Platt Perspective on Business and Technology

Innovation, disruptive innovation and market volatility 32: innovative business development and the tools that drive it 2

Posted in business and convergent technologies, macroeconomics by Timothy Platt on April 20, 2017

This is my 32nd 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 6-31.)

I have been developing what amounts to a case study example in recent installments to this series, and I add in a concurrently running second series: Intentional Management (as can be found at Business Strategy and Operations – 3 and its Page 4 continuation, postings 472 and loosely following. And see in particular that series’ Part 30.) And this case study takes the form of a discussion and analysis of a new approach to business process flow analysis, and to business systems management that I offer here in this blog as a new source of value for improving business modeling and strategic planning, and that would be both hardware and software dependent for its implementation. The hardware called for would be largely off the shelf, but as primarily marketed and sold to very different, non-business market audiences. And much of the underlying software required would be off the shelf in nature too – reducing costs of development among other considerations – but at a cost of increasing resistance to earlier more mainstream use and to acceptance into business practices from its novelty of source too. I discussed by way of comparison, the historical case study example in Part 30, of how businesses were reluctant at best to adapt easier to use graphical user interface computers in their offices, and in large part because of a non-business image that they brought with them. In that, the advent of the graphical user interface approach, represents a case study example of a more general phenomenon that I would argue likely to arise here too, that also fits into our general understanding as to how new innovation and invention diffuse out into general acceptance and use.

I have been discussing the business modeling and imaging innovation case study example of this series, in terms of innovation and disruptive innovation per se and in terms of how the products of change diffuse into and with time throughout consumer, end user-oriented markets and from initial pioneer and early adaptor acceptance outward towards being generally and routinely accepted, and even by late and last adaptor market segments.

The business visualization innovation that I have been developing and presenting here, can in large part be viewed as a resource that would offer increased information availability to business planners and strategists, and for improving overall operational systems – and exception handling contingencies as well if effectively implemented. My focus in this installment is to step away from the specifics of that case study example to consider information flow and availability, and business systems friction in the organization in general, and to discuss the boundaries between and the distinctions between innovation per se and disruptive innovation per se – and from an information flow and accessibility perspective. And I begin by asserting a fundamentally important set of points:

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.

That much is obvious, and should not come across as being particularly new or innovative. But as a next, and I add crucial next step to that progression of thought, I add two more details here:

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.

And it is the last two of these now five points that I will focus on here, building towards a fuller discussion of them in terms laid out in the first three.

Information and knowledge drive innovation, and set the boundaries between innovation per se and disruptive innovation, and certainly as value creating possibilities. And yes, this does mean that the boundary between “simple” innovation that might even just represent a more slow-paced next step of evolutionary change, and disruptive innovation, has a gray area between their more usually considered extremes. Understanding and thinking and developing in terms of that gray area can be leveraged as a capability for making a business, or any innovative effort more effectively innovative and less bound by small and even just cosmetic change.

This all begins with raw data and how it is organized and developed in producing what should be usable, actionable processed knowledge. And this all begins with the questions, issues and challenges of bringing what in fact would be the essential basic information that can be made available, together. And it begins with making this available to the people who would most need it and who could best use it. And that compelling need for more open sharing and for collaborative information value creation, has to take place within the constraints of necessary and even legally mandated information flow restriction and the barriers to free information flow that any real world business has to face, and function within. I have just outlined a few of the highlights of what in practice would become a more extensive process-driven system, and one requiring management and oversight and one that would require ongoing review and change. And I posit all of this as fitting reasonably into a single “and it begins with” as phrased above in this paragraph, as capacity to effectively innovate demands effective basic information access as one of its initial foundational requirements – and certainly if innovations that would be pursued are going to be oriented from the start towards more effectively supporting and enhancing the business that they would be developed in, and if those innovations are to be supportive of ongoing value creating, value center business activities.

Let’s consider this from an operational, rules based information access and use policy perspective and with the absolute basics and with a basic conundrum that actually developing and implementing this type of policy actually entails:

• Effective information access control is a necessary fact of life for any business or organization that gathers in, stores, or uses any information that it might be required to maintain as confidential, and for either internal to the business reasons or to meet outside legal or other regulatory requirements. And this means identifying and classifying both potential information sources and potential information recipients in risk management terms, as to what information they can categorically be allowed access to, and as to whom they could share this information with.
• Database and other storage systems, and channels of information sharing, and of information flow monitoring, and policies and practices for deleting information that should no longer be held: the functional, structural systems in place for carrying out the storage and transmission of this information flow, would all be developed so as to more effectively enable approved information management at the level of the above bullet point while at least ideally preventing unapproved, ad hoc information sharing, and I add local user storage of information that the rules based system in place and its underlying business model thinking would not approve.
• But, and this is where the conundrum of this narrative enters in, when barriers are created in the assembly, storage and sharing of information, that information becomes fragmented and in ways that limit the capability of any one potential user or any group of them as they would create actionable knowledge out of it. The more that the basic information available is partitioned and fragmented, and the more locally known-only, the actionable knowledge can be that is derived from it, the greater the business systems friction there is going to be that would hinder the development of new and less routine knowledge out of it.
• Information access partitioning leads to reduced risk management challenge and certainly in shorter time frames. But it can also actively hinder and even prevent innovative insight, by preventing that information and knowledge from being reconnected in new and potentially valuable unexpected ways. And this creates what can be seen as long-term risk management costs – but ones that can be impossible to see except reactively and even just way after the fact, as for example when a lost opportunity comes to light. Consider the all too common a reality of learning that another competing business has just marketed a breakthrough development, where all of the pieces necessary for it were there in-house in your own business – but disconnected from each other and from the people who could have used them, and never developed from as a result.

I have written repeatedly over the years of businesses that have arrived at even profoundly revolutionary innovation that has gone on to found entire new industries, but with those potentially first mover businesses left out of all of that, because they did not or could not organize and capitalize on what they had in-house and what they had paid to develop there. (See, for example, my series: Keeping Innovation Fresh, as can be found at Business Strategy and Operations – 2, as postings 241 and loosely following, and its Part 2 and Part 3 in particular.)

• And this, of course means people in these systems only having access to the precise information and information types that a simple linear business development, business as usual approach would identify as their legitimately needing. This leaves out the perhaps less expected, and the wildcard factor of how innovators reach out beyond their own day-to-day when arriving at new. Think of this as either a second face of the same conundrum as just noted about or as a second separate conundrum in and of itself; these two considerations do work together in toxic synergy, and either way.

I have primarily been addressing the first of the five initial numbered points offered towards the top of this posting here, as a foundation for discussing the next two. I will turn to them, and in terms raised here, in my next series installment. In anticipation of that, I will consider the issues raised here and those of points four and five, from a financials perspective as well as from a more explicitly risk management one. And as part of the overall discussion to follow, I will also discuss process systems complexity and the role of developing lean and agile systems as innovation enablers.

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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