Platt Perspective on Business and Technology

Innovation, disruptive innovation and market volatility 39: innovative business development and the tools that drive it 9

Posted in business and convergent technologies, macroeconomics by Timothy Platt on February 14, 2018

This is my 39th posting to a series on the economics of innovation, and on how change and innovation can be defined and analyzed in economic and related risk management terms (see Macroeconomics and Business, posting 173 and loosely following for Parts 1-5 and Macroeconomics and Business 2, posting 203 and loosely following for Parts 6-38.)

I have been addressing a set of five interconnected topics points in this series since its Part 32 that I repeat here in full in order to put this posting in perspective (with the postings where I delved into the first four of them, parenthetically noted):

1. Innovation and its realization are information and knowledge driven (Part 32).
2. And the availability and effective use of raw information and of more processed knowledge developed from it, coupled with an ability to look beyond the usual blinders of how that information and knowledge would be more routinely viewed and understood, to see wider possibilities inherent in it (Part 33),
3. Make innovation and its practical realization possible and actively drive them (Part 34, Part 35 and Part 36).
4. Information availability serves as an innovation driver, and business systems friction and the resistance to enabling and using available business intelligence that that creates, significantly set the boundaries that would distinguish between innovation per se and disruptively novel innovation as it would be perceived and understood (37 and 38),
5. And in both the likelihood and opportunity for achieving the later, and for determining the likelihood of a true disruptive innovation being developed and refined to value creating fruition if one is attempted.

And this brings me to the final to-address point on that list, which I will address in terms of my responses to the issues raised in the first four of them as offered up to here, and particularly with Point 4 of that set. I developed a foundational explanation of the first half of that in Part 37. So I focused entirely on the second half it that in Part 38, which I rephrase in simplified form here as: resistance to enabling and using available business intelligence significantly sets the boundaries that would distinguish between innovation per se and disruptively novel innovation as it would be perceived and understood – and acted upon.

I began in fact, to address the last three words, and Point 5 of this list in the process, in Part 38 towards its end. I pick up on that narrative thread here, beginning with a briefly restated set of fundamental points that I have already discussed and developed in this blog, and in this series itself there, but that would serve as an orienting reference point for what follows here:

• When a business, or an individual for that matter confronts new and different or an opportunity to create that, they do not necessarily know up-front where this will lead and either for how disruptively new and novel it could become, or for how consequential it could become and for either cost required or for value creation that might be enabled from it. Perception of change or for opportunity for creating or enabling it, often begins with a single visible possible step of action or of reaction, depending on where motivation for this change is coming from. But it rarely begins with a clearly illuminated path forward beyond that starting point, that could be developed and followed unchanged and as initially perceived from it.

I have been writing extensively about friction as a function of communications challenges and of information limitations in this blog, doing so in terms of overall economic friction as that term is more generally used, and in terms of business systems friction as its basic concepts are reframed into the individual business context and into that of more localized business-to-business interactions (such as supply chain systems for contexts that would facilitate more win-win strategies for all concerned. Think of this as friction in a more mesoeconomic context and see Reexamining Business School Fundamentals – macroeconomics, microeconomics and the gap between for an at least brief orienting note on what that entails.)

Disruptive innovation carries with it the greatest uncertainty of all, and certainly when considered in comparison to other change possibilities that arise along a continuum ranging from simple cosmetic adjustments to established products, towards change into more fully new and unexplored product development territory. And disruptive innovation and the effort to identify and develop it into realized value creating products, is also the most vulnerable of all possible directed change, to the negative impact of friction. Both a free flow of reliable and timely information per se, and a flow of such quality information across novel channels become essential there, and in ways not generally required for simpler and more predictable change, and certainly when simple cosmetic change is considered by way of comparison.

All of this up to here has been well established and discussed and argued for its validity in this blog and in this series itself. And that flow of discussion, analysis and argument also directly addressed Point 5 of my above list, and answers the questions implicit in it.

• The more faulty and limited the flow of crucially relevant information, and the more limited the capacity to bring together the necessary knowledge and perspective, and from whatever diversity of sources that would be required for that, the less likely it is that the right starting innovator even realize an initial insight that a disruptive innovation might be created out of.
• And the more faulty and limited this information sharing, and in fact the more faulty and limited this collaborative information and knowledge creation is: the more friction there is in these business systems and operations, the less likely it is that an initial innovative spark might thrive and develop to any realized fruition.

I find myself thinking back to an analogous point of observation and understanding that I offered as metaphor in another series to this blog in Don’t Invest in Ideas, Invest in People with Ideas 35, regarding sound and noise. If a noise that is never perceived can never be considered to have become a sound as explained there, a glimmer of an idea with potential to become a true innovation that never goes beyond the level of a thought in the mind of an individual, can never become an innovation from that, and of either more standard or more disruptive form or significance.

Think of this posting to this series as representing a point of intersection between this series as a whole and my just-cited and concurrently appearing, Don’t Invest in Ideas, Invest in People series. People innovate, and realizing anything positive from that is communications driven and collaborative information dependent.

And with this noted, I turn to the next set of issues that I would address in this series: issues of research financing, as will be approached in large part from a more accounting and bookkeeping perspective. I will at least begin addressing that in my next series installment.

Meanwhile, you can find this and related postings at Macroeconomics and Business and its Page 2 continuation. And see also Ubiquitous Computing and Communications – everywhere all the time and its Page 2 continuation.

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