Are you letting data from the past drive your future strategy?

By | August 29, 2022

Yesterday, I was deep inside Google looking for datapoints to connect that would add up to give me a sense of what was going on in these turbulent times, and I came across this insightful article on strategy. It was clear the author felt as obsolete as I did about the way strategic planning for innovation and design, as means for competitive advantage in a changing market landscape had evolved from thoughtful analysis to being driven by vast amounts of automated data collection and subsequent number crunching.

Had this evolution rendered strategy and planning skills vestigial? Would they serve in conditions of extreme turbulence?

Turbulence has also been described as VUCA – volatility, uncertainty, complexity, and ambiguity. However, given the acceleration in the rate of turbulence ongoing right now, on a global scale, due to continuous and intense systemic shocks of varying degrees of intensity, duration, and frequency, let me synthesize what is being described in a plethora of recent articles on how to navigate this ‘perfect storm’ of conditions impacting every aspect of the commercial operating environment.

The pandemic, war in Ukraine, the threat to food security, and the resurgence of global poverty. Heatwaves, droughts, and other extreme weather events. These are not random shocks. Nor are they a perfect storm in the conventional sense, a one-off conjuncture of bad events. We face instead a confluence of lasting structural insecurities—geopolitical, economic, and existential—each reinforcing the other. We have entered a perfect long storm. (Shanmugarathnam, 2022)
It is better to anticipate the problem before us as one of managing turbulence rather than to see each shock as separate. This encourages us to avoid the dichotomy between stability and change, to confront their different chronologies, and to recognize the relationship between different types of shocks. (Clavin, 2022)
Environmental turbulence refers to the degree to which firm’s external environment characterized by unpredictability, uncertainty, & volatility. Formally defined as the rate & unpredictability of changes in firm’s external environment (Boyne & Meier, 2009, Danneels & Sethi, 2011) via (Rego, Brady, Leone, Roberts, Srivastava, & Srivastava, 2022)

In the course of my pondering and exploration, I came across my own Strategy professor’s paper on the topic (Camillus & Datta, 1991) where the very first sentence says “a major shortcoming of conventional Strategic Planning Systems (SPS) is their lack of sensitivity in coping with changing environments” before proceeding to lay out an integrative approach for incorporating vision and motivation with a novel approach to respond to ‘weak’ signals and turbulent environments. This is not a blogpost to reference the details of their approach, the world has changed dramatically from the operating context and conditions of 1990, so much as it is a reference to my own positionality when it comes to speaking on strategy, and the legacy of thinking which informs my observation above that I felt obsolete in the way I approach strategy and planning. In this context, I will continue pontificating on the concerns I raise in the title of this piece of writing. Long time readers will note how my well known tendencies to circumlocute and pontificate on this blog have been influenced by my return to academia as a doctoral student in my fifties.

Contextual Situation Analysis

Back to the article that caught my attention yesterday. It focuses on one of the dominant new business sectors in the global economic ecosystem – digital platforms that connect individual humanity. In plain language, the rise of the short video sharing platform TikTok has been so fast and so disruptive to the competitive landscape of dominant incumbents such as Facebook, Instagram, Twitter, and even Google that it has bared to the world the ineptitude of the strategy of copy cat responses to its perceived success factors. Not to mention the irony of Silicon Valley imitating the Chinese in design and  innovation, for a change.

As the author asks,

… while all this TikTokification is going on, all these brands are also losing focus on their own optimum strategic trajectories. Facebook has famously pegged its future to the metaverse, Snapchat to the more myopic opportunities of AR and VR. Why is either brand spending millions of dollars and development hours on short-form video cartoons? It does not fit with the longer-term aspirations of either brand. By copying TikTok, both platforms will fail in the short term, but more importantly fail in the longer term by drifting from their strategic paths.

and concludes,

An appropriate response to TikTok is a very difficult strategic challenge. But it is one that these companies could embark upon, were they not so unable to think strategically in the first place. Of all the many disadvantages of competitor orientation, the biggest one is that it inhibits and ultimately destroys a company’s own strategic capability. When you look at your rival and adopt its approach, you look less and less at your own situation, advantages and options. The more a company copies a competitor’s strategy, the more it loses the ability to create its own.

I would add, based on the author’s analysis of the rationale offered by Instagram and Facebook for their moves – the data shows evidence of increasing attractiveness of short videos – that in addition to the competitor focused orientation destroying one’s own strategic capability, this capability was already being withered by the rise of Big Data as the driver for “user centered” design and innovation, where the data gathered on “users” drove design decision making – much of it automated. Bloomberg’s writers have already noticed this shift in terms of outcomes, even if the analysis has not yet connected reliance on Big Data to rendering strategic thought into a vestigial function, rather like the appendix.

This divergence is ironic given these are the kinds of firms that have been eclipsed by Big Tech’s ability to collect huge amounts of data, capture digitally native Gen-Z eyeballs and swallow a huge slice of marketing spend in recent years. (Bloomberg)

Has reliance on data-driven innovation withered corporate skills in strategic thinking?

The publications of management consultancies like BCG and design-driven innovation scholars like Robert Verganti capture this shift in strategy towards data-driven innovation and market advantage. This implies that its more than just issues of ad spending at play, or new competitors on the horizon. Fragmentation and fracturing of the marketing world’s long established conventions for communication and advertising began with the advent of the long tail concept some 15 or more years ago. What is rapidly becoming more clear, as a very turbulent operating environment increases volatility and ambiguity, is the vulnerabilities inherent in relying on data as the decision maker for strategy.

What is the fatal flaw of this path dependence?

First, the data being gathered is of what has already taken place. Its the past. Turbulence implies constantly changing operating conditions. And, contemporary turbulence, as evidenced by the quotes at the start of this blogpost, isn’t simply a matter of one systemic shock or the other but a series of them that lead observers to conclude that we’re in the midst of a massive systemic transformation and that few, if any, assumptions and frameworks based on stable predictable operating environments will hold. How will the data on what happened in the context of the situation last week help you decide what to do next when the situation is changing dramatically from one day to another?

Second, algorithms are designed based on certain assumptions made to both operating conditions as well as criteria and constraints for decision making. This means that certain data points are collected while others are not deemed relevant to the decision making criteria. It is this foundation of assumptions that is in the midst of upheaval. Or, as I said on Twitter, are you capturing air-quality data while driving underwater?

Third, even if, as many have rightly recommended, the data-driven part of the organization’s new product and innovation decisions are relegated to the micro and meso level, and the humans make the macro decisions, there is a danger that while higher order strategic decisions are taken, the automated process of the micro and meso level drives you off the cliff anyway. A well known discussion point around all these powerful big data algorithms is that they are a mysterious black box and we have no idea what they’re doing anymore. Well, they have no idea what they’re doing anymore either. Unless the human strategist has reins to control the direction of the horsepower of the data driven decision making, the disconnect itself is far more dangerous in turbulent conditions of increasing volatility and uncertainty than any short term efficiency gains or productivity profits.

Finally, there is a massive gap in the kind of data that can be gathered – this is particularly important for the kind of high level strategy and planning that allows navigation through choppy waters. And this is the vast blind spot of experiential and embodied data streams that the complex sensing organ that is our organic brain can capture – in a blink of an eye, to use a management cliche – that current technology is not designed to even recognize much less capture.

When bipeds stood upright and sniffed the air for predators in the savannah grasses of East Africa’s Rift Valley, the data points they were sensing and crunching to arrive at a decision were multifarious, embodied, and experiential. Temporal attributes contributed to instantenous pattern recognition of operating conditions as much as olfactory, aural, and visual. Our artificial intelligence machines and our biggest of big data crunchers are not yet capable – if ever – combining the memory of the smell of spoor with the context of storm clouds on the horizon integrated with the way the birds were taking wing in order to arrive at what is ignorantly called the gut hunch that perhaps there was a predator around to be cautious about.

In contemporary terms, this aspect is well captured in climate change mitigation strategy and planning by the Chadian Indigenous climate changemaker, Hindou Oumarou Ibrahim, who says “Grandmothers are our weather apps“. When my gut is replaced by an algorithm, in inimical circumstances of complexity and ambiguity, where uncertainty and volatility are the norm, then my capacity to survive is rendered null and void. It is this dependence on the digital data driven app that renders embodied skills and experiential capacities into vestigial functions that lead to vulnerabilities when conditions render the operating environment beyond the capacity of the design of the data capturing algorithm.

We need good old fashioned skills in strategy and planning in times of turbulence

Build models that predict and optimize business outcomes say the management consultants when exhorting you to invest in a data-driven strategy for competitive advantage. How can you predict and optimize in conditions of volatility, uncertainty, complexity, and ambiguity? Strategy is not comprised of 24 hour cycles of data analysis and design changes, even though that may add up to continuous incremental innovation at the human-centered scale of a Netflix algorithm or a Uber price shock.  Your strategy might indeed be the decision to take such continuous incremental changes to your pricing model and offerings, a la Amazon’s automated algorithmic business model. But that is a choice and a decision of a strategy and a tactic. This is made during planning, and after evaluating which approach suits you best.

To conflate data-driven insights to inform innovation or automate digital design changes in response to the real time flows of information of “users” – the dumb, blind keystrokes you capture on the back-end in god mode – with what strategy means is indeed to render the function and capacity for strategic thought into a vestigial function. And, this might indeed be the evolutionary direction of first world corporations who embraced data as the foundation for business operations and consider the size of their datapile as measure of their competitive advantage. If so, then, the challenges faced by Facebook and Google by disruptive competitors might just become the early signals of a massive rout in the market. Try not to navigate your sailboat in a rising storm from the shelter of a cabin with an excel spreadsheet of wind direction indicators and water temperature changes. For someone, somewhere is still licking their finger and holding it up in the air to see which way to turn.

Update 31st August 2022, via @incognitosum:

Where big data and AI are concerned, critical data studies has shown time and again that, when ostensibly superior data analytics enter into a social problem, they tend to devalue and invisibilize the kind of data that cannot be captured easily and the kind of judgment that cannot be rationalized through the available data. (Hong, 2022)

Camillus, J. C., & Datta, D. K. (1991). Managing strategic issues in a turbulent environment. Long range planning, 24(2), 67-74.
Clavin, P. (2022). Turbulence and the Lessons from History. Finance & Development. IMF Blog
Hong, S. H. (Online August 24th 2022) Predictions without futures. History and Theory.
Rego, L., Brady, M., Leone, R., Roberts, J., Srivastava, C., & Srivastava, R. (2022). Brand response to environmental turbulence: A framework and propositions for resistance, recovery and reinvention. International Journal of Research in Marketing, 39(2), 583-602.
Shanmugaratnam, T. (2022). Confronting a Perfect Long Storm. Finance & Development, 59(002). IMF Blog

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