A History of Creating and Managing Digital Assets
At the dawn of civilization, bands of Homo Sapiens had been roaming for hundreds of thousands of years. Our nomadic ancestors were one with the Earth, collecting what sources of calories they could, hunting and gathering, playing a small role in thus far untamed ecosystems, sometimes as predators, often as prey.
About 11,500 years ago, we invented agriculture. We harvested wheat, barley, peas and lentils in the Levant. Rice, mung, and soybeans in China. We wrestled with nature and claimed ownership on the first of many asset classes we would create: Land.
An asset is a tradable resource whose value is determined by the expectation of future benefits that it will deliver to its owner. Determining the value of crop and seed was straightforward. Having pried land out of the realm of nature and incorporated it into the market, determining the value of land was also relatively straightforward, based on factors such as fertility, arability or proximity to water.
But the Agricultural Revolution was also a technological revolution, and valuing the technology of agriculture was not so straightforward. The knowledge component of agriculture was largely contained within the heads of people, who, let’s face it, come with an awful lot of baggage, which makes them unreliable. This would soon change. Agriculture introduced the need to plan, store and distribute large quantities of food among a growing population. So, what started as scratches on clay or bone to keep records to aid in the administration of harvests eventually developed into the mathematics and the written word that allowed Newton and Shakespeare to respectively unlock the mysteries of the universe and the human condition.
More prosaically, writing and mathematics also unlocked economic value by decoupling technology from the human brain, pretty much the same way our ancestors decoupled land from Earth. Some written documents became tradable assets: Deeds on land and crops, promissory notes. Technologies could be reduced to a recipe, a blueprint, or an instruction manual, tradable documents containing know-how in the form of math and the written word. Even simple recipes, gunpowder, and Coca-Cola come to mind, could become extremely valuable assets.
The next technological revolution, the Industrial Revolution, further decoupled asset from man and environment. While the Physiocrats still believed that land was the source of all value, Adam Smith started to point to another direction. Sure, resources may be extracted from the land to create machines and tools, but the vast increases in productivity they enabled made it increasingly clear that much of the new value thus created should be attributed to these emerging technologies.
One could unlock value by deploying machines and use them to replace tasks previously done manually. Increasingly though, value was to be found by reorganizing people around the emergent capabilities that newly introduced machines and tools enabled. Business Process is in today’s lingo what Adam Smith called Division of Labor in his seminal inquiry into The Wealth of Nations.
As the stream of benefits attributable to the machines that comprised a factory surpassed that of the land on which the factory sat, the designs of machinery became increasingly valuable assets on their own right. And with tasks ever more narrowly defined and codified, thinkers and doers such as Frederick Taylor, Henry Ford, and W. Edwards Deming unlocked further waves of value creation by changing how we organized ourselves to achieve those tasks.
This codification of task and machine comes in particularly handy, as we find ourselves amid yet another revolution, this time it’s the Digital Revolution. What began as division of labor became business processes so studiously defined and refined that we can now translate them into programs, applications, and algorithms. And now it is not only the design of the machinery, the processes or algorithms that have become valuable assets, but also, these standardized processes create a wealth of data, which is increasingly becoming an asset on its own right.
So, we see throughout history how humanity has added to its stock of capital by progressively adding to the assets at its disposal to create future value: Land, machinery, process, software, and most recently, data.
The Digital Revolution started as process and software-led and is quickly turning into a Data Revolution.
The Human Factor
If you feel that the narrative thus far is one that is long on analysis and short on humanity, you would be right. This history reads like the progressive removal of the human factor from human activity: Written documents replace human memory, machines replace human muscle power, process replaces human judgment. All in the valuable pursuit of efficiency. But, what is business if not humans getting busy?
In the process of creating economic value, the nature of human input can be roughly classified into computational and muscle power. Brain and brawn. For routine and repetitive tasks that have historically made up the bulk of business activity, these contributions are subject to an error rate that is higher for humans than it typically is for machines or processes. It is telling that in a process or engineering context, the human factor is most often mentioned as a proxy for human error.
While some of these human factor-related events genuinely are errors, many actually are what we might better label as creativity and innovation. Those who have worked in an organizational setting long enough would know that sometimes, creativity and innovation are indistinguishable from errors from a hierarchical or process point of view. They can have equally negative effects on short-term efficiency as genuine errors, and are very often dealt with as such. The innovation and its author both. There’s a reason why we often call an innovation disruptive.
And thank god for that! This is the spark that ignites the startup economy.
Of course, the distinction between innovation and efficiency is not clear cut. Firstly, the economic surplus that increased efficiency creates can be reinvested in innovative pursuits. Secondly, innovative approaches can be applied into existing processes to achieve continuous improvement.
What remains true is that as technology: Machine, process, and software, increasingly remove pure error and squeeze efficiency out of a business activity, there remains less incremental value in focusing on removing errors relative to supporting creativity and innovation, which really are innate human capabilities, and will remain our competitive advantage over machine and process for the foreseeable future.
The Digital Asset Landscape
As we have seen, with each subsequent cultural-economic revolution, human civilization has created new types of assets. When an organization embarks on a digital transformation, it is organizing itself to create and operate digital assets. The CapEx and the OpEx of a digital transformation, if you will. Our analysis thus far points at three value areas to focus on:
Not to reinvent the wheel, efficiency-based approaches deliver value by doing more, better and cheaper, broadly in line with the Frederick W Taylor’s ideas, and tend to be rooted in Douglas McGregor’s Theory X assumptions.
When innovation-based approaches value, they tend to disrupt and supplant existing markets and players, as posited by Clayton M Christensen. They also tend to work in environments where Douglas’ assumptions for Theory Y hold.
And finally, the ubiquity of computer-readable sets of instructions made software the premier digital asset of the first half of the Digital Revolution. And it is software’s capacity to consume, create and process information that will make data the oil-wells of the second half of the Digital Revolution, thus becoming a source of value on its own right.
These three types of value combine into seven different approaches to invest in, and operate a digital asset.
Digital Transformations usually start as a straightforward cost optimization exercise. From a pure efficiency perspective, we have classical digitalization or automation of existing tasks. In this case, just like Henry Ford’s assembly lines, your software and processes become your productive assets, churning out not only your product, but also a wealth of data.
This data can then be used to more efficiently monitor and evaluate operations to ensure that it is performing as intended. As organizations start to work with this data, metadata, or data about the data is created.
The volume of metadata can vastly surpass that of data and can be even more valuable than that of the original data itself. Incidentally, this is a source of worry for internet privacy advocates because while users own their personal data, the metadata is owned by the technology companies and is often seen by them as a value-add resulting from their investment in data scientists, data engineers, and data managers.
Sticking to Christensen’s definition, radical and disruptive innovations are often the result of people seeing patterns in one or more fields that can be exploited in another field, so it is no surprise that investing in the exploration of, and experimentation with existing data sets can be a source of disruptive innovation that creates conceptually new business assets.
It is All Interconnected
As mentioned previously, there isn’t a clear cut between innovation and efficiency-driven approaches. Which is why investing in design thinking capabilities is an effective way to bridge the two approaches, bringing innovative thinking into optimizing existing assets, and using existing pain points to create new offerings and the assets that enable such offerings.
Further, organizations can harness every co-worker’s innovative instincts by engaging them to continuously improve existing assets and processes, such as A/B testing in the digital realm, or Toyota’s Kaizen in industry.
Budget is Strategy
It is simple, business strategy boils down to how an organization manages its assets, in other words, how it allocates its capital, and the budget is its distillation.
But simple things can be the hardest to execute. In an organization’s past successes lie the seeds of its future struggles. Past choices and optimizations that create the structure necessary for the execution of past strategies also result in rigidities, the source of resistance to the change that is required now. This is why historically, ice factories did not make the jump to become manufacturers of refrigerators, mainframe computer manufacturers did not make the jump to PC making, and PC makers largely haven’t made the jump into becoming today’s social media titans.
Dear incumbent, there is no need to despair. An oldie but goodie that can be used allocate capital while navigating a changing market is the BCG Growth Matrix, which classifies business lines along the axes of growth and cash generation into cash cows (keep the business and redeploy the cash it generates) stars, (invest in it) question marks, (speculative, invest or discard, based on their probability to become stars) and dogs (divest, discard or reposition these pets).
Just as organizations as varied Wells Fargo (it’s been around since the Gold Rush), GE (founded by Edison), and Microsoft have successfully navigated through such changes, by managing your businesses as a collection of assets, so too can yours.
Article originally published in Medium.