In his recent article for the McKinsey Quarterly, entitled “The Second Economy”, W. Brian Arthur of the Santa Fe Institute states, “In any deep transformation, industries do not so much adopt the new body of technology as encounter it, and as they do they create new ways to profit from its possibilities.” As we speak, industries are encountering, and have been encountering, the ‘Second Economy’ of analytics for some time, and have in fact found a myriad of ways to profit from its adoption. With respect to Arthur’s ‘Second Economy’ theme, analytics creates profitable opportunities through improved decision making in two ways; insight, and automated decision making.
While I consider the full article a must read, a quick synopsis of Arthur’s primary premise is that a second, digital economy is developing and growing underneath the physical economy of goods and services that we are most familiar with, and that this digital economy will transform society more dramatically than any previous technology. Just as steam, gasoline, electric and nuclear power came to supplement and in many cases replace human labor in the physical economy, information technology is augmenting human brain power, and again, in many cases, replacing it completely. That computers have better memories, more storage, and can calculate error-free faster than humans is now a given, but what is new is how IT is replacing direct human intelligence in large portions of the business process value chain. As an example, think of how automated air travel has become, from reservations to check-in to baggage handling to security to things you don’t even see, like aircraft weight distribution and fuel requirements.
The digital transformation of the second economy comes not just from replacing physical human activity in the business processes (think ‘travel agents’), but more significantly, how it is augmenting and even replacing human decision making.
The augmentation of human decision making for insight arises from several analytically-driven areas:
- Short-term and high-volume forecasting can be done with greater accuracy.
- Likely customers can be targeted for specials, cross-selling and up-selling.
- Production, service and operational processes can be optimized.
- Most and least profitable customers and products can be segmented for action.
- Risk can be quantified and managed.
- Text analytics can ferret out customer sentiment.
- Strategic objectives and KPI’s can be validated against expected results.
The common thread running through the above is that analytics removes much of guesswork. Fact-based decisions replace seat-of-the-pants gut instinct and intuition.
But if that was all there was to it, it wouldn’t warrant a claim to be a significant part of the future Arthur sees for the Second Economy. Instead, information technology and analytics is not just augmenting decision making, but in many cases, is replacing it altogether, in real time. Analytics for real time decision making is becoming embedded in the digitized business processes themselves, where the second economy directly affects the physical economy without the intervention of humans. Consider:
- Program trading on Wall Street.
- Credit card fraud detected, accounts frozen and people alerted.
- Revenue and pricing optimization for perishable inventory (i.e. hotel rooms, airplane seats)
- Quality control feedback loops in production processes.
- Point-of-sale discounts and coupons.
As with the previous list, there is a common thread to this one as well, a thread that Arthur identifies as the ‘neural system for the economy’ - the digitized Second Economy “constitutes a neural layer for the physical economy”. And like any proper biology metaphor, this one can be authentically extended to the concept of learning – these analytical processes LEARN and perform better and better as they gain more experience with both the physical economy and the other neural components of the Second Economy that they interact with.
These lists are only going to grow longer as more aspects of more industries ‘encounter’ analytics and create for themselves new ways to put them to use to augment and replace real time human decision making. The above examples tend towards the single case of analytics embedded within the process, but the future will see analytics increasingly embedded at the point-of-inquiry / point-of-sale, and even earlier, at the point-of-decision. Putting analytics to profitable use is limited only by that oh, so very human capacity for creativity and the imagination to see the possibilities.