang, H., & Song, M. (2019). Big Data, Big Data Analytics Capability, and Sustainable Innovation Performance. Sustainability, 11(13), 3647. https://doi.org/10.3390/su11133647
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