The human mind has a burning desire to turn chaos into order, or to assemble random data and events into recognizable patterns. New York Times bestselling author Michael Shermer calls the phenomenon patternicity, or as he defines it, “the tendency to find meaningful patterns in meaningless noise.”
The “noise” is becoming louder. In the digital society in which we live and in which our businesses operate, overall available data is growing exponentially. Making sense of all that data – categorizing it and arranging it in meaningful ways – satisfies a basic human need for order and control. Unfortunately, we can also fool ourselves by seeing patterns that aren’t really there, confusing correlation with causality, or assigning an importance to particular data sets that are not really warranted.
Readily available data may also be assumed to be the most relevant data, which can lead businesses in the wrong direction. Sometimes it is the data that’s missing that may be most telling. Think about a bakery, for example. The fact that apple cinnamon muffins sell better than both bran muffins and cranberry muffins might be valuable information for the baker. What might be more helpful, though, is an insight that blueberry muffins (which the baker doesn’t currently offer) could sell better still. It’s the missing data – and the missing muffins – that may matter most.
Data sets are also, by definition, backward looking. They capture the world as it has been in the recent or distant past, but not necessarily the way it will be moving forward. As artificial intelligence plays an increasing role in our lives and work, that’s an important distinction to remember. Why does Amazon’s AI – certainly a recommendation engine as effective as anyone’s – suggest an assortment of wallets it believes I may like when I just bought a wallet and won’t need another for years? The immediate past is not necessarily a predictor of the immediate future.
But part of our need to bring order to our complex world involves predictive power: how can we use the data available to us to know where our world – or our businesses – are headed? Abundance of data has caused overconfidence or absolute fallacy in the understanding of our predictive ability for years. Our ability to forecast the weather is an example of the first. Are we better at predicting meteorologic events than we were a century ago? Certainly. We do understand more about the causal drivers of storms, for example. Are we perfect with our weather predictions? Far from it, either in terms of timing or severity of adverse impacts.
Some data, however robust, is not appropriate for predictive uses at all. Consider stock market technicians. They actually believe that “head and shoulders” patterns, “cup and handles,” or “double tops” in stock charts mean something. Quite simply, they don’t (except to the extent that a lot of other technicians may believe they do and act accordingly). Yet otherwise-intelligent people dive headlong into realms of data in search of patterns, whether those patterns are real or imagined.
Economists are terrible at predicting outcomes as well. They have been as surprised as anyone that interest rates have been so low for so long following the financial crisis in 2009 without sparking inflation. They routinely miss interest rate predictions, inflation predictions, unemployment predictions and other key components of the global economy. Those failures do not tell us anything about the qualifications of economists. Rather, they remind us that it is not possible with any certainty to predict outcomes in a highly complex system.
There was a wonderful article that appeared in the Harvard Business Review in September 2011 authored by Gokce Sargut and Rita Gunther McGrath. They discuss the important differences between simple systems, complicated systems and complex systems as they pertain to management of businesses. Predictions are possible – and in fact can be highly reliable – for a simple system. Cause and effect is clear, and outputs result from straightforward inputs. A complicated system can have a lot of additional inputs and a lot more interrelated operations, but everything moves according to patterns. While potentially more challenging to understand and manage, outcomes for complicated systems can be predicted and managed.
Complex systems are something else again, such as with our examples of the weather, the stock market or the global economy. There are too many variables, too many interdependencies and a conflation of factors that all defy prognosticators. Remember the concept of the butterfly effect made popular in chaos theory? A small, local change in a complex system can initiate outsized effects across the entire system in ways that are impossible to predict.
To the extent businesses operate as part of complex systems (interacting with customers, the economy, the competition, changing patterns of supply and demand and other factors), how is future planning possible? We need to think in a bifurcated way: we shape the future when we can; we react to the future when we can’t; we stand ready to change our strategic focus and assumptions as we move forward. Having a point of view on where the future is headed is a positive attribute for a business, certainly. Being able to reinvent is equally important.
When IBM was formed from the original C-T-R (Computer-Tabulating-Recording Company), most of the company’s money was made from employee time clocks and punch-card tabulating machines. They would have had difficulty predicting how random-access storage would replace batch processing, or how computers would evolve from the simpler “calculating machines” they sold at the time. But IBM company managers and sales teams stayed very close to customers and understood how their changing needs intersected with available changes in technology. As a result, they dominated the first wave of the computer era in the 1960’s and 1970’s. IBM is trying to reinvent the company again now as customers are moving from on-premise and single-vendor IT solutions to hybrid cloud and multi-vendor, “best in class” systems approaches.
Company capabilities change over time, just as customer needs do. Too many strategic plans lock in organizational capabilities at the point of planning. Take the case of Amazon. Jeff Bezos started with the goal of creating an incredible online bookseller, which he quickly did. He had not thought at the beginning of creating Amazon Web Services (AWS), which in late 2019 was approaching $9 billion in quarterly revenue. As corporate capabilities evolve, the way those capabilities can be leveraged to create further competitive advantage evolves as well.
Berkshire Hathaway’s Warren Buffett has joked that his idea of a strategic plan is to wait for the phone to ring. What he’s telling us that being opportunistic – and always being prepared to use your corporate capabilities effectively as the world around you changes – is not a bad path forward.
A.G. Lafley reinvigorated Procter & Gamble in his second stint as CEO by saying “no” to a large part of the portfolio of brands that had been built over decades. Once known as the training ground for brand marketers the world over, P&G had become bloated and slow to adjust to a rapidly changing consumer landscape. Mr. Lafley saw that becoming nimble and focused would give P&G the best shot at restoring business results and investor confidence. He was right. Since the merger or divestiture of more than half of P&G’s brands, the company has performed well.
For most businesses, strategic planning should be more about evolving focus and less about trying to predict the future or set the plan in stone. Listen to customers as IBM did in the 1950’s. Change your plan when your capabilities change, as Amazon has done much more recently. Be ready when the big opportunity emerges, as Berkshire Hathaway always is. And be prepared to say “no” to long-standing (and sometimes much loved) components of your business or sources of revenue when the world around you changes. Strategy is not predictive. Instead, at its most effective, it is proactive, reactive and an evolving product of the complex world in which we operate.
Interested in discussing these ideas more? Give us a call and we’ll have some coffee and a chat – and maybe one of those missing blueberry muffins.