Platt Perspective on Business and Technology

Innovation, disruptive innovation and market volatility 33: innovative business development and the tools that drive it 3

Posted in business and convergent technologies, macroeconomics by Timothy Platt on June 9, 2017

This is my 33rd 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 6-32.)

I offered a to-address list of topic points towards the top of Part 32, that I repeat here for continuity of discussion, as my goal here is to continue delving into them in follow-up to that posting:

1. Innovation and its realization are information and knowledge driven.
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,
3. Make innovation and its practical realization possible and actively drive them.
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
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.

I focused on Point 1 of this list in Part 32, and then stated at the end of it that I would turn to consider Point 2 here. I am going to do so but to set the stage for that, I want to at least begin by clarifying and explaining a point of terminology that I raised in Part 32 when discussing innovation per se. Innovation might be more minor and incremental in form or it might be more dramatic and overtly consequential, and disruptive innovation usually represents the more fundamental change end of that continuum. I noted that there is a gray area between innovation as a more general term and when more evolutionary change is considered, and overtly disruptive innovation, but without fully clarifying what I mean by that. Part of an explanation as to what this gray area represents, simply means that incremental change can mean smaller or larger increments on the one side, and that new and novel can be profoundly new and unexpected, or only somewhat new and novel; innovation does fall along at least something of a continuum as to the level and degree of novelty arrived at and even if we all tend to think more in terms of extremes and certainly for identifying the more disruptive-facing end of that continuum. But this explanation only tells one half of the story that I seek to convey in that term.

I in effect prepared to explain the other half of “gray area” in this context in Part 32, when I wrote of information and processed knowledge partitioning in an organization, in the context of business systems friction in information management: the ongoing cyclical processes of data collection and vetting, knowledge development from it, access and communications and use of both raw and processed knowledge and information, and their challenges.

I discussed this in terms of conundrums, where the same processes that can lead to risk reduction from prevention of sensitive information falling into the wrong hands, can also lead to the creation of barriers to effective legitimate information sharing too, and certainly where possible disruptive innovation development can call for new and novel patterns of who legitimately should be brought into an information sharing conversation. And as stated up to here at least, that just addresses the more contrived and planned-out, side to information sharing and its control, leaving out more entirely ad hoc decision making options and their use in information policy and information management.

• In practice, businesses face real risk of porosity, and unexpected information transfer out of the areas where specific forms of sensitive data and knowledge belongs, and into areas where it should not go, coupled with the equally unintended development of barriers to acceptable and even essential information and knowledge transfer and sharing.
• Information access and control processes and practices in actual day-to-day use can and all too often do leave the door open to sensitive information transfer and visibility where they should be limited and blocked, while limiting and blocking where they should be more open and communicative – and particularly, for the later, where novel lines of communication and information sharing would be called for, as for example when developing a disruptively new innovative opportunity and in ways that can lead it to a production line and profitability. I repeat this detail intentionally here.
• Let me pick up on and highlight a crucially important aspect of this, to clarify what I am addressing in these points. If the gold standard for controlling and managing sensitive and confidential information is to be found in developing and following explicit rules-based access and storage systems, and ones that can be largely automated with explicitly rules based, established permissions assigned to anyone who in principle might be involved in this information sharing, then novel and unexpected and unplanned for communications requirements that would bring in unusual combinations of experience and expertise into a conversation, would likely be blocked and certainly for proprietary and closely held in-house business intelligence and as both raw data and as more processed knowledge – barring explicit exception making decisions.

And this brings me to a scenario that I briefly touched upon in Part 32 where a business fails to develop a new and potentially very profitable innovation, and even a disruptively novel one, and even though it has had all of the pieces to that puzzle in place, because they cannot bring what they know together and into production. So another, competing business that may have started much later in the race to develop that type of innovation gets there first. And this sheds a whole new light on that “gray area,” where a lack of effective business intelligence development and sharing, and for actionable processed knowledge in particular, can mean real innovation and even disruptively novel innovation going essentially completely unrecognized for the value potential that it carries. Barriers there can prevent even the potentially most valuable disruptive innovation from ever taking place, and from being brought to market if started upon.

• If the gray area in the innovation continuum that I made note of in Part 32 represents a perhaps-rapidly evolving but still just incremental and perhaps-disruptively new, middle ground area along a continuum, where different viewers might rate the level and significance of change differently,
• That gray area also represents an area of friction-clouded uncertainty and of loss of visibility, where innovation and its potential are not going to even be visible and for at least some of those who should be key stakeholders of innovative change and advancement. And when that “some” includes the key gatekeepers who control which developments can be prototyped and which of them can be developed for production and brought into market-facing production, that can have long-term consequences.

I cited an earlier series in Part 32, with essentially this same area of concern in mind that I repeat here for its importance: Keeping Innovation Fresh, as can be found at Business Strategy and Operations – 2, as postings 241 and loosely following. And once again, see its Part 2: Xerox PARC and Menlo Park and its Part 3 continuation of that in particular, for their relevance here. The Xerox PARC research facility is renowned for both the volume and importance of the innovations that its researchers conceived, and the much smaller proportion of those innovation opportunities that its parent company ever allowed to be developed in-house and to their own profitability and benefit.

So the gray area that I touched upon so briefly in Part 32 and that I have been exploring in more detail here, is both a product of more objective reality where not all change is equal in scale, and subjective and subject to information barrier-facilitated friction if not always information barrier-creating friction. And with this stated, I overtly acknowledge that I have written this entire posting up to here as a direct and immediate response to Point 2 of the to-address list that I repeated towards the top of this posting.

Blinders can come from preconception and bias and even when all of the information that could possibly be needed to take a next conceptual leap is right in front of us. But more insidiously, those blinders can come from a lack of essential information that might be fully developed and in-principle available in-house, but that is so scattered in non-communicating offices that the people who would most need it are constrained to be unaware of it. Then they cannot even know enough of what is in-principle available for them to know and act upon, for them to even just realize that they are missing something – and even when that is a vital something.

I am going to continue this discussion in a next series installment where I will address Point 3 in the above list:

• Making innovation and its practical realization possible and actively drive them.

In anticipation of that line of discussion to come, this means my addressing approaches for both identifying where deleterious information and knowledge bottlenecks and barriers have arisen and remediating them, while still meeting genuine information access control requirements. And I will explicitly consider disruptive innovation, where novel and unexpected patterns of information and expertise sharing might be essential for timely success, that would call for both precise and flexible rules-based systems for allowed sensitive information sharing, with explicit exception handling capabilities built into them.

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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